5,388 Matching Annotations
  1. Feb 2025
    1. Author response:

      We genuinely appreciate the reviewers' interest and recognition of our work. The comments and suggestions on the results presentation and interpretation are well taken. We plan to revise the manuscript based on the reviewers' recommendations in the following aspects.

      (1) We fully agree with the reviewer that the aged environment indeed would affect the myeloid and megakaryocyte differentiation behaviors of HSC. As a result, the clonal behaviors of HSCs presented in the current manuscript could be different from how HSCs differentiate in young mice. This point will be discussed in the revised manuscript.

      (2) We agree with the reviewer that the manuscript was not as easy to follow as many other papers in experimental hematology, primarily because the analyses presented in the current manuscript were not frequently used in previous studies. To address this, we will try to revise the manuscript using plain language to describe the results and conclusions. We will also provide graphical summary schematics where appropriate to present the findings better. We will further discuss our results in the context of previous findings to better illustrate the novelty of the current work.

      (3) We will provide more technical details of our analysis in the revised manuscript for readers to better understand how results are obtained and data analyses are performed in the current manuscript.

    1. Author response:

      We thank the reviewers for their thoughtful and constructive assessment of our manuscript. We agree that additional clarity on some key points in the manuscript will be valuable additions to this work. Both reviewers expressed a related concern regarding the basis for design and interpretation of our pyrazinamide ROS synergy experiments. 

      Reviewer 1:

      The in vitro experiments performed in this manuscript mainly report that PZA pre-treatment increases H2O2-mediated killing or inhibition. There is no direct evidence that clearly shows that oxidative stress drives the potent bactericidal activity of PZA. In these settings the oxidative stress is always applied after PZA pre-treatment and is therefore likely displaying the major lethal effect.

      Reviewer 2:

      The manuscript would benefit from a clear statement of the rationale for the protocols used to examine the synergy of PZA with ROS, the possible models their protocols could be testing, and then how their data supports or disproves the models being tested. The manuscript appears to propose, as stated in the title, that "Oxidative stress drives potent bactericidal activity of pyrazinamide...". However their experimental design more likely tests the effect of PZA on ROS sensitivity. Indeed, by the last figure, the authors begin the present their data as PZA sensitizing the bacteria to ROS. More clarity on these possible models and the different interpretations of the data should be considered.

      We agree that the data presented in the current version of the manuscript is incomplete in supporting our assertion that oxidative stress drives bactericidal activity of pyrazinamide. As both reviewers note, pretreatment of bacilli with pyrazinamide followed by challenge with ROS indicates that pyrazinamide enhances susceptibility to oxidative stress but does not address whether oxidative stress enhances susceptibility to pyrazinamide. Further, we neglected to provide information regarding why we chose to pretreat bacilli with pyrazinamide before ROS exposure. Over the course of our work, we had found that pyrazinoic acid, the active form of pyrazinamide, showed potent synergy with hydrogen peroxide.  In contrast to the time-dependent synergy that we observed between pyrazinamide and peroxide, synergy between pyrazinoic acid and peroxide did not require pretreatment. We will revise our manuscript to include results that address these key issues and we will carefully consider revising our interpretations accordingly.

    1. Author response:

      Reviewer #1 (Public review):

      Summary:

      The question of how central nervous system (CNS) lamination defects affect functional integrity is an interesting topic, though it remains a subject of debate. The authors focused on the retina, which is a relatively simple yet well-laminated tissue, to investigate the impact of afadin - a key component of adherens junctions on retinal structure and function. Their findings show that the loss of afadin leads to significant disruptions in outer retinal lamination, affecting the morphology and localization of photoreceptors and their synapses, as illustrated by high-quality images. Despite these severe changes, the study found that some functions of the retinal circuits, such as the ability to process light stimuli, could still be partially preserved. This research offers new insights into the relationship between retinal lamination and neural circuit function, suggesting that altered retinal morphology does not completely eliminate the capacity for visual information processing.

      Strengths:

      The retina serves as an excellent model for investigating lamination defects and functional integrity due to its relatively simple yet well-organized structure, along with the ease of analyzing visual function. The images depicting outer retinal lamination, as well as the morphology and localization of photoreceptors and their synapses, are clear and well-described. The paper is logically organized, progressing from structural defects to functional analysis. Additionally, the manuscript includes a comprehensive discussion of the findings and their implications.

      Weaknesses:

      While this work presents a wealth of descriptive data, it lacks quantification, which would help readers fully understand the findings and compare results with those from other studies. Furthermore, the molecular mechanisms underlying the defects caused by afadin deletion were not explored, leaving the role of afadin and its intracellular signaling pathways in retinal cells unclear. Finally, the study relied solely on electrophysiological recordings to demonstrate RGC function, which may not be robust enough to support the conclusions. Incorporating additional experiments, such as visual behavior tests, would strengthen the overall conclusions.

      Thank you very much for taking the time and thoughtful and valuable comments. Following your suggestions, we will quantify some of the histological data and explore the mechanisms underlying the defects of lamination and cell fate determination observed in afadin cKO retina. We will also try to examine the vision of afadin cKO mice by visual behavior tests.

      Reviewer #2 (Public review):

      Summary:

      Ueno et al. described substantial changes in the afadin knockout retina. These changes include decreased numbers of rods and cones, an increased number of bipolar cells, and disrupted somatic and synaptic organization of the outer limiting membrane, outer nuclear layer, and outer plexiform layer. In contrast, the number and organization of amacrine cells and retinal ganglion cells remain relatively intact. They also observed changes in ERG responses and RGC receptive fields and functions using MEA recordings.

      Strengths:

      The morphological characterization of retinal cell types and laminations is detailed and relatively comprehensive.

      Weaknesses:

      (1) The major weakness of this study, perhaps, is that its findings are predominantly descriptive and lack any mechanistic explanation. As afadin is key component of adherent junctions, its role in mediating retinal lamination has been reported previously (see PMCID: PMC6284407). Thus, a more detailed dissection of afadin's role in processes, such as progenitor generation, cell migration, or the formation of retinal lamination would provide greater insight into the defects caused by knocking out afadin.

      Thank you for taking the time and valuable comments. Following your suggestions, we will perform experiments to evaluate mechanisms of retinal lamination and cell fate determination defects observed in the afadin cKO retina. However, we would like to note that the paper cited in the comment (PMCID: PMC6284407) analyzed the function of afadin in the formation of dendrites of direction selective RGCs in the IPL, and that the word "lamination" refers to the layering of RGC dendrites in the IPL. Here, we analyzed the function of afadin in laminar construction of the retina.

      (2) The authors observed striking changes in the numbers of rods, cones, and BCs, but not in ACs or RGCs. The causes of these distinct changes in specific cell classes remain unclear. Detailed characterizations, such as the expression of afadin in early developing retina, tracing cell numbers across various early developmental time points, and staining of apoptotic markers in developing retinal cells, could help to distinguish between defects in cell generation and survival, providing a better understand of the underlying causes of these phenotypes.

      Following your suggestion, we will perform the experiments to characterize the causes of distinct changes in the afadin cKO retina.

      (3) Although the total number of ACs or RGCs remains unchanged, their localizations are somewhat altered (Figures 2E and 4E). Again, the cause of the altered somatic localization in ACs and RGCs is unclear.

      To clarify the reviewer’s point, we will analyze the progenitor and those cell positions in the developing stage of the afadin cKO retina.

      (4) One conclusion that the authors emphasise is that the function of RGCs remains detectable despite a major disrupted outer plexiform layer. However, the organization of the inner plexiform layer remains largely intact, and the axonal innervation of BCs remains unchanged. This could explain the function integrity of RGCs. In addition, the resolution of detecting RGCs by MEA is low, as they only detected 5 clusters in heterozygous animals. This represents an incomplete clustering of RGC functional types and does not provide a full picture of how functional RGC types are altered in the afadin knockout.

      We appreciate the reviewer’s insightful comments. Although our clustering of RGC subtypes in afadin cHet retinas resulted in only five clusters, the key finding of our study is the preservation of RGC receptive fields in afadin cKO retinas, despite severe photoreceptor loss (reduced to about one-third of normal) and disruption of photoreceptor-bipolar cell synapses in the OPL. This suggests that even with crucial damage to the OPL, the primary photoreceptor-bipolar-RGC pathway can still function as long as the IPL remains intact. Moreover, the presence of rod-driven responses in RGCs indicates that the AII amacrine cell-mediated rod pathway may also continue to function. We agree that our functional clustering in afadin cHet retinas was incomplete. However, we guess that the absence of RGCs with fast temporal responses in afadin cKO retinas may not simply due to the loss of specific RGC subtypes but due to disrupted synaptic connections between photoreceptors and fast-responding bipolar cells. Furthermore, the structural abnormalities in retinal lamination in afadin cKO retinas may alter RGC response properties, making strict functional classification less meaningful. We would like to emphasize the finding that disruption of the retinal lamination in afadin cKO retinas leads to the absence of RGCs with fast temporal response properties, rather than focusing solely on the classification of RGC subtypes.

      Minor Comments:

      (1) Line 56-67: "Overall, these findings provide the first evidence that retinal circuit function can be partially preserved even when there are significant disruptions in retinal lamination and photoreceptor synapses" There is existing evidence showing substantial adaption in retinal function when retinal lamination or photoreceptor synapses are disrupted, such as PMCID: PMC10133175.

      Thank you for your comment. The paper you mentioned is crucial for discussing and considering the results of our study. We will refer the paper and mention in Discussion.  

      (2) Line 114-115: "we focused on afadin, which is a scaffolding protein for nectin and has no ortholog in mice." The term "Ortholog" is misused here, as the mouse has an afadin gene. Should the intended meaning be that afadin has no other isoforms in mouse?

      Thank you for pointing it out. As we misused "Ortholog" as "Paralog", we will revise it.

    1. Author response:

      eLife Assessment

      This useful study integrates experimental methods from materials science with psychophysical methods to investigate how frictional stabilities influence tactile surface discrimination. The authors argue that force fluctuations arising from transitions between frictional sliding conditions facilitate the discrimination of surfaces with similar friction coefficients. However, the reliance on friction data obtained from an artificial finger, together with the ambiguous correlative analyses relating these measurements to human psychophysics, renders the findings incomplete.

      Our main goal with this paper was to show that the most common metric, i.e. average friction coefficient—widely used in tactile perception and device design—is fundamentally unsound, and to offer a secondary parameter that is compatible with the fact that human motion is unconstrained, leading to dynamic interfacial mechanics. In contrast with the summary assessment, we also note that the average friction coefficients in our study were not particularly similar, ranging from differences of 0.4 – 1, a typical range seen in most studies. We believe some of the comments originate from a misinterpretation of our statistically significant, but negative correlation between human results and friction coefficients – which leads to the spurious conclusion that nearly identical objects should be very easy to tell apart, thus supporting our central argument for the need of an alternative. We understand the Reviewers wanting to see that we can demonstrate that humans using instabilities in situ. This is seemingly reasonable, but we explain the significant challenges and fundamental unknowns to those experiments. However, we modified our title to reflect our focus on offering an alternative to the average coefficient of friction.

      We do not think it was feasible, at this stage, to demonstrate that humans use friction instabilities through direct manipulation and observation in human participants. In short, there are still several fundamental unknowns: (1) a decision-making model would need to be created, but it is unknown if tactile decision making follows other models, (2) it is further unknown what constitutes “tactile evidence”, though at our manuscript’s conclusion, we propose that friction instabilities are better suited for to be tactile evidence than the averaging of friction coefficients from a narrow range of human exploration (3) in the design of samples, from a friction mechanics and materials perspective, it is not at this point, possible to pre-program surfaces a priori to deliver friction instabilities and instead must be experimentally determined – especially when attempting to achieve this in controlled surfaces that do not create other overriding tactile cues, like macroscopic bumps or large differences in surface roughness. (4) Given that the basis for tactile percepts, like which object feels “rougher” or “smoother” is not sufficiently established and we have seen leads to confusion, it is necessary to use a 3-alternative forced choice task which avoids asking objects along a preset perceptual dimension – a challenge recognized by Reviewer 3. However, this would bring in issues of memory in the decision-making model. (5) The prior points are compounded by the fact that, we believe, tactile exploration must be performed in an unconstrained manner, i.e., without an apparatus generating motion onto a stationary finger. Work by Liu et al. (IEEE ToH, 2024) showed that recreating friction obtained during free exploration onto a stationary finger was uninterpretable by the participants, hinting at the importance of efference copies(1). We believe that each of the above-mentioned issues constitutes a significant advance in knowledge and would require discussion and dissemination with the community. Finally, one of our overarching goals is to create a consistent method to characterize surfaces, and given individual variability in human fingers and motion, a machine-based method that can rapidly, consistently, and sufficiently replicate tactile exploration is needed.

      Finally, we also justify our use of a mock finger to provide a method to characterize surfaces in tactile studies that other researchers could reasonably recreate, without creating a standard around individual humans, considering the variability in finger shape and motion during exploration. We do not believe this is an “either-or” argument, but rather that standardized methods to characterize surfaces and devices are greatly needed in the field. From these standardized methods, like surface roughness, some tabulated values of friction coefficient, or surface energy, etc., the current metrics to parameterize results are largely incapable of capturing the dynamic changes in forces expected during human tactile exploration.

      Our changes to the manuscript (Page 1 & SI Page 1, Title)

      “Alternatives to Friction Coefficient: Role of Frictional Instabilities for Fine Touch Perception”

      Reviewer 1 (Public review):

      Summary:

      In this paper, Derkaloustian et. al look at the important topic of what affects fine touch perception. The observations that there may be some level of correlation with instabilities are intriguing. They attempted to characterize different materials by counting the frequency (occurrence #, not of vibration) of instabilities at various speeds and forces of a PDMS slab pulled lengthwise over the material. They then had humans make the same vertical motion to discriminate between these samples. They correlated the % correct in discrimination with differences in frequency of steady sliding over the design space as well as other traditional parameters such as friction coefficient and roughness. The authors pose an interesting hypothesis and make an interesting observation about the occurrences of instability regimes in different materials while in contact with PDMS, which is interesting for the community to see in the publication. It should be noted that the finger is complex, however, and there are many factors that may be quite oversimplified with the use of the PDMS finger, and the consideration and discounting of other parameters are not fully discussed in the main text or SI. Most importantly, however, the conclusions as stated do not align with the primary summary of the data in Figure 2.

      Strengths:

      The strength of this paper is in its intriguing hypothesis and important observation that instabilities may contribute to what humans are detecting as differences in these apparently similar samples.

      We thank Reviewer 1 for their time on the manuscript, recognizing the approach we took, and offering constructive feedback. We believe that our conclusions, in fact, are supported by the primary summary of the data in Figure 2 but we believe that our use of R<sup>2</sup> could have led to misinterpretation. The trend with friction coefficient and percent correct was indeed statistically significant but was spurious because the slope was negative. In the revision, we add clarifying comments throughout, change from R<sup>2</sup> to r as to highlight the negative trend, and adjust the figures to better focus on friction coefficient.

      Finally, we added a new section to discuss the tradeoffs between using a real human finger versus a mock finger, and which situations may warrant the use of one or the other. In short, for our goal of characterizing surfaces to be used in tactile experiments, we believe a mock finger is more sustainable and practical than using real humans because human fingers are unique per participant, humans move their fingers at constantly changing pressures and velocities, and friction generated during free exploring human cannot be satisfactorily replicated by moving a sample onto a stationary finger. But, we do not disagree that for other types of experiments, characterizing a human participant directly may be more advantageous.

      Weaknesses:

      Comment 1 - The most important weakness is that the findings do not support the statements of findings made in the abstract. Of specific note in this regard is the primary correlation in Figure 2B between SS (steady sliding) and percent correct discrimination. Of specific note in this regard is the primary correlation in Figure 2B between SS (steady sliding) and percent correct discrimination. While the statistical test shows significance (and is interesting!), the R-squared value is 0.38, while the R-squared value for the "Friction Coefficient vs. Percent Correct" plot has an R-squared of 0.6 and a p-value of < 0.01 (including Figure 2B). This suggests that the results do not support the claim in the abstract: "We found that participant accuracy in tactile discrimination was most strongly correlated with formations of steady sliding, and response times were negatively correlated with stiction spikes. Conversely, traditional metrics like surface roughness or average friction coefficient did not predict tactile discriminability."

      We disagree that the trend with friction coefficient suggests the results do not support the claim because the correlation was found to be negative. However, we could have made the comparison more apparent and expanded on this point, given its novelty.

      While the R<sup>2</sup> value corresponding to the “Friction Coefficient vs. Percent Correct” plot is notably higher, our results show that the slope is negative, which would be statistically spurious. This is because a negative correlation between percent correct (accuracy in discriminating surfaces) and difference in friction coefficient means that the more similar two surfaces are (by friction coefficient), the easier it would be for people to tell them apart. That is, it incorrectly concludes that two identical surfaces would be much easier to tell apart than two surfaces with greatly different friction coefficients.

      This is counterintuitive to nearly all existing results, but we believe our samples were well-positioned to uncover this trend by minimizing variability, by controlling multiple physical parameters in the samples, and that the friction coefficient — typically calculated in the field as an average friction coefficient — ignores all the dynamic changes in forces present in elastic systems undergoing mesoscale friction, i.e., human touch, as seen in Fig. 1 in a mock finger and Fig. 3 in a real finger. By demonstrating this statistically spurious trend, we believe this strongly supports our premise that an alternative to friction coefficient is needed in the design of tactile psychophysics and haptic interfaces.

      We believe that this could have been misinterpreted, so we took several steps to improve clarity, given the importance of this finding: we separated the panel on friction coefficient to its own panel, we changed from R<sup>2</sup> to r throughout, and we added clarifying text. We also added a small section focusing on this spurious trend.

      Our changes to the manuscript (Page 10)

      “To compare the value of looking at frictional instabilities, we also performed GLMM fits on common approaches in the field, like a friction coefficient or material property typically used in tactile discrimination, shown in Fig. 2D-E. Interestingly, in Fig. 2D, we observed a spurious, negative correlation between friction coefficient (typically and often problematically simplified as across all tested conditions) and accuracy (r = -0.64, p < 0.01); that is, the more different the surfaces are by friction coefficient, the less people can tell them apart. This spurious correlation would be the opposite of intuition, and further calls into question the common practice of using friction coefficients in touch-related studies. The alternative, two-term model which includes adhesive contact area for friction coefficient(29) was even less predictive (see Fig. S6A of SI). We believe such a correlation could not have been uncovered previously as our samples are minimal in their physical variations. Yet, the dynamic changes in force even within a single sample are not considered, despite being a key feature of mesoscale friction during human touch.

      We investigate different material properties in Fig. 2E. Differences in average roughness R<sub>a</sub> (or other parameters, like root mean square roughness R<sub>rms</sub> (Fig. S6A of SI) did not show a statistically significant correlation to accuracy. Though roughness is a popular parameter, correlating any roughness parameter to human performance here could be moot: the limit of detecting roughness differences has previously been defined as 13 nm on structured surfaces33 and much higher for randomly rough surfaces,(46) all of which are magnitudes larger than the roughness differences between our surfaces. The differences in contact angle hysteresis – as an approximation of the adhesion contributions(47) – do not present any statistically significant effects on performance.”

      Comment 2, Part 1

      Along the same lines, other parameters that were considered such as the "Percent Correct vs. Difference in Sp" and "Percent Correct vs. Difference in SFW" were not plotted for consideration in the SI. It would be helpful to compare these results with the other three metrics in order to fully understand the relationships.

      We have added these plots to the SI. We note that we had checked these relationships and discussed them briefly, but did not include the plot. The plots show that the type of instability was not as helpful as its presence or absence.

      Our changes to the manuscript (Page 9)

      “Furthermore, a model accounting for slow frictional waves alone specifically shows a significant, negative effect on performance (p < 0.01, Fig. S5 of SI), suggesting that in these samples and task, the type of instability was not as important.”

      Added (SI Page 4)

      “and no correlation between accuracy and stiction spikes (Fig. S5).”

      Comment 2, Part 2

      Other parameters such as stiction magnitude and differences in friction coefficient over the test space could also be important and interesting.

      We agree these are interesting and have thought about them. We are aware that others, like Gueorguiev et al., have studied stiction magnitudes, and though there was a correlation, the physical differences in surface roughness (glass versus PMMA) investigated made it unclear if these could be generalized further(2). We are unsure how to proceed here with a satisfactory analysis of stiction magnitude, given that stiction spikes are not always generated. In fact, Fig. 1 shows that for many velocities and pressures, they do not form. However, we offer some speculation on why stiction spikes may be overrepresented in the literature because:

      (1) They are prone to being created if the finger was loaded for a long time onto a surface prior to movement, thus creating adhesion by contact aging which is unlike active human exploration. We avoid this by discarding the first pull in our measurements, and is a standard practice in mechanical characterization if contact aging needs to be avoided.

      (2) The ranges of velocities and pressures explored were small.

      (3) In an effort to generate strong tactile stimuli, highly adhesive or rough surfaces are used.

      (4) They are visually distinctive on a plot, but we are unaware of any mechanistic reason that mechanoreceptors would be extremely sensitive to this low frequency event over other signals.

      In ongoing work, however, we are always cognizant that if stiction spikes are a dominant factor, then a secondary analysis on their magnitude would be important.

      We interpret “difference in friction coefficient over the test space” to be, for a single surface, like C4, to find the highest average friction for a condition of single velocity and mass and subtract that from the lowest average friction for a condition of single velocity and mass. We calculated the difference in friction coefficient in the typical manner of the field, by averaging all data collected at all velocities and masses and assigning a single value for all of a surface, like C4. We had performed this, and have the data, but we are wary of overinterpreting secondary and tertiary metrics because they do not have any fundamental basis in traditional tribology, and this value, if used by humans, would suggest that they rapidly explore a large parameter space to find a “maximum” and “minimum” friction. Furthermore, the range in friction across the test space, after averaging, may in fact, be smaller than the range of friction in a single measurement. For example, in Fig. 1B, the friction coefficient can be calculated by dividing the data by the normal force ([applied mass + 6 g finger] × gravity). The friction coefficient in a single run varies widely, as expected.

      Fig. 2D shows a GLMM fit between percent correct responses across our pairs and the differences in friction coefficient for each pair, where we see a spurious negative correlation. As we had the data of all average friction coefficients for each condition for a given material, we also looked at the difference in maximum and minimum friction coefficients. For our tested pairs, these differences also lined up on a statistically significant, negative GLMM fit (r = -0.86, p < 0.005). However, the values for a given surface can vary drastically, with an interquartile range of 1.20 to 2.09 on a single surface. We fit participant accuracy to the differences in these IQRs across pairs. This also led to a negative GLMM fit (r = -0.65, p < 0.05). However, we are hesitant to add this to the manuscript for the reasons stated previously.

      Comment 3, Part 1

      Beyond this fundamental concern, there is a weakness in the representativeness of the PDMS finger, the vertical motion, and the speed of sliding to real human exploration.

      Overall, this is a continuous debate that we think offers two solutions. There is always a tradeoff between using a synthetic model of a finger versus a real human finger, and there is a place for both models. That is, while our mock finger will be more successful the closer it is to a human finger, it is not our goal to fully replace a human finger, rather our goal is to provide a method of characterizing surfaces that is indeed relevant on the length scale of human touch.

      The usefulness of the mock finger is in isolating the features of each surface that is independent of human variability, i.e., instabilities that form without changing loading conditions between sliding motions or even within one sliding motion. Of course, with this method, we still require confirmation of these features still forming during human exploration, which we show in Fig. 3.

      We believe that this method of characterizing surfaces at the mesoscale will ultimately lead to more successful human studies on tactile perception. Currently, and as shown in the paper, characterizing surfaces through traditional techniques, such as a commercial tribometer (friction coefficient, using a steel or hard metal ball), roughness (via atomic force microscopy or some other metrology), surface energy are less predictive. Thus, we believe this mock finger is stronger than the current state-of-the-art characterizing surfaces (we are also aware of a commercial mock finger company, but we were unable to purchase or obtain an evaluation model).

      One of the main – and severe – limitations of using a human finger is that all fingers are different, meaning any study focusing on a particular user may not apply to others or be recreated easily by other researchers. We cannot set a standard for replication around a real human finger as that participant may no longer be available, or willing to travel the world as a “standard”. Furthermore, the method in which changes their pressures and velocities is different. We note that this is a challenge unique to touch perception – how an object is touched changes the friction generated, and thus the tactile stimulus generated, whereas a standardized stimulus is more straightforward for light or sound.

      However, we do emphasize that we have strongly considered the balance between feasibility and ecological validity in the design of a mock finger. We have a mock finger, with the three components of stiffness of a human finger (more below). Furthermore, we have also successfully used this mock finger in correlations with human psychophysics in previous work, where findings from our mechanical experiments were predictive of human performance(3-6).

      Our changes to the manuscript Added (Page 2-3)

      “Mock finger as a characterization tool

      In this work, we use a mechanical setup with a PDMS mock finger to derive tactile predictors from controlled friction traces alternative to average friction coefficients. While there is a tradeoff in selecting a synthetic finger over a more accurate, real human finger in modeling touch, our aim to design a method of mesoscale surface characterization for more successful studies on tactile perception cannot be fulfilled using one human participant as a standard. We believe that with sufficient replication of surface and bulk properties as well as contact geometry, and controlled friction measurements collected at loading conditions observed during a tactile discrimination task, we can isolate unique frictional features of a set of surfaces that do not arise from human-to-human variability.

      The major component of a human finger, by volume, is soft tissue (~56%)(22), resulting in an effective modulus close to 100 kPa(23,24). In order to achieve this same softness, we crosslink PDMS in a 1×1×5 cm mold at a 30:1 elastomer:crosslinker ratio. However, two more features impart increased stiffness in a human finger. Most of this added rigidity is derived from the bone at the fingertip, the distal phalanx(23–25), which we mimic with an acrylic bone within our PDMS network. The stratum corneum, the stiffer, glassier outer layer of skin(26), is replicated with the surface of the mock finger glassified, or further crosslinked, after 8 hours of UV-Ozone treatment(27). This treatment also modifies the surface properties of the native PDMS to align with those of a human finger more closely. It minimizes the viscoelastic tack at the surface, resulting in a comparable non-sticky surface. At least one day after treatment, the finger surface returns to moderate hydrophilicity (~60º), as is typically observed for a real finger(28).

      The initial contact area formed before a friction trace is collected is a rectangle of 1×1 cm. While this shape is not entirely representative of a human finger with curves and ridges, human fingers flatten out enough to reduce the effects of curvature with even very light pressures(28–30). This implies that regardless of finger pressure, the contact area is largely load-independent, which is more accurately replicated with a rectangular mock finger. It is still a challenge to control pressure distribution with this planar interface, but non-uniform pressures are also expected during human exploration.

      Lastly, we consider fingerprints vs. flat fingers. A key finding of our previous work is that while fingerprints enhanced frictional dynamics at certain conditions, key features were still maintained with a flat finger.7 Furthermore, for some loading conditions, the more amplified signals could also result in more similar friction traces for different surfaces. We have continued to use flat fingers in our mechanical experiments, and have observed good agreement between these friction traces and human experiments(7,8,21,31).”

      (Page 3-4, Materials and Methods)

      “Mock Finger Preparation

      Friction forces across all six surfaces were measured using a custom apparatus with a polydimethylsiloxane (PDMS, Dow Sylgard 184) mock finger that mimics a human finger’s

      mechanical properties and contact mechanics while exploring a surface relatively closely(7,8). PDMS and crosslinker were combined in a 30:1 ratio to achieve a stiffness of 100 kPa comparable to a real finger, then degassed in a vacuum desiccator for 30 minutes. We are aware that the manufacturer recommended crosslinking ratio for Sylgard 184 is 10:1 due to potential uncrosslinked liquid residues(32), but further crosslinking concentrated at the surface prevents this. The prepared PDMS was then poured into a 1×1×5 cm mold also containing an acrylic 3D-printed “bone” to attach applied masses on top of the “fingertip” area contacting a surface during friction testing. After crosslinking in the mold at 60ºC for 1 hour, the finger was treated with UV-Ozone for 8 hours out of the mold to minimize viscoelastic tack.

      Mechanical Testing

      A custom device using our PDMS mock finger was used to collect macroscopic friction force traces replicating human exploration(7,8). After placing a sample surface on a stage, the finger was lowered at a slight angle such that an initial 1×1 cm rectangle of “fingertip” contact area could be established. We considered a broad range of applied masses (M \= 0, 25, 75, and 100 g) added onto the deadweight of the finger (6 g) observed during a tactile discrimination task. The other side of the sensor was connected to a motorized stage (V-508 PIMag Precision Linear Stage, Physikinstrumente) to control both displacement (4 mm across all conditions) and sliding velocity (v \= 5, 10, 25, and 45 mm s<sup>-1</sup>). Forces were measured at all 16 combinations of mass and velocity via a 250 g Futek force sensor (k \= 13.9 kN m<sup>-1</sup>) threaded to the bone, and recorded at an average sampling rate of 550 Hz with a Keithley 7510 DMM digitized multimeter. Force traces were collected in sets of 4 slides, discarding the first due to contact aging. Because some mass-velocity combinations were near the boundaries of instability phase transitions, not all force traces at these given conditions exhibited similar profiles.

      Thus, three sets were collected on fresh spots for each condition to observe enough occurrences of multiple instabilities, at a total of nine traces per combination for each surface.”

      Added References (Page 13)

      M. Murai, H.-K. Lau, B. P. Pereira and R. W. H. Pho, J. Hand Surg., 1997, 22, 935–941.

      A. Abdouni, M. Djaghloul, C. Thieulin, R. Vargiolu, C. Pailler-Mattei and H. Zahouani, R. Soc. Open Sci., DOI:10.1098/rsos.170321.

      P.-H. Cornuault, L. Carpentier, M.-A. Bueno, J.-M. Cote and G. Monteil, J. R. Soc. Interface, DOI:10.1098/rsif.2015.0495.

      K. Qian, K. Traylor, S. W. Lee, B. Ellis, J. Weiss and D. Kamper, J. Biomech., 2014, 47, 3094– 3099.

      Y. Yuan and R. Verma, Colloids Surf. B Biointerfaces, 2006, 48, 6–12.

      Y.-J. Fu, H. Qui, K.-S. Liao, S. J. Lue, C.-C. Hu, K.-R. Lee and J.-Y. Lai, Langmuir, 2010, 26, 4392–4399.

      Comment 3, Part 2

      “The real finger has multiple layers with different moduli. In fact, the stratum corneum cells, which are the outer layer at the interface and determine the friction, have a much higher modulus than PDMS. The real finger has multiple layers with different moduli. In fact, the stratum corneum cells, which are the outer layer at the interface and determine the friction, have a much higher modulus than PDMS.

      We have approximated the softness of the finger with 100 kPa crosslinked PDMS, which is close to what has been reported for the bulk of a human fingertip(8,9). However, as mentioned in the Materials and Methods, there are two additional features of the mock finger that impart greater strength. The PDMS surrounds a rigid, acrylic bone comparable to the distal phalanx, which provides an additional layer of higher modulus(10). Additionally, the 8-hour UV-Ozone treatment decreases the viscoelastic tack of the pristine PDMS by glassifying, or further crosslinking the surface of the finger(11), therefore imparting greater stiffness at the surface similar to the contributions of the stratum corneum, along with a similar surface energy(12). This technique is widely used in wearables(13), soft robotics(14), and microfluidics(15) to induce both these material changes. Additionally, the finger is used at least a day after UV-Ozone treatment is completed in order for the surface to return to moderate hydrophilicity, similar to the outermost layer of human skin(16).

      Comment 3, Part 3

      In addition, the slanted position of the finger can cause non-uniform pressures across the finger. Both can contribute to making the PDMS finger have much more stick-slip than a real finger.

      To ensure that there is minimal contribution from the slanted position of the finger, an initial contact area of 1×1 cm is established before sliding and recording friction measurements. As the PDMS finger is a soft object, the portion in contact with a surface flattens and the contact area remains largely unchanged during sliding. Any additional stick-slip after this alignment step is caused by contact aging at the interface, but the first trace we collect is always discarded to only consider stick-slip events caused by surface chemistry. We recognize that it is difficult to completely control the pressure distribution due to the planar interface, but this is also expected when humans freely explore a surface.

      Comment 3, Part 4

      In fact, if you look at the regime maps, there is very little space that has steady sliding. This does not represent well human exploration of surfaces. We do not tend to use a force and velocity that will cause extensive stick-slip (frequent regions of 100% stick-slip) and, in fact, the speeds used in the study are on the slow side, which also contributes to more stick-slip. At higher speeds and lower forces, all of the materials had steady sliding regions.

      We are not aware of published studies that extensively show that humans avoid stickslip regimes. In fact, we are aware familiar with literature where stiction spike formation is suppressed – a recent paper by AliAbbasi, Basdogan et. al. investigates electroadhesion and friction with NaCl solution-infused interfaces, resulting in significantly steadier forces(17). We also directly showed evidence of instability formation that we observed during human exploration in Fig. 3B-C. These dynamic events are common, despite the lack of control of normal forces and sliding velocities. We also note that Reviewer 1, Comment 2, was suggesting that we further explore possible trends from parameterizing the stiction spike.

      We note that many studies have often not gone at the velocities and masses required for stiction spikes – even though these masses and velocities would be routinely seen in free exploration – this is usually due to constraints of equipment(18). Sliding events during human free exploration of surfaces can exceed 100 mm/s for rapid touches. However, for the surfaces investigated here, we observe that large regions of stick-slip can emerge at velocities as low as 5 mm/s depending on the applied load. The incidence of steady sliding appears more dependent on the applied mass, with almost no steady sliding observed at or above 75 g. Indeed, the force categorization along our transition zones is the main point of the paper.

      Comment 3, Part 5

      Further, on these very smooth surfaces, the friction and stiction are more complex and cannot dismiss considerations such as finger material property change with sweat pore occlusion and sweat capillary forces. Also, the vertical motion of both the PDMS finger and the instructed human subjects is not the motion that humans typically use to discriminate between surfaces.

      We did not describe the task sufficiently. Humans were only given the instruction to slide their finger along a single axis from top to bottom of a sample, not vertical as in azimuthal to gravity. We have updated our wording in the manuscript to reflect this.

      Our changes to the manuscript (Page 4)

      “Participants could touch for as long as they wanted, but were asked to only use their dominant index fingers along a single axis to better mimic the conditions for instability formation during mechanical testing with the mock finger.”

      (Page 11)

      “The participant was then asked to explore each sample simultaneously, and ran over each surface in strokes along a single axis until the participant could decide which of the two had “more friction”.”

      Comment 3, Part 6

      Finally, fingerprints may not affect the shape and size of the contact area, but they certainly do affect the dynamic response and detection of vibrations.

      We are aware of the nuance. Our previous work on the role of fingerprints on friction experienced by a PDMS mock finger showed enhanced signals with the incorporation of ridges on the finger and used a rate-and-state model of a heterogenous, elastic body to find corresponding trends (though there is no existing model of friction that can accurately model experiments on mesoscale friction)(7). The key conclusion was that a flat finger still preserved key dynamic features, and the presence of stronger or more vibrations could result in more similar forces for different surfaces depending on the sliding conditions.

      This is also in the context that we are seeking to provide a reasonable and experimentally accessible method to characterize surfaces, which will always be better as we get closer in replicating a true human finger. But our goal here was to replicate the finger sufficiently for use in human studies. We believe the more appropriate metric of success is if the mock finger is more successful than replacing traditional characterization experiments, like friction coefficient, roughness, surface energy, etc.

      Comment 4

      This all leads to the critical question, why are friction, normal force, and velocity not measured during the measured human exploration and in a systematic study using the real human finger? The authors posed an extremely interesting hypothesis that humans may alter their speed to feel the instability transition regions. This is something that could be measured with a real finger but is not likely to be correlated accurately enough to match regime boundaries with such a simplified artificial finger.

      We are excited that our manuscript offers a tractable manner to test the hypothesis that tactile decision-making models use friction instabilities as evidence. However, we lay out the challenges and barriers, and how the scope of this paper will lead us in that direction. We also clarify that our goals are to provide a method to characterize samples to better design tactile interfaces in haptics or in psychophysical experiments and raise awareness that the common methods of sample characterization in touch by an average friction coefficient or roughness is fundamentally unsound.

      In short, in our view, to further support our findings on instabilities would require answering:

      (1) Which one, or combination of, of the multiple swipes that people make responsible for a tactile decision? (The need for a decision-making model)

      (2) Establish what is, or may be, tactile evidence.

      (3) Establish tactile decision-making models are similar or different than existing decision-making models.

      (4) Test the hypothesis, in these models, that friction instabilities are evidence, and not some other unknown metric. This requires design samples that vary in the amount of evidence generated, but this evidence cannot be controlled directly. Rather, the samples indirectly vary evidence by how likely it is for a human to generate different types of friction instabilities during standard exploration.

      (5) Design a task that does not require the use of subjective tactile descriptors, like “which one feels rougher”, which we see cause confusion in participants, which will likely require accounting for memory effects.

      We elaborate these points below:

      To successfully perform this experiment, we note that freely exploring humans make multiple strokes on a surface. Therefore, we would need to construct a decision-making model. It has not yet been demonstrated whether tactile decision making follows visual decision making, but perhaps to start, we can assume it does. Then, in the design of our decision-making paradigm, we immediately run into the problem: What is tactile evidence?

      From Fig. 3C, we already can see that identifying evidence is challenging. Prior to this manuscript, people may have chosen the average force, or the highest force. Or we may choose the average friction force. Then, after deciding on the evidence, we need to find a method to manipulate the evidence, i.e., create samples or a machine that causes high friction, etc. We show that during the course of human touch, due to the dynamic nature of friction, the average can change a large amount and sample design becomes a central barrier to experiments. Others may suggest immobilizing the finger and applying a known force, but given how much friction changes with human exploration, there is no known method to make a machine recreate temporally and spatially varying friction forces during sliding onto a stationary finger. Finally, perhaps most importantly, in addition to mechanical challenges, a study by Liu, Colgate et al. showed that even if they recorded the friction (2D) of a finger exploring a surface and then replicated the same friction forces onto a finger, the participant could not determine which surface the replayed friction force was supposed to represent.1 This supports that the efference copy is important, that the forces in response to expected motion are important to determine friction. Finally, there is no known method to design instabilities a priori. They must be found through experiments. Especially since if we were to introduce, say a bump or a trough, then we bring in confounding variables to how participants tell surfaces apart.

      Furthermore, even if we had some consistent method to create tactile “evidence”, the paradigm also deserves some consideration. In our experience, the 3-AFC task we perform is important because the vocabulary for touch has not been established. That is, in 3-AFC, by asking to determine which one sample is unlike the others, we do not have to ask the participant questions like “which one is rougher” or “which one has less friction”. In contrast, 2-AFC, which is better for decision-making models because it does not include memory, requires the asking of a perceptual question like: “which one is rougher?”. In our ongoing work, taking two silane coatings, we found that participants could easily identify which surface is unlike the others above chance in a 3-AFC, but participants, even within their own trials, could not consistently identify one silane as perceptually “rougher” by 2-AFC. To us, this calls into question the validity of tactile descriptors, but is beyond the scope of this manuscript.

      This is not our only goal, but in the context of human exploration, in this manuscript here, we believed it was important to identify a mechanical parameter that was consistent with how humans explore surfaces, but was also a parameter that could characterize to some consistent property of a surface – irrespective of whether a human was touching it. We thought that designing human decision-making models and paradigms around the friction coefficient would not be successful.

      Given the scope of these challenges, we do not think it would be possible to establish these conceptual sequences in a single manuscript.

      Reviewer 2 (Public review):

      Summary:

      In this paper, the authors want to test the hypothesis that frictional instabilities rather than friction are the main drivers for discriminating flat surfaces of different sub-nanometric roughness profiles.

      They first produced flat surfaces with 6 different coatings giving them unique and various properties in terms of roughness (picometer scale), contact angles (from hydrophilic to hydrophobic), friction coefficient (as measured against a mock finger), and Hurst exponent.

      Then, they used those surfaces in two different experiments. In the first experiment, they used a mock finger (PDMS of 100kPA molded into a fingertip shape) and slid it over the surfaces at different normal forces and speeds. They categorized the sliding behavior as steady sliding, sticking spikes, and slow frictional waves by visual inspection, and show that the surfaces have different behaviors depending on normal force and speed. In a second experiment, participants (10) were asked to discriminate pairs of those surfaces. It is found that each of those pairs could be reliably discriminated by most participants.

      Finally, the participant's discrimination performance is correlated with differences in the physical attributes observed against the mock finger. The authors found a positive correlation between participants' performances and differences in the count of steady sliding against the mock finger and a negative correlation between participants' reaction time and differences in the count of stiction spikes against the mock finger. They interpret those correlations as evidence that participants use those differences to discriminate the surfaces.

      Strengths:

      The created surfaces are very interesting as they are flat at the nanometer scale, yet have different physical attributes and can be reliably discriminated.”

      We thank Reviewer 2 for their notes on our manuscript. The responses below address the reviewer’s comments and recommendations for revised work.

      Weaknesses:

      Comment 1

      In my opinion, the data presented in the paper do not support the conclusions. The conclusions are based on a correlation between results obtained on the mock finger and results obtained with human participants but there is no evidence that the human participants' fingertips will behave similarly to the mock finger during the experiment. Figure 3 gives a hint that the 3 sliding behaviors can be observed in a real finger, but does not prove that the human finger will behave as the mock finger, i.e., there is no evidence that the phase maps in Figure 1C are similar for human fingers and across different people that can have very different stiffness and moisture levels.

      The mechanical characterization conducted with the mock finger seeks to extract significant features of friction traces of a set of surfaces to use as predictors of tactile discriminability. The goal is to find a consistent method to characterize surfaces for use in tactile experiments that can be replicated by others and used prior to any human experiments. However, in the overall response and in a response to a similar comment by Reviewer 1, we also explain why we believe experiments on humans to establish this fact is not yet reasonable.

      Comment 2

      I believe that the authors collected the contact forces during the psychophysics experiments, so this shortcoming could be solved if the authors use the actual data, and show that the participant responses can be better predicted by the occurrence of frictional instabilities than by the usual metrics on a trial by trial basis, or at least on a subject by subject basis. I.e. Poor performers should show fewer signs of differences in the sliding behaviors than good performers.

      To fully implement this, a decision-making model is necessary because, as a counter example, a participant could have generated 10 swipes of SFW and 1 swipe of a Sp, but the Sp may have been the most important event for making a tactile decision. This type of scenario is not compatible with the analysis suggested — and similar counterpoints can be made for other types of seemingly straightforward analysis.

      While we are interested and actively working on this, the study here is critical to establish types of evidence for a future decision-making model. We know humans change their friction constantly during real exploration, so it is unclear which of these constantly changing values we should input into the decision making model, and the future challenges we anticipate are explained in Comment 1.

      Comment 3

      The sample size (10) is very small.

      We recognize that, with all factors being equal, this sample size is on the smaller end. However, we emphasize the degree of control of samples is far above typical, with minimal variations in sample properties such as surface roughness, and every sample for every trial was pristine. Furthermore, the sample preparation (> 300 individual wafers were used) and cost became a factor. Although not typically appropriate, and thus not included in the manuscript, a post-hoc power analysis for our 100 trials of our pair that was closest to chance, P4, (53%, closest to chance at 33%) showed a power of 98.2%, suggesting that the study was appropriately powered.

      Reviewer 2 (Recommendations for the authors):

      Comment 1

      Differences in SS and Sp (Table 2) are NOT physical or mechanical differences but are obtained by counting differences in the number of occurrences of each sliding behavior. It is rather a weird choice.

      We disagree that differences in SS and Sp are not physical or mechanical, as these are well-established phenomena in the soft matter and tribology literature(19-21). These are known as “mechanical instabilities” and generated due to the effects of two physical phenomena: the elasticity of the finger (which is constant in our mechanical testing) and the friction forces present (which change per sample type). The motivation behind using these different shapes is that the instabilities, in some conditions, can be invariant to external factors like velocity. This would be quite advantageous for human exploration because, unlike friction coefficient, which changes with nearly any factor, including velocity and mass, the instabilities being invariant to velocity would mean that we are accurately characterizing a unique identifier of the surface even though velocity may be variable.

      This “weird choice” is the central innovation of this paper. This choice was necessary because we demonstrated that the common usage of friction coefficient is fundamentally flawed: we see that friction coefficient suggests that surface which are more different would feel more similar – indeed the most distinctive surfaces would be two surfaces that are identical, which is clearly spurious. One potential explanation for why we were able to see this is effect is because our surfaces have similar (< 0.6 nm variability) roughness, removing potential confounding factors, and this type of low roughness control has not been used in tactile studies to the best of our knowledge.

      Comment 2

      Figures 2B-C: why are the x-data different than Table 2?

      The x-data in Fig. 2B-C are the absolute differences in the number of occurrences measured for a given instability type or material property out of 144 pulls. Modeling the human participant results in our GLMMs required the independent variables to be in this form rather than percentages. We initially chose to list percent differences in Table 2 to highlight the ranges of differences instead of an absolute value, but have added both for clarity.

      Our changes to the manuscript (Page 7)

      “To determine if humans can detect these three different instabilities, we selected six pairs of surfaces to create a broad range of potential instabilities present across all three types. These are summarized in Table 2, where the first column for each instability is the difference in occurrence of that instability formed between each pair, and the second is the percent difference.”

      Comment 3

      "We constructed a set of coated surfaces with physical differences which were imperceptible by touch but created different types of instabilities based on how quickly a finger is slid and how hard a human finger is pressed during sliding." Yet, in your experiment, participants could discriminate them, so this is incoherent.

      To clarify the point, macroscopic objects can differ in physical shape and in chemical composition. What we meant was that the physical differences, i.e., roughness, were below a limit (Skedung et al.) that participants, without a coating, would not be able to tell these apart(22). Therefore, the reason people could tell our surfaces apart was due to the chemical composition of the surface, and not any differences in roughness or physical effects like film stiffness (due to the molecular-scale thinness of the surface coatings, they are mechanically negligible). However, we concede that at the molecular scale, the traditional macroscopic distinction between physical and chemical is blurred.

      We have made minor revisions to the wording in the abstract. We clarify that the surface coatings had physical differences in roughness that were smaller than 0.6 nm, which based purely on roughness, would not be expected to be distinguishable to participants. Therefore, the reason participants can tell these surfaces apart is due to differences in friction generated by chemical composition, and we were able to minimize contributions from physical differences in the sample our study.

      Our changes to the manuscript (Page 1, Abstract)

      “We constructed a set of coated surfaces with minimal physical differences that by themselves, are not perceptible to people, but instead, due to modification in surface chemistry, the surfaces created different types of instabilities based on how quickly a finger is slid and how hard a human finger is pressed during sliding.”

      Reviewer 3 (Public review):

      Strengths:  

      The paper describes a new perspective on friction perception, with the hypothesis that humans are sensitive to the instabilities of the surface rather than the coefficient of friction. The paper is very well written and with a comprehensive literature survey.

      One of the central tools used by the author to characterize the frictional behavior is the frictional instabilities maps. With these maps, it becomes clear that two different surfaces can have both similar and different behavior depending on the normal force and the speed of exploration. It puts forward that friction is a complicated phenomenon, especially for soft materials.

      The psychophysics study is centered around an odd-one-out protocol, which has the advantage of avoiding any external reference to what would mean friction or texture for example. The comparisons are made only based on the texture being similar or not.

      The results show a significant relationship between the distance between frictional maps and the success rate in discriminating two kinds of surface.”

      We thank Reviewer 3 for their notes and interesting discussion points on our manuscript. Below, we address the reviewer’s feedback and comments on related works.

      Weaknesses:

      Comment 1

      The main weakness of the paper comes from the fact that the frictional maps and the extensive psychophysics study are not made at the same time, nor with the same finger. The frictional maps are produced with an artificial finger made out of PDMS which is a poor substitute for the complex tribological properties of skin.

      A similar comment was made by Reviewers 1 and 2 and parts are replicated below. We are not claiming that our PDMS fingers are superior to real fingers, but rather, we cannot establish standards in the field by using real human fingers that vary between subjects and researchers. We believe the mock finger we designed is a reasonable mimic of the human finger by matching surface energy, heterogeneous mechanical structure, and the ability to test multiple physiologically relevant pressures and sliding velocities.

      We achieve a heterogeneous mechanical structure with the 3 primary components of stiffness of a human finger. The effective modulus of ~100 kPa, from soft tissue,8,9 is obtained with a 30:1 ratio of PDMS to crosslinker. The PDMS also surrounds a rigid, acrylic bone comparable to the distal phalanx, which provides an additional layer of higher modulus.10 Additionally, the 8-hour UV-Ozone treatment decreases the viscoelastic tack of the pristine PDMS by glassifying, or further crosslinking the surface of the finger,11 therefore imparting greater stiffness at the surface similar to the contributions of the stratum corneum, along with a similar surface energy.12 The finger is used at least a day after UV-Ozone treatment is completed in order for the surface to return to moderate hydrophilicity, similar to the outermost layer of human skin.16 We also discuss the shape of the contact formed. To ensure that there is minimal contribution from the slanted position of the finger, an initial contact area of 1×1 cm is established before sliding and recording friction measurements. As the PDMS finger is a soft object, the portion in contact with a surface flattens and the contact area remains largely unchanged during sliding. We recognize that it is difficult to completely control the pressure distribution due to the planar interface, but this variation is also expected when humans freely explore a surface. Finally, we consider flat vs. fingerprinted fingers. Our previous work on the role of fingerprints on friction experienced by a PDMS mock finger showed enhanced signals with the incorporation of ridges on the finger and used a rate-andstate model of a heterogenous, elastic body to find corresponding trends.7 The key conclusion was that a flat finger still preserved key dynamic features, and the presence of stronger or more vibrations could result in more similar forces for different surfaces depending on the sliding conditions. We note that we have subsequently used the controlled mechanical data collected with this flat mock finger in correlations with human psychophysics in previous work, where findings from our mechanical experiments were predictive of human performance.3–6 Ultimately, we see from our prior work and here that, despite the drawbacks of our mock finger, it outperforms other standard characterization technique in providing information about the mesoscale that correlates to tactile perception. We have added these details to the manuscript.

      We also note that an intermediate option, replicating real fingers, even in a mold, may also inadvertently limit trends from characterization to a specific finger. One of the main – and severe – limitations of using a human finger is that all fingers are different, meaning any study focusing on a particular user may not apply to others or be recreated easily by other researchers. We cannot set a standard for replication around a real human finger as that participant may no longer be available, or willing to travel the world as a “standard”. Furthermore, the method in which a single person changes their pressures and velocities as they touch a surface is highly variable. We also note that in the Summary Response, we noted that a study by Colgate et al. (IEEE ToH 2024) demonstrated that efference copies may be important, and thus constraining a human finger and replaying the forces recorded during free exploration will not lead to the participant identifying a surface with any consistency. Thus, it is important to allow humans to freely explore surfaces, but creates nearly limitless variability in friction forces.

      This is also against the backdrop that we are seeking to provide a method to characterize surfaces, which will be aided as we get closer in replicate a true human finger. Indeed, the more features we replicate, the more successful the mechanical data will be in correlating to tactile distinguishability. But reasonably, our success would be in replacing traditional characterization experiments, not in recreating the forces of an arbitrary human finger.

      Our changes to the manuscript Added (Page 2-3)

      “Mock finger as a characterization tool

      In this work, we use a mechanical setup with a PDMS mock finger to derive tactile predictors from controlled friction traces alternative to average friction coefficients. While there is a tradeoff in selecting a synthetic finger over a more accurate, real human finger in modeling touch, our aim to design a method of mesoscale surface characterization for more successful studies on tactile perception cannot be fulfilled using one human participant as a standard. We believe that with sufficient replication of surface and bulk properties as well as contact geometry, and controlled friction measurements collected at loading conditions observed during a tactile discrimination task, we can isolate unique frictional features of a set of surfaces that do not arise from human-to-human variability.

      The major component of a human finger, by volume, is soft tissue (~56%)(22), resulting in an effective modulus close to 100 kPa(23,24). In order to achieve this same softness, we crosslink PDMS in a 1×1×5 cm mold at a 30:1 elastomer:crosslinker ratio. However, two more features impart increased stiffness in a human finger. Most of this added rigidity is derived from the bone at the fingertip, the distal phalanx(23-25), which we mimic with an acrylic bone within our PDMS network. The stratum corneum, the stiffer, glassier outer layer of skin(26), is replicated with the surface of the mock finger glassified, or further crosslinked, after 8 hours of UV-Ozone treatment(27). This treatment also modifies the surface properties of the native PDMS to align with those of a human finger more closely. It minimizes the viscoelastic tack at the surface, resulting in a comparable non-sticky surface. At least one day after treatment, the finger surface returns to moderate hydrophilicity (~60º), as is typically observed for a real finger(28).

      The initial contact area formed before a friction trace is collected is a rectangle of 1×1 cm. While this shape is not entirely representative of a human finger with curves and ridges, human fingers flatten out enough to reduce the effects of curvature with even very light pressures(28-30). This implies that regardless of finger pressure, the contact area is largely load-independent, which is more accurately replicated with a rectangular mock finger. It is still a challenge to control pressure distribution with this planar interface, but non-uniform pressures are also expected during human exploration.

      Lastly, we consider fingerprints vs. flat fingers. A key finding of our previous work is that while fingerprints enhanced frictional dynamics at certain conditions, key features were still maintained with a flat finger(7). Furthermore, for some loading conditions, the more amplified signals could also result in more similar friction traces for different surfaces. We have continued to use flat fingers in our mechanical experiments, and have observed good agreement between these friction traces and human experiments(7,8,21,31).”

      (Page 3-4, Materials and Methods)

      “Mock Finger Preparation

      Friction forces across all six surfaces were measured using a custom apparatus with a polydimethylsiloxane (PDMS, Dow Sylgard 184) mock finger that mimics a human finger’s

      mechanical properties and contact mechanics while exploring a surface relatively closely(7,8). PDMS and crosslinker were combined in a 30:1 ratio to achieve a stiffness of 100 kPa comparable to a real finger, then degassed in a vacuum desiccator for 30 minutes. We are aware that the manufacturer recommended crosslinking ratio for Sylgard 184 is 10:1 due to potential uncrosslinked liquid residues(32), but further crosslinking concentrated at the surface prevents this. The prepared PDMS was then poured into a 1×1×5 cm mold also containing an acrylic 3D-printed “bone” to attach applied masses on top of the “fingertip” area contacting a surface during friction testing. After crosslinking in the mold at 60ºC for 1 hour, the finger was treated with UV-Ozone for 8 hours out of the mold to minimize viscoelastic tack.  

      Mechanical Testing

      A custom device using our PDMS mock finger was used to collect macroscopic friction force traces replicating human exploration(7,8). After placing a sample surface on a stage, the finger was lowered at a slight angle such that an initial 1×1 cm rectangle of “fingertip” contact area could be established. We considered a broad range of applied masses (M \= 0, 25, 75, and 100 g) added onto the deadweight of the finger (6 g) observed during a tactile discrimination task. The other side of the sensor was connected to a motorized stage (V-508 PIMag Precision Linear Stage, Physikinstrumente) to control both displacement (4 mm across all conditions) and sliding velocity (v \= 5, 10, 25, and 45 mm s<sup>-1</sup>). Forces were measured at all 16 combinations of mass and velocity via a 250 g Futek force sensor (k \= 13.9 kN m<sup>-1</sup>) threaded to the bone, and recorded at an average sampling rate of 550 Hz with a Keithley 7510 DMM digitized multimeter. Force traces were collected in sets of 4 slides, discarding the first due to contact aging. Because some mass-velocity combinations were near the boundaries of instability phase transitions, not all force traces at these given conditions exhibited similar profiles. Thus, three sets were collected on fresh spots for each condition to observe enough occurrences of multiple instabilities, at a total of nine traces per combination for each surface.”

      Added References (Page 13)

      M. Murai, H.-K. Lau, B. P. Pereira and R. W. H. Pho, J. Hand Surg., 1997, 22, 935–941.

      A. Abdouni, M. Djaghloul, C. Thieulin, R. Vargiolu, C. Pailler-Mattei and H. Zahouani, R. Soc. Open Sci., DOI:10.1098/rsos.170321.

      P.-H. Cornuault, L. Carpentier, M.-A. Bueno, J.-M. Cote and G. Monteil, J. R. Soc. Interface, DOI:10.1098/rsif.2015.0495.

      K. Qian, K. Traylor, S. W. Lee, B. Ellis, J. Weiss and D. Kamper, J. Biomech., 2014, 47, 3094– 3099.

      Y. Yuan and R. Verma, Colloids Surf. B Biointerfaces, 2006, 48, 6–12.

      Y.-J. Fu, H. Qui, K.-S. Liao, S. J. Lue, C.-C. Hu, K.-R. Lee and J.-Y. Lai, Langmuir, 2010, 26, 4392–4399.

      Comment 2

      The evidence would have been much stronger if the measurement of the interaction was done during the psychophysical experiment. In addition, because of the protocol, the correlation is based on aggregates rather than on individual interactions.

      Our Response: We agree that this would have helped further establish our argument, but in the overall statement and in other reviewer responses, we describe the significant challenges to establishing this.

      To fully implement this, a decision-making model is necessary because, as a counter example, a participant could have generated 10 swipes of SFW and 1 swipe of a Sp, but the Sp may have been the most important event for making a tactile decision. We also clarify that our goals are to provide a method to characterize samples to better design tactile interfaces in haptics or in psychophysical experiments.

      In short, in our view, to develop a decision-making model, the challenges are as follows:

      (1) Which one, or combination of, of the multiple swipes that people make responsible for a tactile decision?

      (2) Establish what is, or may be, tactile evidence.

      (3) Establish tactile decision-making models are similar or different than existing decision-making models.

      (4) Test the hypothesis, in these models, that friction instabilities are evidence, and not some other unknown metric.

      (5) Design a task that does not require the use of subjective tactile descriptors, like “which one feels rougher”, which we see cause confusion in participants, which will likely require accounting for memory effects.

      (6) Design samples that vary in the amount of evidence generated, but this evidence cannot be controlled directly. Rather, the samples indirectly vary evidence by how likely it is for a human to generate different types of friction instabilities during standard exploration.

      We elaborate these points below:

      To successfully perform this experiment, we note that freely exploring humans make multiple strokes on a surface. Therefore, we would need to construct a decision-making model. It has not yet been demonstrated whether tactile decision making follows visual decision making, but perhaps to start, we can assume it does. Then, in the design of our decision-making paradigm, we immediately run into the problem: What is tactile evidence?

      From Fig. 3C, we already can see that identifying evidence is challenging. Prior to this manuscript, people may have chosen the average force, or the highest force. Or we may choose the average friction force. Then, after deciding on the evidence, we need to find a method to manipulate the evidence, i.e., create samples or a machine that causes high friction, etc. We show that during the course of human touch, due to the dynamic nature of friction, the average can change a large amount and sample design becomes a central barrier to experiments. Others may suggest to immobilize the finger and applying a known force, but given how much friction changes with human exploration, there is no known method to make a machine recreate temporally and spatially varying friction forces during sliding onto a stationary finger. Finally, perhaps most importantly, in addition to mechanical challenges, a study by Liu, Colgate et al. showed that even if they recorded the friction (2D) of a finger exploring a surface and then replicated the same friction forces onto a finger, the participant could not determine which surface the replayed friction force was supposed to represent.1 This supports that the efference copy is important, that the forces in response to expected motion are important to determine friction. Finally, there is no known method to design instabilities a priori. They must be found through experiments, especially since if we were to introduce, say a bump or a trough, then we bring in confounding variables to how participants tell surfaces apart.

      Furthermore, even if we had some consistent method to create tactile “evidence”, the paradigm also deserves some consideration. In our experience, the 3-AFC task we perform is important because the vocabulary for touch has not been established. That is, in 3-AFC, by asking to determine which one sample is unlike the others, we do not have to ask the participant questions like “which one is rougher” or “which one has less friction”. In contrast, 2-AFC, which is better for decision-making models because it does not include memory, requires the asking of a perceptual question like: “which one is rougher?”. In our ongoing work, taking two silane coatings, we found that participants could easily identify which surface is unlike the others above chance in a 3-AFC, but participants, even within their own trials, could not consistently identify one silane as perceptually “rougher” by 2-AFC. To us, this calls into question the validity of tactile descriptors, but is beyond the scope of the current manuscript.

      This is not our only goal, but in the context of human exploration, in this manuscript here, we believed it was important to identify a mechanical parameter that was consistent with how humans explore surfaces, but was also a parameter that could characterize to some consistent property of a surface – irrespective of whether a human was touching it. We thought that designing human decision-making models and paradigms around the friction coefficient would not be successful.

      Given the scope of these challenges, we do not think it would be possible to establish this conceptual sequence in a single manuscript.

      Comment 3

      The authors compensate with a third experiment where they used a 2AFC protocol and an online force measurement. But the results of this third study, fail to convince the relation.

      With this experiment, our central goal was to demonstrate that the instabilities we have identified with the PDMS finger also occur with a human finger. Several instances of SS, Sp, and SFW were recorded with this setup as a participant touched surfaces in real time.

      Comment 4

      No map of the real finger interaction is shown, bringing doubt to the validity of the frictional map for something as variable as human fingers.

      Real fingers change constantly during exploration, and friction is state-dependent, meaning that the friction will depend on how the person was moving the moment prior. Therefore, a map is only valid for a single human movement – even if participants all were instructed to take a single swipe and start from zero motion, humans are unable to maintain constant velocities and pressures. Clearly, this is not sustainable for any analysis, and these drawbacks apply to any measured parameter, whether instabilities suggested here, or friction coefficients used throughout. We believe the difficulty of this approach emphasizes why a standard map of characterization of a surface by a mock finger, even with its drawbacks, is a viable path forward.

      Reviewer 3 (Recommendations for the authors):

      Comment 1

      It would be interesting to comment on a potential connection between the frictional instability maps and Schalamack waves

      Schallamach waves are a subset of slow frictional waves (SFW). Schallmach waves are very specifically defined. They are a are pockets of air that form between a soft sliding object and rigid surface, and propagate rear-to-front (retrograde waves) as a soft object is slid and buckles due to adhesive pinning. Wrinkles form at the detached portion of the soft material, until the interface reattaches and the process repeats.23 There is typically a high burden of proof to establish a Schallamach wave over a more general slow frictional wave. We note that it would be exceeding difficult to design samples that can reliably create subsets of SFW, but we are aware that this may be an interesting question at a future point in our work.

      Comment 2

      The force sensors look very compliant, and given the dynamic nature of the signal, it is important to characterize the frequency response of the system to make sure that the fluctuations are not amplified.

      Our Response: Thank you for noticing. We mistyped the sensor spring constant as 13.9 N m<sup>-1</sup> instead of kN m<sup>-1</sup>. However, below we show how the instabilities are derived from the mechanics at the interface due to the compliance of the finger. The “springs” of the force sensor and PDMS finger are connected in parallel. Since k<sub>sensor</sub> = 13.9 kN m<sup>-1</sup>, the spring constant of the system overall reflects the compliance of the finger, and highlights the oscillations arising solely from stick-slip. A sample calculation is shown below.

      Author response image 1.

      Fitting a line to the initial slope of the force trace for C6 gives the equation y = 25.679_x_ – 0.2149. The slope here represents force data over time data, and is divided by the velocity (25 mm/s) to determine 𝐹𝐹 the spring constant of the system . This value is lower than ksensor = 13.9 kN/m, indicating that the “springs” representing the force sensor and PDMS finger are connected in parallel: . The finger is the compliant component of the system, with k<sub>finger</sub> = 0.902 N/m, and of course, real human fingers are also compliant so this matches our goals with the design of the mock finger.

      Our changes to the manuscript (Page 4)

      (k \= 13.9 kN m<sup>-1</sup>)

      Comment 3

      The authors should discuss about the stochastic nature of friction:

      Wiertlewski, Hudin, Hayward, IEEE WHC 2011

      Greenspon, McLellan, Lieber, Bensmaia, JRSI 2020”

      We believe that, given the references, this comment on “stochastic” refers to the macroscopically-observable fluctuations (i.e., the mechanical “noise” which is not due to instrument noise) in friction arising from the discordant network of stick-slip phenomena occurring throughout the contact zone, and not the stochastic nature of nanoscale friction that occurs thermal fluctuations nor due to statistical distributions in bond breaking associated with soft contact.

      We first note that our small-scale fluctuations do not arise from a periodic surface texture that dominates in the frequency regime. However, even on our comparatively smooth surfaces, we do expect fluctuations due to nanoscale variation in contact, generation of stick-slip across at microscale length scales that occur either concurrently or discordantly across the contact zone, and the nonlinear dependence of friction to nearly any variation in state and composition(7).

      Perhaps the most relevant to the manuscript is that a major advantage of analysis by friction is that it sidesteps these ever-present microscale fluctuations, leading to more clearly defined classifiers or categories during analysis. Wiertlewski et. al. showed repeated measurements in their systems ultimately gave rise to consistent frequencies(24) (we think their system was in a steady sliding regime and the patterning gave rise to underlying macroscopic waves). These consistent frequencies, at least in soft systems and absent obvious macroscopic patterned features, would be expected to arise from the instability categories and we see them throughout.

      Comment 4

      It is stated that "we observed a spurious, negative correlation between friction coefficient and accuracy”.

      What makes you qualify that correlation as spurious?

      We mean this as in the statistical definition of “spurious”.

      This correlation would indicate that by the metric of friction coefficient, more different surfaces are perceived more similarly. Thus, two very different surfaces, like Teflon and sandpaper, by friction coefficient would be expected to feel very similar. Two nearly identical surfaces would be expected to feel very different – but of course, humans cannot consistently distinguish two identical surfaces. This finding is counterintuitive and refutes that friction coefficient is a reliable classifier of surfaces by touch. We do not think it is productive to determine a mechanism for a spurious correlation, but perhaps one reason we were able to observe this is because our study, to the best of our knowledge, is unique for having samples that are controlled in their physical differences in roughness and surface features.

      Our changes to the manuscript (Page 10)

      “To compare the value of looking at frictional instabilities, we also performed GLMM fits on common approaches in the field, like a friction coefficient or material property typically used in tactile discrimination, shown in Fig. 2D-E. Interestingly, in Fig. 2D, we observed a spurious, negative correlation between friction coefficient (typically and often problematically simplified as across all tested conditions) and accuracy (r = -0.64, p < 0.01); that is, the more different the surfaces are by friction coefficient, the less people can tell them apart. This spurious correlation would be the opposite of intuition, and further calls into question the common practice of using friction coefficients in touch-related studies. The alternative, two-term model which includes adhesive contact area for friction coefficient(29) was even less predictive (see Fig. S6A of SI). We believe such a correlation could not have been uncovered previously as our samples are minimal in their physical variations. Yet, the dynamic changes in force even within a single sample are not considered, despite being a key feature of mesoscale friction during human touch.

      We investigate different material properties in Fig. 2E. Differences in average roughness R<sub>a</sub> (or other parameters, like root mean square roughness R<sub>rms</sub> (Fig. S6A of SI) did not show a statistically significant correlation to accuracy. Though roughness is a popular parameter, correlating any roughness parameter to human performance here could be moot: the limit of detecting roughness differences has previously been defined as 13 nm on structured surfaces(33) and much higher for randomly rough surfaces(46), all of which are magnitudes larger than the roughness differences between our surfaces. The differences in contact angle hysteresis – as an approximation of the adhesion contributions(47) – do not present any statistically significant effects on performance.”

      Comment 5

      The authors should comment on the influence of friction on perceptual invariance. Despite inducing radially different frictional behavior for various conditions, these surfaces are stably perceived. Maybe this is a sign that humans extract a different metric?

      We agree – we are excited that frictional instabilities may offer a more stable perceptual cue because they are not prone to fluctuations (Recommendations for the authors, Comment 3) and instability formation, in many conditions, is invariant to applied pressures and velocities – thus forming large zones where a human may reasonable encounter a given instability.

      Raw friction is highly prone to variation during human exploration (in alignment with Recommendations for the authors, Comment 3), but ongoing work seeks to explain tactile constancy, or the ability to identify objects despite these large changes in force. Very recently published work by Fehlberg et. al. identified the role of modulating finger speed and normal force in amplifying the differences in friction coefficient between materials in order to identify them(25), and we postulate that their work may be streamlined and consistent with the idea of friction instabilities, though we have not had a chance to discuss this in-depth with the authors yet.

      We think that the instability maps show a viable path forward to how surfaces are stably perceived, and instabilities themselves show a potential mechanism: mathematically, instabilities for given conditions can be invariant to velocity or mass, creating zones where a certain instability is encountered. This reduces the immense variability of friction to a smaller, more stable classification of surfaces (e.g., a 30% SS surface or a 60% SS surface). A given surface will typically produce the same instability at a specific condition (we found some boundaries are extremely condition sensitive, but many conditions are not), whereas a single friction trace which is highly prone to variation is not a stable metric.

      Added References (Page 14)

      53 M. Fehlberg, E. Monfort, S. Saikumar, K. Drewing and R. Bennewitz, IEEE Trans. Haptics, 2024, 17, 957–963.

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    1. Author response:

      The following is the authors’ response to the previous reviews.

      Response to Reviewer 1

      Thank you for your recognition of our revised work.

      Response to Reviewer 2

      It would be useful to have a demonstration of where this model outperforms SaProt systematically, and a discussion about what the success of this model teaches us given there is a similar, previously successful model, SaProt.

      As two concurrent works, ProtSSN and SaProt employ different methods to incorporate the structure information of proteins. Generally speaking, for two deep learning models that are developed during a close period, it is challenging to conclude that one model is systematically superior to another. Nonetheless, on DTm and DDG (the two low-throughput datasets that we constructed), ProtSSN demonstrates better empirical performance than SaProt.  

      Moreover, ProtSSN is more efficient in both training and inference compared to SaProt. In terms of training cost, SaProt uses 40 million protein structures for pretraining (requiring 64 A100 GPUs for three months), whereas ProtSSN requires only about 30,000 crystal structures from the CATH database (trained on a single 3090 GPU for two days). Despite SaProt’s significantly higher training cost, its pretrained version does not exhibit superior performance on low-throughput datasets such as DTm, DDG, and Clinvar. Furthermore, the high training cost limits many users from retraining or fine-tuning the model for specific needs or datasets.

      Regarding the inference cost, ProtSSN requires only one embedding computation for a wild-type protein, regardless of the number of mutants (n). In contrast, SaProt computes a separate embedding and score for each mutant. For instance, when evaluating the scoring performance on ProteinGym, ProtSSN only needs 217 inferences, while SaProt needs more than 2M inferences. This inference speed is important in practice, such as high-throughput design and screening.

      Please remove the reference to previous methods as "few shot". This typically refers to their being trained on experimental data, not their using MSAs. A "few shot" model would be ProteinNPT.

      The definition of "few-shot" we used here is following ESM1v [1]. This concept originates from providing a certain number of examples as input to GPT-3 [2]. In the context of protein deep learning models, MSA serves as the wild-type protein examples.

      Also, Reviewer 1 uses the concept in the same way. 

      “Readers should note that methods labelled as "few-shot" in comparisons do not make use of experimental labels, but rather use sequences inferred as homologous; these sequences are also often available even if the protein has never been experimentally tested.”

      In the main text, we also included this definition as well as the reference of ESM-1v in lines 457-458.

      “We extend the evaluation on ProteinGym v0 to include a comparison of our zero-shot ProtSSN with few-shot learning methods that leverage MSA information of proteins (Meier et al., 2021).”

      (1) Meier J, Rao R, Verkuil R, et al. Language models enable zero-shot prediction of the effects of mutations on protein function. Advances in Neural Information Processing Systems, 2021.

      (2) Brown T, Mann B, Ryder N, et al. Language models are few-shot learners. Advances in Neural Information Processing Systems, 2020.

      Furthermore, I don't think it is fair to state that your method is not comparable to these models -- one can run an MSA just as one can predict a structure. A fairer comparison would be to highlight particular assays for which getting an MSA could be challenging -- Transcription did this by showing that they outperform EVE when MSAs are shallow.

      We recognize that there are often differences in the definitions and classifications of various methodologies. Here, we follow the definitions provided by ProteinGym. As the most comprehensive and large scale open benchmark in the community, we believe this classification scheme should be widely accepted. All classifications are available on the official website of ProteinGym (https://proteingym.org/benchmarks), which categorizes methods into PLMs, Structure-based models, and Alignment-based models. For example, GEMME is classified as an alignment-based model, and MSA Transformer is considered a hybrid model combining alignment and PLM features.

      We believe that methodologies with different inputs and architectures can lead to inherent unfairness. Also, it is generally believed that models including evolutionary relationships tend to outperform end-to-end models due to the extra information and efforts involved during the training phase. Some empirical evidence and discussions are in the ablation studies of retrieval factors in Tranception [3]. Moreover, the choice of MSA search parameters can introduce uncertainty, which could have positive or negative impacts. 

      We showcase the impact of MSA depth on model performance with an additional analysis below. Author response image 1 visualizes the Spearman’s correlation between the scores of each model and the number of MSAs on 217 ProteinGym assays, where each point represents one of 217 assays. The summary correlation of each model with respect to all assays are reported in Author response table 1. These results demonstrate no clear correlation between MSA depth and model performance even for MSA-based models.

      Author response image 1.

      Scatter plots of the number of MSA sequences and spearman’s correlation.

      Author response table 1.

      Spearmar’s score of the number of MSA sequences and the model’s performance.

      (3) Notin P, Dias M, Frazer J, et al. Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval. International Conference on Machine Learning, 2022.

      The authors state that DTm and DDG are conceptually appealing because they come from low-throughput assays with lower experimental noise and are also mutations that are particularly chosen to represent the most interesting regions of the protein. I agree with the conceptual appeal but I don't think these claims have been demonstrated in practice. The cited comparison with Frazer as a particularly noisy source of data I think is particularly unconvincing: ClinVar labels are not only rigorously determined from multiple sources of evidence, Frazer et al demonstrates that these labels are actually more reliable than experiment in some cases. They also state that ProteinGym data doesn't come with environmental conditions, but these can be retrieved from the papers the assays came from. The paper would be strengthened by a demonstration of the conceptual benefit of these new datasets, say a comparison of mutations and signal for a protein that may be in one of these datasets vs ProteinGym.

      In the work by Frazer et al. [4], they mentioned that

      "However, these technologies do not easily scale to thousands of proteins, especially not to combinations of variants, and depend critically on the availability of assays that are relevant to or at least associated with human disease phenotypes." 

      It points out that the results of high-throughput experiments are usually based on the design of specific genes (such as BRCA1 and TP53.) and cannot be easily extended to thousands of other genes. At the same time, due to the complexity of the experiment, there may be problems with reproducibility or deviations from clinical relevance.

      This statement aligns with our perspective that high-throughput experiments inherently involve a significant amount of noise and error. It is important to clarify that the noise we discuss here arises from the limitations of high-throughput experiments themselves, instead of from the reliability of the data sources, such as systematic errors in experimental measurements. This latter issue is a complex problem common to all wetlab experiments and falls outside the scope of our study.

      Under this premise, low-throughput datasets like DTm and DDG can be considered to have less noise than high-throughput datasets, as they have undergone manual curation. As for your suggestion, while valuable, unfortunately, we were unable to identify datasets in DTM and DDG that align with those in ProteinGym after a careful search. Thus, we are unable to conduct this comparative experiment at this stage.

      (4) Frazer J, Notin P, Dias M, et al. Disease variant prediction with deep generative models of evolutionary data. Nature, 2021.

    1. Author response:

      eLife Assessment

      This study presents a valuable theoretical exploration on the electrophysiological mechanisms of ionic currents via gap junctions in hippocampal CA1 pyramidal-cell models, and their potential contribution to local field potentials (LFPs) that is different from the contribution of chemical synapses. The biophysical argument regarding electric dipoles appears solid, but the evidence can be more convincing if their predictions are tested against experiments. A shortage of model validation and strictly comparable parameters used in the comparisons between chemical vs. junctional inputs makes the modeling approach incomplete; once strengthened, the finding can be of broad interest to electrophysiologists, who often make recordings from regions of neurons interconnected with gap junctions.

      We gratefully thank the editors and the reviewers for the time and effort in rigorously assessing our manuscript, for the constructive review process, for their enthusiastic responses to our study, and for the encouraging and thoughtful comments. We especially thank you for deeming our study to be a valuable exploration on the differential contributions of active dendritic gap junctions vs. chemical synapses to local field potentials. We thank you for your appreciation of the quantitative biophysical demonstration on the differences in electric dipoles that appear in extracellular potentials with gap junctions vs. chemical synapses.

      However, we are surprised by aspects of the assessment that resulted in deeming the approach incomplete, especially given the following with specific reference to the points raised:

      (1) Testing against experiments: With specific reference to gap junctions, quantitative experimental verification becomes extremely difficult because of the well-established nonspecificities associated with gap junctional modulators (Behrens et al., 2011; Rouach et al., 2003). The non-specific actions of gap junctions are tabulated in Table 2 of (Szarka et al., 2021), reproduced below. In addition, genetic knockouts of gap junctional proteins are either lethal or involve functional compensation (Bedner et al., 2012; Lo, 1999), together making causal links to specific gap junctional contributions with currently available techniques infeasible.

      In addition, the complex interactions between co-existing chemical synaptic, gap junctional, and active dendritic contributions from several cell-types make the delineation of the contributions of specific components infeasible with experimental approaches. A computational approach is the only quantitative route to specifically delineate the contributions of individual components to extracellular potentials, as seen from studies that have addressed the question of active dendritic contributions to field potentials (Halnes et al., 2024; Ness et al., 2018; Reimann et al., 2013; Sinha & Narayanan, 2015, 2022) or spiking contributions to local field potentials (Buzsaki et al., 2012; Gold et al., 2006; Schomburg et al., 2012). The biophysically and morphologically realistic computational modeling route is therefore invaluable in assessing the impact of individual components to extracellular field potentials (Einevoll et al., 2019; Halnes et al., 2024).

      Together, we emphasize that the computational modeling route is currently the only quantitative methodology to delineate the contributions of gap junctions vs. chemical synapses to extracellular potentials.

      (2) Model validation: The model used in this study was adopted from a physiologically validated model from our laboratory (Roy & Narayanan, 2021). Please note that the original model was validated against several physiological measurements along the somatodendritic axis. We sincerely regret our oversight in not mentioning clearly that we have used an existing, thoroughly physiologically-validated model from our laboratory in this study.

      (3) Comparisons between chemical vs. junctional inputs: We had taken elaborate precautions in our experimental design to match the intracellular electrophysiological signatures with reference to synchronous as well as oscillatory inputs, irrespective of whether inputs arrived through gap junctions or chemical synapses.

      In a revised manuscript, we will address all the concerns raised by the reviewers in detail. We have provided point-by-point responses to reviewers’ helpful and constructive comments below. We thank the editors and the reviewers for this constructive review process, which we believe will help us in improving our manuscript with specific reference to emphasizing the novelty of our approach and conclusions.

      Reviewer #1 (Public review):

      This manuscript makes a significant contribution to the field by exploring the dichotomy between chemical synaptic and gap junctional contributions to extracellular potentials. While the study is comprehensive in its computational approach, adding experimental validation, network-level simulations, and expanded discussion on implications would elevate its impact further.

      We gratefully thank you for your time and effort in rigorously assessing our manuscript, for the enthusiastic response, and the encouraging and thoughtful comments on our study. In what follows, we have provided point-by-point responses to the specific comments.

      Strengths

      Novelty and Scope

      The manuscript provides a detailed investigation into the contrasting extracellular field potential (EFP) signatures arising from chemical synapses and gap junctions, an underexplored area in neuroscience. It highlights the critical role of active dendritic processes in shaping EFPs, pushing forward our understanding of how electrical and chemical synapses contribute differently to extracellular signals.

      We thank you for the positive comments on the novelty of our approach and how our study addresses an underexplored area in neuroscience. The assumptions about the passive nature of dendritic structures had indeed resulted in an underestimation of the contributions of gap junctions to extracellular potentials. Once the realities of active structures are accounted for, the contributions of gap junctions increases by several orders of magnitude compared to passive structures (Fig. 1D).

      Methodological Rigor

      The use of morphologically and biophysically realistic computational models for CA1 pyramidal neurons ensures that the findings are grounded in physiological relevance. Systematic analysis of various factors, including the presence of sodium, leak, and HCN channels, offers a clear dissection of how transmembrane currents shape EFPs.

      We thank you for your encouraging comments on the experimental design and methodological rigor of our approach.

      Biological Relevance

      The findings emphasize the importance of incorporating gap junctional inputs in analyses of extracellular signals, which have traditionally focused on chemical synapses. The observed polarity differences and spectral characteristics provide novel insights into how neural computations may differ based on the mode of synaptic input.

      We thank you for your positive comments on the biological relevance of our approach. We also gratefully thank you for emphasizing the two striking novelties unveiling the dichotomy between gap junctions and chemical synapses in their contributions to field potentials: polarity differences and spectral characteristics.

      Clarity and Depth

      The manuscript is well-structured, with a logical progression from synchronous input analyses to asynchronous and rhythmic inputs, ensuring comprehensive coverage of the topic.

      We sincerely thank you for the positive comments on the structure and comprehensive coverage of our manuscript encompassing different types of inputs that neurons typically receive.

      Weaknesses and Areas for Improvement

      Generality and Validation

      The study focuses exclusively on CA1 pyramidal neurons. Expanding the analysis to other cell types, such as interneurons or glial cells, would enhance the generalizability of the findings. Experimental validation of the computational predictions is entirely absent. Empirical data correlating the modeled EFPs with actual recordings would strengthen the claims.

      We thank you for raising this important point. The prime novelty and the principal conclusion of this study is that gap junctional contributions to extracellular field potentials are orders of magnitude higher when the active nature of cellular compartments are accounted for. The lacuna in the literature has been consequent to the assumption that cellular compartments are passive, resulting in the dogma that gap junctional contributions to field potentials are negligible. Despite knowledge about active dendritic structures for decades now, this assumption has kept studies from understanding or even exploring the contributions of gap junctions to field potentials. The rationale behind the choice of a computational approach to address the lacuna were as follows:

      (1) The complex interactions between co-existing chemical synaptic, gap junctional, and active dendritic contributions from several cell-types make the delineation of the contributions of specific components infeasible with experimental approaches. A computational approach is the only quantitative route to specifically delineate the contributions of individual components to extracellular potentials, as seen from studies that have addressed the question of active dendritic contributions to field potentials (Halnes et al., 2024; Ness et al., 2018; Reimann et al., 2013; Sinha & Narayanan, 2015, 2022) or spiking contributions to local field potentials (Buzsaki et al., 2012; Gold et al., 2006; Schomburg et al., 2012). The biophysically and morphologically realistic computational modeling route is therefore invaluable in assessing the impact of individual components to extracellular field potentials (Einevoll et al., 2019; Halnes et al., 2024).

      (2) With specific reference to gap junctions, quantitative experimental verification becomes extremely difficult because of the well-established non-specificities associated with gap junctional modulators (Behrens et al., 2011; Rouach et al., 2003). The non-specific actions of gap junctions are tabulated in Table 2 of (Szarka et al., 2021). In addition, genetic knockouts of gap junctional proteins are either lethal or involve functional compensation (Bedner et al., 2012; Lo, 1999), together making causal links to specific gap junctional contributions with currently available techniques infeasible.

      We highlight the novelty of our approach and of the conclusions about differences in extracellular signatures associated with active-dendritic chemical synapses and gap junctions, against these experimental difficulties. We emphasize that the computational modeling route is currently the only quantitative methodology to delineate the contributions of gap junctions vs. chemical synapses to extracellular potentials. Our analyses clearly demonstrates that gap junctions do contribute to extracellular potentials if the active nature of the cellular compartments is explicitly accounted for (Fig. 1D). We also show theoretically well-grounded and mechanistically elucidated differences in polarity (Figs. 1–3) as well as in spectral signatures (Figs. 5–8) of extracellular potentials associated with gap junctional vs. chemical synaptic inputs. Together, our fundamental demonstration in this study is the critical need to account for the active nature of cellular compartments in studying gap junctional contributions of extracellular potentials, with CA1 pyramidal neuronal dendrites used as an exemplar.

      In a revised version of the manuscript, we will emphasize the motivations for the approach we took, highlighting the specific novelties both in methodological and conceptual aspects, finally emphasizing the need to account for other cell types and gap junctional contributions therein. Importantly, we will emphasize the non-specificities associated with gap-junctional blockers as the reason why experimental delineation of gap junctional vs. chemical synaptic contributions to LFP becomes tedious. We hope that these points will underscore the need for the computational approach that we took to address this important question, apart from the novelties of the manuscript.

      Role of Active Dendritic Currents

      The paper emphasizes active dendritic currents, particularly the role of HCN channels in generating outward currents under certain conditions. However, further discussion of how this mechanism integrates into broader network dynamics is warranted.

      We thank you for this constructive suggestion. We agree that it is important to consider the implications for broader network dynamics of the outward HCN currents that are observed with synchronous inputs. In a revised manuscript, we will elaborate on the implications of the outward HCN current to network dynamics in detail.

      Analysis of Plasticity

      While the manuscript mentions plasticity in the discussion, there are no simulations that account for activity-dependent changes in synaptic or gap junctional properties. Including such analyses could significantly enhance the relevance of the findings.

      We thank you for this constructive suggestion. Please note that we have presented consistent results for both fewer and more gap junctions in our analyses (Figure 1 with 217 gap junctions and Supplementary Figure 1 with 99 gap junctions). Thus, our fundamentally novel result that gap junctions onto active dendrites differentially shape LFPs holds true irrespective of the relative density of gap junctions onto the neuron. Thus, these results demonstrate that the conclusions about their contributions to LFP are invariant to plasticity in their gap junctional numerosity.

      We had only briefly mentioned plasticity in the Introduction to highlight the different modes of synaptic transmission and to emphasize that plasticity has been studied in both chemical synapses and gap junctions, playing a role in learning and adaptation. However, if this wording inadvertently suggests that our study includes plasticity simulations, we would remove it from Introduction in the updated manuscript to ensure clarity.

      In the ‘Limitations of analyses and future studies’ section in Discussion, we suggested investigating the impact of plasticity mechanisms—specifically, activity-dependent plasticity of ion channels—on synaptic receptors vs. gap junctions and their effects on extracellular field potentials under various input conditions and plasticity combinations across different structures. We fully agree with the reviewer that such studies would offer valuable insights and further enhance the broader relevance of our findings. However, while our study implies this direction, it was not the primary focus of our investigation.

      In the revised manuscript, we will expand on intrinsic/synaptic plasticity and how they could contribute to LFPs (Sinha & Narayanan, 2015, 2022), while also pointing to simulations with different numbers of gap junction in this context.

      Frequency-Dependent Effects

      The study demonstrates that gap junctional inputs suppress highfrequency EFP power due to membrane filtering. However, it could delve deeper into the implications of this for different brain rhythms, such as gamma or ripple oscillations.

      We sincerely thank you for these insightful comments that we totally agree with. As it so happens, this manuscript forms the first part of a broader study where we explore the implications of gap junctions to ripple frequency oscillations. The ripple oscillations part of the work was presented as a poster in the Society for Neuroscience (SfN) annual meeting 2024 (Sirmaur & Narayanan, 2024). There, we simulate a neuropil made of hundreds of morphologically realistic neurons to assess the role of different synaptic inputs — excitatory, inhibitory, and gap junctional — and active dendrites to ripple frequency oscillations. We demonstrate there that the conclusions from single-neuron simulations in this current manuscript extend to a neuropil with several neurons, each receiving excitatory, inhibitory and gap-junctional inputs, especially with reference to high-frequency oscillations. Our networkbased analyses unveiled a dominant mediatory role of patterned inhibition in ripple generation, with recurrent excitations through chemical synapses and gap junctions in conjunction with return-current contributions from active dendrites playing regulatory roles in determining ripple characteristics (Sirmaur & Narayanan, 2024).

      Our principal goal in this study, therefore, was to lay the single-neuron foundation for network analyses of the impact of gap junctions on LFPs. We are preparing the network part of the study, with a strong focus on ripple-frequency oscillations, for submission for peer review separately.

      In a revised manuscript, we will mention the results from our SfN abstract with reference to network simulations and high-frequency oscillations, while also presenting discussions from other studies on the role of gap junctions in synchrony and LFP oscillations.

      Visualization

      Figures are dense and could benefit from more intuitive labeling and focused presentations. For example, isolating key differences between chemical and gap junctional inputs in distinct panels would improve clarity.

      We thank you for this constructive suggestion. In the revised manuscript, we will enhance the visualization of the figures to ensure a clearer and more intuitive distinction between chemical synapses and gap junctions.

      Contextual Relevance

      The manuscript touches on how these findings relate to known physiological roles of gap junctions (e.g., in gamma rhythms) but does not explore this in depth. Stronger integration of the results into known neural network dynamics would enhance its impact.

      We sincerely appreciate your valuable suggestion and acknowledge the importance of integrating our results into established neural network dynamics, particularly their implications for gamma rhythms. We will address this aspect more comprehensively in the revised version of our manuscript.

      Reviewer #2 (Public review):

      This computational work examines whether the inputs that neurons receive through electrical synapses (gap junctions) have different signatures in the extracellular local field potential (LFP) compared to inputs via chemical synapses. The authors present the results of a series of model simulations where either electric or chemical synapses targeting a single hippocampal pyramidal neuron are activated in various spatio-temporal patterns, and the resulting LFP in the vicinity of the cell is calculated and analyzed. The authors find several notable qualitative differences between the LFP patterns evoked by gap junctions vs. chemical synapses. For some of these findings, the authors demonstrate convincingly that the observed differences are explained by the electric vs. chemical nature of the input, and these results likely generalize to other cell types. However, in other cases, it remains plausible (or even likely) that the differences are caused, at least partly, by other factors (such as different intracellular voltage responses due to, e.g., the unequal strengths of the inputs). Furthermore, it was not immediately clear to me how the results could be applied to analyze more realistic situations where neurons receive partially synchronized excitatory and inhibitory inputs via chemical and electric synapses.

      We gratefully thank you for your time and effort in rigorously assessing our manuscript, for the enthusiastic response, and the encouraging and thoughtful comments on our study. In what follows, we have provided point-by-point responses to the specific comments.

      Strengths

      The main strength of the paper is that it draws attention to the fact that inputs to a neuron via gap junctions are expected to give rise to a different extracellular electric field compared to inputs via chemical synapses, even if the intracellular effects of the two types of input are similar. This is because, unlike chemical synaptic inputs, inputs via gap junctions are not directly associated with transmembrane currents. This is a general result that holds independent of many details such as the cell types or neurotransmitters involved.

      We gratefully thank you for the positive comments and the encouraging words about the novel contributions of our study. We are particularly thankful to you for your comment on the generality of our conclusions that hold for different cell types and neurotransmitters involved.

      Another strength of the article is that the authors attempt to provide intuitive, non-technical explanations of most of their findings, which should make the paper readable also for non-expert audiences (including experimentalists).

      We sincerely thank you for the positive comments about the readability of the paper.

      Weaknesses

      The most problematic aspect of the paper relates to the methodology for comparing the effects of electric vs. chemical synaptic inputs on the LFP. The authors seem to suggest that the primary cause of all the differences seen in the various simulation experiments is the different nature of the input, and particularly the difference between the transmembrane current evoked by chemical synapses and the gap junctional current that does not involve the extracellular space. However, this is clearly an oversimplification: since no real attempt is made to quantitatively match the two conditions that are compared (e.g., regarding the strength and temporal profile of the inputs), the differences seen can be due to factors other than the electric vs. chemical nature of synapses. In fact, if inputs were identical in all parameters other than the transmembrane vs. directly injected nature of the current, the intracellular voltage responses and, consequently, the currents through voltage-gated and leak currents would also be the same, and the LFPs would differ exactly by the contribution of the transmembrane current evoked by the chemical synapse. This is evidently not the case for any of the simulated comparisons presented, and the differences in the membrane potential response are rather striking in several cases (e.g., in the case of random inputs, there is only one action potential with gap junctions, but multiple action potentials with chemical synapses). Consequently, it remains unclear which observed differences are fundamental in the sense that they are directly related to the electric vs. chemical nature of the input, and which differences can be attributed to other factors such as differences in the strength and pattern of the inputs (and the resulting difference in the neuronal electric response).

      We thank you for raising this important point. We would like to emphasize that our experimental design and analyses quantitatively account for the spatial distribution and temporal pattern of specific kinds of inputs that arrive through gap junctions and chemical synapses. We submit that our analyses quantitatively demonstrates that the fundamental difference between the gap junctional and chemical synaptic contributions to extracellular potentials is the absence of the direct transmembrane component from gap junctional inputs. We elucidate these points below:

      (1) Spatial distribution: The inputs were distributed randomly across the basal dendrites, irrespective of whether they were through gap junctions or chemical synapses. For both chemical synapses and gap junctions, the inputs were of the same nature: excitatory.

      (2) Different numbers of inputs: We have presented consistent results for both fewer and more gap junctions or chemical synapses in our analyses (see Figure 1 with 217 gap junctions or 245 chemical synapses and Supplementary Figure 2 with 99 gap junctions or 30 chemical synapses). Our fundamentally novel result that gap junctions onto active dendrites shape LFPs holds true irrespective of the relative density of gap junctions onto the neuron.

      (3) Synchronous inputs (Figs. 1–3): For chemical synapses, the waveforms are in the shape of postsynaptic potentials. For gap junctional inputs, the waveforms are in the shape of postsynaptic potentials or dendritic spikes (to respect the active nature of inputs from the other cell). Here, the electrical response of the postsynaptic cell is identical irrespective of whether inputs arrive through gap junctions or chemical synapses: an action potential. We quantitatively matched the strengths such that the model generated a single action potential in response to synchronous inputs, irrespective of whether they arrived through chemical synaptic and gap junctional inputs. We mechanistically analyze the contributions of different cellular components and show that the direct transmembrane current in chemical synapses is the distinguishing factor that determines the dichotomy between the contributions of gap junctions vs. chemical synapses to extracellular potentials (Figs. 2–3). In a revised manuscript, we will show the intracellular responses to demonstrate that they are electrically matched.

      (4) Random inputs (Fig. 4): For random inputs, we did not account for the number of action potentials that arrived, as the only observation we made here was with reference to the biphasic nature of the extracellular potentials with gap junctional inputs in the “No Sodium” scenario. We note that in the “No Sodium” scenario, the time-domain amplitudes were comparable for the field potentials (Fig. 4B, Fig. 4D).

      (5) Rhythmic inputs (Fig. 5–8): For rhythmic inputs, please note that the intracellular and extracellular waveforms for every frequency are provided in supplementary figures S5– S11. It may be noted that the intracellular responses are comparable. In simulations for assessing spike-LFP comparison, we tuned the strengths to produce a single spike per cycle, ensuring fair comparison of LFPs with gap junctions vs. chemical synapses.

      Taken together, we demonstrate through explicit sets of simulations and analyses that the differences in LFPs were not driven by the strength or patterns of the inputs but rather by the differences in direct transmembrane currents, which are subsequently reflected in the LFPs. In a revised manuscript, we will add a section to emphasize these points apart from providing intracellular traces for cases where they are not provided.

      Some of the explanations offered for the effects of cellular manipulations on the LFP appear to be incomplete. More specifically, the authors observed that blocking leak channels significantly changed the shape of the LFP response to synchronous synaptic inputs - but only when electric inputs were used, and when sodium channels were intact. The authors seemed to attribute this phenomenon to a direct effect of leak currents on the extracellular potential - however, this appears unlikely both because it does not explain why blocking the leak conductance had no effect in the other cases, and because the leak current is several orders of magnitude smaller than the spike-generating currents that make the largest contributions to the LFP. An indirect effect mediated by interactions of the leak current with some voltage-gated currents appears to be the most likely explanation, but identifying the exact mechanism would require further simulation experiments and/or a detailed analysis of intracellular currents and the membrane potential in time and space.

      We thank you for raising this important question. Leak channels were among the several contributors to the positive deflection observed in LFPs associated with gap junctions. This effect was present not only in gap junctional models with intact sodium conductance but also in the no-sodium model, where the amplitude of the positive deflection was reduced across other models as well (Fig. 2F, I). Furthermore, even in the absence of leak conductance, a small positive deflection was still observed (Fig. 2F), leading us to further investigate other transmembrane currents over time and across spatial locations, from the proximal to the distal dendritic ends relative to the soma (Fig. 3D). We had observed that the dominant contributor in the case of chemical synapses was the inward synaptic current (Fig. 3A), whereas for gap junctions, the primary contributors were leak conductance along with other outward currents, such as potassium and HCN currents (Fig. 3D). Together, the direct transmembrane component of chemical synapses provides a dominant contribution to extracellular potentials. This dominance translates to differences in the relative contributions of indirect currents (including leak currents) to extracellular potentials associated chemical synaptic vs. gap junctional inputs. Our analyses of the exact ionic mechanisms (Fig. 3) demonstrates the involvement of several ion channels contributing to the indirect component in either scenario.

      In every simulation experiment in this study, inputs through electric synapses are modeled as intracellular current injections of pre-determined amplitude and time course based on the sampled dendritic voltage of potential synaptic partners. This is a major simplification that may have a significant impact on the results. First, the current through gap junctions depends on the voltage difference between the two connected cellular compartments and is thus sensitive to the membrane potential of the cell that is treated as the neuron "receiving" the input in this study (although, strictly speaking, there is no pre- or postsynaptic neuron in interactions mediated by gap junctions). This dependence on the membrane potential of the target neuron is completely missing here. A related second point is that gap junctions also change the apparent membrane resistance of the neurons they connect, effectively acting as additional shunting (or leak) conductance in the relevant compartments. This effect is completely missed by treating gap junctions as pure current sources.

      We thank you for raising this important point. We agree with the analyses presented by the reviewer on the importance of network simulations and bidirectional gap junctions that respect the voltages in both neurons. However, the complexities of LFP modeling precludes modeling of networks of morphologically realistic models with patterns of stimulations occurring across the dendritic tree. LFP modeling studies predominantly uses “post-synaptic” currents to analyze the impact of different patterns of inputs arriving on to a neuron, even when chemical synaptic inputs are considered. Explicitly, individual neurons are separately simulated with different patterns of synaptic inputs, the transmembrane current at different locations recorded, and the extracellular potential is then computed using line source approximation (Buzsaki et al., 2012; Gold et al., 2006; Halnes et al., 2024; Ness et al., 2018; Reimann et al., 2013; Schomburg et al., 2012; Sinha & Narayanan, 2015, 2022). Even in scenarios where a network is analyzed, a hybrid approach involving the outputs of a pointneuron-based network being coupled to an independent morphologically realistic neuronal model is employed (Hagen et al., 2016; Martinez-Canada et al., 2021; Mazzoni et al., 2015). Given the complexities associated with the computation of electrode potentials arising as a distance-weighted summation of several transmembrane currents, these simplifications becomes essential.

      Our approach models gap junctional currents in a similar way as the other model incorporate synaptic currents in LFP modeling (Buzsaki et al., 2012; Gold et al., 2006; Halnes et al., 2024; Ness et al., 2018; Reimann et al., 2013; Schomburg et al., 2012; Sinha & Narayanan, 2015, 2022). As gap junctions are typically implemented as resistors from the other neuronal compartment, we accounted for gap-junctional variability in our model by randomizing the scaling-factors and the exact waveforms that arrive through individual gap junctions at specific locations. Thus, the inputs were not pre-determined by “pre” neurons. Instead, the recorded voltages from potential synaptic partner neurons were randomized across locations and scaled using factors at the dendrites before being injected into the target neuron (Supplementary Fig. S1). While incorporating a network of interconnected neurons is indeed important, we utilized biophysical, morphologically realistic CA1 neuron model with different sets of input patterns to model LFPs, which were derived from the total transmembrane currents across all compartments of the multi-compartmental neuron model. Given the complexity of this approach, adding further network-level interactions or pre-post connections would have been computationally demanding.

      In a revised manuscript, we will introduce the general methodology used in LFP modeling studies to introduce synaptic currents. We will emphasize that our study extends this approach to modeling gap junctional inputs, while also highlighting randomization of locations and the scaling process in assigning gap junctional synaptic strengths.

      One prominent claim of the article that is emphasized even in the abstract is that HCN channels mediate an outward current in certain cases. Although this statement is technically correct, there are two reasons why I do not consider this a major finding of the paper. First, as the authors acknowledge, this is a trivial consequence of the relatively slow kinetics of HCN channels: when at least some of the channels are open, any input that is sufficiently fast and strong to take the membrane potential across the reversal potential of the channel will lead to the reversal of the polarity of the current. This effect is quite generic and well-known and is by no means specific to gap junctional inputs or even HCN channels. Second, and perhaps more importantly, the functional consequence of this reversed current through HCN channels is likely to be negligible. As clearly shown in Supplementary Figure S3, the HCN current becomes outward only for an extremely short time period during the action potential, which is also a period when several other currents are also active and likely dominant due to their much higher conductances. I also note that several of these relevant facts remain hidden in Figure 3, both because of its focus on peak values, and because of the radically different units on the vertical axes of the current plots.

      We thank you for raising this point and agree with you on every point. Please note that we do not assert that the outward HCN currents are exclusively associated with gap junctional inputs. Rather, our results show that synchronous inputs generate outward HCN currents in both chemical synapses (Fig. 3B; positive/outward HCN currents, except in the no sodium or leak model) and gap junctions (Fig. 3D; positive/outward HCN currents). We emphasized this in the case of gap junctions because, in the absence of inward synaptic currents, HCN (acting as outward currents with synchronous inputs) contributed to the positive deflection observed in the LFPs. While HCN would also contribute in the case of chemical synapses, its effect was negligible due to the presence of large inward synaptic currents. Since LFPs reflect the collective total transmembrane currents, the dominant contributors differ between these two scenarios, which we aimed to highlight. Since HCN exhibited outward currents in our synchronous input simulations, we have elaborated on this mechanism in the supplementary figure (Fig. S3). Our intention was not to emphasize this effect for only one synaptic mode but rather to highlight HCN's contribution to the positive deflection as one of the contributing factors.

      We agree that HCN currents are relatively small in magnitude; therefore, our conclusions were based on HCN being one of the several contributing factors. Leak conductance and other outward conductances, including HCN currents (Fig. 3D), collectively contribute to the positive deflections observed in the case of gap junctional synchronous inputs.

      We will ensure that we will account for all the points appropriately in a revised manuscript.

      Finally, I missed an appropriate validation of the neuronal model used, and also the characterization of the effects of the in silico manipulations used on the basic behavior of the model. As far as I understand, the model in its current form has not been used in other studies. If this is the case, it would be important to demonstrate convincingly through (preferably quantitative) comparisons with experimental data using different protocols that the model captures the physiological behavior of at least the relevant compartments (in this case, the dendrites and the soma) of hippocampal pyramidal neurons sufficiently well that the results of the modeling study are relevant to the real biological system. In addition, the correct interpretation of various manipulations of the model would be strongly facilitated by investigating and discussing how the physiological properties of the model neuron are affected by these alterations.

      We thank you for raising this important point. The CA1 pyramidal neuronal model used in this study is built with ion-channel models derived from biophysical and electrophysiological recordings from these cells. As mentioned in the Methods section “Dynamics and distribution of active channels” and Supplementary Table S1, models for individual channels, their gating kinetics, and channel distributions across the somatodendritic arbor (wherever known) are all derived from their physiological equivalents. Importantly, these values were derived from previously validated models from the laboratory, which contain these very ion channel models and the exact same morphology (Roy & Narayanan, 2021). Please compare Supplementary Table S1 with the Table 1 from (Roy & Narayanan, 2021). Please note that this model was validated against several physiological measurements along the somatodendritic axis (Fig. 1 of (Roy & Narayanan, 2021)).

      In a revised manuscript, we will explicitly mention this while also mentioning the different physiological properties that were used for the validation process from (Roy & Narayanan, 2021). We sincerely regret not mentioning these details in the current version of our manuscript.

      We will fix these in a revised version of the manuscript.

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      Mazzoni, A., Linden, H., Cuntz, H., Lansner, A., Panzeri, S., & Einevoll, G. T. (2015). Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models. PLoS Comput Biol, 11(12), e1004584. https://doi.org/10.1371/journal.pcbi.1004584

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      Sirmaur, R., & Narayanan, R. (2024). Distinct extracellular signatures of chemical and electrical synapses impinging on active dendrites differentially contribute to ripple-frequency oscillations. Society for Neuroscience annual meeting (https://www.abstractsonline.com/pp8/?_gl=1*1bxo7m*_gcl_au*MTc5MTQ0NjE0NC4xNzI3MDcwOTMw*_ga*MTMxMTE5OTcyMy4xNzI3MDcwOTMx*_ga_T09K 3Q2WDN*MTcyNzA3MDkzMS4xLjEuMTcyNzA3MDkzNy41NC4wLjA.#!/20433/ presentation/13949), Chicago, USA.

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    1. Author response:

      The following is the authors’ response to the previous reviews.

      Reviewer #2:

      The authors indicated that they had added coefficients of variation for within-lineage heterogeneity (line 93), but I can't seem to find this.

      The coefficients of variation were indeed included as suggested, and can be found in lines 94-96 of the current revised version of the manuscript. The sentence states: “Nevertheless, substantial intra-lineage heterogeneity could be observed, particularly within L1 and L2 (coefficients of variation 84.4% [L1] and 66.0% [L2] vs. 32.6% [L3], 34.6% [L4] and 31.9% [L5]).”

      They were unable to address my question on the impact of T-cell depletion from PBMC on bacterial growth? Their discussion should include that this experimental limitation means that they are unable to test cause and effect for the relationship between T cell proliferation and bacterial growth.

      As recommended, this experimental limitation is now included in the discussion in lines 344-346.

      Reviewer #3:

      EM:

      Based on the authors lack of resources, I don't believe that electron microscopy experiments should be required for this publication. However, it should be noted that EM is performed on fixed samples such that implementation of those protocols as it relates to bio-safety is no more demanding than the preparation of samples for other common assays performed outside of the BSL3.

      We appreciate your understanding regarding our lack of resources to carry out the EM experiments, although we recognize the possibility of them being performed on BSL3 samples.

      Granuloma score:

      From the author comments and the manuscript's text, it appears that the "granuloma score" is an attempt at quantitation of PBMC organization. Where every component of the metric [(mean area / mean aspect ratio) / mean n ] is a visual facet of the relative integration of PBMCs into a more organized aggregate. The area and number (n) of aggregates both address regional coalescence of the total number of PBMCs added into the matrix. Whereas the aspect ratio component is an indicator of uniformity of the PBMCs that have been assigned to an individual aggregate. Perhaps another roundness estimation would have been a more precise, but aspect ratio seems fine for their assay. Considering these factors and the author's contention that the aggregates making up (n) are granulomas, the name "granuloma score" is inaccurate and a more appropriate title would be "aggregate organization score" or "aggregate organization index".

      Thank you for the suggested alternative terminology, the term “granuloma score” has been substituted with “aggregate formation score” throughout the manuscript.

      Dormancy:

      In the manuscript, the authors should explicitly reference the validation studies which demonstrate induction of the DosR regulon in the model, lest their previously generated and conducted studies go unappreciated by a broader audience. In the title of that previous work (PMID: 32069329) this group used the designation "dormant-like" to describe the state observed in bacteria within their in vitro granuloma model system, as they also do in LINE 124. This term or a variation of it should be exchanged for dormant/dormancy throughout the manuscript when referring to observations in the model bacteria. It is a more precise description. Further, "dormant-like" allows the latitude to refer to actively growing bacteria in the context of dormancy without running the risk of putting forth confusing or potentially erroneous assertions.

      As recommended, the suffix “-like” has been added to the designation “dormant” when referring to the bacterial phenotype induced in the model. In addition, de induction of the DosR regulon in the model is now mentioned in line 116 and the reference to Kapoor’s work that originally demonstrated it by qPCR included.

      PBMC aggregation:

      I would like to make the authors aware that in well vetted models, cell aggregation as a function of infection does not typically occur in PBMCs on tissue culture plates until day 6 post infection (PMID: 25691598, Fig 2). Further, this group's own published protocol for the model under consideration in this manuscript (PMID: 33659472, Fig1) explicitly states that "Formation of granuloma like structures can be observed after 7-8 days", the implication being that prior to 7 days granuloma like structures cannot be observed reliably. Regardless, it seems evident that the authors will not be conducting additional experiments for this publication, which I find acceptable. However a proper negative control would certainly strengthen evidence for the association of strain specific bacterial and host responses with the granulomatous response in this model.

      We had interpreted the reviewer’s previous comment regarding PBMC aggregation as referring to a different experimental model rather than a matter of timing. Since many other studies have previously assessed the impact of strain/lineage variability in macrophage responses, in this work we decided to focus on later time points and we did include uninfected as a negative control. Nonetheless, we agree it would be indeed very interesting to additionally evaluate monocyte/macrophage early responses and we will take it into account for future studies.

      Use of antiquated terminology:

      I can appreciate the desire to establish continuity between publications by using the same abbreviation for TNF but it will come at a cost. Using outdated terms in general makes people more dismissive of the work. Perhaps something to consider.

      Since this seems an important issue to the reviewer, we have replaced the term TNF-a with TNF throughout the manuscript.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      Chen and Phillips describe the dynamic appearance of cytoplasmic granules during embryogenesis analogous to SIMR germ granules, and distinct from CSR-1-containing granules, in the C. elegans germline. They show that the nuclear Argonaute NRDE-3, when mutated to abrogate small RNA binding, or in specific genetic mutants, partially colocalizes to these granules along with other RNAi factors, such as SIMR-1, ENRI-2, RDE-3, and RRF-1. Furthermore, NRDE-3 RIP-seq analysis in early vs. late embryos is used to conclude that NRDE-3 binds CSR-1-dependent 22G RNAs in early embryos and ERGO-1dependent 22G RNAs in late embryos. These data lead to their model that NRDE-3 undergoes small RNA substrate "switching" that occurs in these embryonic SIMR granules and functions to silence two distinct sets of target transcripts - maternal, CSR-1 targeted mRNAs in early embryos and duplicated genes and repeat elements in late embryos.

      Strengths:

      The identification and function of small RNA-related granules during embryogenesis is a poorly understood area and this study will provide the impetus for future studies on the identification and potential functional compartmentalization of small RNA pathways and machinery during embryogenesis.

      Weaknesses:

      (1) While the authors acknowledge the following issue, their finding that loss of SIMR granules has no apparent impact on NRDE-3 small RNA loading puts the functional relevance of these structures into question. As they note in their Discussion, it is entirely possible that these embryonic granules may be "incidental condensates." It would be very welcomed if the authors could include some evidence that these SIMR granules have some function; for example, does the loss of these SIMR granules have an effect on CSR-1 targets in early embryos and ERGO-1-dependent targets in late embryos?

      We appreciate reviewer 1’s concern that we do not provide enough evidence for the function of the SIMR granules. As suggested, we examined the NRDE-3 bound small RNAs more deeply, and we do observe a slight but significant increased CSR-class 22G-RNAs binding to NRDE-3 in late embryos of simr-1 and enri-2 mutants (see below, right). We hypothesize that this result could be due to a slower switch from CSR to ERGO 22G-RNAs in the absence of SIMR granules. We added these data to Figure 6G.

      (2) The analysis of small RNA class "switching" requires some clarification. The authors re-define ERGO1-dependent targets in this study to arrive at a very limited set of genes and their justification for doing this is not convincing. What happens if the published set of ERGO-1 targets is used? 

      As we mentioned in the manuscript, we initially attempted to use the previously defined ERGO targets. However, the major concern is fewer than half the genes classified as ERGO targets by Manage et al. and Fischer et al. overlap with one another (Figure 6—figure supplement 1D and below). We reason this might because the gene sets were defined as genes that lose small RNAs in various ERGO pathway mutants and because different criteria were used to define the lists as discussed in the manuscript (lines 471-476). As a result, some of the previously defined ERGO target genes may actually be indirect targets of the pathway. Here we focus on genes targeted by small RNAs enriched in an ERGO pathway Argonaute IP, which should be more specific.

      In this manuscript, we are interested specifically in the ERGO targets bound by NRDE-3, thus we utilized the IP-small RNA sequencing data from young adult animals (Seroussi et al, 2023), to define a new ERGO list. We are confident about this list because 1) Most of our new ERGO genes overlap with the overlap between ERGO-Manage and ERGO-Fischer list (see Figure 6—figure supplement 1D in our manuscript and below). 2) We observed the most significant decrease of small RNA levels and increase of mRNA levels in the nrde-3 mutants using our newly defined list (see Figure 6—figure supplement 1E-F in our manuscript).

      To further address reviewer 1’s concern about whether the data would look significantly different when using the ERGO-Manage and ERGO-Fischer lists, we made new scatter plots shown in Author response image 1 panels A-C below (ERGO-Manage – purple, ERGO-Fischer- yellow, and the overlap - yellow with purple ring). We found that the small switching pattern of NRDE-3 is consistent with our newly defined list, particularly if we look at the overlap of ERGO-Manage and ERGO-Fischer list (Author response image 1 panels D-F below, red).

      Author response image 1.

      Further, the NRDE-3 RIP-seq data is used to conclude that NRDE-3 predominantly binds CSR-1 class 22G RNAs in early embryos, while ERGO-1-dependent 22G RNAs are enriched in late embryos. a) The relative ratios of each class of small RNAs are given in terms of unique targets. What is the total abundance of sequenced reads of each class in the NRDE-3 IPs? 

      To address the reviewer’s question about the total abundance of sequenced reads of each class in the NRDE-3 IPs: Author response image 2 panel A-B below show the total RPM of CSR and ERGO class sRNAs in inputs and IPs at different stages. Focusing on late embryos, the total abundance of ERGO-dependent sRNAs is similar to CSR-class sRNAs in input, while much higher in IP, indicating an enrichment of ERGO-dependent 22G-RNAs in NRDE-3 consistent with our log2FC (IP vs input) in Figure 6B. This data supports our conclusion that NRDE-3 preferentially binds to ERGO targets in late embryos.

      Author response image 2.

      b) The "switching" model is problematic given that even in late embryos, the majority of 22G RNAs bound by NRDE-3 is the CSR-1 class (Figure 5D). 

      It is important to keep in mind the difference in the total number of CSR target genes (3834) and ERGO target genes (119).  The pie charts shown in Figure 6D are looking at the total proportion of the genes enriched in the NRDE-3 IP that are CSR or ERGO targets. For the NRDE-3 IP in late embryos, that would be 70/119 (58.8%) of ERGO targets are enriched, while 172/3834 (4.5%) of CSR targets are enriched. These data are also supported by the RPM graphs shown in Author response image 2 panels A-B above, which show that the majority of the small RNA bound by NRDE-3 in late embryos are ERGO targets. Nonetheless, NRDE-3 still binds to some CSR targets shown as Figure 6D and panel B, which may be because the amount of CSR-class 22G-RNAs is reduced gradually across embryonic development as the maternally-deposited NRDE-3 loaded with CSR-class 22G-RNAs is diluted by newly transcribed NRDE-3 loaded with ERGOdependent 22G-RNAs (lines 857-862). 

      c) A major difference between NRDE-3 small RNA binding in eri-1 and simr-1 mutants appears to be that NRDE-3 robustly binds CSR-1 22G RNAs in eri-1 but not in simr-1 in late embryos. This result should be better discussed.

      In the eri-1 mutant, we hypothesize that NRDE-3 robustly binds CSR-class 22G-RNAs because ERGOclass 22G-RNAs are not synthesized during mid-embryogenesis, so either NRDE-3 is unloaded (in granule at 100-cell stage in Figure 2A) or mis-loaded with CSR-class 22G-RNAs (in the nucleus at 100cell stage in Figure 2A). We don’t have a robust method to address the proportion of loaded vs. unloaded NRDE-3 so it is difficult to address the degree to which NRDE-3 is misloaded in the eri-1 mutant. In the simr-1 mutant, both classes of small RNAs are present and NRDE-3 is still preferentially loaded with ERGO-dependent 22G-RNAs, though we do see a subtle increase in association with CSR-class 22GRNAs. These data could suggest a less efficient loading of NRDE-3 with ERGO-dependent 22G-RNAs, but we would need more precise methods to address the loading dynamics in the simr-1 mutant.

      (3) Ultimately, if the switching is functionally important, then its impact should be observed in the expression of their targets. RNA-seq or RT-qPCR of select CSR-1 and ERGO-1 targets should be assessed in nrde-3 mutants during early vs late embryogenesis.

      The function of NRDE-3 at ERGO targets has been well studied (Guang et al, 2008) and is also assessed in our H3K9me3 ChIP-seq analysis in Figure 7E where, in mixed staged embryos, H3K9me3 level on ERGO targets (labeled as ‘NRDE-3 targets in young adults’) is reduced significantly in the nrde-3 mutant.

      To understand the function of NRDE-3 binding on CSR targets in early embryos, we attempted to do RTqPCR, smFISH, and anti-H3K9me3 CUT&Tag-seq on early embryos, and we either failed to obtain enough signal or failed to detect any significant difference (data not shown). We additionally tested the possibility that NRDE-3 functions with CSR-class 22G-RNAs in oocytes. We present new data showing that NRDE-3 represses RNA Pol II in oocytes to promote global transcriptional repression at the oocyteto-embryo transition, we now included these data in Figure 8. 

      Reviewer #2 (Public review):

      Summary:

      NRDE-3 is a nuclear WAGO-clade Argonaute that, in somatic cells, binds small RNAs amplified in response to the ERGO-class 26G RNAs that target repetitive sequences. This manuscript reports that, in the germline and early embryos, NRDE-3 interacts with a different set of small RNAs that target mRNAs. This class of small RNAs was previously shown to bind to a different WAGO-clade Argonaute called CSR1, which is cytoplasmic, unlike nuclear NRDE-3. The switch in NRDE-3 specificity parallels recent findings in Ascaris where the Ascaris NRDE homolog was shown to switch from sRNAs that target repetitive sequences to CSR-class sRNAs that target mRNAs.

      The manuscript also correlates the change in NRDE-3 specificity with the appearance in embryos of cytoplasmic condensates that accumulate SIMR-1, a scaffolding protein that the authors previously implicated in sRNA loading for a different nuclear Argonaute HRDE-1. By analogy, and through a set of corelative evidence, the authors argue that SIMR foci arise in embryogenesis to facilitate the change in NRDE-3 small RNA repertoire. The paper presents lots of data that beautifully documents the appearance and composition of the embryonic SIMR-1 foci, including evidence that a mutated NRDE-3 that cannot bind sRNAs accumulates in SIMR-1 foci in a SIMR-1-dependent fashion.

      Weaknesses:

      The genetic evidence, however, does not support a requirement for SIMR-1 foci: the authors detected no defect in NRDE-3 sRNA loading in simr-1 mutants. Although the authors acknowledge this negative result in the discussion, they still argue for a model (Figure 7) that is not supported by genetic data. My main suggestion is that the authors give equal consideration to other models - see below for specifics.

      We appreciate reviewer 2’s comments on the genetic evidence for the function of SIMR foci.  A similar concern was also brought up by reviewer 1. By re-examining our sequencing data, we found that there is a modest but significant increase in NRDE-3 association with CSR-class sRNAs in simr-1 and enri-2 mutants in late embryos. We believe that this data supports our model that SIMR-1 and ENRI-2 are required for an efficient switch of NRDE-3 bound small RNAs. Please refer our response to the reviewer 1 - point (1), and Figure 6G in the updated manuscript. 

      Reviewer #3 (Public review):

      Summary:

      Chen and Phillips present intriguing work that extends our view on the C. elegans small RNA network significantly. While the precise findings are rather C. elegans specific there are also messages for the broader field, most notably the switching of small RNA populations bound to an argonaute, and RNA granules behavior depending on developmental stage. The work also starts to shed more light on the still poorly understood role of the CSR-1 argonaute protein and supports its role in the decay of maternal transcripts. Overall, the work is of excellent quality, and the messages have a significant impact.

      Strengths:

      Compelling evidence for major shift in activities of an argonaute protein during development, and implications for how small RNAs affect early development. Very balanced and thoughtful discussion.

      Weaknesses:

      Claims on col-localization of specific 'granules' are not well supported by quantitative data

      We have now included zoomed images of individual granules to better show the colocalization in Figure 4 and Figure 4—figure supplement 1, and performed Pearson’s colocalization analysis between different sets of proteins in Figure 4B. 

      Reviewer #2 (Recommendations for the authors):

      - The manuscript is very dense and the gene names are not helpful. For example, the authors mention ERGO-1 without clarifying the type of protein, etc. I suggest the authors include a figure to go with the introduction that describes the different classes of primary and secondary sRNAs, associated Argonautes, and other accessory proteins. Also include a table listing relevant gene names, protein classes, main localizations, and proposed functions for easy reference by the readers.

      We agree that the genes names in different small RNA pathways are easily confused. We added a diagram and table in Figure 1—figure supplement 1 depicting the ERGO/NRDE and CSR pathways and added clarification about the ERGO/NRDE-3 pathway in the text from line 126-128.  

      - Line 424 - the wording here and elsewhere seems to imply that SIMR-1 and ENRI-2, although not essential, contribute to NRDE-3 sRNA loading. The sequencing data, however, do not support this - the authors should be clearer on this. If the authors believe there are subtle but significant differences, they should show them perhaps by adding a panel in Figure 5 that directly compares the NRDE-3 IPs in wildtype versus simr-1 mutants. Figure 5H however does not support such a requirement.

      As brought up by reviewer 1, we do not see difference in binding of ERGO-dependent sRNA in simr-1 mutant in late embryos. We do, however, see a modest, but significant, increase of CSR-sRNAs bound by NRDE-3 in simr-1 and enri-2 mutants, which we hypothesize could be due to a less efficient loading of ERGO-dependent 22G-RNAs by NRDE-3. The updated data are now in Figure 6G. We have also edited the text and model figure to soften these conclusions.

      - Condensates of PGL proteins appear at a similar time and place (somatic cells of early embryos) as the embryonic SIMR-1 foci. The PGL foci correspond to autophagy bodies that degrade PGL proteins. Is it possible that SIMR-1 foci also correspond to degradative structures? The possibility that SIMR-1 foci are targeted for autophagy and not functional would fit with the finding that simr-1 mutants do not affect NRDE-3 loading in embryos.

      We appreciate reviewer 2’s comments on possibility of SIMR granules acting as sites for degradation of SIMR-1 and NRDE-3. We think this is not the case for the following reasons: 1) if SIMR granules are sites of autophagic degradation, then we would expect that embryonic SIMR granules in somatic cells, like PGL granules, should only be observed in autophagy mutants; however we see them in wild-type embryos 2) we would not expect a functional Tudor domain to be required for granule localization; however in Figure 1—figure supplement 2B, we show that a point mutation in the Tudor domain of SIMR-1 abrogates SIMR granule formation, and 3) if NRDE-3(HK-AA) is recruited to SIMR granules for degradation while wild-type NRDE-3 is cytoplasmic, then NRDE-3(HK-AA) should shows a significantly reduced protein level comparing to wild-type NRDE-3. In the western blot in Figure 2—figure supplement 1B, NRDE-3 and NRDE-3(HK-AA) protein levels are similar, indicating that NRDE-3(HK-AA) is not degraded despite being unloaded. This is in contrast to what we have observed previously for HRDE-1, which is degraded in its unloaded state. If SIMR-1 played a role directly in promoting degradation of NRDE-3(HK-AA), we would similarly expect to see a change in NRDE-3 or NRDE-3(HK-AA) expression in a simr-1 mutant. We performed western blot and did not observe a significant change in protein expression for NRDE-3 (Figure 3—figure supplement 1A). 

      Although under wild-type conditions, SIMR granules do not appear to be sites of autophagic degradation, upon treatment with lgg-1 (an autophagy protein) RNAi, we found that SIMR-1, as well as many other germ granule and embryonic granule-localized proteins, increase in abundance in late embryos.  This data demonstrates that ZNFX-1, CSR-1, SIMR-1, MUT-2/RDE-3, RRF-1, and unloaded NRDE-3 are removed by autophagic degradation similar to what have been shown previously for PGL-1 proteins (Zhang et al, 2009, Cell). We added these data to Figure 5. It is important to emphasize, however, that the timing of degradation differs for each granule assayed (Lines 447-450), indicating that there must be multiple waves of autophagy to selectively degrade subsets of proteins when they are no longer needed by the embryo.

      - The observation that an NRDE-3 mutant that cannot load sRNAs localizes to SIMR-1 foci does not necessarily imply that wild-type unloaded NRDE-3 would also localize there. Unless the authors have additional data to support this idea, the authors should acknowledge that this hypothesis is speculative. In fact, why does cytoplasmic NRDE-3 not localize to granules in the rde-3;ego-1degron strain shown in Figure 6B?? Is it possible that the NRDE-3 mutant accumulates in SIMR-1 foci because it is unfolded and needs to be degraded?

      We believe that wild-type NRDE-3 also localize to SIMR foci when unloaded. This is supported by the localization of wild-type NRDE-3 in eri-1 and rde-3 mutants, where a subset of small RNAs are depleted. Wild-type NRDE-3 localizes to both somatic SIMR-1 granules and the nucleus, depending on embryo stage (Figure 2A, Figure 2—figure supplement 1C). The granule numbers in eri-1 and rde-3 mutants are less than the nrde-3(HK-AA) mutant, consistent with the imaging data that NRDE-3 only partially localize to somatic granule (Figure 2A – 100-cell stage).

      In the rde-3; ego-1 double mutant, the embryos have severe developmental defect: they cannot divide properly after 4-8 cell stage and exhibit morphology defects after that stage. In wild-type, SIMR foci does not appear until around 8-28-cell stage (shown in Figure 1C), so we believe that cytoplasmic NRDE-3 does not localize to foci in the double mutant is because of the timing.

      - The authors propose that NRDE-3 functions in nuclei to target mRNAs also targeted in the cytoplasm by CSR-1. If so, how do they propose that NRDE-3 might do this since little transcription occurs in oocytes/early embryos?? Are the authors suggesting that NRDE-3 targets germline genes for silencing specifically at the times that zygotic transcription comes back on, or already in maturing oocytes? Is the transcription of most CSR-1 targets silenced in early embryos??

      We appreciate the suggestions to check the function of NRDE-3 in oocytes. We tested this possibility and found it to be correct. NRDE-3 functions in oocytes for transcriptional repression by inhibiting RNA Pol II elongation. We added these data to Figure 8. We also attempted to do RT-qPCR, smFISH, and antiH3K9me3 Cut&Tag-seq on early embryos to further test the hypothesis that NRDE-3 acts with CSR-class 22G-RNAs in early embryos, but we either failed to obtain enough signal or failed to detect any significant difference (data not shown). Therefore, we think that the primary role for NRDE-3 bound to CSR-class 22G-RNAs may be for global transcriptional repression of oocytes prior to fertilization.

      - Line 684-686: "In summary, this work investigating the role of SIMR granules in embryos, together with our previous study of SIMR foci in the germline (Chen and Phillips 2024), has identified a new mechanism for small RNA loading of nuclear Argonaute proteins in C. elegans". This statement appears overstated/incorrect since there is no evidence that SIMR-1 foci are required for sRNA loading of NRDE3. The authors should emphasize other models, as suggested above.

      We have revised the text on line 869-871 to emphasize that SIMR granule regulate the localization of nuclear Argonaute proteins, rather than suggesting a direct role on controlling small RNA loading. We also edit the title, text, and legend for our model in Figure 9. 

      Reviewer #3 (Recommendations for the authors):

      Issues to be addressed:

      - The authors show a switch in 22G RNA binding by NRDE-3 during embryogenesis. While the data is convincing, it would be great if it could be tested if the preferred NRDE-3 replacement model is indeed correct. This could be done relatively easily by giving NRDE-3 a Dendra tag, allowing one to colour-switch the maternal WAGO-3 pool before the zygotic pool comes up. Such data would significantly enhance the manuscript, as this would allow the authors to follow the fate of maternal NRDE-3 more precisely, perhaps identifying a period of sharp decline of maternal NRDE-3.

      We think the NRDE-3 Dendra tag experiment suggested by the reviewer is a clever approach and we will consider generating this strain in the future. However, we feel that optimization of the color-switching tag between the maternal germline and the developing embryos is beyond the scope of this manuscript. To partially address the question about NRDE-3 fate during embryogenesis, we examined the single-cell sequencing data of C. elegans embryos from 1-cell to 16-cell stage (Tintori et al, 2016, Dev Cell; Visualization tool from John I Murray lab), as shown in Author response image 3 Panel A below, NRDE-3 transcript level increases as embryo develops, indicating that zygotic NRDE-3 is being actively expressed starting very early in development. We hypothesize that maternal NRDE-3 will either be diluted as the embryo develops or actively degraded during early embryogenesis. 

      Author response image 3.

      - Figure 3A: * should mark PGCs, but this seems incorrect. At the 8-cell stage there still is only one PGC (P4), not two, and at 100 cells there are only two, not three germ cells. Also, the identification of PGCs with a maker (PGL for instance) would be much more convincing.

      We apologize for the confusion in Figure 3A. We changed the figure legend to clarify that the * indicate nuclear NRDE-3 localization in somatic cells for 8- and 100-cell stage embryos rather than the germ cells.  

      - Overall, the authors should address colocalization more robustly. In the current manuscript, just one image is provided, and often rather zoomed-out. How robust are the claims on colocalization, or lack thereof? With the current data, this cannot be assessed. Pearson correlation, combined with line-scans through a multitude of granules in different embryos will be required to make strong claims on colocalization. This applies to all figures (main and supplement) where claims on different granules are derived from.

      We thank reviewer 3 for this important suggestion. To better address the colocalization, we included insets of individual granules in Figure 2D and Figure 4. We also performed colocalization analysis by calculating the Pearson’s R value between different groups of proteins in Figure 4B, to highlight that SIMR-1 colocalizes with ENRI-2, NRDE-3(HK-AA), RDE-3, and RRF-1, while CSR-1 colocalizes with EGO-1.

      For the proteins that lack colocalization in Figure 4—figure supplement 1, we also added insets of individual granules. Additionally, we included a new set of panels showing SIMR-1 localization compared to tubulin::GFP (Figure 4—figure supplement 1I) in response to a recent preprint (Jin et al, 2024, BioRxiv), which finds NRDE-3 (expressed under a mex-5 promoter) associating with pericentrosomal foci and the spindle in early embryos. We do not see SIMR-1 (or NRDE-3, data not shown) at centrosomes or spindles in wild-type conditions but made a similar observation for SIMR-1 in a mut-16 mutant (Figure 4E). All of the localization patterns were examined on at least 5 individual 100-cell staged embryos with same localization pattern.

      - Figure 7: Its title is: Function of cytoplasmic granules. This is a much stronger statement than provided in the nicely balanced discussion. The role of the granules remains unclear, and they may well be just a reflection of activity, not a driver. While this is nicely discussed in the text, figure 7 misses this nuance. For instance, the title suggests function, and also the legend uses phrases like 'recruited to granule X'. If granules are the results of activity, 'recruitment' is really not the right way to express the findings. The nuance that is so nicely worded in the discussion should come out fully in this figure and its legend as well.

      We have changed the title of Figure 7 (now Figure 9) to “Model for temporally- and developmentallyregulated NRDE-3 function” to deemphasize the role of the granules and to highlight the different functions of NRDE-3. Similarly, we have rephrased the text in the figure and legend and add a some details about our new results.

      Minor:

      Typo: line 663 Acaris

      We corrected the typo.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      (1) Adding microscopy of the untreated group to compare Figure 2A with would further strengthen the findings here.

      First of all, we would like to thank Reviewer #1 for their comments and efforts on our manuscript. We have carefully revised it. We used a time-lapse method to capture images at 0 minutes, before any drugs were added. We will change '0 min' to 'untreated,' which will further strengthen the findings.

      (2) Quantification of immune infiltration and histological scoring of kidney, liver, and spleen in the various treatment groups would increase the impact of Figure 4.

      Thank you very much to Reviewer #1 for their comments and efforts on our manuscript. We have revised it carefully. We conducted quantitative analysis of immune infiltration in the kidney, liver, and spleen across different treatment groups. However, due to the extremely low number of abnormal cells in the negative control, treatment, and prophylactic groups, neither the instrument nor manual methods could reliably gate the cells. Consequently, quantification of immune infiltration and histological scoring were not performed.

      (3) The data in Figure 6 I is not sufficiently convincing as being significant.

      Thanks so much for Reviewer #1 comments and efforts for our manuscript. We have revised it carefully. Previous researches have shown that antibiotics and other drugs can cause alterations in gut microbiota. Therefore, we plan to study the effects of antibiotics on gut microbiota. To conduct this research, we need to isolate these microbes from the gut. Although this process is challenging, we still aim to explore the gut microbiota. If possible, we will continue to delve into interesting aspects of how antibiotics affect gut microbiota in future studies.

      (4) Comparisons of the global transcriptomic analysis of the untreated group to the PC, LP, and LT groups would strengthen the author's claims about the immunological and transcriptomic changes caused by linalool and provide a true baseline.

      Thanks so much for Reviewer #1 comments and efforts for our manuscript. We have revised it carefully. Due to the initial research design and data analysis strategy, we have focused on comparisons among the PC, LP, and LT groups to more directly explore the differences under various treatment conditions. Specifically, while the transcriptomic data from the untreated group could provide a basic reference, it has shown limited relevance to the core hypotheses of our study. Our research has aimed to investigate the immunological and transcriptomic changes among the treatment groups rather than comparing treated and untreated states. We believe that the current experimental design and data analysis have effectively revealed the mechanisms of linalool and that the additional comparisons among the treatment groups have further supported our conclusions. We hope the reviewer understands the rationale behind our experimental design. If there are additional suggestions, we are more than willing to further optimize the content of our manuscript.

      Reviewer #2 (Public review):

      (1) The authors have taken for granted that the readers already know the experiments/assays used in the manuscript. There was not enough explanation for the figures as well as figure legends.

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. We will provide more detailed explanations of the experiments and assays used in the manuscript, as well as enhance the descriptions in the figure legends, to ensure that readers have a clear understanding of the figures and their context.

      (2) The authors missed adding the serial numbers to the references.

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. We will add serial numbers to the references to ensure proper citation and improve the clarity of our manuscript.

      (3) The introduction section does not provide adequate rationale for their work, rather it is focused more on the assays done.

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. We will add a section to the introduction that provides a rationale for our work, specifically focusing on the impact of plant extract on immunoregulation.

      (4) Full forms are missing in many places (both in the text and figure legends), also the resolution of the figures is not good. In some figures, the font size is too small.

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We will ensure that all abbreviations are expanded where necessary, both in the text and figure legends. Additionally, we will improve the resolution of the figures and increase the font size where needed to enhance clarity.

      (5) There is much mislabeling of the figure panels in the main text. A detailed explanation of why and how they did the experiments and how the results were interpreted is missing.

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. We will improve the labeling of the figure panels, provide detailed explanations of the experimental methods, including their rationale and interpretation, and clarify the connections between the methods.

      (6) There is not enough experimental data to support their hypothesis on the mechanism of action of linalool. Most of the data comes from pathway analysis, and experimental validation is missing.

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Actually, in our manuscript the transcriptomic data are not alone, and we carried out many experiments to substantiate the changes inferred from the transcriptomic data as SEM, TEM, CLSM, molecular docking, RT-qPCR, histopathological examinations. The detailed information is listed as below.

      As shown in Figure 2, we combined the transcriptomic data related to membrane and organelle with SEM, TEM, and CLSM images. After deep analysis of these data and observation together, we illustrated that cell membrane may be a potential target for linalool.

      As shown in Figure 3, we carried out molecular docking to explore the specific binding protein of linalool with ribosome which were screen out as potential target of linalool by transcriptomic data.

      As shown in Figure 5, transcriptomic data illustrated that linalool enhanced the host complement and coagulation system. To substantiate these changes, we carried out RT-qPCR to detect those important immune-related gene expressions, and found that RT-qPCR analysis results were consistent with the expression trend of transcriptome analysis genes.

      As shown in Figure 4 and 5, transcriptomics data revealed that linalool promoted wound healing tissue repair, and phagocytosis (Figure. 5E). To ensure these, we carried out histopathological examinations, and found that linalool alleviated tissue damage caused by S. parasitica infection on the dorsal surface of grass carp and enhancing the healing capacity (Figure. 4G).

      Overall, we will conduct additional experiments to verify the mechanism of action of linalool in the future.

      Reviewer #1 (Recommendations for the authors):

      (1) Figure 1 Panel G is not referenced in the legend, this should be fixed

      Thanks so much for Reviewer #1 comments and efforts for our manuscript. We have revised it carefully. Please check the Figure 1. The order of Panel F and G in Figure 1 is wrong. We have modified the order of Figure 1.

      (2) Statistical comparisons between groups in Figure 4 Panels C-F is lacking and should be added.

      Thanks so much for Reviewer #1 comments and efforts for our manuscript. We have revised it carefully. Please check the Figure 4 C-F. We have added statistical comparisons between groups in Figure 4 Panels C-F.

      (3) Capitalize Kidney label in Figure 4G.

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check the Figure 4G. We have capitalized the K of kidney.

      Reviewer #2 (Recommendations for the authors):

      (1) The authors missed adding the serial numbers to the references. I could not go through the references to cross-check if they cited the right ones because it's extremely difficult to figure out which one corresponds to which reference number.

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check the references. We have added the serial numbers to the references.

      (2) In the last paragraph of the introduction section, most of the techniques in the paper were summarized which does not go with the flow of the paper. The introduction should not be focused on the different techniques used the focus should be more on the rationale of the work. It would be nice if the last paragraph could be rewritten.

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in Line 85-94. We have added a section to the introduction that provides a rationale for our work, specifically focusing on the impact of plant extract on immunoregulation.

      (3) The resolution of the figures is not good.

      Thank you for your suggestion. We have revised it carefully. Please check all the figures. We have increased the resolution and size of all the figures.

      (4) Mostly, the figure legends sound like results, with not enough explanation. Full forms are missing in many places which would make the readers go back to the text/other figures each time.

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it throughout the manuscript and all the figure legends. We have added full names and abbreviations to both the manuscript and all the figure legends so that we don't make the readers go back to the text/other figures each time.

      (5) Figure 1:

      Figure 1A: there is not enough explanation for this panel. It's not clear from the text which other EOs than Linalool are referred to here. Which EOs were extracted from daidai flowers?

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in the Figure 1A. Figure 1A is divided into “Essential oils (EOs)” and “The main compounds of EOs” to make it easier to distinguish.

      Figure 1B: do the three different wells of each set represent three replicates? If so, are they biological/technical replicates? Also, I'm not sure how the MFC was determined from this figure (line 116) because clearly this panel only corresponds to the determination of MICs, not MFCs.

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in Line 126-130. The three different wells of each set represent three biological replicates. After adding 5 μL of resazurin dye, when the color of the wells turned to pink, the linalool concentration in the first non-pink well corresponded to the MIC. The culture liquid in the well where no mycelium growth was seen was marked onto the plate and incubated at 25°C for 7 days. The well with the lowest linalool concentration and no mycelium growth was identified as MFC.

      Figure 1C: the figure legend says that the effect of linalool on mycelium growth inhibition was done over a 6hr timepoint but according to the figure the timepoint was 60hr. I am also confused about the concentrations of linalool used. Although a range of concentration from 0 to 0.4% is mentioned, I only see the time vs diameter curves for 7 concentrations.

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in Line 983 and Figure 1C. We have changed 6 h to 60 h in the figure legends. The reason why only the time vs diameter curves for 7 concentrations in Figure1C is that the growth inhibition of 0.4%, 0.2% and 0.1% linalool on mycelial growth is the same. As a result, the time vs diameter curves coincide. We have shown the time and diameter curves of 0.4%, 0.2% and 0.1% concentration with three dotted lines of different colors and sizes in Figure 1C.

      Figure 1D: mislabeled as 1G in the figure panel.

      Figures 1E and 1G: Figure 1E is missing and I do not see any figure legend for Figure 1G.

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check the Figure 1. The order of Panel F and G in Figure 1 is wrong. We changed the order of Figure 1 ABCDEF, no Figure G.

      Overall, Figure 1 is very confusing and needs rewriting. Also, there is a need to add more explanation of the figure panels in the results section.

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check the Figure 1. We have corrected all the problems in Figure1. And we have added more explanation of the figure panels in the results section, and increased the correlation between methods, in order to show how to carry out the experiment logically and interpret the results, please check them in Line 126-130, 144-147, 174-179, 213-217, 343-345, 677-682.

      (6) Figure 2:

      The authors could justify the reason for doing the experiments before moving into the results they got.

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check the methods and results in the manuscript, please check them in Line 126-130, 144-147, 174-179, 213-217, 343-345, 677-682. We have added more explanation of the figure panels in the results section, and increased the correlation between methods, in order to show how to carry out the experiment logically and interpret the results.

      What concentration of linalool was used?

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in Line 992-996. The mycelium treated with 6×MIC (0.3%) linalool was observed by Confocal laser scanning microscopy (CLSM), and the mycelium treated with 1×MIC 0.05% linalool was observed by Scanning Electron Microscope (SEM) and transmission electron microscopy (TEM).

      The full form of DEGs has been mentioned later, but it should be mentioned in the figure legend of Figure 2 as this is the first time the term was used. Also, what is the full form of DEPs?

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in Line 168, 175, 182, 631, 998, 1001. The word DEPs in Figure 2I was incorrect, and we have changed DEPs to DEGs.

      Is there a particular reason for looking into the cellular component rather than molecular function and biological processes in the GO analysis? (what I see is that Figure 2H indicates the prevalence of catalytic activity, binding, cellular, and metabolic processes as well). Also, there is not enough explanation of the observation from Figure 2I (both in the results section and figure legend).

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in Line 174-179, 998-1002 (Figure 2I). The reason we looked at cellular components rather than molecular functions and biological processes in GO analysis is because we focused more on the effects of cell membranes and cell walls. These results are closely related to and echo the results of our scanning electron microscopy (SEM) and transmission electron microscopy (TEM), and also support the results of electron microscopy. Enough explanations have added to the results and figure legend section to explain the observations from Figure 2I.

      (7) Figure 3:

      Figures 3A and 3B: The adjusted p value is already indicated in the figures, so there is no need to add statistical significance (Asterix) to each bar. The resolution for these panels is not good and the font is too small.

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check the Figure 3A and 3B. We have removed statistical significance (Asterix) from Figure3A and 3B. If we are lucky, we will upload the clearest figures when the manuscript is published.

      Figure 3C: the figure legend is missing (wrongly added as KEGG analysis, which should be network analysis). The numbering for the figure legends is wrong. What are the node sizes (5, 22, 40, 58) mentioned in the figure represent? Also, I wonder why ribosome biogenesis in eukaryotes has been indicated as the most enriched pathway despite its less connection to the other nodes.

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check the Figure 3C. Figure 3C is KEGG analysis generated by software, not network analysis. For the convenience of readers, we have made a new Figure of KEGG analysis.

      Figure 3D: KEGG enrichment and GO analysis: global/local search? Which database was used as a reference?

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check the 633-635. Functional enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. KEGG pathway analysis was conducted using Goatools.

      Figure 3E: why were the RNA pol structures compared? The authors did not mention anything about this panel in their results.

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check the line 207. We found that many DEGs related to ribosome biogenesis (Figure 3D) and RNA polymerase (Figure 3E) are down expressed. Because RNA polymerase is closely related to ribosome biogenesis, the downregulation of RNA polymerase directly affects the synthesis of ribosome-related RNAs, including rRNA, mRNA, and tRNA, thereby inhibiting ribosome production. This relationship is particularly significant in cell growth, division, and the response to external environmental changes.

      Figures 3F and 3G: please mention which model is illustrated (ribbon/sphere model).

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check the line 1010-1015. The tertiary structure of NOP1 was displayed using a cartoon representation. Molecular docking of linalool with NOP1 was performed by enlarging the regions binding to the NOP1 activation pocket to showcase the detailed amino acid structures, which were presented using a surface model, while the small molecule was displayed with a ball-and-stick representation.

      Figure 3H: this panel needs more explanation. Why were some of the ABC transporters upregulated while some were downregulated?

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. It is a common phenomenon that microorganisms adjust the expression of genes related to substance transport in response to different environmental stimuli to optimize their survival strategies. The expression of ATP-binding cassette (ABC) transporters can be upregulated or downregulated due to various factors, such as environmental stimuli, metabolic demands, energy consumption, species specificity, and signaling molecules. This explains why some ABC transporters are upregulated while others are downregulated.

      (8) Figure 4:

      There was no statistical significance shown in the figures (D-F) which makes me wonder how they worked out that there was any significant increase/decrease, as mentioned in the text. What are the p values? What is the number of replicates? What concentration of linalool was used?

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully.  Please check the Figure 4D-F. In this study, 4 groups were established: (1) Positive control (PC) group (10 fish infected with S. parasitica). (2) Linalool therapeutic (LT) group (10 fish infected with S. parasitica, soaked in 0.00039% linalool in a 20L tank for 7 days). (3) Linalool prophylactic (LP) group (10 uninfected fish soaked in 0.00039% linalool in a 20L tank for 2 days, followed by the addition of 1×10<sup>6</sup> spores/mL secondary zoospores). (4) Negative control (NC) group (10 uninfected fish without linalool treatment). Each group had 3 replicate tanks. In each group, 8 fish were utilized for immunological assays, and on day 7, blood samples were collected from the tail veins using heparinized syringes and left to coagulate overnight at 4°C. Kits from Nanjing Jiancheng Institute (Nanjing, China) were used to measure lysozyme (LZY) activity, superoxide dismutase (SOD) activity, and alkaline phosphatase (AKP) activity.

      (9) Figure 5:

      Again, the resolution and font size are off. Please mention the full forms of the terms used in the figure legend. The interpretation of the in vivo protective mechanism of linalool is completely based on GO enrichment and KEGG pathway analysis (also some transcriptional analysis). The only wet lab validation done was by checking the mRNA level of some cytokines but that does not necessarily validate what the authors claim.

      Thank you for your suggestion. We have revised it carefully. Please check all the figures and figure legend. We have increased the resolution and size of all the figures and used the full forms of the terms in figure legend. If we are lucky, we will upload the clearest figures when the manuscript is published. Currently, in the field of aquaculture research, mRNA quantification at the genetic level faces numerous challenges compared to model organisms like mice and zebra fish, primarily due to the lack of available antibodies. For instance, antibodies related to grass carp have not yet been commercialized, making protein-level studies and validations significantly more difficult. This lack of antibodies limits the progress of protein verification. However, we hope to design more experiments and validation tests in the future to gradually overcome these technical bottlenecks and provide stronger support for research in the future.

      (10) Figure 6:

      There is not enough explanation on why and how the experiments were done. It seems like the authors already presumed that the readers know the experiments. The interpretation of the PCA plot is not clear. Why are the quadrant sizes different? How was the heat map plotted? Also, the claim of linalool regulating the gut microbiota is only dependent on the correlation analysis and there is no wet lab validation for this. The data represented in this figure is not enough to prove their hypothesis and needs further investigation.

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check the Figure 6. We will improve the labeling of the figure panels, provide detailed explanations of the experimental methods, including their rationale and interpretation, and clarify the connections between the methods.

      The goal of PCoA is to preserve the distance relationships between samples as much as possible through the principal coordinates, thereby revealing the differences or patterns in microbial composition among different groups. For example, in our study, PCoA analysis demonstrated that the microbial compositions of the positive control (PC), linalool prophylactic (LP), and linalool therapeutic (LT) groups showed significant differences in the reduced dimensional space, possibly indicating that these treatments had a notable impact on the microbial community.

      In our study, the heatmap was generated using the Majorbio Cloud Platform. This platform visualized the preprocessed microbial community data, providing an intuitive representation of the differences in microbial composition and relative abundance among samples. The platform automatically performed steps such as data normalization, color mapping, and clustering analysis, offering convenience for data analysis and interpretation.

      Previous researches have shown that antibiotics and other drugs can cause alterations in gut microbiota. Therefore, we plan to study the effects of antibiotics on gut microbiota. To conduct this research, we need to isolate these microbes from the gut. Although this process is challenging, we still aim to explore the gut microbiota. If possible, we will continue to delve into interesting aspects of how antibiotics affect gut microbiota in future studies.

      (11) Figure 7:

      This figure does not clarify how they did the interpretation. The in vivo study does not phenocopy their in vivo studies.

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. we have carefully reviewed and confirmed the current experimental design and data analysis. Although we have not made any changes to Figure 7, we have further clarified the interpretation of the results in the revised manuscript, especially concerning the discrepancies between the in vivo and in vitro studies. We have added more experimental background information to help better understand the possible reasons for these differences. We hope the reviewer will understand our explanation and we look forward to your further feedback.

      (12) Minor comments:

      Line 61: what's meant by "et al"?

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in Line 61. We have removed "et al".

      Line 87-88: please add a citation referring to the earlier studies.

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in Line 109.

      Line 151-152: the term "related to" has been used a couple of times. Mentioning it once in the beginning and avoiding repeating the same word might be better.

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in Line 168-171.We have rewritten this paragraph to avoid repeating the word “related to”.

      How did they reconstitute the EO compounds?

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. The EO compounds we used in our experiments were partially extracted from essential oils in the laboratory and partially purchased from ThermoFisher (USA).

      Line 544: needs explanation of how there was a 2-fold dilution in the concentrations shown in the figure compared to the concentrations mentioned here.

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. We set the concentration of MIC assay for mycelium to be 0.8%, 0.4%, 0.2%, 0.1%, 0.05%, 0.025%, 0.0125%, and 0.00625%, and the concentration of MIC assay for spores to be 0.4%, 0.2%, 0.1%. 0.05%, 0.025%, 0.0125%, 0.00625%. Figure 1B shows the MIC determination of linalool on spores, while the MIC determination of mycelium is not shown.

      Line 546: remove "were".

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in Line 573. We have removed "were".

      Line 555: what concentration of malachite green and tween 20 was used?

      Thanks so much for Reviewer #2 comments and efforts for our manuscript. We have revised it carefully. Please check it in Line 579-580. 2.5mg /mL malachite green and 1% Tween 20 were used.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer 1:

      (1) Some conclusions are not completely supported by the present data, and at times the manuscript is disjoint and hard to follow. While the work has some interesting observations, additional experiments and controls are warranted to support the claims of the manuscript.

      Thank you for the comments. We revised some of the claims and conclusions to be more objective and result-supportive.

      (2) While the authors present compelling data that is relevant to the development of anti-bacterial vaccinations, the data does not completely match their assertions and there are places where some further investigation would further the impact of their interesting study.

      We do not fully agree with the reviewer's comments. We have demonstrated that changes in CPS levels during infection are associated with pathogenesis, which will guide future studies on the underlying mechanisms. A significant amount of effort is required for studying mechanisms, which is beyond the scope of this research. We concur with the reviewer that assertions should be made cautiously until further studies are conducted. We have revised these assertions to align with the data and to avoid extrapolating the results (pages 7, lines 126, 133-136; page 11, lines 216-218; page 13, line 264; and page 18, lines 378-383).

      (3) The difference in the pathogenesis of a log phase vs. stationary phage intranasal infection would be interesting. Especially because the bacteria is a part of the natural microbial community of swine tonsils, it is curious if the change in growth phase and therefore CPS levels may be a causative reason for pathogenic invasion in some pigs.

      S. suis is a part of the natural microbial community of swine tonsils but not mouse NALT. It is interesting to know if CPS levels are low in pig tonsils since CPS is hydrophilic and not conducive to bacterial adhesion. In the study, mice were i.n. infected with a high dose of the bacteria, which could increase opportunities for dissemination (acidic acid may not be a contributor since with or without it is similar). S. suis getting into other body compartments from pig tonsils might be triggered by other conditions, such as viral coinfection, nasal cavity inflammation, cold weather, and decreased immunity.

      Experiments with pig blood and phagocytes have shown that genes involved in the synthesis of CPS are upregulated in pig blood. In contrast, these genes are downregulated [1]. In addition, the absence of CPS correlated with increased hydrophobicity and phagocytosis, proposing that S. suis undergoes CPS phase variation and could play a role in the different steps of S. suis infection [2]. We showed direct evidence of encapsulation modulation associated with S. suis pathogenesis in mice. A pig infection model is required to confirm these findings.

      (4) The authors should consider taking the bacteria from NALT/CSF and blood and compare the lag times bacteria from different organs take to enter a log growth phase to show whether the difference in CPS is because S. suis in each location is in a different growth phase. If log phase bacteria were intranasally delivered, would it adapt a stationary phase life strategy? How long would that take? 

      What causes CPS regulation in vivo is not known. CPS changes in different culture stages, indicating that stress, such as nutrition levels, is one of the signals triggering CPS regulation. The microenvironment in the body compartments is far more complex than in vitro, in which host cells, immune factors and others may affect CPS regulation, individually or collectively. The reviewer’ question is important but the suggested experiment is impracticable since bacterial numbers taken from organs are few, and culturing the bacteria in vitro would obliterate the in vivo status.  

      (5) Authors should be cautious about claims about S. suis downregulating CPS in the NALT for increased invasion and upregulating CPS to survive phagocytosis in blood. While it is true that the data shows that there are different levels of CPS in these locations, the regulation and mechanism of the recorded and observed cell wall difference are not investigated past the correlation to the growth phase.

      We lower the tone and change the claim as “suggest a correlation between lower CPS in the NALT and a greater capacity for cellular association, whereas elevated CPS levels in the blood are linked to improved resistance against bactericidal activity. However, the mechanisms behind these associations remain unknown.” (page 7, lines 133-136).

      (6) The mouse model used in this manuscript is useful but cannot reproduce the nasal environment of the natural pig host. It is not clear if the NALTs of pigs and mice have similar microbial communities and how this may affect the pathogenesis of S. Suis in the mouse. Because the authors show a higher infection rate in the mouse with acetic acid, they may want to consider investigating what the mouse NALT microenvironment is naturally doing to exclude more bacterial invasion. Is it simply a host mismatch or is there something about the microbiome or steady-state immune system in the nose of mice that is different from pigs?

      It is a very interesting comment. The mice are SPF level. The microenvironment in SPF mouse NALT should be significantly different from conventional pig tonsils. Although NALT in mice resembles pig tonsils in function, many factors may contribute to the sensitivity to S. suis colonization in the pig nasal cavity, such as the microbiome and local steady-state immune system. More complex microbiota in tonsils could be one of the factors. Analyzing what makes S. suis inclined towards colonization in pig tonsils by SPF and conventional pigs are an ideal experiment to answer the question. 

      (7) Have some concerns regarding the images shown for neuroinvasion because I think the authors mistake several compartments of the mouse nasal cavity as well as the olfactory bulb. These issues are critical because neuroinvasion is one of the major conclusions of this work.

      Thank you for your comments. The olfactory epithelium (OE) is located directly underneath the olfactory bulb in the olfactory mucosa area and lines approximately half of the nasal cavities of the nasal cavity. The remaining surface of the nasal cavity is lined by respiratory epithelium, which lacks neurons. The olfactory receptor neuron in OE is stained green in the images by β-tubulin III, a neuron-specific marker. The respiratory epithelium is colorless due to the absence of nerve cells. Similarly, the green color stained by β-tubulin III identifies the olfactory bulb. The accuracy of the anatomic compartments of the mouse nasal cavity has been checked and confirmed by referring to related literature [3, 4].

      References

      (1) Wu Z, Wu C, Shao J, Zhu Z, Wang W, Zhang W, Tang M, Pei N, Fan H, Li J, Yao H, Gu H, Xu X, Lu C. The Streptococcus suis transcriptional landscape reveals adaptation mechanisms in pig blood and cerebrospinal fluid. RNA. 2014 Jun;20(6):882-98.

      (2) Charland N, Harel J, Kobisch M, Lacasse S, Gottschalk M. Streptococcus suis serotype 2 mutants deficient in capsular expression. Microbiology (Reading). 1998 Feb;144 ( Pt 2):325-332.

      (3) Pägelow D, Chhatbar C, Beineke A, Liu X, Nerlich A, van Vorst K, Rohde M, Kalinke U, Förster R, Halle S, Valentin-Weigand P, Hornef MW, Fulde M. The olfactory epithelium as a port of entry in neonatal neurolisteriosis. Nat Commun. 2018;9(1):4269.

      (4) Sjölinder H, Jonsson AB. Olfactory nerve--a novel invasion route of Neisseria meningitidis to reach the meninges. PLoS One. 2010 Nov 18;5(11):e14034.

      Reviewer 2:

      (1) However, there are serious concerns about data collection and interpretation that require further data to provide an accurate conclusion. Some of these concerns are highlighted below:

      Both reviewers were concerned about some of the interpretations of the results. We modified the interpretations in related lines throughout the manuscript (Please see the related responses to Reviewer 1).

      (2) In figure 2, the authors conclude that high levels of CPS confer resistance to phagocytic killing in blood exposed S. suis. However, it seems equally likely that this is resistance against complement mediated killing. It would be important to compare S. suis killing in animals depleted of complement components (C3 and C5-9).

      We thank the reviewer for the comment. The experiment should be Bactericidal Assay instead of anti-phagocytosis killing. CPS is a main inhibitor of C3b deposition [1]. It interferes with complement-mediated and receptor-mediated phagocytosis; and direct killing. Data in Figure 2C is expressed as “% of bacterial survival in whole blood” for clarity (page 8, Fig. 2C and page 23, lines 489-490).

      (3) Intranasal administration non-CPS antisera provides a nice contrast to intravenous administration, especially in light of the recently identified "blood-olfactory barrier". Can the authors provide any insight into how long and where this antibody would be located after intranasal administration? Would this be antibody mediated cellular resistance, or something akin to simple antibody "neutralization"

      Anti-V5 may not stay long locally following intranasal administration. Efficient reduction of S. suis colonization in NALT supports that anti-V5 could recognize and neutralize the bacteria in NALT quickly, thereby reducing further dissemination in the body. Antibody-mediated phagocytosis may not play a major role because neutrophils are mainly present in the blood but not in the tissues.  

      (4) The micrographs in Figure 7 depict anatomy from the respiratory mucosa. While there is no histochemical identification of neurons, the tissues labeled OE are almost certainly not olfactory and in fact respiratory. However, more troubling is that in figures 7A,a,b,e, and f, the lateral nasal organ has been labeled as the olfactory bulb. This undermines the conclusion of CNS invasion, and also draws into question other experiments in which the brain and CSF are measured.

      We understand the significance of your concerns and appreciate your careful review of Figure 7. The olfactory epithelium (OE) is situated directly beneath the olfactory bulb in the olfactory mucosa area and covers about half of the nasal cavity. This positioning allows information transduction between the olfactory and the olfactory epithelium. The remaining surface of the nasal cavity is lined with respiratory epithelium, which does not contain neurons and primarily serves as a protective barrier. In contrast, the olfactory epithelium consists of basal cells, sustentacular cells, and olfactory receptor neurons. The olfactory receptor neurons are specifically stained green in the images using β-tubulin III, a marker that is unique to neurons. The respiratory epithelium appears colorless due to the lack of nerve cells. Similarly, the green staining with β-tubulin III also highlights the olfactory bulb. The anatomical structures indicated in the images are consistent with those described in the literature [2, 3], confirming that the anatomy of the nasal cavity has been accurately identified.

      (5) Micrographs of brain tissue in 7B are taken from distal parts of the brain, whereas if olfactory neuroinvasion were occurring, the bacteria would be expected to arrive in the olfactory bulb. It's also difficult to understand how an inflammatory process would be developed to this point in the brain -even if we were looking at the appropriate region of the brain -within an hour of inoculation (is there a control for acetic acid induced brain inflammation?). Some explanations about the speed of the immune responses recorded are warranted.

      Thank you for highlighting this issue. Cerebrospinal fluid (CSF) flows into the subarachnoid space surrounding the spinal cord and the brain. There are direct connections from this subarachnoid space to lymphatic vessels that wrap around the olfactory nerves as they cross the cribriform plate towards the nasal submucosa. This connection allows for the drainage of CSF into the nasal submucosal lymphatics in mice [4, 5]. Bacteria may utilize this CSF outflow channel in the opposite direction, which explains the development of brain inflammation in the distal areas of brain tissue adjacent to the subarachnoid space. We have included additional relevant information in the revised manuscript (page 16, lines 323-325).

      (6) The detected presence of S. suis in the CSF 0.5hr following intranasal inoculation is difficult to understand from an anatomical perspective. This is especially true when the amount of S. suis is nearly the same as that found within the NALT. Even motile pathogens would need far longer than 0.5hr to get into the brain, so it's exceedingly difficult to understand how this could occur so extensively in under an hour. The authors are quantifying CSF as anything that comes out of the brain after mincing. Firstly, this should more accurately be referred to as "brain", not CSF. Secondly, is it possible that the lateral nasal organ -which is mistakenly identified as olfactory bulb in figure 7- is being included in the CNS processing? This would explain the equivalent amounts of S. suis in NALT and "CSF".

      The high dose of inoculation used in the experiment may explain the rapid presence of S. suis in the CSF. Mice exhibit low sensitivity to S. suis infection, and the range for the effective intranasal infectious dose is quite narrow. Higher doses lead to the quick death of the mice, while lower doses do not initiate an infection at all. The dose used in this study is empirical and is intended to facilitate the observation of the progression of S. suis infection in mice.

      The NALT tissue and CSF samples are collected separately. After obtaining the NALT tissue, the nasal portion was carefully separated from the rest of the head along the line of the eyeballs. The brain tissue was then extracted from the remaining part of the head to collect the CSF, and it was lacerated to expose the subarachnoid space without being minced. This procedure aims to preserve the integrity of the brain tissue as much as possible. Further details about the CSF collection process can be found in the Materials and Methods section (page 24, lines 508-512).

      (7) To support their conclusions about neuroinvasion along the olfactory route and /CSF titer the authors should provide more compelling images to support this conclusion: sections stained for neurons and S. suis, images of the actual olfactory bulb (neurons, glomerular structure etc).

      Thank you. We respectfully disagree with the reviewer. We stained neurons using a neuron-specific marker to identify the anatomical structures of the olfactory bulb and olfactory epithelium (in green). We used an S. suis-specific antibody to highlight the bacteria present in these areas (in orange and red). The images, along with the bacteria found in the cerebrospinal fluid (CSF) and the brain inflammation observed early in the infection, strongly support our conclusion regarding brain invasion through the olfactory pathway. Please see the response to question 4 for further clarification.

      References

      (1) Seitz M, Beineke A, Singpiel A, Willenborg J, Dutow P, Goethe R, Valentin-Weigand P, Klos A, Baums CG. Role of capsule and suilysin in mucosal infection of complement-deficient mice with Streptococcus suis. Infect Immun. 2014 Jun;82(6):2460-71.

      (2) Sjölinder H, Jonsson AB. Olfactory nerve--a novel invasion route of Neisseria meningitidis to reach the meninges. PLoS One. 2010 Nov 18;5(11):e14034.

      (3) Pägelow D, Chhatbar C, Beineke A, Liu X, Nerlich A, van Vorst K, Rohde M, Kalinke U, Förster R, Halle S, Valentin-Weigand P, Hornef MW, Fulde M. The olfactory epithelium as a port of entry in neonatal neurolisteriosis. Nat Commun. 2018;9(1):4269.

      (4) Yoon JH, Jin H, Kim HJ, Hong SP, Yang MJ, Ahn JH, Kim YC, Seo J, Lee Y, McDonald DM, Davis MJ, Koh GY. Nasopharyngeal lymphatic plexus is a hub for cerebrospinal fluid drainage. Nature. 2024 Jan;625(7996):768-777.

      (5) Spera I, Cousin N, Ries M, Kedracka A, Castillo A, Aleandri S, Vladymyrov M, Mapunda JA, Engelhardt B, Luciani P, Detmar M, Proulx ST. Open pathways for cerebrospinal fluid outflow at the cribriform plate along the olfactory nerves. EBioMedicine. 2023 May;91:104558.

      Response to Recommendations for the authors:

      Reviewer 1:

      Minor concerns for the manuscript:

      (1) In the introduction, please consider giving a little more background about the bacteria itself and how it causes pathogenesis.

      We appreciate your suggestion. We have included additional background on the virulent factors and the pathogenesis of the bacteria in the introduction to enhance understanding of the results (page 4, lines 63-69).

      (2) Figure 2C would be more correct to say percent survival as the CFUs before and after are what are being compared and not if the bacteria is being phagocytosed or not. Flow cytometry of the leukocytes and a fluorescent S. Suis would show phagocytosis. Unless that experiment is performed, the authors cannot claim that there is a resistance to phagocytosis.

      Thank you for your feedback. We agree with the reviewer that the experiment should be Bactericidal Assay rather than anti-phagocytosis killing. CPS interferes with complement-mediated phagocytosis and direct killing, and receptor-mediated phagocytosis. To enhance clarity, the data in Fig. 2C has been presented as “% of bacterial survival in whole blood” (page 8).  

      (3) There are two different legends present for Figure 1. Please resolve.

      We apologize for the oversight. The redundant figure legend has been removed (page 6).

      (4) There are places such as in lines 194-195, that there are assertions and interpretations about the data that are not directly drawn from the data. These hypotheses are valuable, but please move them to the discussion.

      Thank you for your suggestion. The hypothesis has been moved to the Discussion section (page 19, lines 402 - 405).

      (5) In Figure 4B, higher resolution images would strengthen the ability of non-microbiologists to see the differences in CPS levels in the cell wall.

      We achieved the highest resolution possible for clearer distinctions in CPS levels. To enhance the visualization of the different CPS levels in the images, we revised the description of the CPS changes in Figure 4B within the results section (page 11, lines 208-213).

      (6) In Figure 5 there is no D. Further, the schematics throughout would be easier to parse with the text if the challenge occurred at time 0. Consider revising them for clarity.

      Thank you for highlighting the error. We have removed "i.v + i.n (Fig. 5)" from Figure 5A and made adjustments to the schematic illustrations in Figures 5 and 6 as recommended by the reviewer (page 14).

      (7) What is the control for the serum? The findings for figures 5 and 6 would be much stronger if a non- S. Suis isotype control serum was also infused.

      We used a naive serum as a control to avoid interference from a non-S. suis isotype control that targets other surface molecules of S. suis serotypes.

      (8) Figure 6 legend does not include the anti-CPS treatment.

      Thank you. We have added anti-CPS serum in the legend (page 15, line 249).

      (9) Figure 7 legend does not include the time point for panel 7A.

      Thank you. The time point is shown on Fig.7A (page 17).

      (10) Figure 7 should show OB micrographs or entire brain including the OB.

      The neuron-specific marker, β-tubulin III, identifies the neuro cells in the olfactory bulb (OB) as shown in Fig. 7A. Unfortunately, we were unable to provide an image of the entire brain that includes the OB due to limitations in our section preparation. We apologize for the mislabeled structure in Fig. 7A, which may have caused confusion. We have corrected the labeling for consistency (see page 15, lines 257-260). Additionally, we included a drawing of the sagittal plane of the rodent's nose, depicting the compartments of the OB, olfactory epithelium (OE), nasal cavity (NC), and brain. This illustration, presented in Fig. 7B on page 17, aims to clarify the structural and functional connections between the nasopharynx and the CNS.

      (11) Some conclusions may be better drawn if figures were to be consolidated. As noted above, the data at times feels disjointed and the importance is more difficult for readers to follow because data are presented further apart. Particularly figures 5 and 6 which are similar with different time points and controls of antisera administrative routes; placing these figures together would be an example of increasing continuity throughout the paper.

      Thank you for the valuable suggestion. Figures 5 and 6, along with their related descriptions in the results section, have been combined for better cohesiveness (pages 14-15).

      Reviewer #2:

      To support their conclusions about neuroinvasion along the olfactory route and /CSF titer the authors should provide more compelling images to support this conclusion: sections stained for neurons and S. suis, images of the actual olfactory bulb (neurons, glomerular structure etc).

      Please refer to our responses to Reviewer 1's Question 7, Reviewer 2's Questions 4 and 7 in the public reviews, and Reviewer 1's Question 10 in the authors' recommendations.

    1. Author response:

      The following is the authors’ response to the current reviews.

      Reviewer #1 (Public review):

      The authors have strengthened their conclusions by providing additional information about the specificity of their antibodies, but at the same time the authors have revealed concerning information about the source of their antibodies.

      It appears that many of the antibodies used in this study have been discontinued because the supplier company was involved in a scandal of animal cruelty and all their goats and rabbits Ab products were sacrificed. The authors acknowledge that this is unfortunate but they also claim that the issue is out of their hands.

      The authors' statement is false; the authors ought to not use these antibodies, just as the providing company chose to discontinue them, as those antibodies are tied to animal cruelty. The issue that the authors feel OK with using them is of concern. In short, please remove any results from unethical antibodies.

      Removal of such results also best serves science. That is, any of their results using the discontinued antibodies means that the authors' results are non-reproducible and we should be striving to publish good, reproducible science.

      For the antibodies that do not have unethical origins the authors claim that their antibodies have been appropriately validated, by "testing in positive control tissue and/or Western blot or in situ hybridization". This is good but needs to be expanded upon. It is a strong selling point that the Abs are validated and I want to see additional information in their Supplementary Table 2 stating for each Ab specifically:

      (1) What +ve control tissue was used in the validation of each Ab and which species that +ve control came from. Likewise, if competition assays to confirm validity was used, please also specify.

      (2) Which assay was the Ab validated for (WB, IHC, ELISA, all etc)

      (3) For Antibodies that were validated for, or using WBs please let the reader know if there were additional bands showing.

      (4) Include references to the literature that supports these validations. That is, please make it easy for the reader to appreciate the hard work that went into the validation of the Antibodies.

      Finally, for the Abs, when the authors write that "All antibodies used have been validated by testing in positive control tissue and/or Western blot or in situ hybridization" I fail to understand what in situ hybridisation means in this context. I am under the impression that in situ hybridisation is some nucleic acid -hybridising-to-organ or tissue slice. Not polypeptide binding.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Remove results that have been obtained by unethically-sourced antibody reagents.

      Strengthen the readers' confidence about the appropriateness & validity of your antibodies.

      First, we want to stress that reviewer 1 has raised his critique related to the used of antibodies from Santa Cruz biotechnology not only through the journal. The head of our department and two others were contacted by reviewer 1 directly without going through the journal or informing/approaching the corresponding or first author. It is our opinion that this debate and critique should be handled through the journal and editorial office and not with people without actual involvement in the project.

      It is correct that we have purchased antibodies from Santa Cruz Biotechnologies both mouse, rabbit and goat antibodies as stated in the correspondence with the reviewer.

      As stated in our previous rebuttal – the goat antibodies from Santa Cruz were discontinued due to inadequate treatment of goats after settling with the authorities in 2016.

      https://www.nature.com/articles/nature.2016.19411

      https://www.science.org/content/blog-post/trouble-santa-cruz-biotechnology

      We have used 11 mouse, rabbit or goat antibodies from Santa Cruz biotechnologies in the manuscript as listed in supplementary table 2 of the manuscript and all of them have been carefully validated in other control tissues supported by ISH and/or WB and many of them already used in several publications by our group (https://pubmed.ncbi.nlm.nih.gov/34612843/, https://pubmed.ncbi.nlm.nih.gov/33893301/, https://pubmed.ncbi.nlm.nih.gov/32931047/, https://pubmed.ncbi.nlm.nih.gov/32729975/, https://pubmed.ncbi.nlm.nih.gov/30965119/, https://pubmed.ncbi.nlm.nih.gov/29029242/, https://pubmed.ncbi.nlm.nih.gov/23850520/, https://pubmed.ncbi.nlm.nih.gov/23097629/, https://pubmed.ncbi.nlm.nih.gov/22404291/, https://pubmed.ncbi.nlm.nih.gov/20362668/, https://pubmed.ncbi.nlm.nih.gov/20172873/,  and other research groups. All antibodies used in this manuscript were purchased before the whole world was aware of mistreatment of goats that was evident several years later.

      We do not support animal cruelty in anyway but the purchase of antibodies from Santa Cruz biotechnologies were conducted long before mistreatment was reported. Moreover, antibodies from Santa Cruz biotechnologies are being used in thousands of publications annually. The company has been punished for their misconduct, and subsequently granted permission to produce antibodies from the relevant authorities again.


      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Despite the study being a collation of important results likely to have an overall positive effect on the field, methodological weaknesses and suboptimal use of statistics make it difficult to give confidence to the study's message.

      Strengths:

      Relevant human and mouse models approached with in vivo and in vitro techniques.

      Weaknesses:

      The methodology, statistics, reagents, analyses, and manuscripts' language all lack rigour.

      (1) The authors used statistics to generate P-values and Rsquare values to evaluate the strength of their findings.

      However, it is unclear how stats were used and/or whether stats were used correctly. For instance, the authors write: "Gaussian distribution of all numerical variables was evaluated by QQ plots". But why? For statistical tests that fall under the umbrella of General Linear Models (line ANOVA, t-tests, and correlations (Pearson's)), there are several assumptions that ought to be checked, including typically:

      (a) Gaussian distribution of residuals.

      (b) Homoskedasticity of the residuals.

      (c) Independence of Y, but that's assumed to be valid due to experimental design.

      So what is the point of evaluating the Gaussian distribution of the data themselves? It is not necessary. In this reviewer's opinion, it is irrelevant, not a good use of statistics, and we ought to be leading by example here.

      Additionally, it is not clear whether the homoscedasticity of the residuals was checked. Many of the data appear to have particularly heteroskedastic residuals. In many respects, homoscedasticity matters more than the normal distribution of the residuals. In Graphpad analyses if ANOVA is used but equal variances are assumed (when variances among groups are unequal then standard deviations assigned in each group will be wrong and thus incorrect p values are being calculated.

      Based on the incomplete and/or wrong statistical analyses it is difficult to evaluate the study in greater depth.

      We agree with the reviewer that we should lead by example and improve clarity on the use of the different statistical tests and their application. In response to the reviewer’s suggestion, we have extended the statistical section, focusing on the analyses used. Additionally, we have specified the statistical test used in the figure legends for each figure. Additionally, we did check for Gaussian distribution and homoskedasticity of residuals before conducting a general linear model test, and this has now been specified in the revised manuscript. In case the assumptions were not met, we have specified which non-parametric test we used. If the assumptions were not met, we specified which non-parametric test was used.

      While on the subject of stats, it is worth mentioning this misuse of statistics in Figure 3D, where the authors added the Slc34a1 transcript levels from controls in the correlation analyses, thereby driving the intercept down. Without the Control data there does not appear to be a correlation between the Slc34a1 levels and tumor size.

      We agree with the reviewer that a correlation analysis is inappropriate here and have removed this part of the figure.

      There is more. The authors make statements (e.g. in the figure levels as: "Correlations indicated by R2.". What does that mean? In a simple correlation, the P value is used to evaluate the strength of the slope being different from zero. The authors also give R2 values for the correlations but they do not provide R2 values for the other stats (like ANOVAs). Why not?

      We agree with the reviewer and have replaced the R2 values with the Pearson correlation coefficient in combination with the P value.

      (2) The authors used antibodies for immunos and WBs. I checked those antibodies online and it was concerning:

      (a) Many are discontinued.

      Many of the antibodies we have used were from the major antibody provider Santa Cruz Biotechnology (SCBT). SCBT was involved in a scandal of animal cruelty and all their goats and rabbits were sacrificed, which explains why several antibodies were discontinued, while the mice antibodies were allowed to continue. This is unfortunate but out of our hands.

      (b) Many are not validated.

      We agree with the reviewer that antibody validation is essential. All antibodies used in this manuscript have been validated. The minimal validation has been to evaluate cellular expression in positive control tissue for instance bone, kidney, or mamma. Moreover, many of the antibodies have been used and validated in previous publications (doi: 10.1593/neo.121164, doi:10.1096/fj.202000061RR, doi: 10.1093/cvr/cvv187) including knockout models. Moreover, many antibodies but not all have been validated by western blot or in situ hybridization. We have included the following in the Materials and Methods section: “All antibodies used have been validated by testing in positive control tissue and/or Western blot or in situ hybridization”.

      (c) Many performed poorly in the Immunos, e.g. FGF23, FGFR1, and Kotho are not really convincing. PO5F1 (gene: OCT4) is the one that looks convincing as it is expressed at the correct cell types.

      We fail to understand the criticism raised by the reviewer regarding the specificity of these specific antibodies. We believe the FGF23 and Klotho antibodies are performing exceptionally well, and FGFR1 is abundantly expressed in many cell types in the testis. As illustrated in Figure 2E, the expression of Klotho, FGF23, and FGFR1 is very clear, specific, and convincing. FGF23 is not expressed in normal testis – which is in accordance with no RNA present there either. However, it is abundantly expressed in GCNIS where RNA is present. On the other hand, Klotho is abundantly expressed in germ cells from normal testis but not expressed in GCNIS.

      (d) Others like NPT2A (product of gene SLC34A1) are equally unconvincing. Shouldn't the immuno show them to be in the plasma membrane?

      If there is some brown staining, this does not mean the antibodies are working. If your antibodies are not validated then you ought to omit the immunos from the manuscript.

      We acknowledge your concerns regarding the NPT2A, NPT2B, and NPT2C staining. While the NPT2A antibody is performing well, we understand your reservations about the other antibodies. It's worth noting that NPT2A is not expressed in normal testis (no RNA either) but is expressed in GCNIS where the RNA is also present. Although it is typically present in the plasma membrane, cytoplasmic expression can be acceptable as membrane availability is crucial for regulating NPT2A function, particularly in the kidney where FGF23 controls membrane availability. We are currently involved in a comprehensive study exploring these phosphate transporters in the organs lining the male reproductive tract. In functional animal models, we have observed very specific staining with this NPT2A antibody following exposed to high phosphate or FGF23. Additionally, we are conducting Western Blot analyses with this antibody, which reinforces our belief that the antibody has a specific binding.

      Reviewer #2 (Public Review):

      Summary:

      This study set out to examine microlithiasis associated with an increased risk of testicular germ cell tumors (TGCT). This reviewer considers this to be an excellent study. It raises questions regarding exactly how aberrant Sertoli cell function could induce osteogenic-like differentiation of germ cells but then all research should raise more questions than it answers.

      Strengths:

      Data showing the link between a disruption in testicular mineral (phosphate)homeostasis, FGF23 expression, and Sertoli cell dysfunction, are compelling.

      Weaknesses:

      Not sure I see any weaknesses here, as this study advances this area of inquiry and ends with a hypothesis for future testing.

      We thank the reviewer for the acknowledgment and highlighting that this is an important message that addresses several ways to develop testicular microlithiasis, which indicates that it is not only due to malignant disease but also frequent in benign conditions.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      I applaud the authors' approach to nomenclature for rodent and human genes and proteins (italicised for genes, all caps for humans, capitalised only for rodents, etc), but the authors frequently got it wrong when referring to genes or proteins. A couple of examples include:

      (1) SLC34A1 (italics) refers to gene (correct use by the authors) but then again the authors use e.g. SLC34A1 (not italics) to refer to the protein product of SLC34A1(italics) gene. In fact, the protein product of the SLC34A1 (italics) gene is called NPT2A (non-italics).

      (2) OCT4 (italics) refers to gene (correct use by the authors) but then again the authors use e.g. OCT4 (not italics) to refer to the protein product of OCT4 (italics)gene. In fact, the protein product of the OCT4 gene (italics) gene is called PO5F1(non-italics).

      The problem with their incorrect and inconsistent nomenclature is widespread in the manuscript making further evaluation difficult.

      Please consult a reliable protein-based database like Uniprot to derive the correct protein names for the genes. You got NANOG correct though.

      We thank the reviewer for addressing this important point. We have corrected the nomenclature throughout the manuscript as suggested.

      (3) The authors use the word "may" too many times. Also often in conjunction with words like "indicates", and "suggests". Examples of phrases that reflect that the authors lack confidence in their own results, conclusions, and understanding of the literature are:

      "...which could indicate that the bone-specific RUNX2 isoform may also be expressed... "

      "...which indicates that the mature bone may have been..."

      Are we shielding ourselves from being wrong in the future because "may" also means "may not"? It is far more engaging to read statements that have a bit more tooth to them, and some assertion too. How about turning the above statements around, to :

      "...which shows that the bone-specific RUNX2 isoform is also expressed... "

      "...which reveals that the mature bone were..."

      ...then revisit ambiguous language ("may", "might" "possibly", "could", "indicate" etc.) throughout the manuscript?

      It's OK to make a statement and be found wrong in the future. Being wrong is integral to Science.

      Thank you for addressing this. We agree with the reviewer that it is fair to be more direct and have revised many of these vague phrases throughout the manuscript.

      (4) The authors use the word "transporter" which in itself is confusing. For instance, is SLC34A1 an importer or an exporter of phosphate? Or both? Do SLC34As move phosphate in or out of the cells or cellular compartments? "Transporter" sounds too vague a word.

      We understand that it might be easier for the reader with the term "importer". However, we should use the specific nomenclature or "wording" that applies to these transporters. The exact terminology is a co-transporter or sodium-dependent phosphate cotransporter as reported here (doi: 10.1152/physrev.00008.2019). Thus, we will use the terms “co-transporter” and “transporter” throughout the revised manuscript.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      We would like to remind the editors and reviewers that the present project is a pilot study that does not claim to produce definitive results. Pilot studies are exploratory preliminary studies to test the validity of hypotheses, the feasibility of a study as well as the research methods and the study design. From our point of view, our hypotheses and the feasibility of the pilot study have been confirmed to such an extent that the implementation of a larger study is justified. At the same time, it became clear during the pilot that the methods and design need to be adapted in some areas in order to increase the reliability of the results - a finding that pilot studies are usually conducted to obtain. We discussed these limitations in detail in order to explain the planned changes in the follow-up study. What the reviewers and editors interpret as incompleteness is therefore due to the nature of a pilot study.  We consider it necessary that appropriate standards are taken into account in the evaluation of the present work.

      In addition, we would like to make a counterstatement as to what our main claims, which should be used to assess the strength of evidence, are - and what they are not:

      In the introduction, we describe the background that led to the formation of our hypotheses: Previous animal and human studies show that food, along with light, serves as the main Zeitgeber for circadian clocks. It has also been shown that chrononutrition can lead to weight loss and improved well-being. Based on this, we hypothesized that individualized meal timing can enhance these positive effects. This hypothesis has been validated on the basis of the available results. Contrary to what the editors and reviewers stated, the assumption that the observed beneficial effects are indeed related to an alteration or resetting of endogenous circadian rhythms was not intended to be investigated in this study and is not one of our main claims. This has already been sufficiently demonstrated and, in our view, need not and should not be repeated in every study on chrononutrition. Accordingly, this assumption was not formulated as a working hypothesis or main claim. It is described in the paper as a potential mechanism, the assumption of which is justified on the basis of previous studies. The lack of a corresponding examination and the erroneous insinuation that corresponding results were nevertheless listed by us in the paper as a main claim should therefore not be used as a criterion for downgrading the assessment of the strength of evidence.

      The main criticism of our study is the collection of data using self-reported food and food quantities. This form of data collection is indeed prone to error, as there is little control over the accuracy of the reported data. However, we believe that this problem is limited in scope.

      (1) Contrary to what the editors and reviewers claim, at no point do we write that we are convinced that food intake has not changed. On the contrary, in Figure 2 we explicitly show that there was a change in what some participants reported to us regarding their food intake. We make it clear throughout the text that we could not find any correlation between weight change and the changes in the reports of food quantities/meals. These statements are correct and only what are actual and formulated main claims should be included in the evaluation of the study.

      (2) As previously stated, we conducted analyses that suggest that an unreported reduction in food intake is unlikely to be the cause of weight loss. For the most part, participants did not change their reporting behavior during the exploration and intervention phases. That is, participants who underreported food intake reported similar amounts in both phases of the study, but lost weight only in the intervention phase. To explain their weight loss with imprecise reporting, it would have to be assumed that these participants began to eat less in the intervention phase and at the same time report more in order to achieve similar calorie counts and food composition in the evaluation. We consider such behavior to be very unlikely, especially since it would apply to numerous participants.

      (3) The editors and reviewers reduce the results to the absence of a correlation between weight loss and reported food quantity and composition. In their assessment of the significance of the findings, however, they ignore the fact that we did find a significant correlation in our analyses, namely between weight loss and an increase in the regularity of food intake. There is no correlation between an increase in regularity and a reduction in reported calories (R<sup>2</sup> = 0.01472). This is credible in our view, as it is unlikely that the more regularly participants ate, the more pronounced the error in their reports was (while in reality they ate less than before).

      (4) We also had the requirement for the study design that the participants could carry out the intervention in their normal everyday life and environment in order to test and ensure implementation in real life. We consider it unrealistic to be able to monitor food intake continuously and without interruption over a period of several weeks under these conditions. We therefore see no alternative to self-reporting. As the reviewers and editors did not suggest any alternative methods of data collection that would fulfil the requirements of our study, we assume that, despite criticism and reservations, they generally agree with our assessment and take this into account in their evaluation.

      It is still criticized that some confounding factors are present. The reviewer makes no reference to the fact that we either eliminated these in the last version submitted (age range), identified them as unproblematic (unmatched cohorts, menstrual cycle, shift work) or even deliberately used them in order to be able to test our hypothesis more validly (inclusion of individuals with normal weight, overweight, and obesity).

      Besides, the use of actimeters to determine circadian rhythms as proposed by the editors and reviewers is not valid for this study and the requirement to use them to determine a circadian reset in the eLife assessment is misleading and inappropriate. This instrument only measures physical activity, but not the physiological parameters that are relevant for an investigation in this field of research.

      For the assessment of chronotype alone, the MCTQ questionnaire is a valid instrument that has been validated several times against actimetry (e.g., DOIs: 10.1080/07420528.2022.2025821, 10.1080/07420528.2023.2202246, 10.1016/j.ijpsycho.2016.07.433, 10.1155/2018/5646848). The reviewer's statement that the MCTQ questionnaire is unreliable for determining chronotype is unsupported and incorrect.

      Equally unproven is the statement that any form of imposed diet appears to lead to weight loss over a period of several months.

      Nevertheless, in order to prevent further misunderstandings, we have revised our text in a number of places and clarified that our statements are not irrefutable assertions, but potential interpretations of the results obtained in the pilot study, which are to be analyzed in more detail with regard to the planned more comprehensive study.

    1. Author response:

      Public Reviews: 

      Reviewer #1 (Public review): 

      Summary: 

      The authors found that IL-1b signaling is pivotal for hypoxemia development and can modulate NETs formation in LPS+HVV ALI model.  

      Strengths: 

      They used IL1R1 ko mice and proved that IL1R1 is involved in ALI model proving that IL1b signalling leads towards ARDS. In addition, hypothermia reduces this effect, suggesting a therapeutic option.  

      We thank the Reviewer for recognizing the strengths of our study and their positive feedback.

      Weaknesses: 

      (1) IL1R1 binds IL1a and IL1b. What would be the role of IL1a in this scenario? 

      Thank you for asking this question. We have addressed this in our previous paper (Nosaka et al. Front Immunol 2020;11; 207) where we used  anti-IL-1a and IL-1a KO mice (Nosaka et al. Front Immunol 2020;11; 207) in our model and found that neither anti-IL-1a treated mice nor IL-1a KO mice were protected. Thus, IL-1b plays a role in inducing hypoxemia during LPS+HVV but not IL-1a. We will now add this point in our revised manuscript discussion.

      (2) The authors depleted neutrophils using anti-Ly6G. What about MDSCs? Do these latter cells be involved in ARDS and VILI?  

      Anti-Ly6G neutrophils depletion may potentially affect G-MDSCs as well (Blood Adv 2022 Jul 29;7(1):73–86), however, we have not looked directly at G-MDSCs.  If these cells were depleted we would have expected to see an increase in inflammation, which we did not.   

      Instead, anti-Ly6G treated mice were protected. Thus, we can not comment on any presumed role of G-MDSCs in LPS+HVV induced severe ALI model that we used.  

      (3) The authors found that TH inhibited IL-1β release from macrophages led to less NETs formation and albumin leakage in the alveolar space in their lung injury model. A graphical abstract could be included suggesting a cellular mechanism.  

      Thanks for summarizing our findings and the suggestion. Unfortunately, eLIFE does not publish a graphical abstract. We tried to mention this mechanism in the discussion.

      (4) If Macrophages are responsible for IL1b release that via IL1R1 induces NETosis, what happens if you deplete macrophages? what is the role of epithelial cells?  

      Previous studies have found that macrophage depletion is protective in several models of ALI (Eyal. Intensive Care Med. 2007;33:1212–1218., Lindauer.  J Immunol. 2009;183:1419–1426.), and other researchers have found that airway epithelial cells did not contribute to IL-1β secretion (Tang. PLoS ONE. 2012;7:e37689.). We have previously reported that epithelial cells produce IL-18 without LPS priming signal during LPS+HVV (Nosaka et al. Front Immunol 2020;11; 207). Thus, IL-18 is not sufficient to induce Hypoxemia as Saline+HVV treated mice do not develop hypoxemia (Nosaka et al. Front Immunol 2020;11; 207). We will now add this point to the revised discussion of the manuscript.

      Reviewer #2 (Public review): 

      Summary: 

      The manuscript by Nosaka et al is a comprehensive study exploring the involvement of IL1beta signaling in a 2-hit model of lung injury + ventilation, with a focus on modulation by hypothermia. 

      Strengths: 

      The authors demonstrate quite convincingly that interleukin 1 beta plays a role in the development of ventilator-induced lung injury in this model, and that this role includes the regulation of neutrophil extracellular trap formation. The authors use a variety of in vivo animal-based and in vitro cell culture work, and interventions including global gene knockout, cell-targeted knockout and pharmacological inhibition, which greatly strengthen the ability to make clear biological interpretations. 

      We thank the Reviewer for their positive feedback 

      Weaknesses: 

      A primary point for open discussion is the translatability of the findings to patients. The main model used, one of intratracheal LPS plus mechanical ventilation is well accepted for research exploring the pathogenesis and potential treatments for acute respiratory distress syndrome (ARDS). However, the interpretation may still be open to question - in the model here, animals were exposed to LPS to induce inflammation for only 2 hours, and seemingly displayed no signs of sickness, before the start of ventilation. This would not be typical for the majority of ARDS patients, and whether hypothermia could be effective once substantial injury is already present remains an open question. The interaction between LPS/infection and temperature is also complicated - in humans, LPS (or infection) induces a febrile, hyperthermic response, whereas in mice LPS induces hypothermia (eg. Ganeshan K, Chawla A. Nat Rev Endocrinol. 2017;13:458-465). Given this difference in physiological response, it is therefore unclear whether hypothermia in mice and hypothermia in humans are easily comparable. Finally, the use of only young, male animals such as in the current study has been typical but may be criticised as limiting translatability to people. 

      Therefore while the conclusions of the paper are well supported by the data, and the biological pathways have been impressively explored, questions still remain regarding the ultimate interpretations.  

      We agree with the reviewer that at two hours post LPS, there is only minimal pulmonary inflammation at that time (Dagvadorj et al Immunity 42, 640–653). This is a limitation to the experimental model we used in our study. Additionally, as the reviewer pointed out that LPS induces hyperthermia in human, but it is also well-established that physiological hypothermia occurs in humans with severe infections and sepsis (Baisse. Am J Emerg Med. 2023 Sep: 71: 134-138., Werner.  Am J Emerg Med. 2025 Feb;88:64-78.). Therefore, the difference between human and mouse responses to sepsis or infections may be more nuanced.  Furthermore, it is important to distinguish between physiological hypothermia (just <36°C) and therapeutic hypothermia (typically 32-34°C). We will add to the discussion whether hypothermia serves as a protective response, and the transition from normothermia to hyperthermia could have detrimental effects. We only used young male mice in our study as the Reviewer points out; we will also add this point to the revised discussion as a limitation of our study.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      DiPeso et al. develop two tools to (i) classify micronucleated (MN) cells, which they call VCS MN, and (ii) segment micronuclei and nuclei with MMFinder. They then use these tools to identify transcriptional changes in MN cells.

      The strengths of this study are:

      (1) Developing highly specialized tools to speed up the analysis of specific cellular phenomena such as MN formation and rupture is likely valuable to the community and neglected by developers of more generalist methods.

      (2) A lot of work and ideas have gone into this manuscript. It is clearly a valuable contribution.

      (3) Combining automated analysis, single-cell labeling, and cell sorting is an exciting approach to enrich phenotypes of interest, which the authors demonstrate here.

      Weaknesses:

      (1) Images and ground truth labels are not shared for others to develop potentially better analysis methods.

      We regret this omission and thank the reviewer for pointing it out. Both the images and ground truth labels for VCS MN and MNFinder are now available on the lab’s github page and described in the README.txt files. VCS MN: https://github.com/hatch-lab/fast-mn. MNFinder: https://github.com/hatch-lab/mnfinder.

      (2) Evaluations of the methods are often not fully explained in the text.

      The text has been extensively updated to include a full description of the methods and choices made to develop the VCS MN and MNFinder image segmentation modules.

      (3) To my mind, the various metrics used to evaluate VCS MN reveal it not to be terribly reliable. Recall and PPV hover in the 70-80% range except for the PPV for MN+. It is what it is - but do the authors think one has to spend time manually correcting the output or do they suggest one uses it as is?

      VCS MN attempts to balance precision and recall with speed to reduce the fraction of MN changing state from intact to ruptured during a single cell cycle during a live-cell isolation experiment. In addition, we chose to prioritize inclusion of small MN adjacent to the nucleus in our positive calls. This meant that there were more false positives (lower PPV) than obtained by other methods but allowed us to include this highly biologically relevant class of MN in our MN+ population. Thus, for a comprehensive understanding of the consequences of MN formation and rupture, we recommend using the finder as is. However, for other visual cell sorting applications where a small number of highly pure MN positive and negative cells is preferred, such as clonal outgrowth or metastasis assays, we would recommend using the slower, but more precise, MNFinder to get a higher precision at a cost of temporal resolution. In addition, MNFinder, with its higher flexibility and object coverage, is recommended for all fixed cell analyses.

      Reviewer #2 (Public review):

      Summary:

      Micronuclei are aberrant nuclear structures frequently seen following the missegregation of chromosomes. The authors present two image analysis methods, one robust and another rapid, to identify micronuclei (MN) bearing cells. The authors induce chromosome missegregation using an MPS1 inhibitor to check their software outcomes. In missegregation-induced cells, the authors do not distinguish cells that have MN from those that have MN with additional segregation defects. The authors use RNAseq to assess the outcomes of their MN-identifying methods: they do not observe a transcriptomic signature specific to MN but find changes that correlate with aneuploidy status. Overall, this work offers new tools to identify MN-presenting cells, and it sets the stage with clear benchmarks for further software development.

      Strengths:

      Currently, there are no robust MN classifiers with a clear quantification of their efficiency across cell lines (mIoU score). The software presented here tries to address this gap. GitHub material (tools, protocols, etc) provided is a great asset to naive and experienced computational biologists. The method has been tested in more than one cell line. This method can help integrate cell biology and 'omics' studies.

      Weaknesses:

      Although the classifier outperforms available tools for MN segmentation by providing mIOU, it's not yet at a point where it can be reliably applied to functional genomics assays where we expect a range of phenotypic penetrance.

      We agree that the MNFinder module has limitations with regards to the degree of nuclear atypia and cell density that can be tolerated. Based on the recall and PPV values and their consistency across the majority conditions analyzed, we believe that MNFinder can provide reliable results for MN frequency, integrity, shape, and label characteristics in a functional genomics assay in many commonly used adherent cell lines. We also added a discussion of caveats for these analyses, including the facts that highly lobulated nuclei will have higher false positive rates and that high cell confluency may require additional markers to ensure highly accurate assignment of MN to nuclei.

      Spindle checkpoint loss (e.g., MPS1 inhibition) is expected to cause a variety of nuclear atypia: misshapen, multinucleated, and micronucleated cells. It may be difficult to obtain a pure MN population following MPS1 inhibitor treatment, as many cells are likely to present MN among multinucleated or misshapen nuclear compartments. Given this situation, the transcriptomic impact of MN is unlikely to be retrieved using this experimental design, but this does not negate the significance of the work. The discussion will have to consider the nature, origin, and proportion of MN/rupture-only states - for example, lagging chromatids and unaligned chromosomes can result in different states of micronuclei and also distinct cell fates.

      We appreciate the reviewer’s comments and now quantify the frequency of other nuclear atypias and MN chromosome content in RPE1 cells after 24 h Mps1 inhibition (Fig. S1). In summary, we find only small increases in nuclear atypia, including multinucleate cells, misshapen nuclei, and chromatin bridges, compared to the large increase in MN formation. This contrasts with what is observed when mitosis is delayed using nocodazole or CENPE inhibitors where nuclear atypia is much more frequent. Importantly, after Mps1 inhibition, RPE1 cells with MN were only slightly more likely to have a misshapen nucleus compared to cells without MN (Fig. S1C).

      Interestingly, this analysis showed that the VCS MN pipeline, which uses the Deep Retina segmenter to identify nuclei, has a strong bias against lobulated nuclei and frequently fails to find them (Fig. S2B). Therefore, the cell populations analyzed by RNAseq were largely depleted of highly misshapen nuclei and differences in nuclear atypia frequency between MN+ and MN- cells in the starting population were lost (Fig. S9A, compare to Fig. S1C). This strongly suggests that the transcript changes we observed reflect differences in MN frequency and aneuploidy rather than differences in nuclei morphology.

      We agree with the reviewer that MN rupture frequency and formation, and downstream effects on cell proliferation and DNA damage, are sensitive to the source of the missegregated chromatin. In the revised manuscript we make clear that we chose Mps1 inhibition because it is strongly biased towards whole chromosome MN (Fig. S1E), limiting signal from DNA damage products, including chromosome fragments and chromatin bridges. This provides a base line to disambiguate the consequences of micronucleation and DNA damage in more complex chromosome missegregation processes, such as DNA replication disruption and irradiation. 

      Reviewer #3 (Public review):

      Summary:

      The authors develop a method to visually analyze micronuclei using automated methods. The authors then use these methods to isolate MN post-photoactivation and analyze transcriptional changes in cells with and without micronuclei of RPE-1 cells. The authors observe in RPE-1 cells that MN-containing cells show similar transcriptomic changes as aneuploidy, and that MN rupture does not lead to vast changes in the transcriptome.

      Strengths:

      The authors develop a method that allows for automating measurements and analysis of micronuclei. This has been something that the field has been missing for a long time. Using such a method has the potential to advance micronuclei biology. The authors also develop a method to identify cells with micronuclei in real time and mark them using photoconversion and then isolate them via FACS. The authors use this method to study the transcriptome. This method is very powerful as it allows for the sorting of a heterogenous population and subsequent analysis with a much higher sample number than could be previously done.

      Weaknesses:

      The major weakness of this paper is that the results from the RNA-seq analysis are difficult to interpret as very few changes are found to begin with between cells with MN and cells without. The authors have to use a 1.5-fold cut-off to detect any changes in general. This is most likely due to the sequencing read depth used by the authors. Moreover, there are large variances between replicates in experiments looking at cells with ruptured versus intact micronuclei. This limits our ability to assess if the lack of changes is due to truly not having changes between these populations or experimental limitations. Moreover, the authors use RPE-1 cells which lack cGAS, which may contribute to the lack of changes observed. Thus, it is possible that these results are not consistent with what would occur in primary tissues or just in general in cells with a proficient cGAS/STING pathway.

      We agree with the reviewer’s assessment of the limitations of our RNA-Seq analysis. After additional analysis, we propose an alternative explanation for the lower expression changes we observe in the MN+ and Mps1 inhibitor RNA-Seq experiments. In summary, we find that VCS MN has a strong bias against highly lobulated nuclei that depletes this class of cells from both the bulk analysis and the micronucleated cell populations (Fig. S9A). Based on this result, we propose that our analysis reduces the contribution of nuclear atypia to these transcriptional changes and that nuclear morphology changes are likely a signaling trigger associated with aneuploidy.

      We believe that this finding strengthens our overall conclusion that MN formation and rupture do not cause transcriptional changes, as suppressing the signaling associated with nuclei atypia should increase sensitivity to changes from the MN. However, we cannot completely rule out that MN formation or rupture cause a broad low-level change in transcription that is obscured by other signals in the dataset.

      As to cGAS signaling, several follow up papers and even the initial studies from the Greenburg lab show that MN rupture does not activate cGAS and does not cause cGAS/STING-dependent signaling in the first cell cycle (see citations and discussion in text). Therefore, we expect the absence of cGAS in RPE1 cells will have no effect in the first cell cycle, but could alter the transcriptional profile after mitosis. Although analysis of RPE1  cGAS+ cells or primary cells in these experiments will be required to definitively address this point, we believe that our interpretation of our RNAseq results is sufficiently backed up by the literature to warrant our conclusion that MN formation and rupture do not induce a transcriptional response in the first cell cycle.

      Reviewer #1 (Recommendations for the authors):

      I do not recommend additional experimental or computational work. Instead, I just recommend adapting the claims of the manuscript to what has been done. I am just asking for further clarification and minor rewriting.

      (1) The manuscript is written like a molecular biology paper with sparse explanations of the authors' reasoning, especially in the development of their algorithms. I was often lost as to why they did things in one way or another.

      The revised manuscript has thorough explanations and additional data and graphics defining how and why the VCS MN and MNFinder modules were developed. We hope that this clears up many of the questions the reviewer had and appreciate their guidance on making it more readable for scientists from different backgrounds.

      (2) Evaluations of their method are often not fully explained, for example:

      "On average, 75% of nuclei per field were correctly segmented and cropped."

      "MN segments were then assigned to 'parent' nuclei by proximity, which correctly associated 97% of MN."

      Were there ground truth images and labels created? How many? For example, I don't know how the authors could even establish a ground-truth for associating MNs to nuclei if MNs happened to be almost equidistant between two nuclei in their images.

      I suggest a separate subsection early in the Results section where the underlying imaging data + labels are presented.

      We added new sections to the text and figures at the beginning of the VCS MN and MNFinder subsections (Fig. S2 and Fig. S5) with specific information about how ground truth images and labels were generated for both modules and how these were broken up for training, validation, and testing.

      We also added information and images to explain how ground truth MN/nucleus associations were derived. In summary, we took advantage of the fact that 2xDendra-NLS is present at low levels in the cytoplasm to identify cell boundaries. This combined with a subconfluent cell population allowed us to unambiguously group MN and nuclei for 98% of MN, we estimate. These identifications were used to generate ground truth labels and analyze how well proximity defines MN/nuclei groups (Fig.s S1 and S2).

      (3) Overall, I find the sections long and more subtitles would help me better navigate the manuscript.

      Where possible, we have added subtitles.

      (4) Everything following "To train the model, H2B channel images were passed to a Deep Retina neural net ..." is fully automated, it seems to me. Thus, there seems to be no human intervention to correct the output before it is used to train the neural network. Therefore, I do not understand why a neural network was trained at all if the pipeline for creating ground truth labels worked fully automatically. At least, the explanations are insufficient.

      We apologize for the initial lack of clarity in the text and included additional details in the revision. We used the Deep Retina segmenter to crop the raw images to areas around individual nuclei to accelerate ground truth labeling of MN. A trained user went through each nucleus crop and manually labeled pixels belonging to MN to generate the ground truth dataset for training, validation, and imaging in VCS MN (Fig. S2A).

      (5) To my mind, the various metrics used to evaluate VCS MN reveal it not to be terribly reliable. Recall and PPV hover in the 70-80% range except for the PPV for MN+. It is what it is - but do the authors think one has to spend time manually correcting the output or do they suggest one uses it as is? I understand that for bulk transcriptomics, enrichment may be sufficient but for many other questions, where the wrong cell type could contaminate the population, it is not.

      Remarks in the Results section on what the various accuracies mean for different applications would be good (so one does not need to wait for the Discussion section).

      One of the strengths of the visual cell sorting system is that any image analysis pipeline can be used with it. We used VCS MN for the transcriptomics experiment, but for other applications a user could run visual cell sorting in conjunction with MNFinder for increased purity while maintaining a reasonable recall or use a pre-existing MN segmentation program that gives 100% purity but captures only a specific subgroup of micronucleated cells (e.g. PIQUE). 

      To maintain readability, especially with the expansion of the results sections, we kept the discussion of how we envision using visual cell sorting for other MN-based applications in the discussion section.

      (6) I am confused about what "cell" is referring to in much of the manuscript. Is it the nucleus + MNs only? Is it the whole cell, which one would ordinarily think it is? If so, are there additional widefield images, where one can discern cell boundaries? I found the section "MNFinder accurately ..." very hard to read and digest for this reason and other ambiguous wording. I suggest the authors take a fresh look at their manuscript and see whether the text can be improved for clarity. I did not find it an easy read overall, especially the computational part.

      After re-examining how “cell” was used, we updated the text to limit its use to the MNFinder arm tasked with identifying MN-nucleus associations where the convex hull defined by these objects is used to determine the “cell” boundary. In all other cases we have replaced cell with “nucleus” because, as the reviewer points out, that is what is being analyzed and converted. We hope this is clearer.

      (7) Post-FACS PPVs are not that great (Figure 3c). It depends on the question one wants to answer whether ~70% PPV is good enough. Again, would be good to comment on.

      We added discussion of this result to the revision. In summary, a likely reason for the reduced PPV is that, although we maintain the cells in buffer with a Cdk1 inhibitor, we know that some proportion of the cells go through mitosis post-sorting. Since MN are frequently reincorporated into the nucleus after mitosis (Hatch et al, 2013; Zhang et al., 2015), we expect this to reduce the MN+ population. Thus, we expect that the PPV in the RNAseq population is higher than what we can measure by analyzing post-sorted cells that have been plated and analyzed later.

      (8) I am thoroughly confused as to why the authors claim that their system works in the "absence of genetic perturbations" and why they emphasize the fact that their cells are non-transformed: They still needed a fluorescent label and they induce MNs with a chemical Mps1 inhibitor. (The latter is not a genetic manipulation, of course, but they still need to enrich MNs somehow. That is, their method has not been tested on a cell population in which MNs occur naturally, presumably at a very low rate, unless I missed something.) A more careful description of the benefits of their method would be good.

      We apologize for the confusion on these points and hope this is clarified in the revision. We were comparing our system, which can be made using transient transfection, if desired, to current tools that disambiguate aneuploidy and MN formation by deleting parts of chromosomes or engineering double strand breaks with CRISPR to generate single chromosome-specific missegregation events. Most of these systems require transformed cancer cells to obtain high levels of recombination. In contrast, visual cell sorting can isolate micronucleated cells from any cell line that can exogenously express a protein, including primary cells and non-transformed cells like RPE1s.

      Other minor points:

      (1) The authors should not refer to "H2B channels" but to "H2B-emiRFP703 channels". It may seem obvious to the authors but for someone reading the manuscript for the very first time, it was not. I was not sure whether there were additional imaging modalities used for H2B/nucleus/chromatin detection before I went back and read that only fluorescence images of H2B-emiRFP703 were used. To put it another way, the authors are detecting fluorescence, not histones -- unless I misunderstood something.

      To address this point, we altered the text to read “H2B-emiRFP703” when discussing images of this construct. For MNFinder some images were of cells expressing H2B-GFP, which has also been clarified.

      (2) If the level of zoom on my screen is such that I can comfortably read the text, I cannot see much in the figure panels. The features that I should be able to see are the size of a title. The image panels should be magnified.

      In the revision, the images are appended to the end at full resolution to overcome this difficulty. Thank you for your forbearance.

      Reviewer #2 (Recommendations for the authors):

      The methods are adequately explained. The Results text narrating experiments and data analysis is clear. Interpretation of a few results could be clarified and strengthened as explained below.

      (1) RNAseq experiments are a good proof of principle. To strengthen their interpretation in Figures 4 and 6, I would recommend the authors cite published work on checkpoint/MPS1 loss-induced chromosome missegregation (PMID: 18545697, PMID: 33837239, PMC9559752) and consider in their discussion the 'origin' and 'proportion' of micronucleated cells and irregularly shaped nuclei expected in RPE1 lines. This will help interpret Figure 6 findings on aneuploidy signature accurately. Not being able to see an MN-specific signature could be due to the way the biological specimen is presented with a mixture of cells with 'MN only' or 'rupture' or 'MN along with misshapen nuclei'. These features may all link to aneuploidy rather than 'MN' specifically.

      We appreciate the reviewer’s suggestion and added a new analysis of nuclear atypia after Mps1 inhibition in RPE1 cells to Fig. S1. Overall, we found that Mps1 inhibition significantly, but modestly, increased the proportion of misshapen nuclei and chromatin bridges. Multinucleate cells were so rare that instead of giving them their own category we included them in “misshapen nuclei.” These results are consistent with images of Msp1i treated RPE1 cells from He et al. 2019 and Santaguida et al. 2017 and distinct from the stronger changes in nuclear morphology observed after delaying mitosis by nocodazole or CENPE inhibition.

      We also found that the Deep Retina segmenter used to identify nuclei in VCS MN had a significant bias against highly lobulated nuclei (Fig. S2B) that led to misshapen nuclei being largely excluded from the RNAseq analyses. As a result we found no enrichment of misshapen nuclei, chromatin bridges, or dead/mitotic nuclear morphologies in MN+ compared to MN- nuclei in our RNASeq experiments (Fig. S9A).

      (2) As the authors clarify in the response letter, one round of ML is unlikely to result in fully robust software; additional rounds of ML with other markers will make the work robust. It will be useful to indicate other ML image analysis tools that have improved through such reiterations. They could use reviews on challenges and opportunities using ML approaches to support their statement. Also in the introduction, I would recommend labelling as 'rapid' instead of 'rapid and precise' method.

      We updated the text to reference review articles that discuss the benefit of additional training for increasing ML accuracy and changed the text to “rapid.”

      (3) The lack of live-cell studies does not allow the authors to distinguish the origin of MN (lagging chromatids or unaligned chromosomes). As explained in 1, considering these aspects in discussion would strengthen their interpretation. Live-cell studies can help reduce the dependencies on proximity maps (Figure S2).

      The revised text includes new references and data (Fig. S1E) demonstrating that Mps1 inhibition strongly biases towards whole chromosome missegregation and that MN are most likely to contain a single centromere positive chromosome rather than chromatin fragments or multiple chromosomes.

      (4) Mean Intersection over Union (mIOU) is a good measure to compare outcomes against ground truth. However, the mIOU is relatively low (Figure 2D) for HeLa-based functional genomics applications. It will help to discuss mIOU for other classifiers (non-MN classifiers) so that they can be used as a benchmark (this is important since the authors state in their response that they are the first to benchmark an MN classifier). There are publications for mitochondria, cell cortex, spindle, nuclei, etc. where IOU has been discussed.

      We added references to classifiers for other small cellular structures. We also evaluated major sources of error in MNFinder found that false negatives are enriched in very small MN (3 to 9 pixels, or about 0.4 µm<sup>2</sup> – 3 µm<sup>2</sup>, Fig. S6B). A similar result was obtained for VCS MN (Fig. S3B). Because small changes in the number of pixels identified in small objects can have outsized effects on mIoU scores, we suspect that this is exerting downward pressure on the mIoU value. Based on the PPV and recall values we identified, we believe that MNFinder is robust enough to use for functional genomics and screening applications with reasonable sample sizes.

      (5) Figure 5 figure legend title is an overinterpretation. MN and rupture-initiated transcriptional changes could not be isolated with this technique where several other missegregation phenotypes are buried (see point 1 above).

      We decided to keep the figure title legend based on our analysis of known missegregation phenotypes in Fig. S1 and S9 showing that there is no difference in major classes of nuclear atypia between MN+ and MN- populations in this analysis. Although we cannot rule out that other correlated changes exist, we believe that the title represents the most parsimonious interpretation.

      Minor comments

      (1) The sentence in the introduction needs clarification and reference. "However, these interventions cause diverse "off-target" nuclear and cellular changes, including chromatin bridges, aneuploidy, and DNA damage." Off-target may not be the correct description since inhibiting MPS1 is expected to cause a variety of problems based on its role as a master kinase in multiple steps of the chromosome segregation process. Consider one of the references in point 1 for a detailed live-cell view of MPS1 inhibitor outcomes.

      We have changed “off-target” to “additional” for clarity.

      (2) In Figure 3 or S3, did the authors notice any association between the cell cycle phase and MN or rupture presence? Is this possible to consider based on FACS outcomes or nuclear shapes?

      Previous work by our lab and others have shown that MN rupture frequency increases during the cell cycle (Hatch et al., 2013; Joo et al., 2023). Whether this is stochastic or regulated by the cell cycle may depend on what chromosome is in the MN (Mammel et al., 2021) and likely the cell line. Unfortunately, the H2B-emiRFP703 fluorescence in our population is too variable to identify cell cycle stage from FACS or nuclear fluorescence analysis.

      (3) Figure 5 - Please explain "MA plot".

      An MA plot, or log fold-change (M) versus average (A) gene expression, is a way to visualize differently expressed genes between two conditions in an RNASeq experiment and is used as an alternative to volcano plots. We chose them for our paper because most of the expression changes we observed were small and of similar significance and the MA plot spreads out the data compared to a volcano plot and allowed a better visualization of trends across the population.

      (4) Page 7: "our results strongly suggest that protein expression changes in MN+ and rupture+ cells are driven mainly by increased aneuploidy rather than cellular sensing of MN formation and rupture.". This is an overstatement considering the mIOU limits of the software tool and the non-exclusive nature of MN in their samples.

      We agree that we cannot rule out that an unknown masking effect is inhibiting our ability to observe small broad changes in transcription after MN formation or rupture. However, we believe we have minimized the most likely sources of masking effects, including nuclear atypia and large scale aneuploidy differences, and thus our interpretation is the most likely one.

      Reviewer #3 (Recommendations for the authors):

      Overall, the authors need to explain their methods better, define some technical terms used, and more thoroughly explain the parameters and rationale used when implementing these two protocols for identifying micronuclei; primarily as this is geared toward a more general audience that does not necessarily work with machine learning algorithms.

      (1) A clearer description in the methods as to how accuracy was calculated. Were micronuclei counted by hand or another method to assess accuracy?

      We significantly expanded the section on how the machine learning models were trained and tested, including how sensitivity and specificity metrics were calculated, in both the results and the methods sections. The code used to compare ground truth labels to computed masks is also now included in the MNFinder module available on the lab github page. 

      (2) Define positive predictive value.

      The text now says “the positive predictive value (PPV, the proportion of true positives, i.e. specificity) and recall (the proportion of MN found by the classifier, i.e. sensitivity)…”.

      (3) Why is it a problem to use the VCS MN at higher magnifications where undersegmentation occurs? What do the authors mean by diminished performance (what metrics are they using for this?).

      We have included a representative image and calculated mIoU and recall for 40x magnification images analyzed by MNFinder after rescaling in Fig. 2A. In summary, VCS MN only correctly labeled a few pixels in the MN, which was sufficient to call the adjacent nucleus “MN+” but not sufficient for other applications, such as quantifying MN area. In addition, VCS MN did much worse at identifying all the MN in 40x images with a recall, or sensitivity, metric of 0.36. We are not sure why. Developing MNFinder provided a module that was well suited to quantify MN characteristics in fixed cell images, an important use case in MN biology.

      (4) The authors should compare MN that are analyzed and not analyzed using these methods and define parameters. Is there a size limitation? Closeness to the main nucleus?

      We added two new figures defining what contributes to module error for both VCS MN (Fig. S3) and MNFinder (Fig. S6). For VCS MN, false negatives are enriched in very large or very small MN and tend to be dimmer and farther from the nucleus than true positives. False positives are largely misclassification of small dim objects in the image as MN. For MNFinder, the most missed class of MN are very small ones (3-9 px in area) and the majority of false positives are misclassifications of elongated nuclear blebs as MN.

      (5) Are there parameters in how confluent an image must be to correctly define that the micronucleus belongs to the correct cell? The authors discussed that this was calculated based on predicted distance. However, many factors might affect proper calling on MN. And the authors should test this by staining for a cytosolic marker and calculating accuracy.

      We updated the text with more information about how the cytoplasm was defined using leaky 2x-Dendra2-NLS signal to analyze the accuracy of MN/nucleus associations (Fig. S2G-H). In addition, we quantified cell confluency and distance to the first and second nearest neighbor for each MN in our training and testing image datasets. We found that, as anticipated, cells were imaged at subconfluent concentrations with most fields having a confluency around 30% cell coverage (Fig. S2E) and that the average difference in distance between the closest nucleus to an MN and the next closest nucleus was 3.3 fold (Fig. S2F). We edited the discussion section to state that the ability of MN/nuclear proximity to predict associations at high cell confluencies would have to be experimentally validated.

      (6) The authors measure the ratio of Dendra2(Red) v. Dendra2 (Green) in Figure 3B to demonstrate that photoconversion is stable. This measurement, to me, is confusing, as in the end, the authors need to show that they have a robust conversion signal and are able to isolate these data. The authors should directly demonstrate that the Red signal remains by analyzing the percent of the Red signal compared to time point 0 for individual cells.

      We found a bulk analysis to be more powerful than trying to reidentify individual cells due to how much RPE1 cells move during the 4 and 8 hours between image acquisitions. In addition, we sort on the ratio between red and green fluorescence per cell, rather than the absolute fluorescence, to compensate for variation in 2xDendra-NLS protein expression between cells. Therefore, demonstrating that distinct ratios remained present throughout the time course is the most relevant to the downstream analysis.

      To address the reviewer’s concern, we replotted the data in Fig. 3B to highlight changes over time in the raw levels of red and green Dendra fluorescence (Fig. S7D). As expected, we see an overall decrease in red fluorescence intensity, and complementary increase in green fluorescence intensity, over 8 hours, likely due to protein turnover. We also observe an increase in the number of nuclei lacking red fluorescence. This is expected since the well was only partially converted and we expect significant numbers of unconverted cells to move into the field between the first image and the 8 hour image.

      (7) The authors isolate and subsequently use RNA-sequencing to identify changes between Mps1i and DMSO-treated cells. One concern is that even with the less stringent cut-off of 1.5 fold there is a very small change between DMSO and MPS1i treated cells, with only 63 genes changing, none of which were affected above a 2-fold change. The authors should carefully address this, including why their dataset sees changes in many more pathways than in the He et al. and Santaguida et al. studies. Is this due to just having a decreased cut-off?

      The reviewer correctly points out that we observed an overall reduction in the strength of gene expression changes between our dataset of DMSO versus Mps1i treated RPE1 cells compared to similar studies. We suggest a couple reasons for this. One is that the log<sub>2</sub> fold changes observed in the other studies are not huge and vary between 2.5 and -3.8 for He et al., 3.3 and -2.3 for Santaguida et al., and -0.8 and 1.6 for our study. This variability is within a reasonable range for different experimental conditions and library prep protocols. A second is that our protocol minimizes a potential source of transcriptional change – nuclear lobulation – that is present in the other datasets.

      For the pathway analysis we did not use a fold-change cut-off for any data set, instead opting to include all the genes found to be significantly different between control and Mps1i treated cells for all three studies. Our read-depth was higher than that of the two published experiments, which could contribute to an increased DEG number. However, we hypothesize that our identification of a broader number of altered pathways most likely arises from increased sensitivity due to the loss of covering signal from transcriptional changes associated with increased nuclear atypia. Additional visual cell sorting experiments sorting on misshapen nuclei instead of MN would allow us to determine the accuracy of this hypothesis.

      (8) Moreover, clustering (in Figure 5E) of the replicates is a bit worrisome as the variances are large and therefore it is unclear if, with such large variance and low screening depth, one can really make such a strong conclusion that there are no changes. The authors should prove that their conclusion that rupture does not lead to large transcriptional changes, is not due to the limitations of their experimental design.

      We agree with the reviewers that additional rounds of RNAseq would improve the accuracy of our transcriptomic analysis and could uncover additional DEGs. However, we believe the overall conclusion to be correct based on the results of our attempt to validate changes in gene expression by immunofluorescence. We analyzed two of the most highly upregulated genes in the ruptured MN dataset, ATF3 and EGR1. Although we saw a statistically significant increase in ATF3 intensity between cells without MN and those with ruptured MN, the fold change was so small compared to our positive control (100x less) that we believe it is it is more consistent with a small increase in the probability of aneuploidy rather than a specific signature of MN rupture.

      (9) The authors also need to address the fact that they are using RPE-1 cells more clearly and that the lack of effect in transcriptional changes may be simply due to the loss of cGAS-STING pathway (Mackenzie et al., 2017; Harding et al., 2017; etc.).

      As we discuss above in the public comments section, the literature is clear that MN do not activate cGAS in the first cell cycle after their formation, even upon rupture. Therefore, we do not expect any changes in our results when applied to cGAS-competent cells. However, this expectation needs to be experimentally validated, which we plan to address in upcoming work.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Reviewer #2:

      (1) The use of two m<sup>5</sup>C reader proteins is likely a reason for the high number of edits introduced by the DRAM-Seq method. Both ALYREF and YBX1 are ubiquitous proteins with multiple roles in RNA metabolism including splicing and mRNA export. It is reasonable to assume that both ALYREF and YBX1 bind to many mRNAs that do not contain m<sup>5</sup>C.

      To substantiate the author's claim that ALYREF or YBX1 binds m<sup>5</sup>C-modified RNAs to an extent that would allow distinguishing its binding to non-modified RNAs from binding to m<sup>5</sup>C-modified RNAs, it would be recommended to provide data on the affinity of these, supposedly proven, m<sup>5</sup>C readers to non-modified versus m<sup>5</sup>C-modified RNAs. To do so, this reviewer suggests performing experiments as described in Slama et al., 2020 (doi: 10.1016/j.ymeth.2018.10.020). However, using dot blots like in so many published studies to show modification of a specific antibody or protein binding, is insufficient as an argument because no antibody, nor protein, encounters nanograms to micrograms of a specific RNA identity in a cell. This issue remains a major caveat in all studies using so-called RNA modification reader proteins as bait for detecting RNA modifications in epitranscriptomics research. It becomes a pertinent problem if used as a platform for base editing similar to the work presented in this manuscript.

      The authors have tried to address the point made by this reviewer. However, rather than performing an experiment with recombinant ALYREF-fusions and m<sup>5</sup>C-modified to unmodified RNA oligos for testing the enrichment factor of ALYREF in vitro, the authors resorted to citing two manuscripts. One manuscript is cited by everybody when it comes to ALYREF as m<sup>5</sup>C reader, however none of the experiments have been repeated by another laboratory. The other manuscript is reporting on YBX1 binding to m<sup>5</sup>C-containing RNA and mentions PAR-CLiP experiments with ALYREF, the details of which are nowhere to be found in doi: 10.1038/s41556-019-0361-y.<br /> Furthermore, the authors have added RNA pull-down assays that should substitute for the requested experiments. Interestingly, Figure S1E shows that ALYREF binds equally well to unmodified and m<sup>5</sup>C-modified RNA oligos, which contradicts doi:10.1038/cr.2017.55, and supports the conclusion that wild-type ALYREF is not specific m<sup>5</sup>C binder. The necessity of including always an overexpression of ALYREF-mut in parallel DRAM experiments, makes the developed method better controlled but not easy to handle (expression differences of the plasmid-driven proteins etc.)

      Thank you for pointing this out. First, we would like to correct our previous response: the binding ability of ALYREF to m<sup>5</sup>C-modified RNA was initially reported in doi: 10.1038/cr.2017.55, (and not in doi: 10.1038/s41556-019-0361-y), where it was observed through PAR-CLIP analysis that the K171 mutation weakens its binding affinity to m<sup>5</sup>C -modified RNA.

      Our previous experimental approach was not optimal: the protein concentration in the INPUT group was too high, leading to overexposure in the experimental group. Additionally, we did not conduct a quantitative analysis of the results at that time. In response to your suggestion, we performed RNA pull-down experiments with YBX1 and ALYREF, rather than with the pan-DRAM protein, to better validate and reproduce the previously reported findings. Our quantitative analysis revealed that both ALYREF and YBX1 exhibit a stronger affinity for m<sup>5</sup>C -modified RNAs. Furthermore, mutating the key amino acids involved in m<sup>5</sup>C recognition significantly reduced the binding affinity of both readers. These results align with previous studies (doi: 10.1038/cr.2017.55 and doi: 10.1038/s41556-019-0361-y), confirming that ALYREF and YBX1 are specific readers of m<sup>5</sup>C -modified RNAs. However, our detection system has certain limitations. Despite mutating the critical amino acids, both readers retained a weak binding affinity for m<sup>5</sup>C, suggesting that while the mutation helps reduce false positives, it is still challenging to precisely map the distribution of m<sup>5</sup>C modifications. To address this, we plan to further investigate the protein structure and function to obtain a more accurate m<sup>5</sup>C sequencing of the transcriptome in future studies. Accordingly, we have updated our results and conclusions in lines 294-299 and discuss these limitations in lines 109-114.

      In addition, while the m<sup>5</sup>C assay can be performed using only the DRAM system alone, comparing it with the DRAM<sup>mut</sup>C control enhances the accuracy of m<sup>5</sup>C region detection. To minimize the variations in transfection efficiency across experimental groups, it is recommended to use the same batch of transfections. This approach not only ensures more consistent results but also improve the standardization of the DRAM assay, as discussed in the section added on line 308-312.

      (2) Using sodium arsenite treatment of cells as a means to change the m<sup>5</sup>C status of transcripts through the downregulation of the two major m<sup>5</sup>C writer proteins NSUN2 and NSUN6 is problematic and the conclusions from these experiments are not warranted. Sodium arsenite is a chemical that poisons every protein containing thiol groups. Not only do NSUN proteins contain cysteines but also the base editor fusion proteins. Arsenite will inactivate these proteins, hence the editing frequency will drop, as observed in the experiments shown in Figure 5, which the authors explain with fewer m<sup>5</sup>C sites to be detected by the fusion proteins.

      The authors have not addressed the point made by this reviewer. Instead the authors state that they have not addressed that possibility. They claim that they have revised the results section, but this reviewer can only see the point raised in the conclusions. An experiment would have been to purify base editors via the HA tag and then perform some kind of binding/editing assay in vitro before and after arsenite treatment of cells.

      We appreciate the reviewer’s insightful comment. We fully agree with the concern raised. In the original manuscript, our intention was to use sodium arsenite treatment to downregulate NSUN mediated m<sup>5</sup>C levels and subsequently decrease DRAM editing efficiency, with the aim of monitoring m<sup>5</sup>C dynamics through the DRAM system. However, as the reviewer pointed out, sodium arsenite may inactivate both NSUN proteins and the base editor fusion proteins, and any such inactivation would likely result in a reduced DRAM editing. This confounds the interpretation of our experimental data.

      As demonstrated in Appendix A, western blot analysis confirmed that sodium arsenite indeed decreased the expression of fusion proteins. In addition, we attempted in vitro fusion protein purification using multiple fusion tags (HIS, GST, HA, MBP) for DRAM fusion protein expression, but unfortunately, we were unable to obtain purified proteins. However, using the Promega TNT T7 Rapid Coupled In Vitro Transcription/Translation Kit, we successfully purified the DRAM protein (Appendix B). Despite this success, subsequent in vitro deamination experiments did not yield the expected mutation results (Appendix C), indicating that further optimization is required. This issue is further discussed in line 314-315.

      Taken together, the above evidence supports that the experiment of sodium arsenite treatment was confusing and we determined to remove the corresponding results from the main text of the revised manuscript.

      Author response image 1.

      (3) The authors should move high-confidence editing site data contained in Supplementary Tables 2 and 3 into one of the main Figures to substantiate what is discussed in Figure 4A. However, the data needs to be visualized in another way then excel format. Furthermore, Supplementary Table 2 does not contain a description of the columns, while Supplementary Table 3 contains a single row with letters and numbers.

      The authors have not addressed the point made by this reviewer. Figure 3F shows the screening process for DRAM-seq assays and principles for screening high-confidence genes rather than the data contained in Supplementary Tables 2 and 3 of the former version of this manuscript.

      Thank you for your valuable suggestion. We have visualized the data from Supplementary Tables 2 and 3 in Figure 4A as a circlize diagram (described in lines 213-216), illustrating the distribution of mutation sites detected by the DRAM system across each chromosome. Additionally, to improve the presentation and clarity of the data, we have revised Supplementary Tables 2 and 3 by adding column descriptions, merging the DRAM-ABE and DRAM-CBE sites, and including overlapping m<sup>5</sup>C genes from previous datasets.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Reviewer #1 (Public Review): 

      Summary: 

      The paper begins with phenotyping the DGRP for post-diapause fecundity, which is used to map genes and variants associated with fecundity. There are overlaps with genes mapped in other studies and also functional enrichment of pathways including most surprisingly neuronal pathways. This somewhat explains the strong overlap with traits such as olfactory behaviors and circadian rhythm. The authors then go on to test genes by knocking them down effectively at 10 degrees. Two genes, Dip-gamma and sbb, are identified as significantly associated with post-diapause fecundity, and they also find the effects to be specific to neurons. They further show that the neurons in the antenna but not the arista are required for the effects of Dip-gamma and sbb. They show that removing the antenna has a diapause-specific lifespan-extending effect, which is quite interesting. Finally, ionotropic receptor neurons are shown to be required for the diapause-associated effects. 

      Strengths and Weaknesses: 

      Overall I find the experiments rigorously done and interpretations sound. I have no further suggestions except an ANOVA to estimate the heritability of the post-diapause fecundity trait, which is routinely done in the DGRP and offers a global parameter regarding how reliable phenotyping is. 

      We added to the Methods: “We performed a one-way ANOVA to get the mean squares for between-group and withingroup variances and calculated broad-sense heritability using the formula: H<sup>2</sup> = MS<sub>G</sub> - MS<sub>E</sub> / MS<sub>G</sub> + (k-1) MS<sub>E</sub> where MS<sub>G</sub> - Mean square between groups and MS<sub>G</sub> - Mean square within groups and k - Number of individuals per group. Using this formula, the broad-sense heritability for normalized post-diapause fecundity was found to be 0.51.” 

      We added to the Results: “The broad-sense heritability for normalized post-diapause fecundity was found to be 0.51 (see Methods).”

      A minor point is I cannot find how many DGRP lines are used. 

      Response: We screened 193 lines and have added that to the Results. 

      Reviewer #2 (Public Review):

      Summary

      In this study, Easwaran and Montell investigated the molecular, cellular, and genetic basis of adult reproductive diapause in Drosophila using the Drosophila Genetic Reference Panel (DGRP). Their GWAS revealed genes associated with variation in post-diapause fecundity across the DGRP and performed RNAi screens on these candidate genes. They also analyzed the functional implications of these genes, highlighting the role of genes involved in neural and germline development. In addition, in conjunction with other GWAS results, they noted the importance of the olfactory system within the nervous system, which was supported by genetic experiments. Overall, their solid research uncovered new aspects of adult diapause regulation and provided a useful reference for future studies in this field.

      Strengths:

      The authors used whole-genome sequenced DGRP to identify genes and regulatory mechanisms involved in adult diapause. The first Drosophila GWAS of diapause successfully uncovered many QTL underlying post-diapause fecundity variations across DGRP lines. Gene network analysis and comparative GWAS led them to reveal a key role for the olfactory system in diapause lifespan extension and post-diapause fecundity.

      Comments on revised version:

      While the authors have addressed many of the minor concerns raised by the reviewers, they have not fully resolved some of the key criticisms. Notably, two reviewers highlighted significant concerns regarding the phenotype and assay of post-diapause fecundity, which are critical to the study. The authors acknowledged that this assay could be confounded by the 'cold temperature endurance phenotype,' potentially altering the interpretation of their results.

      However, they responded by stating that it is not obvious how to separate these effects experimentally. This leaves the analysis in this research ambiguous, as also noted by Reviewer #3.

      We should have clarified earlier that we actually chose to measure post-diapause fecundity in order to minimize any impact of ‘cold temperature endurance.” In fact, we chose post-diapause fecundity as the appropriate measure of successful diapause for both technical and conceptual reasons. Conceptually, the benefit of diapause is to perpetuate the species. It seems obvious to us that post-diapause fecundity is more relevant to species propagation than other measures of diapause such as how many egg chambers contain yolk or how many eggs are laid. Technically, we chose 5-week diapause and recovery based on pilot studies that showed that nearly all DGRP lines showed excellent survival at 5 weeks in diapause conditions. Therefore, our experimental design minimized as much as possible any effect of cold temperature endurance - in the sense of the ability to survive at 10°C - on our phenotype. 

      We apologize for not clarifying that point earlier and have added this text to the Results: “We chose 5 weeks based on pilot studies that showed that nearly all DGRP lines showed excellent survival at 5 weeks in diapause conditions while exhibiting sufficient variation in post-diapause fecundity to carry out GWAS. Beyond 5 weeks, fecundity was low and there was insufficient variation to conduct a GWAS.”

      Additionally, I raised concerns about the validity of prioritizing genes with multiple associated variants. Although the authors agreed with this point, they did not revise the manuscript accordingly. The statement that 'Genes with multiple SNPs are good candidates for influencing diapause traits' is not a valid argument within the context of population and quantitative genetics.

      We apologize for neglecting to revise the manuscript accordingly. We have revised Supplemental Table: S4 and ranked the genes by p-value.

    1. Author Response:

      Reviewer #1 (Public Review):

      [...] Strengths: This study utilized multiple in vitro approaches, such as proteomics, siRNA, and overexpression, to demonstrate that PCBP2 is an intrinsic factor of BMSC aging.

      Weaknesses:

      This study did not perform in vivo experiments.

      Response: We will continue to conduct animal experiments in subsequent studies.

      Reviewer #2 (Public Review):

      [...] Weaknesses: It is unclear if PCBP2 can also function as an intrinsic factor for BMSC cells in female individuals. More work may be needed to further dissect the mechanism of how PCBP2 impacts FGF2 expression. Could PCBP2 impact the FGF2 expression independent of ROS?

      Response: Thank you very much for your valuable comments, which is also the focus of our follow-up work. We will sort out the data and publish the relevant research results as soon as possible.

      Additional context that would help readers interpret or understand the significance of the work: In the current work, the authors studied the aging process of BMSC cells, which are related to osteoporosis. Aging processes also impact many other cell types and their function, such as in muscle, skin, and the brain.

      Response: Thank you very much for your valuable comments, we will continue to improve the writing logic of the article to make the article more understandable.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife assessment

      This useful manuscript reports mechanisms behind the increase in fecundity in response to sub-lethal doses of pesticides in the crop pest, the brown plant hopper. The authors hypothesize that the pesticide works by inducing the JH titer, which through the JH signaling pathway induces egg development. Evidence for this is, however, inadequate.

      We greatly appreciate your valuable comments and constructive suggestions for our work. All in all, the manuscript has been carefully edited and improved following your suggestions. We also provide more evidence to support our statements by conducting new experiments. First, we found that also EB treatment of adult females can stimulate egg-laying. Second, EB treatment in female adults increases the number of mature eggs in the ovary and ovarioles. Third, EB treatment in females enhances the expression of the kr-h1 gene in the whole body of BPH. Finally, EB treatment in female adults increases the JHIII titer, but has no impact on the 20E titer.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Gao et al. have demonstrated that the pesticide emamectin benzoate (EB) treatment of brown planthopper (BPH) leads to increased egg-laying in the insect, which is a common agricultural pest. The authors hypothesize that EB upregulates JH titer resulting in increased fecundity.

      Strengths:

      The finding that a class of pesticide increases the fecundity of brown planthopper is interesting.

      We greatly appreciate your positive comments on our work.

      Weaknesses:

      (1) EB is an allosteric modulator of GluCl. That means EB physically interacts with GluCl initiating a structural change in the cannel protein. Yet the authors' central hypothesis here is about how EB can upregulate the mRNA of GluCl. I do not know whether there is any evidence that an allosteric modulator can function as a transcriptional activator for the same receptor protein. The basic premise of the paper sounds counterintuitive. This is a structural problem and should be addressed by the authors by giving sufficient evidence about such demonstrated mechanisms before.

      Thank you for your question. As the reviewer points out, EB physically interacts with its target protein GluCl and thus affects its downstream signaling pathway. In the manuscript, we reported that EB-treated brown planthoppers display increased expression of GluCl in the adult stage (Fig. 5A). Actually, there are many studies showing that insects treated with insecticides can increase the expression of target genes. For example, the relative expression level of the ryanodine receptor gene of the rice stem borer, Chilo suppressalis was increased 10-fold after treatment with chlorantraniliprole, an insecticide which targets the ryanodine receptor (Peng et al., 2017). Besides this, in Drosophila, starvation (and low insulin) elevates the transcription level of the sNPF and tachykinin receptors (Ko et al., 2015; Root et al., 2011). In brown planthoppers, reduction in mRNA and protein expression of a nicotinic acetylcholine receptor α8 subunit is associated with resistance to imidacloprid (Zhang et al., 2015). RNA interference knockdown of α8 gene decreased the sensitivity of N. lugens to imidacloprid (Zhang et al., 2015). Hence, expression of receptor genes can be regulated by diverse factors including insecticide treatment. In our case, we found that EB can upregulate its target gene GluCl. However, we did not claim that EB functions as transcriptional activator for GluCl, and we still do not know why EB treatment changes the expression of GluCl in the brown planthopper. Considering our experiments are lasting several days, it might be an indirect (or secondary) effect caused by other factors, which change the expression of GluCl gene upon EB action of the channel. One reason is maybe that the allosteric interaction with GluCl by EB makes it dysfunctional and the cellular response is to upregulate the channel/receptor to compensate. We have inserted text on lines 738 - 757 to explain these possibilities.

      (2) I am surprised to see a 4th instar larval application or treatment with EB results in the upregulation of JH in the adult stages. Complicating the results further is the observation that a 4th instar EB application results in an immediate decrease in JH titer. There is a high possibility that this late JH titer increase is an indirect effect.

      Thank you for your question. Treatment with low doses or sublethal doses of insecticides might have a strong and complex impact on insects (Gandara et al., 2024; Gong et al., 2022; Li et al., 2023; Martelli et al., 2022). We kept the 4th instar of brown planthoppers feeding on EB for four days. They will develop to 5th instar after four days treatment, which is the final nymphal stage of BPH. Since the brown planthopper is a hemimetabolous insect, we cannot rule out the possibility that an indirect effect of treatment with EB results in the upregulation of JH in the adult stages. In this new revised manuscript, we investigated the impact of EB treatment in the adult stage. We found that female adults treated with EB also laid more eggs than controls (Figure 1-figure supplement 1A). The following experiments were performed in adults to address how EB treated stimulates egg-laying in adult brown planthopper.

      (1) We found that EB treatment in adults increases the number of mature eggs in ovary (new Figure 2-figure supplement 1). We add this results in lines 234 – 238 and 281-285.

      (2) We measured the JH titer after the female adults had been treated with EB. We found that EB can also increase the JH titer but has no impact on the 20E titer in the female adult (Figure 3-S3A and B). We add this results in lines 351 – 356 and 281-285.

      (3) EB treatment in adults increases the gene expression of JHAMT and Kr-h1 (Figure 3-S3C and D). We add this results in lines 378 – 379, lines 387-390 and lines 457-462.

      (3) The writing quality of the paper needs improvement. Particularly with respect to describing processes and abbreviations. In several instances the authors have not adequately described the processes they have introduced, thus confusing readers.

      Thank you for your suggestion. We have thoroughly revised the paper to improve clarity.

      (4) In the section 'EB promotes ovarian development' the authors have shown that EB treatment results in increased detention of eggs which contradicts their own results which show that EB promotes egg laying. Again, this is a serious contradiction that nullifies their hypothesis.

      Thank you for pointing this out. We revised the figure 2B to show number of mature eggs in the ovary. The number of mature eggs in ovaries of females that fed on EB was higher than in control females. We also show that BPH fed with EB laid more eggs than controls. Thus, our results suggest that EB promotes ovary maturation (and egg production) and also increases egg laying (Figure 1 and Table S1). Thus, we found that EB treatment can increase both the production of eggs and increase egg laying. We add this results in lines 234 – 238.

      (5) Furthermore, the results suggest that oogenesis is not affected by EB application. The authors should devote a section to discussing how they are observing increased egg numbers in EB-treated insects while not impacting Oogenesis.

      Thank you for your suggestions, and apologies for the lack of clarity in our initial explanation. First, we found that EB treatment led to an increase in the number of eggs laid by female brown planthoppers (Figure 1). Through dissection experiments, we observed that EB-treated females had more mature eggs in their ovaries (Figure 2A and B), indicating that the increased egg-laying was due to a larger production of mature eggs in the ovaries after EB treatment. This is now explained on lines 229-238.

      Additionally, since there is no systematic description of oogenesis in the brown planthopper, we were the first to observe the oogenesis process in this species using immunohistochemistry and laser confocal microscopy. Based on the developmental characteristics, we defined the different stages of oogenesis (Figure 2C, Figure 2-figure supplement 2). We did not observe any significant effect of EB treatment on the various stages of oogenesis, indicating that EB treatment does not impair normal egg development (Figure 2D). Instead, the increase in vitellogenin accelerates the production of mature eggs. This is now explained on lines 243-262.

      During the maturation process, eggs require uptake of vitellogenin, and an increase in vitellogenin (Vg) content can accelerate egg maturation, producing more mature eggs. Our molecular data suggest that EB treatment leads to an upregulation of vg expression. Based on these findings, we conclude that the increase in egg-laying caused by EB treatment is due to the upregulation of vg (Figure 3I), which raises vitellogenin content, promoting the uptake of vitellogenin by maturing eggs and resulting in the production of more mature eggs. We have revised the text on lines 389-395 to clarify this point.

      (6) Met is the receptor of JH and to my understanding, remains mostly constant in terms of its mRNA or protein levels throughout various developmental periods in many different insects. Therefore, the presence of JH becomes the major driving factor for physiological events and not the presence of the receptor Met. Here the authors have demonstrated an increase in Met mRNA as a result of EB treatment. Their central hypothesis is that EB increases JH titer to result in enhanced fecundity. JH action will not result in the activation of Met. Although not contradictory to the hypothesis, the increase in mRNA content of Met is contrary to the findings of the JH field thus far.

      Thank you for your comment. Our results showed that EB treatment can mildly increase (about 2-fold) expression of the Met gene in brown planthoppers (Figure 3G). And our data indicated that Met and FAMeT expression levels were not influenced so much by EB compared with kr-h1 and vg (Figure 3H and I). We agree that JH action will not result in the increase of Met. However, we cannot rule out the possibility of other factors (indirect effects), induced by EB treatment that increase the mRNA expression level of Met. One recent paper reported that downregulation of transcription factor CncC will increase met expression in beetles (see Figure 6A in this reference) (Jiang et al., 2023). Many studies have reported that insecticide treatment will activate the CncC gene signaling pathway, which regulates detoxification gene expression (Amezian et al., 2023; Fu et al., 2024; Hu et al., 2021). Hence, it is possible that EB might influence the CncC gene pathway which then induces met expression. This EB effect on met upregulation may be similar to the upregulation of GluCl and some other secondary effects. We have discussed this on lines 725-738.

      (7) As pointed out before, it is hard to rationalize how a 4th instar exposure to EB can result in the upregulation of key genes involved in JH synthesis at the adult stage. The authors must consider providing a plausible explanation and discussion in this regard.

      Thank you for your comments. It must be mentioned that although we exposed the BPH to EB at 4th instar, we make the insect feed on the EB-treated rice plants for four days. After that, the insect will develop into 5<sup>th</sup> instar, the final nymphal stage of brown planthopper. Since brown planthoppers do not have a pupal stage, this might cause the EB presented to the insects last a longer time even in the adult stage. Besides this, we found that EB treatment will increase the weight of adult females (Figure 1-figure supplement 3E and F), which indicates that EB might increase food intake in BPHs that might produce more insulin peptide. Insulin might increase the JH synthesis at the adult stage. In our revised study we also investigate EB impairment in adult BPHs. We found that, similar to the nymphal stage, EB treatment in adult BPHs also increases the egg laying. Furthermore, the JH titer was increased after treatment of BPH with EB in adults. Besides this, GluCl and kr-h1 genes were also up-regulated after EB treatment in the adult stage. We have discussed this on lines 739-746.

      (8) I have strong reservations against such an irrational hypothesis that Met (the receptor for JH) and JH-Met target gene Kr-h1 regulate JH titer (Line 311, Fig 3 supplemental 2D). This would be the first report of such an event on the JH field and therefore must be analysed in depth. I strongly suggest the authors remove such claims from the manuscript without substantiating it.

      Thank you for your suggestions and comments. We have changed our claims in this revised MS. We found that EB treatment can enhance Kr-h1 expression. We have no evidence to support that JH can induce met expression. We have rewritten the manuscript to avoid confusion (see text on lines 725-735).

      (9) Kr-h1 is JH/Met target gene. The authors demonstrate that silencing of Kr-h1 results in inhibition of FAMeT, which is a gene involved in JH synthesis. A feedback loop in JH synthesis is unreported. It is the view of this reviewer that the authors must go ahead with a mechanistic detail of Kr-h1 mediated JH upregulation before this can be concluded. Mere qPCR experiments are not sufficient to substantiate a claim that is completely contrary to the current understanding of the JH signalling pathway.

      Thank you for your suggestions and comments. We agree that only qPCR experiments are not enough to provide this kind of claim. More evidences need to be provided to support this. We have revised the MS to avoid confusion (see text on lines 725-735).

      (10) The authors have performed knockdowns of JHAMT, Met, and Kr-h1 to demonstrate the effect of these factors on fecundity in BPH. Additionally, they have performed rescue experiments with EB application on these knockdown insects (Figure 3K-M). This, I believe, is a very flawed experiment. The authors demonstrate EB works through JHAMT in upregulating JH titer. In the absence of JHAMT, EB application is not expected to rescue the phenotype. But the authors have reported a complete rescue here. In the absence of Met, the receptor of JH, either EB or JH is not expected to rescue the phenotype. But a complete rescue has been reported. These two experimental results contradict their own hypothesis.

      Thank you for your comments. We thought that this rescue is possible since knockdown of the genes is incomplete when using dsRNA injection (and residual gene expression allows for EB action). It is not a total knockout and actually, these genes still have a low level of expression in the dsRNA-injected insects. Since EB can upregulate the expression of JHAMT, Met, and Kr-h1, it is reasonable that EB treatment can rescue the down-regulation effects of these three genes and make fecundity completely rescued. We have clarified this on lines 411-413).

      (11) A significant section of the paper deals with how EB upregulates JH titer. JH is a hormone synthesized in the Corpora Allata. Yet the authors have chosen to use the whole body for all of their experiment. Changes in the whole body for mRNA of those enzymes involved in JH synthesis may not reflect the situation in Corpora Allata. Although working with Corpora Allata is challenging, discarding the abdomen and thorax region and working with the head and neck region of the insect is easily doable. Results from such sampling are always more convincing when it comes to JH synthesis studies.

      Thank you for your suggestions. Because the head is very difficult to separate from the thorax region in brown planthoppers as you can see in Author response image 1. We are now trying to answer how EB regulates JH synthesis using Drosophila as a model.

      Author response image 1.

      The brown planthopper

      (12) The phenomenon reported was specific to BPH and not found in other insects. This limits the implications of the study.

      Thank you for your comments. The brown planthopper is a serious insect pest on rice in Asia. Our findings can guide the use of this insecticide in the field. Besides this, our findings indicated that EB, which targets GluCl can impair the JH titer. Our findings added new implications for how a neuronal system influences the JH signaling pathway. We will further investigate how EB influences JH in the future and will use Drosophila as a model to study the molecular mechanisms.

      (13) Overall, the molecular experiments are very poorly designed and can at best be termed superficial. There are several contradictions within the paper and no discussion or explanation has been provided for that.

      Thank you for your comments. We have revised the paper according to your suggestions and added further explanation of our results in the discussion parts and hope the conclusions are better supported in the new version. We have discussed this on lines 725-746 and 778-799.

      Reviewer #2 (Public Review):

      The brown plant hopper (BPH) is a notorious crop pest and pesticides are the most widespread means of controlling its population. This manuscript shows that in response to sublethal doses of the pesticide (EB), BPH females show enhanced fecundity. This is in keeping with field reports of population resurgence post-pesticide treatment. The authors work out the mechanism behind this increase in fecundity. They show that in response to EB exposure, the expression of its target receptor, GluCl, increases. This, they show, results in an increase in the expression of genes that regulate the synthesis of juvenile hormone (JH) and JH itself, which, in turn, results in enhanced egg-production and egg-laying. Interestingly, these effects of EB exposure are species-specific, as the authors report that other species of plant hoppers either don't show enhanced fecundity or show reduced fecundity. As the authors point out, it is unclear how an increase in GluCl levels could result in increased JH regulatory genes.

      We greatly appreciate your valuable comments and constructive suggestion to our work. We will try to figure out how EB interacts with its molecular target GluCl and then increases JH regulatory genes in the future work using Drosophila as models.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Overall, the molecular experiments are very poorly designed and can at best be termed superficial. There are several contradictions within the paper and no discussion or explanation has been provided for that.

      The authors should consider a thorough revision.

      Thank you for your comments. We have thoroughly revised the paper according to your suggestions and added further experiments and explanations of our results in the discussion parts.

      Reviewer #2 (Recommendations For The Authors):

      It would help the reader to have more schematics along with the figures. The final figure is helpful, but knowing the JH pathway, and where it acts would help with the interpretations as one reads the manuscript and the figures. The pathways represented in 4N or 5J are helpful but could be improved upon for better presentation.

      It would be nice to have some discussion on how the authors think EB exposure results in an increase in GluCl expression, and how that in turn affects the expression of so many genes.

      Thank you for your comments. We have thoroughly revised the paper according to your suggestions and added further experiments and explanations of how we think EB exposure results in an increase in JH titer and other genes in the discussion parts. We have added the test on lines 753-761.

      References

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      Fu, B., Liang, J., Hu, J., Du, T., Tan, Q., He, C., Wei, X., Gong, P., Yang, J., Liu, S., Huang, M., Gui, L., Liu, K., Zhou, X., Nauen, R., Bass, C., Yang, X., Zhang, Y., 2024. GPCR–MAPK signaling pathways underpin fitness trade-offs in whitefly. Proceedings of the National Academy of Sciences 121, e2402407121.

      Gandara, L., Jacoby, R., Laurent, F., Spatuzzi, M., Vlachopoulos, N., Borst, N.O., Ekmen, G., Potel, C.M., Garrido-Rodriguez, M., Böhmert, A.L., Misunou, N., Bartmanski, B.J., Li, X.C., Kutra, D., Hériché, J.-K., Tischer, C., Zimmermann-Kogadeeva, M., Ingham, V.A., Savitski, M.M., Masson, J.-B., Zimmermann, M., Crocker, J., 2024. Pervasive sublethal effects of agrochemicals on insects at environmentally relevant concentrations. Science 386, 446-453.

      Gong, Y., Cheng, S., Desneux, N., Gao, X., Xiu, X., Wang, F., Hou, M., 2022. Transgenerational hormesis effects of nitenpyram on fitness and insecticide tolerance/resistance of Nilaparvata lugens. Journal of Pest Science.

      Hu, B., Huang, H., Hu, S., Ren, M., Wei, Q., Tian, X., Esmail Abdalla Elzaki, M., Bass, C., Su, J., Reddy Palli, S., 2021. Changes in both trans- and cis-regulatory elements mediate insecticide resistance in a lepidopteron pest, Spodoptera exigua. PLOS Genetics 17, e1009403.

      Jiang, H., Meng, X., Zhang, N., Ge, H., Wei, J., Qian, K., Zheng, Y., Park, Y., Reddy Palli, S., Wang, J., 2023. The pleiotropic AMPK–CncC signaling pathway regulates the trade-off between detoxification and reproduction. Proceedings of the National Academy of Sciences 120, e2214038120.

      Ko, K.I., Root, C.M., Lindsay, S.A., Zaninovich, O.A., Shepherd, A.K., Wasserman, S.A., Kim, S.M., Wang, J.W., 2015. Starvation promotes concerted modulation of appetitive olfactory behavior via parallel neuromodulatory circuits. eLife 4, e08298.

      Li, Z., Wang, Y., Qin, Q., Chen, L., Dang, X., Ma, Z., Zhou, Z., 2023. Imidacloprid disrupts larval molting regulation and nutrient energy metabolism, causing developmental delay in honey bee Apis mellifera. eLife

      Martelli, F., Hernandes, N.H., Zuo, Z., Wang, J., Wong, C.-O., Karagas, N.E., Roessner, U., Rupasinghe, T., Robin, C., Venkatachalam, K., Perry, T., Batterham, P., Bellen, H.J., 2022. Low doses of the organic insecticide spinosad trigger lysosomal defects, elevated ROS, lipid dysregulation, and neurodegeneration in flies. eLife 11, e73812.

      Peng, Y.C., Sheng, C.W., Casida, J.E., Zhao, C.Q., Han, Z.J., 2017. Ryanodine receptor genes of the rice stem borer, Chilo suppressalis: Molecular cloning, alternative splicing and expression profiling. Pestic. Biochem. Physiol. 135, 69-77.

      Root, Cory M., Ko, Kang I., Jafari, A., Wang, Jing W., 2011. Presynaptic facilitation by neuropeptide signaling mediates odor-driven food search. Cell 145, 133-144.

      Zhang, Y., Wang, X., Yang, B., Hu, Y., Huang, L., Bass, C., Liu, Z., 2015. Reduction in mRNA and protein expression of a nicotinic acetylcholine receptor α8 subunit is associated with resistance to imidacloprid in the brown planthopper, Nilaparvata lugens. Journal of Neurochemistry 135, 686-694.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review): 

      Summary: 

      The authors aimed to confirm the association between the human leukocyte antigen (HLA)-II region and tuberculosis (TB) susceptibility within admixed African populations. Building upon previous findings from the International Tuberculosis Host Genetics Consortium (ITHGC), this study sought to address the limitations of small sample size and the inclusion of admixed samples by employing the Local Ancestry Allelic Adjusted (LAAA) model, as well as identify TB susceptibility loci in an admixed South African cohort. 

      Strengths: 

      The major strengths of this study include the use of six TB case-control datasets collected over 30 years from diverse South African populations and ADMIXTURE for global ancestry inference. The former represents comprehensive dataset used in this study and the later ensures accurate determination of ancestral contributions. In addition, the identified association in the HLA-DPB1 gene shows near-genomewide significance, enhancing the credibility of the findings. 

      Weaknesses: 

      The major weakness of this study includes insufficient significant discoveries and reliance on crossvalidation. This study only identified one variant significantly associated with TB status, located in an intergenic region with an unclear link to TB susceptibility. Despite identifying multiple lead SNPs, no other variants reached the genome-wide significance threshold, limiting the overall impact of the findings. The absence of an independent validation cohort, with the study relying solely on crossvalidation, is also a major limitation. This approach restricts the ability to independently confirm the findings and evaluate their robustness across different population samples. 

      Appraisal: 

      The authors successfully achieved their aims of confirming the association between the HLA-II region and TB susceptibility in admixed African populations. However, the limited number of significant discoveries, reliance on cross-validation, and insufficient discussion of model performance and SNP significance weaken the overall strength of the findings. Despite these limitations, the results support the conclusion that considering local ancestry is crucial in genetic studies of admixed populations. 

      Impact:  

      The innovative use of the LAAA model and the comprehensive dataset in this study make substantial contributions to the field of genetic epidemiology. 

      Reviewer #2 (Public review): 

      Summary: 

      This manuscript is about using different analytical approaches to allow ancestry adjustments to GWAS analyses amongst admixed populations. This work is a follow-on from the recently published ITHGC multi-population GWAS (https://doi.org/10.7554/eLife.84394), with a focus on the admixed South African populations. Ancestry adjustment models detected a peak of SNPs in the class II HLA DPB1, distinct from the class II HLA DQA1 loci significant in the ITHGC analysis. 

      Strengths: 

      Excellent demonstration of GWAS analytical pipelines in highly admixed populations. Further confirmation of the importance of the HLA class II locus in genetic susceptibility to TB. 

      Weaknesses: 

      Limited novelty compared to the group's previous existing publications and the body of work linking HLA class II alleles with TB susceptibility in South Africa or other African populations. This work includes only ~100 new cases and controls from what has already been published. High-resolution HLA typing has detected significant signals in both the DQA1 and DPB1 regions identified by the larger ITHGC and in this GWAS analysis respectively (Chihab L et al. HLA. 2023 Feb; 101(2): 124-137). Despite the availability of strong methods for imputing HLA from GWAS data (Karnes J et Plos One 2017), the authors did not confirm with HLA typing the importance of their SNP peak in the class II region. This would have supported the importance of this ancestry adjustment versus prior ITHGC analysis. 

      The populations consider active TB and healthy controls (from high-burden presumed exposed communities) and do not provide QFT or other data to identify latent TB infection. 

      Important methodological points for clarification and for readers to be aware of when reading this paper: 

      (1) One of the reasons cited for the lack of African ancestry-specific associations or suggestive peaks in the ITHGC study was the small African sample size. The current association test includes a larger African cohort and yields a near-genome-wide significant threshold in the HLA-DPB1 gene originating from the KhoeSan ancestry. The investigation is needed as to whether the increase in power is due to increased African samples and not necessarily the use of the LAAA model as stated on lines 295 and 296? 

      Thank you for your comment. The Manhattan plot in Figure 3 includes the results for all four models: the traditional GWAS model (GAO), the admixture mapping model (LAO), the ancestry plus allelic (APA) model and the LAAA model. In this figure, it is evident that only the LAAA model identified the association peak on chromosome 6, which lends support the argument that the increase in power is due to the use of the LAAA model and not solely due to the increase in sample size. 

      (2) In line 256, the number of SNPs included in the LAAA analysis was 784,557 autosomal markers; the number of SNPs after quality control of the imputed dataset was 7,510,051 SNPs (line 142). It is not clear how or why ~90% of the SNPs were removed. This needs clarification. 

      Thank you for your recommendation. In our manuscript (line 194), we mention that “…variants with minor allele frequency (MAF) < 1% were removed to improve the stability of the association tests.” A large proportion of imputed variants fell below this MAF threshold, and were subsequently excluded from this analysis. Below, we show the number of imputed variants across MAF bins for one of our datasets [RSA(A)] to substantiate this claim:  

      Author response image 1.

      (3) The authors have used the significance threshold estimated by the STEAM p-value < 2.5x10<sup>-6</sup> in the LAAA analysis. Grinde et al. (2019 implemented their significance threshold estimation approach tailored to admixture mapping (local ancestry (LA) model), where there is a reduction in testing burden. The authors should justify why this threshold would apply to the LAAA model (a joint genotype and ancestry approach). 

      Thank you for your recommendation. We describe in the methods (line 189 onwards) that the LAAA model is an extension of the APA model. Since the APA model itself simultaneously performs the null global ancestry only model and the local ancestry model (utilised in admixture mapping), we thus considered the use of a threshold tailored to admixture mapping appropriate for the LAAA model.  

      (4) Batch effect screening and correction (line 174) is a quality control check. This section is discussed after global and local ancestry inferences in the methods. Was this QC step conducted after the inferencing? If so, the authors should justify how the removed SNPs due to the batch effect did not affect the global and local ancestry inferences or should order the methods section correctly to avoid confusion. 

      Thank you for your comments. The batch effect correction method utilised a pseudo-case-control comparison which included global ancestry proportions. Thus, batch effect correction was conducted after ancestry inference. We excluded 36 627 SNPs that were believed to have been affected by the batch effect. We have amended line 186 to include the exact number of SNPs excluded due to batch effect. 

      The ancestry inference by RFMix utilised the entire merged dataset of 7 510 051 SNPs. Thus, the SNPs removed due to the batch effect make up a very small proportion of the SNPs used to conduct global and local ancestry inferences (less than 0.5%). As a result, we do not believe that the removed SNPs would have significantly affected the global and local ancestry inferences. However, we did conduct global ancestry inference with RFMix on each separate dataset as a sanity check. In the tables below, we show the average global ancestry proportions inferred for each separate dataset, the average global ancestry proportions across all datasets and the average global ancestry proportions inferred using the merged dataset. The SAC and Xhosa cohorts are shown in two separate tables due to the different number of contributing ancestral populations to each cohort. The differences between the combined average global ancestry proportions across the separate cohorts does not differ significantly to the global ancestry proportions inferred using the merged dataset. 

      Author response table 1.

      Comparison of global ancestry proportions across the separate SAC datasets and the merged cohort.

      Author response table 2.

      Comparison of global ancestry proportions in the Xhosa dataset and the merged cohort. 

      Reviewer #1 (Recommendations for the authors): 

      Suggestions for Improved or Additional Experiments, Data, or Analyses:   

      (1) It might be beneficial to consider splitting the data into separate discovery and validation cohorts rather than relying solely on cross-validation. This approach could provide a stronger basis for independently confirming the findings. 

      Thank you for your suggestion. However, we are hesitant to divide our already modest dataset (n=1544) into separate discovery and validation cohorts, as this would reduce the statistical power to detect significant associations.

      (2) Clearly stating the process of cross-validation in the methods section and reporting relevant validation statistics, such as accuracy, sensitivity, specificity, and area under the curve (AUC), would provide a more comprehensive assessment of the model's performance.  

      Thank you for your recommendation. We would like to highlight this article, “GWAS in the southern African context” (1), which evaluated the performance of the LAAA model compared to other models in three- and five-way admixed populations. Given the thorough evaluation of the model’s performance in that study, we did not find it necessary to reassess its performance in this manuscript.   

      (3) Analysing racial cohorts separately to see if you can replicate previous results and find significant markers in combined non-African populations that are not evident in African-only samples might be useful. 

      Thank you for your suggestion. We would like to respectfully note that race is a social construct, and its use as a proxy for genetic ancestry can be problematic (2). In our study, we rather rely on genetic ancestry inferred using ancestry inference software to provide a more accurate representation of our cohort's genetic diversity. Additionally, our cohort consists mostly of a highly admixed population group, with some individuals exhibiting ancestral contributions from up to five different global populations. Therefore, it is not possible to categorize our samples into distinct “Africanonly” or “non-African” groups.

      (4) It might be worthwhile to consider using polygenic risk scores (PRS) to combine multiple genetic influences. This approach could help in identifying cumulative genetic effects that are not apparent when examining individual SNPs.  

      Thank you for your recommendation. While constructing a polygenic risk score (PRS) is beyond the scope of the current study, but an ongoing interest in our group, we recognize its potential value and will consider incorporating this approach in future research endeavours or a separate publication. A recent publication by Majara et al showed that that PRS accuracy is low for all traits and varies across ancestrally and ethnically diverse South African groups (3).

      Recommendations for Improving the Writing and Presentation: 

      Including a more thorough discussion of the methodological limitations, such as the challenges of studying admixed populations and the potential limitations of the LAAA model, would provide a more balanced perspective. 

      Thank you for your suggestion. To provide a more balanced perspective, we included the limitations of our study in the discussion, from line 429 to like 451.

      Minor Corrections to the Text and Figures: 

      Including all relevant statistics would improve clarity. For example, providing confidence intervals for the odds ratios and discussing any observed trends or outliers would be beneficial. 

      Thank you for your recommendation. We have added 95% confidence intervals to all odds ratios reported in Table 3. However, beyond the association peak identified in the HL-II region associated with the phenotype, we do not observe any other trends or outliers in or LAAA analysis.  

      Reviewer #2 (Recommendations for the authors): 

      Points for improvement: 

      (1) Related to the different datasets and inclusions in previous publications, it would also be good to better understand the different numbers of cases and controls included across the previous and current analyses, or discussion thereof. For instance, the RSA(M) dataset includes 555/440 cases/controls for this analysis and only 410/405 cases/controls in the ITHGC analysis. Other discrepancies are noted across the other published datasets compared to those included in this analysis, and these always need to be detailed in a supplement or similar to better understand if this could have introduced bias or was in fact correct based on the additional ancestry-related restriction applied.  

      Thank you for your comments. Table 1 of our manuscript lists number of individuals in the RSA(M) dataset, including related individuals. As described in line 131, related individuals were subsequently excluded during quality control: “Individual datasets were screened for relatedness using KING software (Manichaikul et al., 2010) and individuals up to second degree relatedness were removed.” The ITHGC only reported the number of unrelated individuals included their analyses, which would account for the discrepancies in the reported number of cases and controls.  

      (2) The imbalance between cases and controls in this analysis is quite striking, and it is unusual to have the imbalance favour cases over controls. This contrasts with the ITHGC, where there are substantially more controls. There is no comment on how this could potentially impact this analysis. 

      Thank you for your comment. We have included a note on our case-control imbalance in the discussion:

      “While many studies discuss methods for addressing case-control imbalances with more controls than cases (which can inflate type 1 error rates (Zhou et al. 2018; Dai et al. 2021; Öztornaci et al. 2023), few address the implications of a large case-to-control ratio like ours (952 cases to 592 controls). To assess the impact of this imbalance, we used the Michigan genetic association study (GAS) power calculator (Skol et al. 2006). Under an additive disease model with an estimated prevalence of 0.15, a disease allele frequency of 0.3, a genotype relative risk of 1.5, and a default significance level of 7 × 10<sup>-6</sup>, we achieved an expected power of approximately 75%. With a balanced sample size of 950 cases and 950 controls, power would exceed 90%, but it would drop significantly with a smaller balanced cohort of 590 cases and 590 controls. Given these results, we proceeded with our analysis to maximize statistical power despite the case-control imbalance.” 

      Author response image 2.

      Minor comments 

      (1) Referencing around key points of TB epidemiology and disease states seems out of date, given recent epidemiology reviews and seminal nature or lancet review articles. Please update.  

      Thank you for your suggestion. We have included the following recent publications in the introductory paragraph: 

      Zaidi, S. M. A., Coussens, A. K., Seddon, J. A., Kredo, T., Warner, D., Houben, R. M. G. J., & Esmail, H. (2023). Beyond latent and active tuberculosis: a scoping review of conceptual frameworks. EClinicalMedicine, 66, 102332. https://doi.org/10.1016/j.eclinm.2023.102332

      Menzies, N. A., Swartwood, N., Testa, C., Malyuta, Y., Hill, A. N., Marks, S. M., Cohen, T., & Salomon, J. A. (2021). Time Since Infection and Risks of Future Disease for Individuals with Mycobacterium tuberculosis Infection in the United States. Epidemiology, 32(1), 70–78. https://doi.org/10.1097/EDE.0000000000001271  

      Cudahy, P. G. T., Wilson, D., & Cohen, T. (2020). Risk factors for recurrent tuberculosis after successful treatment in a high burden setting: a cohort study. BMC Infectious Diseases, 20(1), 789. https://doi.org/10.1186/s12879-020-05515-4  

      Escombe, A. R., Ticona, E., Chávez-Pérez, V., Espinoza, M., & Moore, D. A. J. (2019). Improving natural ventilation in hospital waiting and consulting rooms to reduce nosocomial tuberculosis transmission risk in a low resource setting. BMC Infectious Diseases, 19(1), 88. https://doi.org/10.1186/s12879-019-3717-9  

      Laghari, M., Sulaiman, S. A. S., Khan, A. H., Talpur, B. A., Bhatti, Z., & Memon, N. (2019). Contact screening and risk factors for TB among the household contact of children with active TB: a way to find source case and new TB cases. BMC Public Health, 19(1), 1274. https://doi.org/10.1186/s12889-0197597-0  

      Matose, M., Poluta, M., & Douglas, T. S. (2019). Natural ventilation as a means of airborne tuberculosis infection control in minibus taxis. South African Journal of Science, 115(9/10). https://doi.org/10.17159/sajs.2019/5737

      Smith, M. H., Myrick, J. W., Oyageshio, O., Uren, C., Saayman, J., Boolay, S., van der Westhuizen, L., Werely, C., Möller, M., Henn, B. M., & Reynolds, A. W. (2023). Epidemiological correlates of overweight and obesity in the Northern Cape Province, South Africa. PeerJ, 11, e14723. https://doi.org/10.7717/peerj.14723  

      (2) Lines 46 to 48 appear to have two contradictory statements next to each other. The first says there are numerous GWAS investigating TB susceptibility; the second says there are sparse. Please clarify.

      Thank you for bringing this to our attention. We have amended the lines as follows: 

      “Numerous genome-wide association studies (GWASs) investigating TB susceptibility have been conducted across different population groups. However, findings from these studies often do not replicate across population groups (Möller & Kinnear, 2020; Möller et al., 2018; Uren et al., 2017).”

      (3) Add ref in line 69 for two SAC populations.

      Thank you for your recommendation. We have included the citation for the ITHGC meta-analysis paper here: 

      “The authors described possible reasons for the lack of associations, including the smaller sample size compared to the other ancestry-specific meta-analyses, increased genetic diversity within African individuals and population stratification produced by two admixed cohorts from the South African Coloured (SAC) population (Schurz et al. 2024).”

      (4) Write out abbreviations the first time they appear (Line 121).

      Thank you for your recommendation. We have corrected the sentence as follows: 

      “Monomorphic sites were removed. Individuals were screened for deviations in Hardy-Weinberg Equilibrium (HWE) for each SNP and sites deviating from the HWE threshold of 10-5 were removed.”

      (5) It would be good in the supplement to see if there is a SNP peak in chromosome 20 with a hit that reached significance in the Bantu-speaking African ancestry.

      Thank you for your recommendation. We have included a regional plot for the lead variant identified on chromosome 20 originating from Bantu-speaking African ancestry in the supplementary material (Supplementary Figure 3).

      (6) It would be good to mention the p-values of rs28383206 from the ITHGC paper in this cohort for KhoeSan and Bantu-speaking African ancestries. 

      Thank you for your suggestion. We have included the following paragraph from line 352:

      “The lead variant identified in the ITHGC meta-analysis, rs28383206, was not present in our genotype or imputed datasets. The ITHGC imputed genotypes using the 1000 Genomes (1000G) reference panel (4). Variant rs28383206 has an alternate allele frequency of 11.26% in the African population subgroup within the 1000G dataset (https://www.ncbi.nlm.nih.gov/snp/rs28383206). However, rs28383206 is absent from our in-house whole-genome sequencing (WGS) datasets, which include Bantu-speaking African and KhoeSan individuals. This absence suggests that rs28383206 might not have been imputed in our datasets using the AGR reference panel, potentially due to its low alternate allele frequency in southern African populations. Our merged dataset contained two variants located within 800 base pairs of r_s28383206: rs482205_ (6:32576009) and rs482162 (6:32576019). However, these variants were not significantly associated with TB status in our cohort (Supplementary Table 1).” Supplementary Table 1 can be found in the supplementary material:

      (7) It would improve the readability of the ancestry proportions listed on lines 236 and 237 if these population groups were linked with the corresponding specific population used in Figure 1, as has been done in Table 2.

      Thank you for your suggestion. We have amended Figure 1 to include the corresponding population labels mentioned in Table 2.  

      (8) In line 209, it is not clear why the number of alleles of a specific ancestry at a locus is referred to as a covariate in admixture mapping when the corresponding marginal effect is the parameter of interest. 

      Thank you for bringing this to our attention. We have amended the description as follows: 

      “(2) Local ancestry (LA) model:

      This model is used in admixture mapping to identify ancestry-specific variants associated with a specific phenotype. The LA model evaluates the number of alleles of a specific ancestry at a locus and includes the corresponding marginal effect as a covariate in association analyses.”

      (9) Table 3 would benefit from a column on whether the SNP was genotyped or imputed. 

      Thank you for your suggestion. We have included a column indicating whether the SNP was genotyped or imputed, as well as an additional column with the INFO score for imputed genotypes. 

      (10) The authors should remove the print and download icons in Figure 1 on lines 240 and 241.

      Thank you for your suggestion. We have amended the figure as requested.  

      (11) In the quality control, the authors use a more relaxed threshold for missingness in individuals (90%) and genotypes (5%) and have strayed away from the conventional 97%-98%. An explanation of the choice of these thresholds will be helpful to the reader.

      Thank you for your suggestion. We aimed to use similar genotype and individual missingness thresholds outline by the ITHGC meta-analysis (which utilised a threshold of 10% for both genotype and individual missingness) and the previous LAAA analysis paper performed by Swart et al. in 2021. We have amended line 116 for more clarity: 

      “Individuals with genotype call rates less than 90% and SNPs with more than 5% missingness were removed as described previously (5).”

      References  

      (1) Swart Y, van Eeden G, Uren C, van der Spuy G, Tromp G, Moller M. GWAS in the southern African context. Cold Spring Harbor Laboratory. 2022;

      (2) Byeon YJJ, Islamaj R, Yeganova L, Wilbur WJ, Lu Z, Brody LC, et al. Evolving use of ancestry, ethnicity, and race in genetics research-A survey spanning seven decades. Am J Hum Genet. 2021 Dec 2;108(12):2215–23.

      (3) Majara L, Kalungi A, Koen N, Tsuo K, Wang Y, Gupta R, et al. Low and differential polygenic score generalizability among African populations due largely to genetic diversity. HGG Adv. 2023 Apr 13;4(2):100184.

      (4) Schurz H, Naranbhai V, Yates TA, Gilchrist JJ, Parks T, Dodd PJ, et al. Multi-ancestry metaanalysis of host genetic susceptibility to tuberculosis identifies shared genetic architecture. eLife. 2024 Jan 15;13.

      (5) Swart Y, Uren C, van Helden PD, Hoal EG, Möller M. Local ancestry adjusted allelic association analysis robustly captures tuberculosis susceptibility loci. Front Genet. 2021 Oct 15;12:716558.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary: The authors of this study sought to define a role for IgM in responses to house dust mites in the lung.

      Strengths:

      Unexpected observation about IgM biology

      Combination of experiments to elucidate function

      Weaknesses:

      Would love more connection to human disease

      We thank the reviewer for these comments. At the time of this publication, we have not made a concrete link with human disease. While there is some anecdotal evidence of diseases such as Autoimmune glomerulonephritis, Hashimoto’s thyroiditis, Bronchial polyp, SLE, Celiac disease and other diseases in people with low IgM. Allergic disorders are also common in people with IgM deficiency, other studies have reported as high as 33-47%. The mechanisms for the high incidence of allergic diseases are unclear as generally, these patients have normal IgG and IgE levels. IgM deficiency may represent a heterogeneous spectrum of genetic defects, which might explain the heterogeneous nature of disease presentations. 

      Reviewer #2 (Public Review):

      Summary:

      The manuscript by Hadebe and colleagues describes a striking reduction in airway hyperresponsiveness in Igm-deficient mice in response to HDM, OVA and papain across the B6 and BALB-c backgrounds. The authors suggest that the deficit is not due to improper type 2 immune responses, nor an aberrant B cell response, despite a lack of class switching in these mice. Through RNA-Seq approaches, the authors identify few differences between the lungs of WT and Igm-deficient mice, but see that two genes involved in actin regulation are greatly reduced in IgM-deficient mice. The authors target these genes by CRISPR-Cas9 in in vitro assays of smooth muscle cells to show that these may regulate cell contraction. While the study is conceptually interesting, there are a number of limitations, which stop us from drawing meaningful conclusions.

      Strengths:

      Fig. 1. The authors clearly show that IgMKO mice have striking reduced AHR in the HDM model, despite the presence of a good cellular B cell response.

      Weaknesses:

      Fig. 2. The authors characterize the cd4 t cell response to HDM in IGMKO mice.<br /> They have restimulated medLN cells with antiCD3 for 5 days to look for IL-4 and IL-13, and find no discernible difference between WT and KO mice. The absence of PBS-treated WT and KO mice in this analysis means it is unclear if HDM-challenged mice are showing IL-4 or IL-13 levels above that seen at baseline in this assay.

      We thank the Reviewer for this comment. We would like to mention that a very minimal level of IL-4 and IL-13 in PBS mice was detected. We have indicated with a dotted line on the Figure to show levels in unstimulated or naïve cytokines. Please see Author response image 1 below from anti-CD3 stimulated cytokine ELISA data. The levels of these cytokines are very low and are not changed between WT and IgM<sup>-/-</sup> mice, this is also true for PMA/ionomycin-stimulated cells.

      Author response image 1.

      The choice of 5 days is strange, given that the response the authors want to see is in already primed cells. A 1-2 day assay would have been better.

      We agree with the reviewer that a shorter stimulation period would work. Over the years we have settled for 5-day re-stimulation for both anti-CD3 and HDM. We have tried other time points, but we consistently get better secretion of cytokines after 5 days.

      It is concerning that the authors state that HDM restimulation did not induce cytokine production from medLN cells, since countless studies have shown that restimulation of medLN would induce IL-13, IL-5 and IL-10 production from medLN. This indicates that the sensitization and challenge model used by the authors is not working as it should.

      We thank the reviewer for this observation. In our recent paper showing how antigen load affects B cell function, we used very low levels of HDM to sensitise and challenge mice (1 ug and 3 ug respectively). See below article, Hadebe et al., 2021 JACI. This is because Labs that have used these low HDM levels also suggested that antigen load impacts B cell function, especially in their role in germinal centres. We believe the reason we see low or undetectable levels of cytokines is because of this low antigen load sensitisation and challenge. In other manuscripts we have published or about to publish, we have shown that normal HDM sensitisation load (1 ug or 100 ug) and challenge (10 ug) do induce cytokine release upon restimulation with HDM. See the below article by Khumalo et al, 2020 JCI Insight (Figure 4A).

      Sabelo Hadebe, Jermaine Khumalo, Sandisiwe Mangali, Nontobeko Mthembu, Hlumani Ndlovu, Amkele Ngomti, Martyna Scibiorek, Frank Kirstein, Frank Brombacher. Deletion of IL-4Ra signalling on B cells limits hyperresponsiveness depending on antigen load. doi.org/10.1016/j.jaci.2020.12.635).

      Jermaine Khumalo, Frank Kirstein, Sabelo Hadebe, Frank Brombacher. IL-4Rα signalling in regulatory T cells is required for dampening allergic airway inflammation through inhibition of IL-33 by type 2 innate lymphoid cells. JCI Insight. 2020 Oct 15;5(20):e136206. doi: 10.1172/jci.insight.136206

      The IL-13 staining shown in panel c is also not definitive. One should be able to optimize their assays to achieve a better level of staining, to my mind.

      We agree with the reviewer that much higher IL-13-producing CD4 T cells should be observed. We don’t think this is a technical glitch or non-optimal set-up as we see much higher levels of IL-13-producing CD4 T cells when using higher doses of HDM to sensitise and challenge, say between 7 -20% in WT mice (see Author response image 2, lung stimulated with PMA/ionomycin+Monensin, please note this is for illustration purposes only and it not linked to the current manuscript, its merely to demonstrate a point from other experiments we have conducted in the lab).

      Author response image 2.

      In d-f, the authors perform a serum transfer, but they only do this once. The half life of IgM is quite short. The authors should perform multiple naïve serum transfers to see if this is enough to induce FULL AHR.

      We thank the reviewer for this comment. We apologise if this was not clear enough on the Figure legend and method, we did transfer serum 3x, a day before sensitisation, on the day of sensitisation and a day before the challenge to circumvent the short life of IgM. In our subsequent experiments, we have now used busulfan to deplete all bone marrow in IgM-deficient mice and replace it with WT bone marrow and this method restores AHR (Figure 3).

      This now appears in line 165 to 169 and reads

      “Adoptive transfer of naïve serum

      Naïve wild-type mice were euthanised and blood was collected via cardiac puncture before being spun down (5500rpm, 10min, RT) to collect serum. Serum (200mL) was injected intraperitoneally into IgM-deficient mice. Serum was injected intraperitoneally at day -1, 0, and a day before the challenge with HDM (day 10).”

      The presence of negative values of total IgE in panel F would indicate some errors in calculation of serum IgE concentrations.

      We thank the reviewer for this observation. For better clarity, we have now indicated these values as undetected in Figure , as they were below our detection limit.

      Overall, it is hard to be convinced that IgM-deficiency does not lead to a reduction in Th2 inflammation, since the assays appear suboptimal.

      We disagree with the reviewer in this instance, because we have shown in 3 different models and in 2 different strains and 2 doses of HDM (high and low) that no matter what you do, Th2 remains intact. Our reason for choosing low dose HDM was based on our previous work and that of others, which showed that depending on antigen load, B cells can either be redundant or have functional roles. Since our interest was to tease out the role of B cells and specifically IgM, it was important that we look at a scenario where B cells are known to have a function (low antigen load). We did find similar findings at high dose of HDM load, but effects on AHR were not as strong, but Th2 was not changed, in fact in some instances Th2 was higher in IgM-deficient mice.

      Fig. 3. Gene expression differences between WT and KO mice in PBS and HDM challenged settings are shown. PCA analysis does not show clear differences between all four groups, but genes are certainly up and downregulated, in particular when comparing PBS to HDM challenged mice. In both PBS and HDM challenged settings, three genes stand out as being upregulated in WT v KO mice. these are Baiap2l1, erdr1 and Chil1.

      Noted

      Fig. 4. The authors attempt to quantify BAIAP2L1 in mouse lungs. It is difficult to know if the antibody used really detects the correct protein. A BAIAP2L1-KO is not used as a control for staining, and I am not sure if competitive assays for BAIAP2L1 can be set up. The flow data is not convincing. The immunohistochemistry shows BAIAP2L1 (in red) in many, many cells, essentially throughout the section. There is also no discernible difference between WT and KO mice, which one might have expected based on the RNA-Seq data. So, from my perspective, it is hard to say if/where this protein is located, and whether there truly exists a difference in expression between wt and ko mice.

      We thank the reviewer for this comment. We are certain that the antibody does detect BAIAP2L1, we have used it in 3 assays, which we admit may show varying specificities since it’s a Polyclonal antibody. However, in our western blot, the antibody detects 1 band at 56.7kDa and no other bands, apart from what we think are isoforms. We agree that BAIAP2L1 is expressed by many cell types, including CD45+ cells and alpha smooth muscle negative cells and we show this in our supplementary Figure 9. Where we think there is a difference in expression between WT and IgM-deficient mice is in alpha-smooth muscle-positive cells. We have tested antibodies from different companies, and we find similar findings. We do not have access to BAIAP2L1 KO mice and to test specificity, we have also used single stain controls with or without secondary antibody and isotype control which show no binding in western blot and Immunofluorescence assays and Fluorescence minus one antibody in Flow cytometry, so that way we are convinced that the signal we are seeing is specific to BAIAP2L1.

      Fig. 5 and 6. The authors use a single cell contractility assay to measure whether BAIAP2L1 and ERDR1 impact on bronchial smooth muscle cell contractility. I am not familiar with the assay, but it looks like an interesting way of analysing contractility at the single cell level.

      The authors state that targeting these two genes with Cas9gRNA reduces smooth muscle cell contractility, and the data presented for contractility supports this observation. However, the efficiency of Cas9-mediated deletion is very unclear. The authors present a PCR in supp fig 9c as evidence of gene deletion, but it is entirely unclear with what efficiency the gene has been deleted. One should use sequencing to confirm deletion. Moreover, if the antibody was truly working, one should be able to use the antibody used in Fig 4 to detect BAIAP2L1 levels in these cells. The authors do not appear to have tried this.

      We thank the reviewer for these observations. We are in a process to optimise this using new polyclonal BAIAP2L1 antibodies from other companies, since the one we have tried doesn’t seem to work well on human cells via western blot. So hopefully in our new version, we will be able to demonstrate this by immunofluorescence or western blot.

      Other impressions:

      The paper is lacking a link between the deficiency of IgM and the effects on smooth muscle cell contraction.

      The levels of IL-13 and TNF in lavage of WT and IGMKO mice could be analysed.

      We have measured Th2 cytokine IL-13 in BAL fluid and found no differences between IgM-deficient mice and WT mice challenged with HDM (Author response image 1). We could not detected TNF-alpha in the BAL fluid, it was below detection limit.

      Author response image 3.

      IL-13 levels are not changed in IgM-deficient mice in the lung. Bronchoalveolar lavage fluid in WT or IgM-deficient mice sensitised and challenged with HDM. TNF-a levels were below the detection limit.

      Moreover, what is the impact of IgM itself on smooth muscle cells? In the Fig. 7 schematic, are the authors proposing a direct role for IgM on smooth muscle cells? Does IgM in cell culture media induce contraction of SMC? This could be tested and would be interesting, to my mind.

      We thank the Reviewer for these comments. We are still trying to test this, unfortunately, we have experienced delays in getting reagents such as human IgM to South Africa. We hope that we will be able to add this in our subsequent versions of the article. We agree it is an interesting experiment to do even if not for this manuscript but for our general understanding of this interaction at least in an in vitro system.

      Reviewer #3 (Public Review):

      Summary:

      This paper by Sabelo et al. describes a new pathway by which lack of IgM in the mouse lowers bronchial hyperresponsiveness (BHR) in response to metacholine in several mouse models of allergic airway inflammation in Balb/c mice and C57/Bl6 mice. Strikingly, loss of IgM does not lead to less eosinophilic airway inflammation, Th2 cytokine production or mucus metaplasia, but to a selective loss of BHR. This occurs irrespective of the dose of allergen used. This was important to address since several prior models of HDM allergy have shown that the contribution of B cells to airway inflammation and BHR is dose dependent.

      After a description of the phenotype, the authors try to elucidate the mechanisms. There is no loss of B cells in these mice. However, there is a lack of class switching to IgE and IgG1, with a concomitant increase in IgD. Restoring immunoglobulins with transfer of naïve serum in IgM deficient mice leads to restoration of allergen-specific IgE and IgG1 responses, which is not really explained in the paper how this might work. There is also no restoration of IgM responses, and concomitantly, the phenotype of reduced BHR still holds when serum is given, leading authors to conclude that the mechanism is IgE and IgG1 independent. Wild type B cell transfer also does not restore IgM responses, due to lack of engraftment of the B cells. Next authors do whole lung RNA sequencing and pinpoint reduced BAIAP2L1 mRNA as the culprit of the phenotype of IgM<sup>-/-</sup> mice. However, this cannot be validated fully on protein levels and immunohistology since differences between WT and IgM KO are not statistically significant, and B cell and IgM restoration are impossible. The histology and flow cytometry seems to suggest that expression is mainly found in alpha smooth muscle positive cells, which could still be smooth muscle cells or myofibroblasts. Next therefore, the authors move to CRISPR knock down of BAIAP2L1 in a human smooth muscle cell line, and show that loss leads to less contraction of these cells in vitro in a microscopic FLECS assay, in which smooth muscle cells bind to elastomeric contractible surfaces.

      Strengths:

      (1) There is a strong reduction in BHR in IgM-deficient mice, without alterations in B cell number, disconnected from effects on eosinophilia or Th2 cytokine production

      (2) BAIAP2L1 has never been linked to asthma in mice or humans

      Weaknesses:

      (1) While the observations of reduced BHR in IgM deficient mice are strong, there is insufficient mechanistic underpinning on how loss of IgM could lead to reduced expression of BAIAP2L1. Since it is impossible to restore IgM levels by either serum or B cell transfer and since protein levels of BAIAP2L1 are not significantly reduced, there is a lack of a causal relationship that this is the explanation for the lack of BHR in IgM-deficient mice. The reader is unclear if there is a fundamental (maybe developmental) difference in non-hematopoietic cells in these IgM-deficient mice (which might have accumulated another genetic mutation over the years). In this regard, it would be important to know if littermates were newly generated, or historically bred along with the KO line.

      We thank the reviewer for asking this question and getting us to think of this in a different way. This prompted us to use a different method to try and restore IgM function and since our animal facility no longer allows irradiation, we opted for busulfan. We present this data as new data in Figure 3. We had to go back and breed this strain and then generated bone marrow chimeras. What we have shown now with chimeras is that if we can deplete bone marrow from IgM-deficient mice and replace it with congenic WT bone marrow when we allow these mice to rest for 2 months before challenge with HDM (new Supplementary Figure 6 a-c) We also show that AHR (resistance and elastance) is partially restored in this way (Figure 3 a and b) as mice that receive congenic WT bone marrow after chemical irradiation can mount AHR and those that receive IgM-deficient bone marrow, can’t mount AHR upon challenge with HDM. If the mice had accumulated an unknown genetic mutation in non-hematopoietic cells, the transfer of WT bone marrow would not make a difference. So, we don’t believe the colony could have gained a mutation that we are unaware of. We have also shipped these mice to other groups and in their hands, this strains still only behaves as an IgM only knockout mice. See their publication below.

      Mark Noviski, James L Mueller, Anne Satterthwaite, Lee Ann Garrett-Sinha, Frank Brombacher, Julie Zikherman 2018. IgM and IgD B cell receptors differentially respond to endogenous antigens and control B cell fate. eLife 2018;7:e35074. DOI: https://doi.org/10.7554/eLife.35074 we have also added methods for bone marrow chimaeras and added results sections and new Figures related to this methods.

      Methods (line 171-182).

      “Busulfan Bone marrow chimeras

      WT (CD45.2) and IgM<sup>-/-</sup> (CD45.2) congenic mice were treated with 25 mg/kg busulfan (Sigma-Aldrich, Aston Manor, South Africa) per day for 3 consecutive days (75 mg/kg in total) dissolved in 10% DMSO and Phosphate buffered saline (0.2mL, intraperitoneally) to ablate bone marrow cells. Twenty-four hours after last administration of busulfan, mice were injected intravenously with fresh bone marrow (10x10<sup>6</sup> cells, 100mL) isolated from hind leg femurs of either WT (CD45.1) or IgM<sup>-/-</sup> mice(33). Animals were then allowed to complement their haematopoietic cells for 8 weeks. In some experiments the level of bone marrow ablation was assessed 4 days post-busulfan treatment in mice that did not receive donor cells. At the end of experiment level of complemented cells were also assessed in WT and IgM<sup>-/-</sup> mice that received WT (CD45.1) bone marrow.”

      Results (line 491-521)

      “Replacement of IgM-deficient mice with functional hematopoietic cells in busulfan mice chimeric mice restores airway hyperresponsiveness.

      We then generated bone marrow chimeras by chemical radiation using busulfan(33). We treated mice three times with busulfan for 3 consecutive days and after 24 hrs transferred naïve bone marrow from congenic CD45.1 WT mice or CD45.2 IgM<sup>-/-</sup> mice (Fig. 3a and Supplementary Fig. 5a). We showed that recipient mice that did not receive donor bone marrow after 4 days post-treatment have significantly reduced lineage markers (CD45+Sca-1+) or lineage negative (Lin-) cells in the bone marrow when compared to untreated or vehicle (10% DMSO) treated mice (Supplementary Figure 5b-c). We allowed mice to reconstitute bone marrow for 8 weeks before sensitisation and challenge with low dose HDM (Figure 3a). We showed that WT (CD45.2) recipient mice that received WT (CD45.1) donor bone marrow had higher airway resistance and elastance and this was comparable to IgM<sup>-/-</sup> (CD45.2) recipient mice that received donor WT (CD45.1) bone marrow (Figure 3b). As expected, IgM<sup>-/-</sup> (CD45.2) recipient mice that received donor IgM<sup>-/-</sup> (CD45.2) bone marrow had significantly lower AHR compared to WT (CD45.2) or IgM<sup>-/-</sup> (CD45.2) recipient mice that received WT (CD45.1) bone marrow (Figure 3b). We confirmed that the differences observed were not due to differences in bone marrow reconstitution as we saw similar frequencies of CD45.1 cells within the lymphocyte populations in the lungs and other tissues (Supplementary Fig. 5d). We observed no significant changes in the lung neutrophils, eosinophils, inflammatory macrophages, CD4 T cells or B cells in WT or IgM<sup>-/-</sup> (CD45.2) recipient mice that received donor WT (CD45.1/CD45.2) or IgM<sup>-/-</sup> (CD45.2) bone marrow when sensitised and challenged with low dose HDM (Fig. 3c)

      Restoring IgM function through adoptive reconstitution with congenic CD45.1 bone marrow in non-chemically irradiated recipient mice or sorted B cells into IgM<sup>-/-</sup> mice (Supplementary Fig.  6a) did not replenish IgM B cells to levels observed in WT mice and as a result did not restore AHR, total IgE and IgM in these mice (Supplementary Fig.  6b-c).”

      The 2 new figures are

      Figure 3 which moved the rest of the Figures down and Supplementary Figure 5, which also moved the rest of the supplementary figures down.

      Discussion appears in line 757-766 of the untracked version of the article.

      To resolve other endogenous factors that could have potentially influenced reduced AHR in IgM-deficient mice, we resorted to busulfan chemical irradiation to deplete bone marrow cells in IgM-deficient mice and replace bone marrow with WT bone marrow. While it is well accepted that busulfan chemical irradiation partially depletes bone marrow cells, in our case it was not possible to pursue other irradiation methods due to changes in ethical regulations and that fact that mice are slow to recover after gamma rays irradiation. Busulfan chemical irradiation allowed us to show that we could mostly restore AHR in IgM-deficient recipient mice that received donor WT bone marrow when challenged with low dose HDM.

      (2) There is no mention of the potential role of complement in activation of AHR, which might be altered in IgM-deficient mice 

      We thank the reviewer for this comment. We have not directly looked at complement in this instance, however, from our previous work on C3-/- mice, there have been comparable AHR to WT mice under the HDM challenge.

      (3) What is the contribution of elevated IgD in the phenotype of the IgM-deficient mice. It has been described by this group that IgD levels are clearly elevated

      We thank the reviewer for this question. We believe that IgD is essentially what drives partial class switching to IgG, we certainly have shown that in the case of VSV virus and Trypanosoma congolense and Trypanosoma brucei brucei that elevated IgD drive delayed but effective IgG in the absence of IgM (Lutz et al, 2001, Nature). This is also confirmed by Noviski studies where they show that both IgM and IgD do share some endogenous antigens, so its likely that external antigens can activate IgD in a similar manner to prompt class switching.

      (4) How can transfer of naïve serum in class switching deficient IgM KO mice lead to restoration of allergen specific IgE and IgG1?

      We thank the Reviewer for these comments, we believe that naïve sera transferred to IgM deficient mice is able to bind to the surface of B cells via IgM receptors (FcμR / Fcα/μR), which are still present on B cells and this is sufficient to facilitate class switching. Our IgM<sup>-/-</sup> mouse lacks both membrane-bound and secreted IgM, and transferred serum contains at least secreted IgM which can bind to surfaces via its Fc portion. We measured HDM-specific IgE and we found very low levels, but these were not different between WT and IgM<sup>-/-</sup> adoptively transferred with WT serum. We also detected HDM-specific IgG1 in IgM<sup>-/-</sup> transferred with WT sera to the same level as WT, confirming a possible class switching, of course, we can’t rule out that transferred sera also contains some IgG1. We also can’t rule out that elevated IgD levels can partially be responsible for class switched IgG1 as discussed above.

      In the discussion line 804-812, we also added the following

      “We speculate that IgM can directly activate smooth muscle cells by binding a number of its surface receptors including FcμR, Fcα/μR and pIgR(52-54). IgM binds to FcμR strictly, but shares Fcα/μR and pIgR with IgA(5,52,54). Both Fcα/μR and pIgR can be expressed by non-structural cells at mucosal sites(54,55). We would not rule out that the mechanisms of muscle contraction might be through one of these IgM receptors, especially the ones expressed on smooth muscle cells(54,55). Certainly, our future studies will be directed towards characterizing the mechanism by which IgM potentially activates the smooth muscle.”

      We have discussed this section under Discussion section, line 731 to 757. In addition, since we have now performed bone marrow chimaeras we have further added the following in our discussion in line 757-766.

      To resolve other endogenous factors that could have potentially influenced reduced AHR in IgM-deficient mice, we resorted to busulfan chemical irradiation to deplete bone marrow cells in IgM-deficient mice and replace bone marrow with WT bone marrow. While it is well accepted that busulfan chemical irradiation partially depletes bone marrow cells, in our case it was not possible to pursue other irradiation methods due to changes in ethical regulations and that fact that mice are slow to recover after gamma rays irradiation. Busulfan chemical irradiation allowed us to show that we could mostly restore AHR in IgM-deficient recipient mice that received donor WT bone marrow when challenged with low dose HDM.

      We removed the following lines, after performing bone marrow chimaeras since this changed some aspects.

      Our efforts to adoptively transfer wild-type bone marrow or sorted B cells into IgM-deficient mice were also largely unsuccessful partly due to poor engraftment of wild-type B cells into secondary lymphoid tissues. Natural secreted IgM is mainly produced by B1 cells in the peritoneal cavity, and it is likely that any transfer of B cells via bone marrow transfer would not be sufficient to restore soluble levels of IgM(3,10).

      (5) Alpha smooth muscle antigen is also expressed by myofibroblasts. This is insufficiently worked out. The histology mentions "expression in cells in close contact with smooth muscle". This needs more detail since it is a very vague term. Is it in smooth muscle or in myofibroblasts.

      Response: We appreciate that alpha-smooth muscle actin-positive cells are a small fraction in the lung and even within CD45 negative cells, but their contribution to airway hyperresponsiveness is major. We also concede that by immunofluorescence BAIAP2L1 seems to be expressed by cells adjacent to alpha-smooth muscle actin (Fig. 5b), however, we know that cells close to smooth muscle (such as extracellular matrix and myofibroblasts) contribute to its hypertrophy in allergic asthma.

      James AL, Elliot JG, Jones RL, Carroll ML, Mauad T, Bai TR, et al. Airway Smooth Muscle Hypertrophy and Hyperplasia in Asthma. Am J Respir Crit Care Med [Internet]. 2012;185:1058–64. Available from: https://doi.org/10.1164/rccm.201110-1849OC

      (6) Have polymorphisms in BAIAP2L1 ever been linked to human asthma?

      No, we have looked in asthma GWAS studies, at least summary statics and we have not seen any SNPs can could be associated with human asthma.

      (7) IgM deficient patients are at increased risk for asthma. This paper suggests the opposite. So the translational potential is unclear

      We thank the reviewer for these comments. At the time of this publication, we have not made a concrete link with human disease. While there is some anecdotal evidence of diseases such as Autoimmune glomerulonephritis, Hashimoto’s thyroiditis, Bronchial polyp, SLE, Celiac disease and other diseases in people with low IgM. Allergic disorders are also common in people with IgM deficiency as the reviewer correctly points out, other studies have reported as high as 33-47%. The mechanisms for the high incidence of allergic diseases are unclear as generally, these patients have normal or higher IgG and IgE levels. IgM deficiency may represent a heterogeneous spectrum of genetic defects, which might explain the heterogeneous nature of disease presentations.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The manuscript performs a comprehensive biochemical, structural, and bioinformatic analysis of TseP, a type 6 secretion system effector from Aeromonas dhakensis that includes the identification of a domain required for secretion and residues conferring target organism specificity. Through targeted mutations, they have expanded the target range of a T6SS effector to include a gram-positive species, which is not typically susceptible to T6SS attack.

      Strengths:

      All of the experiments presented in the study are well-motivated and the conclusions are generally sound.

      Thank you.

      Weaknesses:

      There are some issues with the clarity of figures. For example, the microscopy figures could have been more clearly presented as cell counts/quantification rather than representative images. Similarly, loading controls for the secreted proteins for the westerns probably should be shown.

      Also, some of the minor/secondary conclusions reached regarding the "independence" of the N and C term domains of the TseP are a bit overreaching.

      We thank the reviewer for pointing out the issues and have carefully revised the manuscript accordingly. We acknowledge the reviewer’s concern regarding the independence of the N- and C-terminal domains, and have toned down the relevant claims.

      Reviewer #2 (Public review):

      Summary:

      Wang et al. investigate the role of TseP, a Type VI secretion system (T6SS) effector molecule, revealing its dual enzymatic activities as both an amidase and a lysozyme. This discovery significantly enhances the understanding of T6SS effectors, which are known for their roles in interbacterial competition and survival in polymicrobial environments. TseP's dual function is proposed to play a crucial role in bacterial survival strategies, particularly in hostile environments where competition between bacterial species is prevalent.

      Strengths:

      (1) The dual enzymatic function of TseP is a significant contribution, expanding the understanding of T6SS effectors.

      (2) The study provides important insights into bacterial survival strategies, particularly in interbacterial competition.

      (3) The findings have implications for antimicrobial research and understanding bacterial interactions in complex environments.

      Thank you.

      Weaknesses:

      (1) The manuscript assumes familiarity with previous work, making it difficult to follow. Mutants and strains need clearer definitions and references.

      Thank you for raising the issue. We have revised the manuscript accordingly to improve the clarity by including more detailed descriptions of the mutants and strains, along with references to prior work where relevant, to improve clarity.

      (2) Figures lack proper controls, quantification, and clarity in some areas, notably in Figures 1A and 1C.

      We have now added the controls as requested by reviewers.

      (3) The Materials and Methods section is poorly organized, hindering reproducibility. Biophysical validation of Zn<sup>2+</sup> interaction and structural integrity of proteins need to be addressed.

      We have now included more details in the Materials and Methods section. While we recognize the importance of biophysical validation of the Zn<sup>2+</sup> interaction, this analysis lies beyond the primary scope of the current study. We plan to investigate the role of Zn²⁺ interaction and the EF-hand domain in greater depth as part of our follow-up studies. Thank you for this suggestion.

      (4) Discrepancies in protein degradation patterns and activities across different figures raise concerns about data reliability.

      We acknowledge the concern about discrepancies in protein degradation patterns. TseP exhibits inherent instability, which might explain the observed variations. We have added an explanation in the detailed response letter and the manuscript.

      Reviewer #3 (Public review):

      Summary:

      Type VI secretion systems (T6SS) are employed by bacteria to inject competitor cells with numerous effector proteins. These effectors can kill injected cells via an array of enzymatic activities. A common class of T6SS effector are peptidoglycan (PG) lysing enzymes. In this manuscript, the authors characterize a PG-lysing effector-TseP-from the pathogen Aeromonas dhakensis. While the C-terminal domain of TseP was known to have lysozyme activity, the N-terminal domain was uncharacterized. Here, the authors functionally characterize TsePN as a zinc-dependent amidase. This discovery is somewhat novel because it is rare for PG-lysing effectors to have amidase and lysozyme activity.

      In the second half of the manuscript, the authors utilize a crystal structure of the lysozyme TsePC domain to inform the engineering of this domain to lyse gram-positive peptidoglycan.

      Strengths:

      The two halves of the manuscript considered together provide a nice characterization of a unique T6SS effector and reveal potentially general principles for lysozyme engineering.

      Thank you.

      Weaknesses:

      The advantage of fusing amidase and lysozyme domains in a single effector is not discussed but would appear to be a pertinent question. Labeling of the figures could be improved to help readers understand the data.

      Thank you for the suggestions. We have revised the manuscript and figures to improve clarity.

      The advantage of having dual-domain functions relative to having just one of the two functions is likely for increasing competitive fitness. Although such dual functional cell-wall targeting effectors have not been characterized prior to this study, there are some examples that dual functions are encoded by the same secretion module, for example the VgrG1-TseL pair in Vibrio cholerae. The C-terminal of VgrG1 not only catalyzes actin crosslinking but also recognizes and delivers the downstream encoded lipase effector TseL through direct interaction. In this context, the VgrG1-TseL pair also represent a dual-functional module. Therefore, it is likely that fusing effector domains and coupling effector functions are parallel strategies for the evolution of T6SS effectors.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      The paper explored cross-species variance in albumin glycation and blood glucose levels in the function of various life-history traits. Their results show that

      (1) blood glucose levels predict albumin gylcation rates

      (2) larger species have lower blood glucose levels

      (3) lifespan positively correlates with blood glucose levels and

      (4) diet predicts albumin glycation rates.

      The data presented is interesting, especially due to the relevance of glycation to the ageing process and the interesting life-history and physiological traits of birds. Most importantly, the results suggest that some mechanisms might exist that limit the level of glycation in species with the highest blood glucose levels.

      While the questions raised are interesting and the amount of data the authors collected is impressive, I have some major concerns about this study:

      (1) The authors combine many databases and samples of various sources. This is understandable when access to data is limited, but I expected more caution when combining these. E.g. glucose is measured in all samples without any description of how handling stress was controlled for. E.g glucose levels can easily double in a few minutes in birds, potentially introducing variation in the data generated. The authors report no caution of this effect, or any statistical approaches aiming to check whether handling stress had an effect here, either on glucose or on glycation levels.

      (2) The database with the predictors is similarly problematic. There is information pulled from captivity and wild (e.g. on lifespan) without any confirmation that the different databases are comparable or not (and here I'm not just referring to the correlation between the databases, but also to a potential systematic bias (e.g. captivate-based sources likely consistently report longer lifespans). This is even more surprising, given that the authors raise the possibility of captivity effects in the discussion, and exploring this question would be extremely easy in their statistical models (a simple covariate in the MCMCglmms).

      (3) The authors state that the measurement of one of the primary response variables (glycation) was measured without any replicability test or reference to the replicability of the measurement technique.

      (4) The methods and results are very poorly presented. For instance, new model types and variables are popping up throughout the manuscript, already reporting results, before explaining what these are e.g. results are presented on "species average models" and "model with individuals", but it's not described what these are and why we need to see both. Variables, like "centered log body mass", or "mass-adjusted lifespan" are not explained. The results section is extremely long, describing general patterns that have little relevance to the questions raised in the introduction and would be much more efficiently communicated visually or in a table.

      Reviewer #2 (Public review):

      Summary

      In this extensive comparative study, Moreno-Borrallo and colleagues examine the relationships between plasma glucose levels, albumin glycation levels, diet, and lifehistory traits across birds. Their results confirmed the expected positive relationship between plasma blood glucose level and albumin glycation rate but also provided findings that are somewhat surprising or contradicting findings of some previous studies (relationships with lifespan, clutch mass, or diet). This is the first extensive comparative analysis of glycation rates and their relationships to plasma glucose levels and life history traits in birds that are based on data collected in a single study and measured using unified analytical methods.

      Strengths

      This is an emerging topic gaining momentum in evolutionary physiology, which makes this study a timely, novel, and very important contribution. The study is based on a novel data set collected by the authors from 88 bird species (67 in captivity, 21 in the wild) of 22 orders, which itself greatly contributes to the pool of available data on avian glycemia, as previous comparative studies either extracted data from various studies or a database of veterinary records of zoo animals (therefore potentially containing much more noise due to different methodologies or other unstandardised factors), or only collected data from a single order, namely Passeriformes. The data further represents the first comparative avian data set on albumin glycation obtained using a unified methodology. The authors used LC-MS to determine glycation levels, which does not have problems with specificity and sensitivity that may occur with assays used in previous studies. The data analysis is thorough, and the conclusions are mostly wellsupported (but see my comments below). Overall, this is a very important study representing a substantial contribution to the emerging field of evolutionary physiology focused on the ecology and evolution of blood/plasma glucose levels and resistance to glycation.

      Weaknesses

      My main concern is about the interpretation of the coefficient of the relationship between glycation rate and plasma glucose, which reads as follows: "Given that plasma glucose is logarithm transformed and the estimated slope of their relationship is lower than one, this implies that birds with higher glucose levels have relatively lower albumin glycation rates for their glucose, fact that we would be referring as higher glycation resistance" (lines 318-321) and "the logarithmic nature of the relationship, suggests that species with higher plasma glucose levels exhibit relatively greater resistance to glycation" (lines 386-388). First, only plasma glucose (predictor) but not glycation level (response) is logarithm transformed, and this semi-logarithmic relationship assumed by the model means that an increase in glycation always slows down when blood glucose goes up, irrespective of the coefficient. The coefficient thus does not carry information that could be interpreted as higher (when <1) or lower (when >1) resistance to glycation (this only can be done in a log-log model, see below) because the semi-log relationship means that glycation increases by a constant amount (expressed by the coefficient of plasma glucose) for every tenfold increase in plasma glucose (for example, with glucose values 10 and 100, the model would predict glycation values 2 and 4 if the coefficient is 2, or 0.5 and 1 if the coefficient is 0.5). Second, the semi-logarithmic relationship could indeed be interpreted such that glycation rates are relatively lower in species with high plasma glucose levels. However, the semi-log relationship is assumed here a priori and forced to the model by log-transforming only glucose level, while not being tested against alternative models, such as: (i) a model with a simple linear relationship (glycation ~ glucose); or (ii) a loglog model (log(glycation) ~ log(glucose)) assuming power function relationship (glycation = a * glucose^b). The latter model would allow for the interpretation of the coefficient (b) as higher (when <1) or lower (when >1) resistance in glycation in species with high glucose levels as suggested by the authors.

      Besides, a clear explanation of why glucose is log-transformed when included as a predictor, but not when included as a response variable, is missing.

      We apologize for missing an answer to this part before. Indeed, glucose is always log transformed and this is explained in the text.

      The models in the study do not control for the sampling time (i.e., time latency between capture and blood sampling), which may be an important source of noise because blood glucose increases because of stress following the capture. Although the authors claim that "this change in glucose levels with stress is mostly driven by an increase in variation instead of an increase in average values" (ESM6, line 46), their analysis of Tomasek et al.'s (2022) data set in ESM1 using Kruskal-Wallis rank sum test shows that, compared to baseline glucose levels, stress-induced glucose levels have higher median values, not only higher variation.

      Although the authors calculated the variance inflation factor (VIF) for each model, it is not clear how these were interpreted and considered. In some models, GVIF^(1/(2*Df)) is higher than 1.6, which indicates potentially important collinearity; see for example https://www.bookdown.org/rwnahhas/RMPH/mlr-collinearity.html). This is often the case for body mass or clutch mass (e.g. models of glucose or glycation based on individual measurements).

      It seems that the differences between diet groups other than omnivores (the reference category in the models) were not tested and only inferred using the credible intervals from the models. However, these credible intervals relate to the comparison of each group with the reference group (Omnivore) and cannot be used for pairwise comparisons between other groups. Statistics for these contrasts should be provided instead. Based on the plot in Figure 4B, it seems possible that terrestrial carnivores differed in glycation level not only from omnivores but also from herbivores and frugivores/nectarivores.

      Given that blood glucose is related to maximum lifespan, it would be interesting to also see the results of the model from Table 2 while excluding blood glucose from the predictors. This would allow for assessing if the maximum lifespan is completely independent of glycation levels. Alternatively, there might be a positive correlation mediated by blood glucose levels (based on its positive correlations with both lifespan and glycation), which would be a very interesting finding suggesting that high glycation levels do not preclude the evolution of long lifespans.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Line 84: "glycation scavengers" such as polyamines - can you specify what these polyamines do exactly?

      A clarification of what we mean with "glycation scavengers" is added.

      (2) Line 87-89: specify that the work of Wein et al. and this sentence is about birds.

      This is now clarified.

      (3) Line 95: "88 species" add "OF BIRDS". Also, I think it would be nice if you specified here that you are relying on primary data.

      This is now clarified (line 96).

      (4) Line 90-119: I find this paragraph very long and complex, with too many details on the methodology. For instance, I agree with listing your hypothesis, e.g. that with POL, but then what variables you use to measure the pace of life can go in the materials and methods section (so all lines between 112-119).

      This is explained here as a previous reviewer considered this presentation was indeed needed in the introduction.

      (5) Line 122-124: The first sentence should state that you collected blood samples from various sources, and list some examples: zoos? collaborators? designated wild captures? Stating the sample size before saying what you did to get them is a bit weird. Besides, you skipped a very important detail about how these samples were collected, when, where, and using what protocols. We know very well, that glucose levels can increase quickly with handling stress. Was this considered during the captures? Moreover, you state that you had 484 individuals, but how many samples in total? One per individual or more?

      We kindly ask the reviewer to read the multiple supplementary materials provided, in which the questions of source of the samples, potential stress effects and sample sizes for each model are addressed. All individuals contributed with one sample. More details about the general sources employed are given now in lines 125-127.

      (6) Line 135-36: numbers below 10 should be spelled out.

      Ok. Now that is changed.

      (7) Line 136: the first time I saw that you had both wild and captive samples. This should be among the first things to be described in the methods, as mentioned above.

      As stated above, details on this are included in the supplementary materials, but further clarifications have now been included in the main text (question 5).

      (8) Line 137-138: not clear. So you had 46 samples and 9 species. But what does the 3-3-3 sample mean? or for each species you chose 9 samples (no, cause that would be 81 samples in total)?

      This has now been clarified (lines 139-140).

      (9) Line 139-141: what methodological constraints? Too high glucose levels? Too little plasma?

      There were cases in which the device (glucometer) produced an unspecific error. This did not correspond to too high nor too low glucose levels, as these are differently signalled errors. Neither the manual nor the client service provided useful information to discern the cause. This may perhaps be related to the composition of the plasma of certain species, interfering with the measurement. Some clarifications have been added (lines 143-146).

      (10) Line 143: should be ZIMS.

      Corrected.

      (11) Line 120-148: you generally talk about individuals here, but I feel it would be more precise to use 'samples'.

      The use is totally interchangeable, as we never measured more than one sample for a given individual within this study. Besides, in some cases, saying “sample” could result less informative.

      (12) Line 150: missing the final number of measurements for glucose and glycation.

      Please, read the ESM6 (Table ESM6.1), where this information is given.

      (13) Line 154-155: so you took multiple samples from the same individual? It's the first time the text indicates so. Or do you mean technical replicates were not performed on the same samples?

      As previously indicated, each individual included only one sample. Replicates were done only for some individuals to validate the technique, as it would be unfeasible to perform replicates of all of them. This part of the text is referring to the fact that not all samples were analysed at the same time, as it takes a considerable amount of time, and the mass spectrometry devices are shared by other teams and project. Clarifications in this sense are now added (lines 160-163).

      (14) Line 171-172: "After realizing that diet classifications from AVONET were not always suitable for our purpose" - too informal. Try rephrasing, like "After determining that AVONET diet classifications did not align with our research needs...", but you still need to specify what was wrong with it and what was changed, based on what argument?

      The new formulation suggested by the reviewer has now been applied (lines 181-183). The details are given in the ESM6, as indicated in the text. 

      (15) Line 174-176: You start a new paragraph, talking about missing values, but you do not specify what variable are you talking about. you talk about calculating means, but the last variable you mentioned was diet, so it's even more strange.

      We refer to life history traits. It has now been clarified in the text (line 185).

      (16) Line 177: what longevity records? Coming from where? How did you measure longevity? Maximum lifespan ever recorded? 80-90% longevity, life expectancy???

      We refer to maximum lifespan, as indicated in the introduction and in every other case throughout the manuscript. Clarifications have now been introduced (188-190).

      (17) Line 180-183: using ZIMS can be problematic, especially for maximum longevity. There are often individuals who had a wrong date of birth entered or individuals that were failed to be registered as dead. The extremes in this database are often way off. If you want to combine though, you can check the correlation of lifespans obtained from different sources for the overlapping species. If it's a strong correlation it can be ok, but intuitively this is problematic.

      The species for which we used ZIMS were those for which no other databases reported any values. We could try correlations for other species, but this issue is not necessarily restricted to ZIMS, as the primary origin of the data from other databases is often difficultly traceable. Also, ZIMS is potentially more updated that some of the other databases, mainly Amniotes database, from which we rely the most, as it includes the highest number of species in the most easily accessible format.

      (18) Line 181-186: in ZIMS you calculate the average of the competing records, otherwise you choose the max. Why use different preferences for the same data?

      This constitutes a misunderstanding, for which we include clarifications now (line 196). We were referring here to the fact that for maximum lifespan the maximum is always chosen, while for other variables an average is calculated. 

      (19) Line 198: Burn-in and thinning interval is quite low compared to your number of iterations. How were model convergences checked?

      Please, check ESM1.

      (20) Line 201-203: What's the argument using these priors? Why not use noninformative ones? Do you have some a priori expectations? If so, it should be explained.

      Models have now been rerun with no expectations on the variance partitions so the priors are less informative, given the lack of firm expectations, and results are similar. Smaller nu values are also tried.

      (21) Line 217: "carried" OUT.

      Corrected (now in line 229).

      (22) Line 233-234: "species average model" - what is this? it was not described in the methods.

      Please, read the ESM6.

      (23) Line 232-246: (a) all this would be better described by a table or plot. You can highlight some interesting patterns, but describing it all in the text is not very useful I think, (b) statistically comparing orders represented by a single species is a bit odd.

      (a) Figure 1 shows this graphically, but this part was found to be quite short without descriptions by previous reviewers. (b) We recognise this limitation, but this part is not presented as one of the main results of the article, and just constitutes an attempt to illustrate very general patterns, in order to guide future research, as in most groups glycation has never been measured, so this still constitutes the best illustration of such patterns in the literature.

      (24) Line 281: the first time I saw "mass-adjusted maximum lifespan" - what is this, and how was it calculated? It should be described in the methods. But in any case, neither ratios, nor residuals should be used, but preferably the two variables should be entered side by side in the model.

      Please, see ESM6 for the explanations and justifications for all of this.

      (25) Line 281: there was also no mention of quadratic terms so far. How were polynomial effects tested/introduced in the models? Orthogonal polynomials? or x+ x^2?

      Please, read ESM6.

      (26) Table 1. What is 'Centred Log10Body mass', should be added in the methods.

      Please, read ESM6.

      (27) Table 1: what's the argument behind separating terrestrial and aquatic carnivores?

      This was mostly based on the a priori separation made in AVONET, but it is also used in a similar way by Szarka and Lendvai 2024 (comparative study on glucose in birds), where differences in glucose levels between piscivorous and carnivorous are reported. We had some reasons to think that certain differences in dietary nutrient composition, as discussed later, can make this difference relevant.

      (28) Table 1: The variable "Maximum lifespan" is discussed and plotted as 'massadjusted maximum lifespan' and 'residual maximum lifespan'. First, this is confusing, the same name should be used throughout and it should be defined in the methods section. Second, it seems that non-linear effects were tested by using x + x^2. This is problematic statistically, orthogonal polynomials should be used instead (check polyfunction in R). Also, how did you decide to test for non-linear effects in the case of lifespan but not the other continuous predictors? Should be described in the methods again.

      Please, read ESM6. Data exploration was performed prior to carry out these models. Orthogonal polynomials were considered to difficult the interpretation of the estimates and therefore the patterns predicted by the models, so raw polynomials were used. Clarifications have now been included in line 297.

      (29) Figure 2. From the figure label, now I see that relative lifespan is in fact residual. This is problematic, see Freckleton, R. P. (2009). The seven deadly sins of comparative analysis. Journal of evolutionary biology, 22(7), 1367-1375. Using body mass and lifespan side by side is preferred. This would also avoid forcing more emphasis on body mass over lifespan meaning that you subjectively introduce body mass as a key predictor, but lifespan and body size are highly correlated, so by this, you remove a large portion of variance that might in fact be better explained by lifespan.

      Please, read ESM6 for justifications on the use of residuals.

      Reviewer #2 (Recommendations for the authors):

      (1) If the semi-logarithmic relationship (glycation ~ log10(glucose)) is to be used to support the hypothesis about higher glycation resistance in species with high blood glucose (lines 318-321 and 386-388), it should be tested whether it is significantly better than the model assuming a simple linear relationship (i.e., glycation ~ glucose). Alternatively, if the coefficient is to be used to determine whether glycation rate slows down or accelerates with increasing glucose levels, log-log model (log10(glycation) ~ log10(glucose)) assuming power function relationship (glycation = a * glucose^b) should be used (as is for example in the literature about relationships between metabolic rates and body size). Probably the best approach would be to compare all three models (linear, semi-logarithmic, and log-log) and test if one performs significantly better. If none of them, then the linear model should be selected as the most parsimonious.

      Different options (linear, both semi-logarithmic combinations and log-log) have now been tested, with similar results. All of the models confirm the pattern of a significant positive relationship between glucose and glycation. Moreover, when standardizing the variables (both glucose and glycation, either log transformed or not), the estimate of the slope is almost equal for all the models. It is also lower than one, which in the case of both the linear and log-log confirms the stated prediction. The log-log model, showing a much lower DIC than the linear version, is now shown as the final model.

      (2) ESM6, line 46: Please note that Kruskal-Wallis rank sum test in ESM1 shows that, compared to baseline glucose levels, stress-induced glucose levels have higher median values (not only higher variation). With this in mind, what is the argument here about increased variation being the main driver of stress-induced change in glucose levels based on? It seems that both the median values and variation differ between baseline and stress-induced levels, and this should be acknowledged here.

      As discussed in the public answers, Kruskal Wallis does not allow to determine differences in mean, but just says that the groups are “different” (implicitly, in their ranksums, which does not mean necessarily in mean), while the Levene test performed signals heteroskedasticity. This makes this feature of the data analytically more grounded. Of course, when looking at the data, a higher mean can be perceived, but nothing can be said about its statistical significance. Still, some subtle changes have been introduced in corresponding section of the ESM6.

      (3) Have you recorded the sampling times? If yes, why not control them in the models? It is at least highly advisable to include the sampling times in the data (ESM5).

      As indicated in ESM6 lines 42-43, we do not have sampling times for most of the individuals (only zebra finches and swifts), so this cannot be accounted for in the models.

      (4) If sampling times will remain uncontrolled statistically, I recommend mentioning this fact and its potential consequences (i.e., rather conservative results) in the Methods section of the main text, not only in ESM6.

      A brief description of this has now been included in the main text (lines 129-132), referencing the more detailed discussion on the supplementary materials. Some subtle changes have also been included in the “Possible effects of stress” section of the ESM6.

      (5) ESM6, lines 52-53: The lower repeatability in Tomasek et al.' study compared to your study is irrelevant to the argument about the conservative nature of your results (the difference in repeatability between both studies is most probably due to the broader taxonomic coverage of the current study). The important result in this context is that repeatability is lower when sampling time is not considered within Tomasek et al's data set (ESM1). Therefore, I suggest rewording "showing a lower species repeatability than that from our data" to "showing lower species repeatability when sampling time is not considered" to avoid confusion. Please also note that you refer here to species repeatability but, in ESM1, you calculate individual repeatability. Nevertheless, both individual and species repeatabilities are lower when not controlling for sampling time because the main driver, in that case, is an increased residual variance.

      We recognize the current confusion in the way the explanation is exposed, and have significantly changed the redaction of the section. However, we would like to indicate that ESM1 shows both species and individual repeatability (for Tomasek et al. 2022 data, for ours only species as we do not have repeated individual values). Changes are now made to make it more evident.

      (6) I recommend providing brief guidelines for the interpretation of VIFs to the readers, as well as a brief discussion of the obtained values and their potential importance.

      Thank you for the recommendation. We included a brief description in lines 230-231. Also in the results section (lines 389-393).

      (7) Line: 264: Please note that the variance explained by phylogeny obtained from the models with other (fixed) predictors does not relate to the traits (glucose or glycation) per se but to model residuals.

      We appreciate the indication, and this has been rephrased accordingly (lines 280-286).

      (8) Change the term "confidence intervals" to "credible intervals" throughout the paper, since confidence interval is a frequentist term and its interpretations are different from Bayesian credible interval.

      Thank you for the remark, this has now been changed.

      (9) Besides lifespan, have you also considered quadratic terms for body mass? The plot in Figure 2A suggests there might be a non-linear relationship too.

      A quadratic component of body mass has not shown any significant effect on glucose in an alternative model. Also, a model with linear instead of log glucose (as performed in other studies) did not perform better by comparing the DICs, despite both showing a significant relationship between glucose and body mass. Therefore, this model remains the best option considered as presented in the manuscript.

      (10) ESM6, lines 115-116: It is usually recommended that only factors with at least 6 or 8 levels are included as random effects because a lower number of levels is insufficient for a good estimation of variance.

      In a Bayesian approach this does not apply, as random and fixed factors are estimated similarly. 

      (11) Typos and other minor issues:

      a) Line 66: Delete "related".

      b) Figure 2: "B" label is missing in the plot.

      c) Reference 9: Delete "Author".

      d) References 15 and 83 are duplicated. Keep only ref. 83, which has the correct citation details.

      e) ESM6, line 49: Change "GLLM" to "GLMM".

      Thank you for indicating this. Now it’s corrected.

    1. Author response:

      The following is the authors’ response to the current reviews.

      Response to Reviewer 2’s comments:

      I am concerned that the results in Figure 8D may not be correct, or that the authors may be mis-interpreting them. From my reading of the paper they cite (Lammers & Flamholz 2023), the equilibrium sharpness limit for the network they consider in Figure 8 should be 0.25. But both solutions shown in Figure 8D fall below this limit, which means that they have sharpness levels that could have been achieved with no energy expenditure. If this is the case, then it would imply that while both systems do dissipate energy, they are not doing so productively; meaning that the same results could be achieved while holding Phi=0.

      I acknowledge that this could be due to a difference in how they measure sharpness, but wanted to raise it here in case it is, in fact, a genuine issue with the analysis.There should be an easy fix for this: just set the sharper "desired response" curve in 8b to be such that it demands non-equilibrium sharpness levels (0.25<S<0.5).

      Thank you for raising this point regarding the interpretation of our results in Figure 8D. We agree that if the equilibrium sharpness limit for this particular network is around 0.25 (as shown by Lammers & Flamholz 2023), then achieving a sharpness below this threshold could, in principle, be accomplished without any energy expenditure. However, in our current design approach, the loss function is solely designed to enforce agreement with a target mean mRNA level at different input concentrations; it does not explicitly constrain energy dissipation, noise, or other metrics. Consequently, the DGA has no built-in incentive to minimize or optimize energy consumption, which means the resulting solutions may dissipate energy without exceeding the equilibrium sharpness limit.

      In other words, the same input–output relationship could theoretically be achieved with \Phi =0 if an explicit constraint or regularization term penalizing energy usage had been included. As noted, adding such a term (e.g., penalizing \Phi^2) is conceptually straightforward but falls outside the scope of this study. Our primary goal is to demonstrate the flexibility of the DGA in designing a desired response, rather than to delve into energy–sharpness trade-offs or other biological considerations

      While we appreciate the suggestion to set a higher target sharpness that exceeds the equilibrium limit, we believe the current example effectively demonstrates the DGA’s ability to design circuits with desired input-output relationships, which is the primary focus of this study. Researchers interested in optimizing energy efficiency, burst size, burst frequency, noise, response time, mutual information, or other system properties can easily extend our approach by incorporating additional terms into the loss function to target these specific objectives.

      We hope this explanation addresses your concern and clarifies that the manuscript provides sufficient context for readers to interpret the results in Figure 8D correctly.


      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      We thank Reviewer #1 for their thoughtful feedback and appreciation of the manuscript's clarity. Our primary goal is to introduce the DGA  as a foundational tool for integrating stochastic simulations with gradient-based optimization. While we recognize the value of providing detailed comparisons with existing methods and a deeper analysis of the DGA’s limitations (such as rare event handling), these topics are beyond the scope of this initial work. Our focus is on presenting the core concept and demonstrating its potential, leaving more extensive evaluations for future research.

      Reviewer #2 (Public review):

      We thank Reviewer #2 for their detailed and constructive feedback. We appreciate the recognition of the DGA as a significant conceptual advancement for stochastic biochemical network analysis and design.

      Weaknesses:

      (1) Validation of DGA robustness in complex systems:

      Our primary goal is to introduce the DGA framework and demonstrate its feasibility. While validation on high-dimensional and non-steady-state systems is important, it is beyond the scope of this initial work. Future studies may improve scalability by employing techniques such as dynamically adjusting the smoothness of the DGA's approximations during simulation or using surrogate models that remain differentiable but more accurately capture discrete behaviors in critical regions, thus preserving gradient computation while improving accuracy.

      (2) Inference accuracy and optimization:

      We acknowledge that the non-convex loss landscape in the DGA can hinder parameter inference and convergence to global minima, as seen in Figure 5A. While techniques like multi-start optimization or second-order methods (e.g., L-BFGS) could improve performance, our focus here is on establishing the DGA framework. We plan to explore better optimization methods in future work to improve the accuracy of parameter inference in complex systems.

      (3) Use of simple models for demonstration:

      We selected well-understood systems to clearly illustrate the capabilities of the DGA. These examples were intended to demonstrate how the DGA can be applied, rather than to solve problems better addressed by analytical methods. Applying DGA to more complex, analytically intractable systems is an exciting avenue for future work, but introducing the method was our main objective in this study.

      Reviewer #3 (Public review):

      We thank the reviewer for their detailed and insightful feedback. We appreciate the recognition of the DGA as a significant advancement for enabling gradient-based optimization in stochastic systems.

      Weaknesses:

      (1) Application beyond steady-state analysis

      We acknowledge the limitation of focusing solely on steady-state properties. To extend the DGA for analyzing transient dynamics, time-dependent loss functions can be incorporated to capture system evolution over time. This could involve aligning simulated trajectories with experimental time-series data or using moment-matching across multiple time points. 

      (2) Numerical instability in gradient computation

      The reviewer correctly highlights that large sharpness parameters (a and b) in the sigmoid and Gaussian approximations can induce numerical instability due to vanishing or exploding gradients. To address this, adaptive tuning of a and b during optimization could balance smoothness and accuracy. Additionally, alternative smoothing functions (e.g., softmax-based reaction selection) and gradient regularization techniques (such as gradient clipping and trust-region methods) could improve stability and convergence.

      Reviewer #1 (recommendations):

      We thank the reviewer for their thoughtful and constructive feedback on our manuscript. Below, we address each of the comments and suggestions raised.

      Main points:

      (1) It would have been useful to have a brief discussion, based on a concrete example, of what can be achieved with the DGA and is totally beyond the reach of the Gillespie algorithm and the numerous existing stochastic simulation methods.

      Thank you for your comment. We would like to clarify that the primary aim of this work is to introduce the DGA and demonstrate its feasibility for tasks such as parameter estimation and network design. Unlike traditional stochastic simulation methods, the DGA’s differentiable nature enables gradient-based optimization, which is not possible with the classical Gillespie algorithm or its variants.

      (2) As often with machine learning techniques, there is a sense of black box, with a lack of mathematical details of the proposed method: as opposite to the exact Gillespie algorithm, whose foundations lie on solid mathematical results (exponentially-distributed waiting times of continuous-time Markov processes), the DGA involves uncontrolled approximations, that are only briefly mentioned in the paper. For instance, it is currently simply noted that "the approximations introduced by the DGA may be pronounced in more complex settings such as the calculation of rare events", without specifying how limiting these errors are. It would be useful to include a clearer and more comprehensive discussion of the limitations of the DGA: When does it work accurately? What are the approximations/errors and can they be controlled? When is it worth paying the price for those approximations/errors, and when is it better to stick to the Gillespie algorithm? Is this notably the case for problems involving rare events? Clearly, these are difficult questions, and the answers are problem specific. However, it would be important to draw the readers' attention on the issues, especially if the DGA is presented as a potentially significant tool in computational and synthetic biology.

      We acknowledge the importance of discussing the limitations of the DGA in more detail. While we have noted that the approximations introduced by the DGA may impact its accuracy in certain scenarios, such as rare-event problems, a deeper exploration of these trade-offs is outside the scope of this work. Instead, we provide sufficient context in the manuscript to guide readers on when the DGA is appropriate.

      (3) The DGA is here introduced and discussed in the context of non-spatial problems (simple gene regulatory networks). However, numerous problems in the life sciences and computational/synthetic biology, involve stochasticity and spatial degrees of freedom (e.g. for problems involving diffusion, migration, etc). It is notoriously challenging to use the Gillespie algorithm to efficiently simulate stochastic spatial systems, especially in the context of rare events (e.g., extinction or fixation problems). It would be useful to comment on whether, and possibly how, the DGA can be used to efficiently simulate stochastic spatial systems, and if it would be better suited than the Gillespie algorithm for this purpose.

      Thank you for pointing this out. Although our current work centers on non-spatial systems, we agree that many biological contexts incorporate both stochasticity and spatial degrees of freedom. Extending the DGA to efficiently simulate such systems would indeed require substantial modifications—for instance, coupling it with reaction-diffusion frameworks or spatial master equations. We believe this is an exciting direction for future research and mention it briefly in the discussion as a potential extension.

      Minor suggestions:

      (1) After Eq.(10): it would be useful to explain and motivate the choice of the ratio JSD/H.

      Done.

      (2) On page 6, just below the caption of Fig.4: it would be useful to clarify what is actually meant by "... convergence towards the steady-state distribution of the exact Gillespie simulation, which is obtained at a simulation time of 10^4".

      Done.

      (3) At the end of Section B on page 7: please clarify what is meant here by "soft directions".

      Done.

      Reviewer #2 (recommendations):

      We thank the reviewer for their thoughtful comments and constructive feedback. Below, we address each of the comments/suggestions.

      Main points:

      (1) Enumerate the conditions under which DGA assumptions hold (and when they do not). There is currently not enough information for the interested reader to know whether DGA would work for their system of interest. Without this information, it is difficult to assess what the true scope of DGA's impact will be. One simple idea would be to test DGA performance along two axes: (i) increasing number of model states and (ii) presence/absence of non-steady state dynamics. I acknowledge that these are very open-ended directions, but looking at even a single instance of each would greatly strengthen this work. Alternatively, if this is not feasible, then the authors should provide more discussion of the attendant difficulties in the main text.

      We agree that a detailed exploration of the conditions under which the DGA assumptions hold would be a valuable addition to the field. However, this paper primarily aims to introduce the DGA methodology and demonstrate its proof-of-concept applications. A comprehensive analysis along axes such as increasing model states or non-steady-state dynamics, while important, would require significant additional simulations and is beyond the scope of this work. In Appendix A, we have discussed the trade-off between accuracy and numerical stability. Additionally, we encourage future users to tune the hyperparameters a and b for their specific systems.

      (2) Demonstrate DGA performance in a more complex biochemical system. Clearly the authors were aware that analytic solutions exist for the 2-state system in Figure 7, but it this is actually also the case (I think) for mean mRNA production rate of the non-equilibrium system in Figure 8. To really demonstrate that DGA is practically viable, I encourage the authors to seek out an interesting application that is not analytically tractable.

      We appreciate the suggestion to validate DGA on a more complex biochemical system. However, the goal of this study is not to provide an exhaustive demonstration of all possible applications but to introduce the DGA and validate it in systems where ground-truth comparisons are available. While the non-equilibrium system in Figure 8 might be analytically tractable, its complexity already provides a meaningful demonstration of DGA’s ability to optimize parameters and design systems. Extending this work to analytically intractable systems is an exciting direction for future studies, and we hope this paper will inspire others to explore these applications.

      (3) Take steps to improve the robustness of parameter optimization and error bar calculations. (3a) When the loss landscape is degenerate, shallow, or otherwise "difficult," a common solution is to perform multiple (e.g. 25-100) inference runs starting from different random positions in parameter space. Doing this, and then taking the parameter set that minimizes the loss should, in theory, lead to a more robust recovery of the optimal parameter set.

      (3b) It seems clear that the Hessian approximation is underestimating the true error in your inference results. One alternative is to use a "brute force" approach like bootstrap resampling to get a better estimate for the statistical dispersion in parameter estimates. But I recognize that this is only viable if the inference is relatively fast. Simply recovering the true minimum will, of course, also help.

      (3a) We acknowledge the challenge posed by degenerate or shallow loss landscapes during parameter optimization. While performing multiple inference runs from different initializations is a common strategy, this approach is computationally intensive. Instead, we rely on standard optimization techniques (e.g., ADAM) to find a robust local minimum. 

      (3b) Thank you for your comment. We agree that Hessian-based error bars can underestimate uncertainty, particularly in degenerate or poorly conditioned loss landscapes. While methods like bootstrap and Monte Carlo can provide more robust estimates, they can be computationally prohibitive for larger-scale simulations. A simpler reason for not using them is the high resource demand from repeated simulations, which quickly becomes infeasible for complex or high-dimensional models. We note these trade-offs between robust estimation and practicality as an important area for further exploration.

      Moderate comments:

      (1) Figure 7: is it possible to also show the inferred kon values? Specifically, it would be of interest to see how kon varies with repressor concentration.

      Thank you for the suggestion. We have updated Figure 7 to include the inferred kon values, showing their variation with the mean mRNA copy number. However, we could not plot them against repressor concentration due to the lack of available data.

      (2) Figure 8B & D: the authors claim that the sharper system dissipates more energy, but doesn't 8D show the opposite of this? More importantly, it does not look like either network drives sharpness levels that exceed the upper equilibrium limit cited in [36]. So it is not clear that it is appropriate to look at energy dissipation here. In fact, it is likely that equilibrium networks could produce the curves in 8B, and might be worth checking.

      Thank you for pointing this out. We realized that the plotted values in Figure 8D were incorrect, as we had mistakenly plotted noise instead of energy dissipation. The plot has now been corrected. 

      (3) Figure 8: I really like this idea of using DGA to "design" networks with desired input-output properties, but I wonder if you could explore more a biologically compelling use-case. Specifically, what about some kind of switch-like logic where, as the activator concentration increases, you have first 0 genes on, then 1 promoter on, then 2 promoters on. This would achieve interesting regulatory logic, and having DGA try to produce step functions would ensure that you force the networks to be maximally sharp (i.e. about double what you're currently achieving).

      Thank you for this intriguing suggestion. While the proposed switch-like logic use case is indeed compelling, implementing such a system would require significant work. This goes beyond the scope of the current study, which focuses on demonstrating the feasibility of DGA for network design with simple input-output properties.

      Minor comments:

      (1) Figure 4B & C: the bar plots do not do a good job conveying the points made by the authors. Consider alternatives, such as scatter plots or box plots that could convey inference uncertainty.

      Done.

      (2) Figure 4B: consider using a log y-axis.

      The y-axis in Figure 4B is already plotted on a log scale.

      (3) Figure 4D is mentioned prior to 4C in the text. Consider reordering.

      Done. 

      (4) Figure 5B: it is difficult to assess from this plot whether or not the landscape is truly "flat," as the authors claim. Flat relative to what? Consider alternative ways to convey your point.

      Thank you for highlighting this ambiguity. By describing the loss landscape as “flat,” we intend to convey its relative insensitivity to parameter variations in certain regions, rather than implying a completely level surface. While we believe Figure 5B still provides a useful qualitative depiction of this behavior, we acknowledge that it does not quantitatively establish “flatness.” In future work, we plan to incorporate more rigorous measures—such as gradient magnitudes or Hessian eigenvalues—to more accurately characterize and communicate the geometry of the loss landscape.

      Reviewer #3 (recommendations):

      We sincerely thank the reviewer for their thoughtful feedback and constructive suggestions, which have helped us improve the clarity and rigor of our manuscript. Below, we address each of the comments.

      (1) Precision is lacking in the introduction section. Do the authors mean the Direct SSA, sorted SSA, which is usually faster, and how about rejection sampling methods?

      Thank you for pointing this out. We have updated the introduction to explicitly mention the Direct SSA.

      (2) When mentioning PyTorch and Jax, would be good to also talk about Julia, as they have fast stochastic simulators.

      We have now mentioned Julia alongside PyTorch and Jax.

      (3) Mentioned references 22-27. Reference 26 is an odd choice; a better reference is from the same author the Automatic Differentiation of Programs with Discrete Randomness, G Arya, M Schauer, F Schäfer, C Rackauckas, Advances in Neural Information Processing Systems, NeurIPS 2022

      We have now cited the suggested reference.

      (4) Page 1, Section: 'To circumnavigate these difficulties, the DGA modifies....' Have you thought about how you would deal with the bias that will be introduced by doing this?

      Thank you for your insightful comment. We acknowledge the potential for bias due to the differentiable approximations in the DGA; however, our analysis has not revealed any systematic bias compared to the exact Gillespie algorithm. Instead, we observe irregular deviations from the exact results as the smoothness of the approximations increases.

      (5) Page 2, first sentence '... traditional Gillespie...' be more precise here - the direct algorithm.

      Thank you for your comment. We believe that the context of the paper, particularly the schematic in Figure 1, makes it clear that we are focusing on the Direct SSA. 

      (6) Page 2, second paragraph: ' In order to simulate such a system...' This doesn't fit here as this section is about tau-leaping. As this approach approximates discrete operations, it is unclear if it would work for large models, snap-shot data of larger scale and if it would be possible to extend it for time-lapse data

      Thank you for your comment. We respectfully disagree that this paragraph is misplaced. The purpose of this paragraph is to explain why the standard Gillespie algorithm does not use fixed time intervals for simulating stochastic processes. By highlighting the inefficiency of discretizing time into small intervals where reactions rarely occur, the paragraph provides necessary context for the Gillespie algorithm’s event-driven approach, which avoids this inefficiency.

      Regarding the applicability of the DGA to larger models, snapshot data, or time-lapse data, we acknowledge these are important directions and have noted them as potential extensions in the discussion section.

      (7) Page 2 Section B: 'In order to make use of modern deep-learning techniques...' It doesn't appear from the paper that any modern deep learning is used.

      Thank you for your comment. Although the DGA does not utilize deep learning architectures such as neural networks, it employs automatic differentiation techniques provided by frameworks like PyTorch and Jax. These tools allow efficient gradient computations, making the DGA compatible with modern optimization workflows.

      (8) Page 3, Fig 1(a). S matrix last row, B and C should swap places: B should be 1 and C is -1.

      Corrected the typo.

      (9) Fig1 needs a more detailed caption.

      Expanded the caption slightly for clarity.

      (10) Page 3 last paragraph: 'The hyperparameter b...' Consequences of this are relevant, for example can we now go below zero. Also, we lose more efficient algorithms here. It would be good to discuss this in more detail that this is an approx.. algorithm that is good for our case study, but for other to use it more tests are needed.

      Thank you for the comment. Appendix A discusses the trade-offs related to a and b, but we agree that more detailed analysis is needed. The hyperparameters are tailored to our case study and must be tuned for specific systems.

      (11) Page 4, Section C, first paragraph, 'The goal of making...' This is snapshot data. Would the framework also translate to time-lapse data? Also, it would be better to make it clearer earlier which type of data are the target of this study.

      Thank you for your suggestion. While the current study focuses on snapshot data and steady-state properties, we believe the DGA could be extended to handle time-lapse data by incorporating multiple recorded time points into its inference objective. Specifically, one could modify the loss function to penalize discrepancies across observed transitions between these time points, effectively capturing dynamic trajectories. We consider this an exciting area for future development, but it lies beyond our present scope.

      (12) Page 4 Section C, sentence '...experimentally measured moments'. Should later be mentioned as error, as moments are imperfect

      Thank you for your comment. We agree that experimentally measured moments are inherently noisy and may not perfectly represent the true system. However, within the context of the DGA, these moments serve as target quantities, and the discrepancy between simulated and measured moments is already accounted for in the loss function. 

      (13) Page 4 Section C, last sentence '...second-order...such as ADAM'. Another formulation would be better as second order can be confusing, especially in the context of parameter estimation

      We have revised the language to avoid confusion regarding “second-order” methods.

      (14) Fig 4(a) a density plot would fit better here

      Fig. 4(a) has been updated to a scatter density plot as suggested.

      (15) Fig 4(c) Would be interesting to see closer analysis of trade of between gradient and accuracy when changing a and b parameters

      Thank you for this suggestion. We acknowledge that an in-depth exploration of these trade-offs could provide deeper insights into the method’s performance. However, for now, we believe the current analysis suffices to highlight the utility of the DGA in the contexts examined.

      (16) Page 6 Section III, first sentence: This fits more to intro. Further the reference list is severely lacking here, with no comparison to other methods for actually fitting stochastic models.

      Thank you for the suggestion. We have added a few references there.

      (17) Page 6, Section A, sentence: '....experimental measured mean...' Why is it a good measure here (moment matching is not perfect), also do you have distribution data, would that not be better? How about accounting for measurement error?

      Thank you for the comment. While we do not have full distribution data, we acknowledge that incorporating experimental measurement error could enhance the framework. A weighted loss function could model uncertainty explicitly, but this is beyond the scope of the current study. 

      (18) Page 7, section B, first paragraph: 'Motivated by this, we defined the...'Why using Fisher-Information when profile-likelihood have proven to be better, especially for systems with few parameters like this.

      Thank you for the suggestion. While profile-likelihood is indeed a powerful tool for parameter uncertainty analysis, we chose Fisher Information due to its computational efficiency and compatibility with the differentiable nature of the DGA framework.

      (19)  Page 7, section C, sentence '...set kR/off=1..'. In this case, we cannot infer this parameter.

      Thank you for the comment. You are correct that setting kR/off = 1 effectively normalizes the rates, making this parameter unidentifiable. In steady-state analyses, not all parameters can be independently inferred because observable quantities depend on relative—rather than absolute—rate values (as evident when setting the time derivative to zero in the master equation). To infer all parameters, one would need additional information, such as time-series data or moments at finite time.

      (20)  Page 7 Section 2. Estimating parameters .... Sentence: '....as can be seen, there is very good agreement..' How many times the true value falls within the CI (because corr 0.68 is not great).

      Thank you for your comment. While a correlation coefficient of 0.68 indicates moderate agreement, the primary goal was to demonstrate the feasibility of parameter estimation using the DGA rather than achieving perfect accuracy. The coverage of the CI was not explicitly calculated, as the focus was on the overall trends and relative agreement.

      (21) Page 7 Section 2. Estimating parameters .... Sentence: 'Fig5(c) shows....' Is this when using exact simulator?

      Thank you for your question. Yes, the exact values in x-axis of Fig. 5(c) are obtained using the exact Gillespie simulation.

      (22) Page 7 Section 3 Estimating parameters for the... Sentence: 'Fig6(a) shows...' Why Cis are not shown?

      Thank you for your comment. CIs are not shown in Fig. 6(a) because this particular case is degenerate, making the calculation and meaningful representation of CIs challenging. 

      (23) Page 10, Sentence: 'As can be seen in Fig 7(b)...' Can you show uncertainty in measured value? It would be good to see something of a comparison against an exact method, at least on simulated synthetic data

      Thank you for the comment. Fig. 7(a) already includes error bars for the experimental data, which account for measurement uncertainty. However, in Fig. 7(b), we do not include error bars for the experimental values due to limitations in the available data.

      (24) Page 12, Section B Loss function '...n=600...' This is on a lower range. Have you tested with n=1000?

      Yes, we have tested with n=1000 and observed no significant difference in the results. This indicates that n=600 is sufficient for the purposes of this study. 

      (25) Fig 8(c) why there are no CI shown?

      Thank you for your comment. CIs were not included in Fig. 8(c) due to degeneracy, which makes meaningful confidence intervals difficult to compute.

      (26) Page 12 Conclusion, sentence: '..gradients via backpropagation...' Actually, by making the function continuous, both forward and reverse mode might be used. And in this case, forward-mode would actually be the fastest by quite a margin

      Thank you for your insightful comment. You are correct that by making the function continuous, both forward-mode and reverse-mode automatic differentiation can be used. We have now mentioned this point in the discussion.

      (27) Overall comment for the Conclusion section: It would be good to discuss how this framework compares to other model-fitting frameworks for models with stochastic dynamics. The authors mention dynamic data and more discussion on this would be very welcomed. Why use ADAM and not something established like BFGS for model fitting? It would be interesting to discuss how this can fit with other SSA algorithms (e.g. in practice sorting SSA is used when models get larger). Also, inference comparison against exact approaches would be very nice. As it is now, the authors truly only check the accuracy of the SSA on 1 model -it would be interesting to see for other models.

      Thank you for your detailed comments. While this study focuses on introducing the DGA and demonstrating its feasibility, we agree that comparisons with other model-fitting frameworks, testing on additional models, and integrating with other SSA variants like sorted SSA are important directions for future work. Similarly, extending the DGA to handle transient dynamics and exploring alternatives to ADAM, such as BFGS, are promising areas to investigate further.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      We are grateful for the positive evaluation of the work and the critical points raised by the reviewers. We thank all reviewers for their excellent comments. We believe that these revisions have significantly improved the quality of our study.

      In response to the 2nd reviewer, we apologise for the missing data, we failed to provide a P-value of the RM ANOVA post-hoc test, we are very grateful that this was brought to our attention. We have revised the RM ANOVA by using the Tukey HSD post-hoc test, which is generally recommended for pairwise comparisons as it is more robust to unequal sample sizes. The controversial statistical analysis of the overall comparison of speed differences was deleted, as were three supplementary figures (Fig. S4, Fig. S9 and S10), which are less informative in support of the manuscript.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This study is useful as it provides further analysis of previously published data to address which specific genes are part of the masculinizing actions of E2 on female zebra finches, and where these key genes are expressed in the brain. However the data supporting the conclusion of masculinizing the song system are incomplete as the current manuscript is a re-analysis of differential gene expression modulated by E2 treatment between male/female zebra finches without manipulation of gene expression. The conclusions (and title) regarding song learning are also incompletely supported with no gene manipulation or song analysis. Importantly, the use of WGCNA for a question of sex-chromosome expression in species without dosage compensation is considered inadequate. As the experimental design did not include groups to directly test for song learning, and there was also no analysis of song performance, these data were also considered inadequate in that regard.

      We are sorry the editor felt the manuscript so incomplete and inadequate. Though the tone of this assessment seems more severe than the below reviewer comments, we are also happy to see that the editor has considered our paper further for a revised publication, based on the reviewer’s comments. We address the editor’s comments as follows:

      While we agree that manipulation of some of the genes we discovered, whose expression levels are E2-sensitive in the song system, would take the study further in validating some proposed hypothesis in the discussion of the paper, we don’t think the outcome of gene manipulations would change the major conclusions from the results of the paper. In this study we performed estrogen hormone manipulations, with causal consequences on gene expression in song nuclei and associated song behavior. In a way this is analogous to gene manipulations, but manipulating directly the action of estrogen. The categories of genes impacted, and the differences among the sex chromosomes wouldn’t change.

      For the comment on WGCNA being inadequate for addressing questions on sex chromosome expression in species without dosage compensation, we think the evidence in our data does not bear that out. One main result of this paper is the separation of Z chromosome transcripts whose expression is most strongly regulated by chromosomal dosage (WGCNA module E) across regions from those subject to additional sources of regulation in song nuclei (other modules). It seems to us that rather than being confounded by the lack of dosage compensation, WGCNA allowed us to better resolve the effects of dosage on different genes within the sex chromosomes. We have added a new figure more directly examining sex chromosome transcript abundance within different modules. Briefly, we found that module E assigned Z chromosome genes exhibited almost exactly the male-biased expression ratio expected from no dosage compensation while the Z chromosome genes in song nuclei assigned to other modules were expressed below the dosage predicted value, consistent with module E containing those genes whose expression are most strongly regulated by dose across all brain regions sampled.

      At its core, WGCNA finds sets of correlated genes. The biological reality of the zebra finch transcriptome is that Z chromosome expression is largely anti-correlated with W chromosome due to dosage. However, this dosage effect is not felt equally by all genes and WGCNA provides an unbiased computational framework which can be used to separate dose from other potential sources of gene regulation. This is why roughly ⅓ of Z chromosome genes are not assigned to module E; for example the growth hormone receptor is assigned to module G based on its correlation with genes upregulated within HVC.

      “As the experimental design did not include groups to directly test for song learning, and there was also no analysis of song performance, these data were also considered inadequate in that regard.”

      Concerning the comment on no analysis on song performance in the paper, all such analyses were conducted on our previous study on the same animals (Choe et al. 2021, Hormones & Behavior). The birds considered here were sacrificed at PHD30, prior to the onset of learned song behavior. However, females treated with E2 the same at the same time and allowed to mature into adulthood, went onto to develop rudimentary song. Further, induction of rudimentary song learning in females following E2 treatment has been well established since the early ‘80s. We have added the following text toward the end of the intro to make this more clear:

      “While the birds for this study were sacrificed prior to the developmental presentation of song behavior, we have previously shown that female finches treated in exactly the say way with E2 go on to produce rudimentary imitative songs as adults (Choe et al 2021), consistent with the known induction of vocal learning in females by E2 (REF).”

      Reviewer #1 (Recommendations For The Authors):

      Overall, this is a wonderfully designed and executed study that takes full advantage of new resources, such as the most complete zebra finch genome assembly yet, as well as the latest methods. I have very few suggestions as to the improvement of the manuscript. They are as follows:

      Results Section:

      In the paragraph "Identification of gene expression modules in song nuclei":

      "The E2-treated females in this study had similarly sized song system nuclei as males, indicating that E2 treatment prevented atrophy."

      Clarify if this comparison is to treated and/or untreated males.

      We thank the reviewer for their comment. The relative differences in the song nuclei sizes between the E2-treated females and the other groups is more complex that our original sentence implied. We have revised the main the text as follows

      “In our previous study, we found that estradiol treatment in PHD30 females caused HVC to enlarge and Area X to appear when it normally does not develop in females, but both at sizes less than in untreated or treated males.The sizes of PHD30 female LMAN RA were already the sizes as seen in males, as the later has not atrophied yet at this age(25).”

      In the paragraph "Sex- and micro-chromosome gene expression across the telencephalon": "These animal and chromosome specific shifts in the transcriptomes could represent the systemic effects of allelic chromosomal structural variation..."

      The authors should clarify the meaning of a"llelic chromosomal structural variation" in this context, as it is an unusual phrase. Major chromosomal structural variation seems unlikely to produce these effects. Is it also possible that animal-specific modules with brain-wide higher could also result from laboratory contamination between all samples from one animal? This is not too likely but perhaps should be acknowledged or ruled out.

      We have removed the word allelic, which was unnecessary. We can’t envision how laboratory contamination could occur such that all of one animal’s samples would be affected to produce the observed result which is module and chromosome specific. An animal wide effect could emerge during sacrifice, but we can think of no reason that would affect these modules and not others. Rather, the most likely explanation is biological natural difference between animals. We have added this consideration of alternative explanations.

      In the section "Candidate gene drivers of HVC specialization in E2-treated females":

      When discussing GHR's role in cell growth and proliferation, the authors' argument could be expanded by including the documented role of GH signaling in anti-apoptotic protection of neurons from rounds of neural pruning during development as documented in the chicken, e.g. • Harvey S, Baudet M-L, Sanders EJ. 2009. Growth Hormone-induced Neuroprotection in the Neural Retina during Chick Embryogenesis. Annals of the New York Academy of Sciences, 1163: 414-416. https://doi.org/10.1111/j.1749-6632.2008.03641.x

      We thank the reviewer for sharing this publication with us.. We have added the following sentence to our discussion with the above citation. “Further, our results are consistent with growth hormone’s known role in avian anti-apoptotic protection, with elevated signaling associated with the survival of chicken neurons during rounds of pruning in the developing

      retina.”

      The authors' argument of the relevance of the passerine GH duplication would be strengthened by citing:

      • Rasband SA, Bolton PE, Fang Q, Johnson PLF, Braun MJ. 2023. Evolution of the Growth Hormone Gene Duplication in Passerine Birds, Genome Biol Evol, 15(3) https://doi.org/10.1093/gbe/evad033. Greatly expands on the Yuri et al. paper cited by characterizing of the molecular evolution of these genes across hundreds of avian species, supporting positive selection on multiple amino acid sites identified in both ancestral and duplicate (passerine) growth hormone.

      • Xie F, London SE, Southey BR et al. 2010. The zebra finch neuropeptidome: prediction, detection and expression. BMC Biol 8, 28. https://doi.org/10.1186/1741-7007-8-28 The authors report significantly different expression of the ancestral GH gene in the adult male zebra finch auditory forebrain after different song exposure experiences.

      We have amended the results section sentence and added all suggested citations. The sentence now reads: “The gene which encodes growth hormone receptor’s ligand, growth hormone, is interestingly duplicated and undergoing accelerated evolution in the genomes of songbirds (Rasband et al 2023); the GH ligand has been found to be upregulated in the zebra finch auditory forebrain following the presentation of familiar song (Xie et al 2010).”

      Figures:

      - Figure 1B. "Duration of sex typing" being a shorter bar compared to the others is not fully explained in the experimental design. Presumably at the end of this time period, the sex is non-invasively, phenotypically evident. I suggest an arrow pointing to the PHD/PHD range when sex is apparent in plumage/anatomy.

      - Figure 4. Caption appears to be truncated; "across all... genes"?

      Fixed

      - Figure 5. For 5E, 5F, 5G, 5H, consider enlarging the plots so overlapping gene symbols are readable. Alternately, smaller numbers or symbols could be used with a key in areas where overlapping symbols are hard to prevent.

      We agree that these are not the easiest to read; we originally offset the symbols in R to minimize overlaps, but it can only do so much for the more crammed panels. We have now added a supplemental .xlsx file with the underlying data from each of the 4 tests for readers that want to examine the data in more detail.

      Reviewer #2 (Recommendations For The Authors):

      Since WGCNA methods will inherently draw together sex-chromosome genes into the same module in systems without dosage compensation, I suggest the authors rerun the WGCNA using only female samples and only male samples. Then identify the composition of modules that differ between E2 and vehicle-treated females and compare these genes to males. Then from male WGCNA identify the composition of modules that differ between E2 and vehicle-treated males and compare to female modules.

      We thank the reviewer for their suggestions. However, we believe it is not as strong as the approach we used, which is grouping data from both sexes in the WGCNA analyses in a study that is looking for sex differences. The reviewer's proposed approach amounts to computing modules twice (once per sex), determining song system specialized modules and E2 responsive modules in both settings, then intersecting the two sets to find corresponding modules, all done to prevent the non-dose compensated sex chromosome genes from being drawn into the same module.

      While WGCNA does group the majority of sex chromosome genes into module E, it does not categorize them all this way (Fig 3). The module classification instead differentiates those sex chromosome genes whose expression are most explained by chromosome dosage / sex across regions (modE) from those whose expression is controlled by other sources of regulation; for an example of the latter, the growth hormone receptor (GHR) is one of several Z chromosome genes classified into modG as its expression better correlates with the genes specialized to HVC than it does with the majority of dosage-dependent Z chromosome genes found in modE. Further, to remove biological sex as a variable in a WGCNA analysis that is focused on sex differences seems counterintuitive.

      Instead, to quantitatively address the reviewer’s concern, we conducted additional analyses, that led to an added new figure, associated text, and tables, that better describes sex/chromosome dosage effects on the abundance (FPKM) and expression ratios of sex chromosome transcripts by module irrespective of brain region (Fig. 5). We find that the Z chromosome genes in modE were expressed at the expected chromosome dosage in the non-vocal surrounding regions (65.06% observed vs 66.6% expected) while in other modules, other Z chromosome genes were expressed at intermediate levels between equal expression and the expected chromosomal dosage. For example, the Z chromosome content of modules D and H exhibited near equal expression between sexes. Within the song system, Z chromosome gene content of modG was highly expressed in males beyond what is expected from chromosome dosage, consistent with modG’s male-specific upregulation in song nuclei relative to surrounds in the absence of E2. These results better demonstrate that in our WGCNA on the combined dataset we are able to separate those Z chromosome genes whose expression is predominantly dosage controlled from those subject to additional regulation such as song system specialization.

      Fig. S3 Legend: 'Black arrow' -> 'Red arrow'

      Change made.

      Fig. S5 - What part of the figure shows the 'human convergent signature'? Also, simply listing the number of genes mapped to a chromosome is misleading to readers unfamiliar with the zebra finch genome, you should either provide the number of genes on each chromosome or present as corrected by that number.

      Fig. S5 was the same type of analyses in Fig. 3 but with an older zebra finch genome assembly, where we had not included the panel a for enrichments with genes convergent in expression between songbird song regions and humans speech brain regions. However, we see that Fig. S5 was not adding any new important information to the paper, so we removed it.

      For the chromosome analyses in Fig. 3b, we provide both the total raw number of module assigned genes broken down by chromosome (The black bar plots on the right) as well as a statistical fold-enrichment value of modules per chromosome. Given the number of genes per chromosome and genes per module in our data, we computed the fold-enrichment for each intersection (observed intersection size / expected intersection size). To test for the significance of these enrichments, we bootstrapped FDR corrected p values for the enrichment of each chromosome-module pairing by randomizing the mapping of genes to modules to construct a null distribution of fold enrichments for each intersection. Our intent was not to describe the size of the chromosomes themselves, information readily available elsewhere, but to show the disproportionate chromosomal origins of the gene sets considered by this study. Performing this enrichment test using all annotated genes per chromosome would artificially increase enrichment values and make the analysis less conservative by confounding the results with the inherent enrichment for “brain function” in the assigned genes relative to all genes.

      At several places you say "we correlated expression of each sex chromosome transcript with sexual dimorphism within each region, such that expressed W genes would be positively correlated and depleted Z chromosome genes would be anticorrelated." What was the sexual dimorphism that was being correlated with? Is this the eigengene?

      We thank you for this comment. Our language was less clear than it could be. We tested for correlations of both the eigengene and the individual gene expression profiles with the biological sex of the animals. We have changed the text to:

      “To do this, we tested for a correlation between the expression of each sex chromosome transcript to the animals’ sex within each brain region. We found that female-enriched transcripts were positively correlated with sex and male-enriched transcripts were anticorrelated (Fig. 4f,g).”

      Fig. 4A: The 'true/false' boxes and animal A-L is confusing and unnecessary. I'd suggest just using M and F (or sex symbols) with a horizontal line below each set of 3 for respective E2 and Veh.

      Change made.

      Reviewer #3 (Recommendations For The Authors):

      General comments:

      After the initial characterization of the datasets and module identification, it is quite hard to follow the logic of the data presentation in the various other Results sections or to clearly understand how they relate to the main stated goal to identify factors related to sex differences in vocal learning. The most relevant findings relate to the presumed actions of hormone treatment and sex chromosome gene dosage in song nuclei, whereas analyses of other brain areas, other chromosomes, or speech-related genes serve more as controls and/or appear as distractions from the main theme. A suggestion to increase the clarity of the presentation and potential impact of the study is to change the order of the presentation, focusing first on the specific analyses and comparisons that most directly speak to the main goals of the study, and then secondarily and more briefly presenting the controls or less related comparisons.

      The reviewer’s suggestion for the results section organization is exactly what we had tried to do. We opened the first paragraph on identification of modules, then presented the song nuclei specific modules, followed by E2-changes to those modules; and the followed by other specific results for the remainder of the paper, including module enrichments to specific chromosomes. The reviewer mentioned our analyses of “other brain areas” (which we assume to mean the non-vocal surround regions), other chromosomes (which we assume means autosomes) and speech-related genes as controls were a distraction in the paper; but within our analysis, these other brain regions are essential controls needed to assess the song-system specificity of any observed sex differences observed from the very first paragraphs of the results; the autosomes were not controls for sex chromosome results, but primary results in of themselves; the overlap with speech-related genes was also not a control, but a novel discovery. We have revised these points in the paper to make them clearer, and revised some of the section titles and transitions between sections to help increase clarity of the main storyline of the paper.

      A related comment is that many of the inferences drawn from the WGCNA analysis were quite complex, thus independent verification of some predictions would be quite valuable. For example, consider the passage: "In non-vocal learning juvenile females, interestingly LMAN was specialized relative to the AN by the same gene modules as in males (B, F, and I) as well as an additional module G (Fig. 2b); RA was specialized by module A as in males, but not module L and by additional modules A and G. In contrast, neither juvenile female HVC nor Area X exhibited significant gene module expression specializations relative to their surrounds." Providing in situ hybridization verification of these regional gene expression predictions with a few representative genes seems quite feasible given the group's expertise and would considerably strengthen confidence in the module-based inferences.

      We performed in-situ independent validation of 36 candidate genes in our first study with this dataset (Choe et al 2021). We now mention this validation in the revised paper. The reviewer’s selection of one of our sentences though made us realize that our grammar used to explain the results was not as clear as it needs to be. We thus cleaned up the grammar of our module descriptions so that it should be communicated with less complexity, the main issue noted by the reviewer.

      Because this is a re-analysis of a previously published dataset, the authors should more explicitly describe somewhere in the Discussion how the present analysis advances the understanding of sex differences in songbird neuroanatomy and behavior beyond the previous analysis.

      We have added an additional sentence into the discussion more clearly separating the results of the current study from our previous work.

      Specific comments:

      Abstract:

      There is evidence (from Frank Johnson's lab) that RA does not completely atrophy in female zebra finches, but is still present with more preserved connectivity than previously thought, possibly related to non-singing function(s). A term like 'marked reduction' of female RA may more accurately reflect the current state of knowledge.

      We have changed the text to “partial atrophy”.

      The term "driver" is undefined and unclear at this point of the paper; a clear definition for "driver" is also lacking in the Intro.

      We now define “driver” or “genetic driver” as understood to mean “a genetic locus whose expression and/or inheritance strongly regulates the trait of interest”.

      When citing the literature on studies that identified "specific genes with specialized up- or down-regulated expression in song and speech circuits relative to the surrounding motor control circuits", the authors should also cite studies from other labs (e.g. Li et al., PNAS, 2007; Lovell et al, Plos One 2008; Lovell et al, BMC Genomics 2018; Nevue et al, Sci Rep. 2020), to be accurate and fair.

      Citations added

      For clarity, the authors should explicitly formulate the hypothesis they are proposing at the end of the Summary.

      We thank the reviewer for this comment. We have replaced the final sentence of the summary with: “We present a hypothesis where reduced dosage and expression of these Z chromosome genes changes the developmental trajectory of female HVC, partially preventable by estrogen treatment, contributing to the loss of song learning behavior.”

      Introduction:

      Vocal learning is arguably the ability to imitate 'vocal' sounds, this could be clarified here.

      We have amended the sentence to “Vocal learning is the ability to imitate heard sounds using a vocal organ…”

      Given they are currently considered sister taxa, can the author briefly explain what is the basis for assuming that songbirds and parrots independently evolved vocal learning?

      Although songbirds and parrots belong to a monophyletic clade, they are not sister taxa. There are two clades separating them that are vocal non-learners. We have cited the reference that demonstrated this (e.g. Jarvis et al 2014 Science).

      Why use Taeniopygia castanotis rather than the more broadly used Taeniopygia guttata?

      Zebra finches were recently reclassified and T.castanotis is now more accurate. The Indonesian Timor zebra finch retained T.guttata while the Australian finch, used here, was classified as T.castanotis.

      The authors state: "...vocal learning is strongly sexually dimorphic in zebra finches and many other vocal learning species" and cite Nottebohm and Arnold, Science, 1978. That landmark paper only shows dimorphism in song nuclei (not learning) in two songbird species. The authors should provide citations for other species and behavior, or modify the statement.

      We have added an additional citation (Odom et al.) to this sentence which covers the phylogeny more broadly.

      The authors refer to the nucleus RA as being located in the lateral intermediate arcopallium (LAI). Other labs have described this domain as the dorsal part of the intermediate arcopallium, thus AId or AID (Mello et al., JCN, 2019; Yuan and Bottjer, J Neurophys 2019; Yuan and Bottjer, eNeuro, 2020; Nevue et al., BCM Genomics, 2020). The authors should acknowledge this discrepancy in nomenclature so that data and conclusions can be more readily compared across studies.

      We thank the reviewer and agree that this is helpful. We have added a note at the first mention of LAI.

      The authors state that data from the gynandromorph bird described by Agate et al implicates "sex chromosome gene expression within the song system" as involved in the song system sexual dimorphism. That study, however, only rules out circulating gonadal steroids, and while suggesting a cell-autonomous mechanism like sex chromosome genes, it does not necessarily exclude other brain-autonomous factors like sex differences in local production of sex steroids.

      We say that this study “implicated” sex chromosome gene expression, which is accurate per the results and discussion of that study. We are unsure what “brain autonomous factors like sex differences in local production of sex steroids” means?. “Brain autonomous” and “local production” in the brain seem contradictory in this context?

      Results:

      The authors state that "the E2-treated females in this study had similarly sized song system nuclei as males, indicating that E2 treatment prevented atrophy". Can they clarify whether the VEH-treated females actually had smaller RAs than E2-treated females or VEH-treated males at this age? This is still quite early in development and it is unclear to what extent RA's marked sexual dimorphism in adults or later developmental ages has already taken place in untreated (or VEH-treated) birds. A related comment is that the authors state later on: "We interpret these findings to indicate that: LMAN and RA atrophy later in juvenile female development..." Does this mean these nuclei actually did not show the marked decreases predicted earlier in the text? Clarifying this point would be helpful.

      We thank the reviewer for pointing out this discrepancy, which reviewer #1 asked for clarification as well. RA size at this age is similar in males and females. However, HVC and Area X is smaller and absent respectively in females and E2 treatment partially prevents this atrophy. The text now reads:

      “In our previous study, we found that estradiol treatment in PHD30 females caused HVC to enlarge and Area X to appear when it normally does not develop in females, but both at sizes less than in untreated or treated males.The sizes of PHD30 female LMAN RA were already the sizes as seen in males, as the later has not atrophied yet at this age(25).”

      The authors acknowledge that area X is absent in untreated and VEH-treated females. Could they please clarify how area X and the surrounding stratal tissue that excludes area X were identified for laser capture dissections in juvenile females?

      We have added the following statement to the main text portion discussing the dissections.

      “In the case of vehicle-treated females which lack Area X, a piece of striatum from the same location of where Area X is found in males was taken. “

      Some passages in Results discussing the authors' interpretation of the modules seem quite speculative and possibly belong instead in the Discussion. For example: "... that module A and G genes could be associated with the start of this atrophy; HVC and Area X are likely the first to atrophy or not develop; and lack of any gene module specialization in them at this age could mean that they would be more sensitive to estrogen prevention of vocal learning loss."

      As suggested, we have removed this text from the results; these ideas were already presented in the Discussion. We have merged the resulting small paragraph with the preceding paragraph.

      The authors state: "To assess the effects of chronic exogenous estrogen on the developing song system, we first performed a control analysis of modules in the E2-treated juvenile males." How can an assessment of estrogen effects be a "control" analysis? Does this refer to a contrast with females? Please clarify the language here.

      The reviewer is correct, that E2 treatment in males should not be considered a control experiment. We removed the word “control”.

      When discussing the GO-enriched terms for module G, it is unclear how the authors reached the conclusion about "proliferative", as the enriched terms do not refer to processes more directly indicative of proliferation like "cell division" or "cell cycle regulation". Rather, these terms seem more related to differentiation and growth, which do not necessarily imply proliferation. The authors also refer to "HVC proliferation" later on in the Discussion. However, there is conclusive evidence from several labs that proliferative events associated with postnatal neuronal addition and/or replacement in song nuclei occur in the subventricular zone, not in song nuclei like HVC itself, and that the growth of song nuclei largely reflects cell survival, as well as growth in size and complexity under the regulation of sex steroids.

      We agree that “proliferative” may have been a poor word choice here. We did not mean to indicate that cell division was occuring in HVC itself. Instead we meant to indicate that HVC is able to accommodate the new born neurons from the SVZ. We have replaced the word “proliferative” throughout. In the instance the reviewer mentions specifically we replaced it with,“...potentially act to integrate and differentiate late born neurons.”

      With regard to module E, referring to a telencephalon-wide sexually dimorphic gene expression program seems quite a stretch, given that only a few regions were sampled and compared between sexes. These related statements should be toned down.

      We have replaced “telencephalon-wide” with “more distributed across the finch telencephalon” and other similar language in each instance.

      The following passage is very speculative and should shortened and/or moved to the Discussion: "Based on the findings in these gene sets, we hypothesize that without excess estrogen in females, HVC expansion is prevented by not specializing the growth and neuronal migration promoting genes in module G to the HVC lineage by late development. This is potentially enacted by depleting necessary gene products from the Z sex chromosome, such as GHR, which are already present in only one copy."

      We have deleted this portion of the text, as the idea is already present in the discussion.

      Figure 5: To this reviewer, the comparisons of sex differences and of female response to E2 are the most relevant and informative ones, whereas the regional differences between song nuclei and surrounds refer to different cell populations and cell types where other processes may be occurring, independently of what occurs in song nuclei. It thus seems like the intersection analysis in panel 5i may be subtracting out important "core genes" in terms of E2 effects and/or sex differences in the most relevant cell populations, i.e. in this case within song nucleus HVC.

      Song learning and the vocal learning brain regions are specialized behaviors and associated nuclei which have a set of hundreds of specialized genes compared to the surrounds. Our previous findings shows that E2 drives the appearance of these specializations in female zebra finches. Thus, we considered this the most interesting question to focus on, which we have further highlighted. Nevertheless, in response to the reviewers suggestion, we have added a .xlsx supplemental file containing the results from each of the individual tests so readers may examine any single comparison, or set of comparisons, in more detail.

      Discussion:

      It is unclear what the term "critical period" refers to in: "during the critical period of atrophy for the female vocal circuit"; please clarify.

      We agree that our language was nebulous. We have replaced it with “as several male song control nuclei begin to expand and female nuclei partially atrophy”

      In: "HVC appeared unspecialized at the level of gene module expression in control females", does "unspecialized" refer to a lack of difference in gene expression when compared to surroundings? Please clarify. The same comment applies to other uses of "unspecialized" in this paragraph.

      Yes, unspecialized means lack of difference in gene expression in the song nucleus. To clarify this point, we have reworked that and the following sentence as follows:

      “HVC appeared unspecialized compared to the surrounding nidopallium at the level of gene module expression in control females, with no significantly differentially expressed MEGs . However, in E2-treated females, HVC exhibited a subset of the observed male HVC gene expression specializations. Similarly, the vehicle-treated female striatum located where Area X would be also lacked any specialized gene module expression, but the E2-treated female Area X exhibited a subset of the male Area X specializations, consistent with the known absence of Area X in vehicle-treated females and presence in E2-treated females.”

      The authors state: "...we surprisingly found that the most specialized genes were disproportionately from the Z chromosome", when discussing module G in HVC. Why is this so surprising? In a sense, this could be taken as consistent with the findings of Friedrich et al, 2022, where sex differences in the RA transcriptome were predominantly Z related on 20 dph. Arguably 20 dph is still quite close to 30 dph in the present study, when compared to 50 dph in Friedrich et al, when autosomes predominate.

      Our bioRxiv was originally posted in July 2021, prior to the publication of Friedrich et al, 2022; however we had previously added to our discussion that several of our results are consistent with the observations of Friedrich et al..

      We have a different interpretation of Z chromosome gene results in Friedrich et al.. While the percentage of specialized genes from the Z chromosome decreased, the absolute number of specialized Z chromosome genes actually increased over this interval. In Fig. 3a from Friedrich et al. it appears that ~28% of Z chromosome genes were sexually dimorphic in their expression in RA at PHD20 but that ~39% of Z chromosome genes were similarly dimorphic at PHD50. We interpret this result as the Z chromosome genes being among the earliest genes differentially expressed between the sexes, not that their differential expression or role ever subsequently decreased. We have reworked this portion of the discussion to make our point more clear:

      “This model of sex chromosome influenced song system development is consistent with recent observations comparing male and female zebra finch transcriptomes from RA at young juvenile (PHD20) and young adult (PHD50) ages in un-manipulated birds (Friedrich et al. 2022)57. While that study proposes that the role of the sex chromosome in maintaining transcriptomic sex differences diminishes across development, as the proportion of specialized genes that originate on the sex chromosomes diminishes, this effect was driven by large increases in differentially expressed autosomal genes rather than by any reduction in sex chromosome dimorphism; the percentage of differentially expressed Z chromosome genes increased from PHD20 (28%) to PHD50 (39%) (Friedrich et al). This leads us to conclude that sexually dimorphic Z chromosome expression at juvenile ages precedes the sexually dimorphic expression of the autosomes seen in adults. This is consistent with our hypothesis that sufficient expression of select Z chromosome gene products (GHR, etc..) is necessary for subsequent autosomal song system specializations (modG).”

      Further, when we write ”When examining the module G HVC specialization induced by E2-treatment in female HVC, we surprisingly found that the most specialized genes were disproportionately from the Z chromosome” we are referring to the upregulation of module G by E2 in female HVC, not the sex difference described in RA by Friedrich et al. which only utilized un-treated RA samples and thus is more likely related to our observations of module E.

      The term "sexual dimorphism" has been more traditionally used for sex differences that are very marked, like features that are highly regressed or absent in one sex, most often in females. Quantitative differences in gene expression, including dosage differences like those related to module E, are more appropriately described as sex differences rather than dimorphisms. That usage would be more consistent with most of the literature, and thus preferable.

      We did a google search for common definitions, and found more the opposite. Sexual dimorphism being used more often as differences of degree (with the zebra finch example as one of the top hits), and sex differences being used often as more absolute differences (like presence vs absence of the Y chromosome). Further, as in the reviewer’s first sentence, the definition of sexual dimorphism is a sex difference. That is, the two phrases can be interchangeable. Thus, we prefer to keep sexual dimorphism.

      Several references are incomplete or seem truncated, like 9 and 10.

      Fixed

      Table S2: Please examine and take into account the W gene curation presented in Table S3 of Friedrich et al., 2022.

      We have added additional supplementals (supplemetal_w_chrom_express.csv and supplemetal_z_chrom_express.csv) of the data provided in new Fig 5 incorporating the curation information from Table S3 from Friedrich et al.

      Data availability:

      Genes for all the main modules identified should be presented in a Supplemental Table, or through a link to a stable data repository.

      We have added an additional Supplemental Table supplemental_gene_module_assignment.csv with this information.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews

      Reviewer #1 (Public Review):

      Summary:

      The authors have created a system for designing and running experimental pipelines to control and coordinate different programs and devices during an experiment, called Heron. Heron is based around a graphical tool for creating a Knowledge Graph made up of nodes connected by edges, with each node representing a separate Python script, and each edge being a communication pathway connecting a specific output from one node to an iput on another. Each node also has parameters that can be set by the user during setup and runtime, and all of this behavior is concisely specified in the code that defines each node. This tool tries to marry the ease of use, clarity, and selfdocumentation of a purely graphical system like Bonsai with the flexibility and power of a purely code-based system like Robot Operating System (ROS).

      Strengths:

      The underlying idea behind Heron, of combining a graphical design and execution tool with nodes that are made as straightforward Python scripts seems like a great way to get the relative strengths of each approach. The graphical design side is clear, selfexplanatory, and self-documenting, as described in the paper. The underlying code for each node tends to also be relatively simple and straightforward, with a lot of the complex communication architecture successfully abstracted away from the user. This makes it easy to develop new nodes, without needing to understand the underlying communications between them. The authors also provide useful and well-documented templates for each type of node to further facilitate this process. Overall this seems like it could be a great tool for designing and running a wide variety of experiments, without requiring too much advanced technical knowledge from the users.

      The system was relatively easy to download and get running, following the directions and already has a significant amount of documentation available to explain how to use it and expand its capabilities. Heron has also been built from the ground up to easily incorporate nodes stored in separate Git repositories and to thus become a large community-driven platform, with different nodes written and shared by different groups. This gives Heron a wide scope for future utility and usefulness, as more groups use it, write new nodes, and share them with the community. With any system of this sort, the overall strength of the system is thus somewhat dependent on how widely it is used and contributed to, but the authors did a good job of making this easy and accessible for people who are interested. I could certainly see Heron growing into a versatile and popular system for designing and running many types of experiments.

      Weaknesses:

      (1) The number one thing that was missing from the paper was any kind of quantification of the performance of Heron in different circumstances. Several useful and illustrative examples were discussed in depth to show the strengths and flexibility of Heron, but there was no discussion or quantification of performance, timing, or latency for any of these examples. These seem like very important metrics to measure and discuss when creating a new experimental system.

      Heron is practically a thin layer of obfuscation of signal passing across processes. Given its design approach it is up to the code of each Node to deal with issues of timing, synching and latency and thus up to each user to make sure the Nodes they author fulfil their experimental requirements. Having said that, Heron provides a large number of tools to allow users to optimise the generated Knowledge Graphs for their use cases. To showcase these tools, we have expanded on the third experimental example in the paper with three extra sections, two of which relate to Heron’s performance and synching capabilities. One is focusing on Heron’s CPU load requirements (and existing Heron tools to keep those at acceptable limits) and another focusing on post experiment synchronisation of all the different data sets a multi Node experiment generates.   

      (2) After downloading and running Heron with some basic test Nodes, I noticed that many of the nodes were each using a full CPU core on their own. Given that this basic test experiment was just waiting for a keypress, triggering a random number generator, and displaying the result, I was quite surprised to see over 50% of my 8-core CPU fully utilized. I don’t think that Heron needs to be perfectly efficient to accomplish its intended purpose, but I do think that some level of efficiency is required. Some optimization of the codebase should be done so that basic tests like this can run with minimal CPU utilization. This would then inspire confidence that Heron could deal with a real experiment that was significantly more complex without running out of CPU power and thus slowing down.

      The original Heron allowed the OS to choose how to manage resources over the required process. We were aware that this could lead to significant use of CPU time, as well as occasionally significant drop of packets (which was dependent on the OS and its configuration). This drop happened mainly when the Node was running a secondary process (like in the Unity game process in the 3rd example). To mitigate these problems, we have now implemented a feature allowing the user to choose the CPU that each Node’s worker function runs on as well as any extra processes the worker process initialises. This is accessible from the Saving secondary window of the node. This stops the OS from swapping processes between CPUs and eliminates the dropping of packages due to the OS behaviour. It also significantly reduces the utilised CPU time. To showcase this, we initially run the simple example mentioned by the reviewer. The computer running only background services was using 8% of CPU (8 cores). With Heron GUI running but with no active Graph, the CPU usage went to 15%. With the Graph running and Heron’s processes running on OS attributed CPU cores, the total CPU was at 65% (so very close to the reviewer’s 50%). By choosing a different CPU core for each of the three worker processes the CPU went down to 47% and finally when all processes were forced to run on the same CPU core the CPU load dropped to 30%.  So, Heron in its current implementation running its GUI and 3 Nodes takes 22% of CPU load. This is still not ideal but is a consequence of the overhead of running multiple processes vs multiple threads. We believe that, given Heron’s latest optimisation, offering more control of system management to the user, the benefits of multi process applications outweigh this hit in system resources. 

      We have also increased the scope of the third example we provide in the paper and there we describe in detail how a full-scale experiment with 15 Nodes (which is the upper limit of number of Nodes usually required in most experiments) impacts CPU load. 

      Finally, we have added on Heron’s roadmap projects extra tasks focusing only on optimisation (profiling and using Numba for the time critical parts of the Heron code).

      (3) I was also surprised to see that, despite being meant specifically to run on and connect diverse types of computer operating systems and being written purely in Python, the Heron Editor and GUI must be run on Windows. This seems like an unfortunate and unnecessary restriction, and it would be great to see the codebase adjusted to make it fully crossplatform-compatible.

      This point was also mentioned by reviewer 2. This was a mistake on our part and has now been corrected in the paper. Heron (GUI and underlying communication functionality) can run on any machine that the underlying python libraries run, which is Windows, Linux (both for x86 and Arm architectures) and MacOS. We have tested it on Windows (10 and 11, both x64), Linux PC (Ubuntu 20.04.6, x64) and Raspberry Pi 4 (Debian GNU/Linux 12 (bookworm), aarch64). The Windows and Linux versions of Heron have undergone extensive debugging and all of the available Nodes (that are not OS specific) run on those two systems. We are in the process of debugging the Nodes’ functionality for RasPi. The MacOS version, although functional requires further work to make sure all of the basic Nodes are functional (which is not the case at the moment). We have also updated our manuscript (Multiple machines, operating systems and environments) to include the above information. 

      (4) Lastly, when I was running test experiments, sometimes one of the nodes, or part of the Heron editor itself would throw an exception or otherwise crash. Sometimes this left the Heron editor in a zombie state where some aspects of the GUI were responsive and others were not. It would be good to see a more graceful full shutdown of the program when part of it crashes or throws an exception, especially as this is likely to be common as people learn to use it. More problematically, in some of these cases, after closing or force quitting Heron, the TCP ports were not properly relinquished, and thus restarting Heron would run into an "address in use" error. Finding and killing the processes that were still using the ports is not something that is obvious, especially to a beginner, and it would be great to see Heron deal with this better. Ideally, code would be introduced to carefully avoid leaving ports occupied during a hard shutdown, and furthermore, when the address in use error comes up, it would be great to give the user some idea of what to do about it.

      A lot of effort has been put into Heron to achieve graceful shut down of processes, especially when these run on different machines that do not know when the GUI process has closed. The code that is being suggested to avoid leaving ports open has been implemented and this works properly when processes do not crash (Heron is terminated by the user) and almost always when there is a bug in a process that forces it to crash. In the version of Heron available during the reviewing process there were bugs that caused the above behaviour (Node code hanging and leaving zombie processes) on MacOS systems. These have now been fixed. There are very seldom instances though, especially during Node development, that crashing processes will hang and need to be terminated manually. We have taken on board the reviewer’s comments that users should be made more aware of these issues and have also described this situation in the Debugging part of Heron’s documentation. There we explain the logging and other tools Heron provides to help users debug their own Nodes and how to deal with hanging processes.

      Heron is still in alpha (usable but with bugs) and the best way to debug it and iron out all the bugs in all use cases is through usage from multiple users and error reporting (we would be grateful if the errors the reviewer mentions could be reported in Heron’s github Issues page). We are always addressing and closing any reported errors, since this is the only way for Heron to transition from alpha to beta and eventually to production code quality.

      Overall I think that, with these improvements, this could be the beginning of a powerful and versatile new system that would enable flexible experiment design with a relatively low technical barrier to entry. I could see this system being useful to many different labs and fields. 

      We thank the reviewer for positive and supportive words and for the constructive feedbacks. We believe we have now addressed all the raised concerns.  

      Reviewer #2 (Public Review):

      Summary:

      The authors provide an open-source graphic user interface (GUI) called Heron, implemented in Python, that is designed to help experimentalists to

      (1) design experimental pipelines and implement them in a way that is closely aligned with their mental schemata of the experiments,

      (2) execute and control the experimental pipelines with numerous interconnected hardware and software on a network.

      The former is achieved by representing an experimental pipeline using a Knowledge Graph and visually representing this graph in the GUI. The latter is accomplished by using an actor model to govern the interaction among interconnected nodes through messaging, implemented using ZeroMQ. The nodes themselves execute user-supplied code in, but not limited to, Python.

      Using three showcases of behavioral experiments on rats, the authors highlighted three benefits of their software design:

      (1) the knowledge graph serves as a self-documentation of the logic of the experiment, enhancing the readability and reproducibility of the experiment,

      (2) the experiment can be executed in a distributed fashion across multiple machines that each has a different operating system or computing environment, such that the experiment can take advantage of hardware that sometimes can only work on a specific computer/OS, a commonly seen issue nowadays,

      (3) he users supply their own Python code for node execution that is supposed to be more friendly to those who do not have a strong programming background.

      Strengths:

      (1) The software is light-weight and open-source, provides a clean and easy-to-use GUI,

      (2) The software answers the need of experimentalists, particularly in the field of behavioral science, to deal with the diversity of hardware that becomes restricted to run on dedicated systems.

      (3) The software has a solid design that seems to be functionally reliable and useful under many conditions, demonstrated by a number of sophisticated experimental setups.

      (4) The software is well documented. The authors pay special attention to documenting the usage of the software and setting up experiments using this software.

      Weaknesses:

      (1) While the software implementation is solid and has proven effective in designing the experiment showcased in the paper, the novelty of the design is not made clear in the manuscript. Conceptually, both the use of graphs and visual experimental flow design have been key features in many widely used softwares as suggested in the background section of the manuscript. In particular, contrary to the authors’ claim that only pre-defined elements can be used in Simulink or LabView, Simulink introduced MATLAB Function Block back in 2011, and Python code can be used in LabView since 2018. Such customization of nodes is akin to what the authors presented.

      In the Heron manuscript we have provided an extensive literature review of existing systems from which Heron has borrowed ideas. We never wished to say that graphs and visual code is what sets Heron apart since these are technologies predating Heron by many years and implemented by a large number of software. We do not believe also that we have mentioned that LabView or Simulink can utilise only predefined nodes. What we have said is that in such systems (like LabView, Simulink and Bonsai) the focus of the architecture is on prespecified low level elements while the ability for users to author their own is there but only as an afterthought. The difference with Heron is that in the latter the focus is on the users developing their own elements. One could think of LabView style software as node-based languages (with low level visual elements like loops and variables) that also allow extra scripting while Heron is a graphical wrapper around python where nodes are graphical representations of whole processes. To our knowledge there is no other software that allows the very fast generation of graphical elements representing whole processes whose communication can also be defined graphically. Apart from this distinction, Heron also allows a graphical approach to writing code for processes that span different machines which again to our knowledge is a novelty of our approach and one of its strongest points towards ease of experimental pipeline creation (without sacrificing expressivity). 

      (2) The authors claim that the knowledge graph can be considered as a self-documentation of an experiment. I found it to be true to some extent. Conceptually it’s a welcoming feature and the fact that the same visualization of the knowledge graph can be used to run and control experiments is highly desirable (but see point 1 about novelty). However, I found it largely inadequate for a person to understand an experiment from the knowledge graph as visualized in the GUI alone. While the information flow is clear, and it seems easier to navigate a codebase for an experiment using this method, the design of the GUI does not make it a one-stop place to understand the experiment. Take the Knowledge Graph in Supplementary Figure 2B as an example, it is associated with the first showcase in the result section highlighting this self-documentation capability. I can see what the basic flow is through the disjoint graph where 1) one needs to press a key to start a trial, and 2) camera frames are saved into an avi file presumably using FFMPEG. Unfortunately, it is not clear what the parameters are and what each block is trying to accomplish without the explanation from the authors in the main text. Neither is it clear about what the experiment protocol is without the help of Supplementary Figure 2A.

      In my opinion, text/figures are still key to documenting an experiment, including its goals and protocols, but the authors could take advantage of the fact that they are designing a GUI where this information, with properly designed API, could be easily displayed, perhaps through user interaction. For example, in Local Network -> Edit IPs/ports in the GUI configuration, there is a good tooltip displaying additional information for the "password" entry. The GUI for the knowledge graph nodes can very well utilize these tooltips to show additional information about the meaning of the parameters, what a node does, etc, if the API also enforces users to provide this information in the form of, e.g., Python docstrings in their node template. Similarly, this can be applied to edges to make it clear what messages/data are communicated between the nodes. This could greatly enhance the representation of the experiment from the Knowledge graph.

      In the first showcase example in the paper “Probabilistic reversal learning.

      Implementation as self-documentation” we go through the steps that one would follow in order to understand the functionality of an experiment through Heron’s Knowledge Graph. The Graph is not just the visual representation of the Nodes in the GUI but also their corresponding code bases. We mention that the way Heron’s API limits the way a Node’s code is constructed (through an Actor based paradigm) allows for experimenters to easily go to the code base of a specific Node and understand its 2 functions (initialisation and worker) without getting bogged down in the code base of the whole Graph (since these two functions never call code from any other Nodes). Newer versions of Heron facilitate this easy access to the appropriate code by also allowing users to attach to Heron their favourite IDE and open in it any Node’s two scripts (worker and com) when they double click on the Node in Heron’s GUI. On top of this, Heron now (in the versions developed as answers to the reviewers’ comments) allows Node creators to add extensive comments on a Node but also separate comments on the Node’s parameters and input and output ports. Those can be seen as tooltips when one hovers over the Node (a feature that can be turned off or on by the Info button on every Node).  

      As Heron stands at the moment we have not made the claim that the Heron GUI is the full picture in the self-documentation of a Graph. We take note though the reviewer’s desire to have the GUI be the only tool a user would need to use to understand an experimental implementation. The solution to this is the same as the one described by the reviewer of using the GUI to show the user the parts of the code relevant to a specific Node without the user having to go to a separate IDE or code editor. The reason this has not been implemented yet is the lack of a text editor widget in the underlying gui library (DearPyGUI). This is in their roadmap for their next large release and when this exists we will use it to implement exactly the idea the reviewer is suggesting, but also with the capability to not only read comments and code but also directly edit a Node’s code (see Heron’s roadmap). Heron’s API at the moment is ideal for providing such a text editor straight from the GUI.

      (3) The design of Heron was primarily with behavioral experiments in mind, in which highly accurate timing is not a strong requirement. Experiments in some other areas that this software is also hoping to expand to, for example, electrophysiology, may need very strong synchronization between apparatus, for example, the record timing and stimulus delivery should be synced. The communication mechanism implemented in Heron is asynchronous, as I understand it, and the code for each node is executed once upon receiving an event at one or more of its inputs. The paper, however, does not include a discussion, or example, about how Heron could be used to address issues that could arise in this type of communication. There is also a lack of information about, for example, how nodes handle inputs when their ability to execute their work function cannot keep up with the frequency of input events. Does the publication/subscription handle the queue intrinsically? Will it create problems in real-time experiments that make multiple nodes run out of sync? The reader could benefit from a discussion about this if they already exist, and if not, the software could benefit from implementing additional mechanisms such that it can meet the requirements from more types of experiments.

      In order to address the above lack of explanation (that also the first reviewer pointed out) we expanded the third experimental example in the paper with three more sections. One focuses solely on explaining how in this example (which acquires and saves large amounts of data from separate Nodes running on different machines) one would be able to time align the different data packets generated in different Nodes to each other. The techniques described there are directly implementable on experiments where the requirements of synching are more stringent than the behavioural experiment we showcase (like in ephys experiments). 

      Regarding what happens to packages when the worker function of a Node is too slow to handle its traffic, this is mentioned in the paper (Code architecture paragraph): “Heron is designed to have no message buffering, thus automatically dropping any messages that come into a Node’s inputs while the Node’s worker function is still running.” This is also explained in more detail in Heron’s documentation. The reasoning for a no buffer system (as described in the documentation) is that for the use cases Heron is designed to handle we believe there is no situation where a Node would receive large amounts of data in bursts while very little data during the rest of the time (in which case a buffer would make sense). Nodes in most experiments will either be data intensive but with a constant or near constant data receiving speed (e.g. input from a camera or ephys system) or will have variable data load reception but always with small data loads (e.g. buttons). The second case is not an issue and the first case cannot be dealt with a buffer but with the appropriate code design, since buffering data coming in a Node too slow for its input will just postpone the inevitable crash. Heron’s architecture principle in this case is to allow these ‘mistakes’ (i.e. package dropping) to happen so that the pipeline continues to run and transfer the responsibility of making Nodes fast enough to the author of each Node. At the same time Heron provides tools (see the Debugging section of the documentation and the time alignment paragraph of the “Rats playing computer games”  example in the manuscript) that make it easy to detect package drops and either correct them or allow them but also allow time alignment between incoming and outgoing packets. In the very rare case where a buffer is required Heron’s do-it-yourself logic makes it easy for a Node developer to implement their own Node specific buffer.

      (4) The authors mentioned in "Heron GUI’s multiple uses" that the GUI can be used as an experimental control panel where the user can update the parameters of the different Nodes on the fly. This is a very useful feature, but it was not demonstrated in the three showcases. A demonstration could greatly help to support this claim.

      As the reviewer mentions, we have found Heron’s GUI double role also as an experimental on-line controller a very useful capability during our experiments. We have expanded the last experimental example to also showcase this by showing how on the “Rats playing computer games” experiment we used the parameters of two Nodes to change the arena’s behaviour while the experiment was running, depending on how the subject was behaving at the time (thus exploring a much larger set of parameter combinations, faster during exploratory periods of our shaping protocols construction). 

      (5) The API for node scripts can benefit from having a better structure as well as having additional utilities to help users navigate the requirements, and provide more guidance to users in creating new nodes. A more standard practice in the field is to create three abstract Python classes, Source, Sink, and Transform that dictate the requirements for initialisation, work_function, and on_end_of_life, and provide additional utility methods to help users connect between their code and the communication mechanism. They can be properly docstringed, along with templates. In this way, the com and worker scripts can be merged into a single unified API. A simple example that can cause confusion in the worker script is the "worker_object", which is passed into the initialise function. It is unclear what this object this variable should be, and what attributes are available without looking into the source code. As the software is also targeting those who are less experienced in programming, setting up more guidance in the API can be really helpful. In addition, the self-documentation aspect of the GUI can also benefit from a better structured API as discussed in point 2 above.

      The reviewer is right that using abstract classes to expose to users the required API would be a more standard practice. The reason we did not choose to do this was to keep Heron easily accessible to entry level Python programmers who do not have familiarity yet with object oriented programming ideas. So instead of providing abstract classes we expose only the implementation of three functions which are part of the worker classes but the classes themselves are not seen by the users of the API. The point about the users’ accessibility to more information regarding a few objects used in the API (the worker object for example) has been taken on board and we have now addressed this by type hinting all these objects both in the templates and more importantly in the automatically generated code that Heron now creates when a user chooses to create a Node graphically (a feature of Heron not present in the version available in the initial submission of this manuscript).  

      (6) The authors should provide more pre-defined elements. Even though the ability for users to run arbitrary code is the main feature, the initial adoption of a codebase by a community, in which many members are not so experienced with programming, is the ability for them to use off-the-shelf components as much as possible. I believe the software could benefit from a suite of commonly used Nodes.

      There are currently 12 Node repositories in the Heron-repositories project on Github with more than 30 Nodes, 20 of which are general use (not implementing a specific experiment’ logic). This list will continue to grow but we fully appreciate the truth of the reviewer’s comment that adoption will depend on the existence of a large number of commonly used Nodes (for example Numpy, and OpenCV Nodes) and are working towards this goal.

      (7) It is not clear to me if there is any capability or utilities for testing individual nodes without invoking a full system execution. This would be critical when designing new experiments and testing out each component.

      There is no capability to run the code of an individual Node outside Heron’s GUI. A user could potentially design and test parts of the Node before they get added into a Node but we have found this to be a highly inefficient way of developing new Nodes. In our hands the best approach for Node development was to quickly generate test inputs and/or outputs using the “User Defined Function 1I 1O” Node where one can quickly write a function and make it accessible from a Node. Those test outputs can then be pushed in the Node under development or its outputs can be pushed in the test function, to allow for incremental development without having to connect it to the Nodes it would be connected in an actual pipeline. For example, one can easily create a small function that if a user presses a key will generate the same output (if run from a “User Defined Function 1I 1O” Node) as an Arduino Node reading some buttons. This output can then be passed into an experiment logic Node under development that needs to do something with this input. In this way during a Node development Heron allows the generation of simulated hardware inputs and outputs without actually running the actual hardware. We have added this way of developing Nodes also in our manuscript (Creating a new Node).

      Reviewer #3 (Public Review):

      Summary:

      The authors present a Python tool, Heron, that provides a framework for defining and running experiments in a lab setting (e.g. in behavioural neuroscience). It consists of a graphical editor for defining the pipeline (interconnected nodes with parameters that can pass data between them), an API for defining the nodes of these pipelines, and a framework based on ZeroMQ, responsible for the overall control and data exchange between nodes. Since nodes run independently and only communicate via network messages, an experiment can make use of nodes running on several machines and in separate environments, including on different operating systems.

      Strengths:

      As the authors correctly identify, lab experiments often require a hodgepodge of separate hardware and software tools working together. A single, unified interface for defining these connections and running/supervising the experiment, together with flexibility in defining the individual subtasks (nodes) is therefore a very welcome approach. The GUI editor seems fairly intuitive, and Python as an accessible programming environment is a very sensible choice. By basing the communication on the widely used ZeroMQ framework, they have a solid base for the required non-trivial coordination and communication. Potential users reading the paper will have a good idea of how to use the software and whether it would be helpful for their own work. The presented experiments convincingly demonstrate the usefulness of the tool for realistic scientific applications.

      Weaknesses:

      (1) In my opinion, the authors somewhat oversell the reproducibility and "selfdocumentation" aspect of their solution. While it is certainly true that the graph representation gives a useful high-level overview of an experiment, it can also suffer from the same shortcomings as a "pure code" description of a model - if a user gives their nodes and parameters generic/unhelpful names, reading the graph will not help much. 

      This is a problem that to our understanding no software solution can possibly address. Yet having a visual representation of how different inputs and outputs connect to each other we argue would be a substantial benefit in contrast to the case of “pure code” especially when the developer of the experiment has used badly formatted variable names.

      (2) Making the link between the nodes and the actual code is also not straightforward, since the code for the nodes is spread out over several directories (or potentially even machines), and not directly accessible from within the GUI. 

      This is not accurate. The obligatory code of a Node always exists within a single folder and Heron’s API makes it rather cumbersome to spread scripts relating to a Node across separate folders. The Node folder structure can potentially be copied over different machines but this is why Heron is tightly integrated with git practices (and even politely asks the user with popup windows to create git repositories of any Nodes they create whilst using Heron’s automatic Node generator system). Heron’s documentation is also very clear on the folder structure of a Node which keeps the required code always in the same place across machines and more importantly across experiments and labs. Regarding the direct accessibility of the code from the GUI, we took on board the reviewers’ comments and have taken the first step towards correcting this. Now one can attach to Heron their favourite IDE and then they can double click on any Node to open its two main scripts (com and worker) in that IDE embedded in whatever code project they choose (also set in Heron’s settings windows). On top of this, Heron now allows the addition of notes both for a Node and for all its parameters, inputs and outputs which can be viewed by hovering the mouse over them on the Nodes’ GUIs. The final step towards GUI-code integration will be to have a Heron GUI code editor but this is something that has to wait for further development from Heron’s underlying GUI library DearPyGUI.

      (3) The authors state that "[Heron’s approach] confers obvious benefits to the exchange and reproducibility of experiments", but the paper does not discuss how one would actually exchange an experiment and its parameters, given that the graph (and its json representation) contains user-specific absolute filenames, machine IP addresses, etc, and the parameter values that were used are stored in general data frames, potentially separate from the results. Neither does it address how a user could keep track of which versions of files were used (including Heron itself).

      Heron’s Graphs, like any experimental implementation, must contain machine specific strings. These are accessible either from Heron’s GUI when a Graph json file is opened or from the json file itself. Heron in this regard does not do anything different to any other software, other than saving the graphs into human readable json files that users can easily manipulate directly.

      Heron provides a method for users to save every change of the Node parameters that might happen during an experiment so that it can be fully reproduced. The dataframes generated are done so in the folders specified by the user in each of the Nodes (and all those paths are saved in the json file of the Graph). We understand that Heron offers a certain degree of freedom to the user (Heron’s main reason to exist is exactly this versatility) to generate data files wherever they want but makes sure every file path gets recorded for subsequent reproduction. So, Heron behaves pretty much exactly like any other open source software. What we wanted to focus on as the benefits of Heron on exchange and reproducibility was the ability of experimenters to take a Graph from another lab (with its machine specific file paths and IP addresses) and by examining the graphical interface of it to be able to quickly tweak it to make it run on their own systems. That is achievable through the fact that a Heron experiment will be constructed by a small amount of Nodes (5 to 15 usually) whose file paths can be trivially changed in the GUI or directly in the json file while the LAN setup of the machines used can be easily reconstructed from the information saved in the secondary GUIs.

      Where Heron needs to improve (and this is a major point in Heron’s roadmap) is the need to better integrate the different saved experiments with the git versions of Heron and the Nodes that were used for that specific save. This, we appreciate is very important for full reproducibility of the experiment and it is a feature we will soon implement. More specifically users will save together with a graph the versions of all the used repositories and during load the code base utilised will come from the recorded versions and not from the current head of the different repositories. This is a feature that we are currently working on now and as our roadmap suggests will be implemented by the release of Heron 1.0. 

      (4) Another limitation that in my opinion is not sufficiently addressed is the communication between the nodes, and the effect of passing all communications via the host machine and SSH. What does this mean for the resulting throughput and latency - in particular in comparison to software such as Bonsai or Autopilot? The paper also states that "Heron is designed to have no message buffering, thus automatically dropping any messages that come into a Node’s inputs while the Node’s worker function is still running."- it seems to be up to the user to debug and handle this manually?

      There are a few points raised here that require addressing. The first is Heron’s requirement to pass all communication through the main (GUI) machine. We understand (and also state in the manuscript) that this is a limitation that needs to be addressed. We plan to do this is by adding to Heron the feature of running headless (see our roadmap). This will allow us to run whole Heron pipelines in a second machine which will communicate with the main pipeline (run on the GUI machine) with special Nodes. That will allow experimenters to define whole pipelines on secondary machines where the data between their Nodes stay on the machine running the pipeline. This is an important feature for Heron and it will be one of the first features to be implemented next (after the integration of the saving system with git). 

      The second point is regarding Heron’s throughput latency. In our original manuscript we did not have any description of Heron’s capabilities in this respect and both other reviewers mentioned this as a limitation. As mentioned above, we have now addressed this by adding a section to our third experimental example that fully describes how much CPU is required to run a full experimental pipeline running on two machines and utilising also non python code executables (a Unity game). This gives an overview of how heavy pipelines can run on normal computers given adequate optimisation and utilising Heron’s feature of forcing some Nodes to run their Worker processes on a specific core. At the same time, Heron’s use of 0MQ protocol makes sure there are no other delays or speed limitations to message passing. So, message passing within the same machine is just an exchange of memory pointers while messages passing between different machines face the standard speed limitations of the Local Access Network’s ethernet card speeds. 

      Finally, regarding the message dropping feature of Heron, as mentioned above this is an architectural decision given the use cases of message passing we expect Heron to come in contact with. For a full explanation of the logic here please see our answer to the 3rd comment by Reviewer 2.

      (5) As a final comment, I have to admit that I was a bit confused by the use of the term "Knowledge Graph" in the title and elsewhere. In my opinion, the Heron software describes "pipelines" or "data workflows", not knowledge graphs - I’d understand a knowledge graph to be about entities and their relationships. As the authors state, it is usually meant to make it possible to "test propositions against the knowledge and also create novel propositions" - how would this apply here?

      We have described Heron as a Knowledge Graph instead of a pipeline, data workflow or computation graph in order to emphasise Heron’s distinct operation in contrast to what one would consider a standard pipeline and data workflow generated by other visual based software (like LabView and Bonsai). This difference exists on what a user should think of as the base element of a graph, i.e. the Node. In all other visual programming paradigms, the Node is defined as a low-level computation, usually a language keyword, language flow control or some simple function. The logic in this case is generated by composing together the visual elements (Nodes). In Heron the Node is to be thought of as a process which can be of arbitrary complexity and the logic of the graph is composed by the user both within each Node and by the way the Nodes are combined together. This is an important distinction in Heron’s basic operation logic and it is we argue the main way Heron allows flexibility in what can be achieved while retaining ease of graph composition (by users defining their own level of complexity and functionality encompassed within each Node). We have found that calling this approach a computation graph (which it is) or a pipeline or data workflow would not accentuate this difference. The term Knowledge Graph was the most appropriate as it captures the essence of variable information complexity (even in terms of length of shortest string required) defined by a Node.

      Recommendations for the authors:  

      Reviewer #1 (Recommendations For The Authors):

      -  No buffering implies dropped messages when a node is busy. It seems like this could be very problematic for some use cases... 

      This is a design principle of Heron. We have now provided a detailed explanation of the reasoning behind it in our answer to Reviewer 2 (Paragraph 3) as well as in the manuscript. 

      -  How are ssh passwords stored, and is it secure in some way or just in plain text?  

      For now they are plain text in an unencrypted file that is not part of the repo (if one gets Heron from the repo). Eventually, we would like to go to private/public key pairs but this is not a priority due to the local nature of Heron’s use cases (all machines in an experiment are expected to connect in a LAN).  

      Minor notes / copyedits:

      -  Figure 2A: right and left seem to be reversed in the caption. 

      They were. This is now fixed. 

      -  Figure 2B: the text says that proof of life messages are sent to each worker process but in the figure, it looks like they are published by the workers? Also true in the online documentation.  

      The Figure caption was wrong. This is now fixed.

      -  psutil package is not included in the requirements for GitHub

      We have now included psutil in the requirements.

      -  GitHub readme says Python >=3.7 but Heron will not run as written without python >= 3.9 (which is alluded to in the paper)

      The new Heron updates require Python 3.11. We have now updated GitHub and the documentation to reflect this.

      -  The paper mentions that the Heron editor must be run on Windows, but this is not mentioned in the Github readme.  

      This was an error in the manuscript that we have now corrected.

      -  It’s unclear from the readme/manual how to remove a node from the editor once it’s been added.  

      We have now added an X button on each Node to complement the Del button on the keyboard (for MacOS users that do not have this button most of the times).

      -  The first example experiment is called the Probabilistic Reversal Learning experiment in text, but the uncertainty experiment in the supplemental and on GitHub.  

      We have now used the correct name (Probabilistic Reversal Learning) in both the supplemental material and on GitHub

      -  Since Python >=3.9 is required, consider using fstrings instead of str.format for clarity in the codebase  

      Thank you for the suggestion. Latest Heron development has been using f strings and we will do a refactoring in the near future.

      -  Grasshopper cameras can run on linux as well through the spinnaker SDK, not just Windows.  

      Fixed in the manuscript. 

      -  Figure 4: Square and star indicators are unclear.

      Increased the size of the indicators to make them clear.

      -  End of page 9: "an of the self" presumably a typo for "off the shelf"?  

      Corrected.

      -  Page 10 first paragraph. "second root" should be "second route"

      Corrected.

      -  When running Heron, the terminal constantly spams Blowfish encryption deprecation warnings, making it difficult to see the useful messages.  

      The solution to this problem is to either update paramiko or install Heron through pip. This possible issue is mentioned in the documentation.

      -  Node input /output hitboxes in the GUI are pretty small. If they could be bigger it would make it easier to connect nodes reliably without mis-clicks.

      We have redone the Node GUI, also increasing the size of the In/Out points.

      Reviewer #2 (Recommendations For The Authors):

      (1) There are quite a few typos in the manuscript, for example: "one can accessess the code", "an of the self", etc.  

      Thanks for the comment. We have now screened the manuscript for possible typos.

      (2) Heron’s GUI can only run on Windows! This seems to be the opposite of the key argument about the portability of the experimental setup.  

      As explained in the answers to Reviewer 1, Heron can run on most machines that the underlying python libraries run, i.e. Windows and Linux (both for x86 and Arm architectures). We have tested it on Windows (10 and 11, both x64), Linux PC (Ubuntu 20.04.6, x64) and Raspberry Pi 4 (Debian GNU/Linux 12 (bookworm), aarch64). We have now revised the manuscript and the GitHub repo to reflect this.

      (3) Currently, the output is displayed along the left edge of the node, but the yellow dot connector is on the right. It would make more sense to have the text displayed next to the connectors.  

      We have redesigned the Node GUI and have now placed the Out connectors on the right side of the Node.

      (4) The edges are often occluded by the nodes in the GUI. Sometimes it leads to some confusion, particularly when the number of nodes is large, e.g., Fig 4.

      This is something that is dependent on the capabilities of the DearPyGUI module. At the moment there is no way to control the way the edges are drawn.

      Reviewer #3 (Recommendations For The Authors):

      A few comments on the software and the documentation itself:

      - From a software engineering point of view, the implementation seems to be rather immature. While I get the general appeal of "no installation necessary", I do not think that installing dependencies by hand and cloning a GitHub repository is easier than installing a standard package.

      We have now added a pip install capability which also creates a Heron command line command to start Heron with. 

      -The generous use of global variables to store state (minor point, given that all nodes run in different processes), boilerplate code that each node needs to repeat, and the absence of any kind of automatic testing do not give the impression of a very mature software (case in point: I had to delete a line from editor.py to be able to start it on a non-Windows system).  

      As mentioned, the use of global variables in the worker scripts is fine partly due to the multi process nature of the development and we have found it is a friendly approach to Matlab users who are just starting with Python (a serious consideration for Heron). Also, the parts of the code that would require a singleton (the Editor for example) are treated as scripts with global variables while the parts that require the construction of objects are fully embedded in classes (the Node for example). A future refactoring might make also all the parts of the code not seen by the user fully object oriented but this is a decision with pros and cons needing to be weighted first. 

      Absence of testing is an important issue we recognise but Heron is a GUI app and nontrivial unit tests would require some keystroke/mouse movement emulator (like QTest of pytest-qt for QT based GUIs). This will be dealt with in the near future (using more general solutions like PyAutoGUI) but it is something that needs a serious amount of effort (quite a bit more that writing unit tests for non GUI based software) and more importantly it is nowhere as robust as standard unit tests (due to the variable nature of the GUI through development) making automatic test authoring an almost as laborious a process as the one it is supposed to automate.

      -  From looking at the examples, I did not quite see why it is necessary to write the ..._com.py scripts as Python files, since they only seem to consist of boilerplate code and variable definitions. Wouldn’t it be more convenient to represent this information in configuration files (e.g. yaml or toml)?  

      The com is not a configuration file, it is a script that launches the communication process of the Node. We could remove the variable definitions to a separate toml file (which then the com script would have to read). The pros and cons of such a set up should be considered in a future refactoring.

      Minor comments for the paper:

      -  p.7 (top left): "through its return statement" - the worker loop is an infinite loop that forwards data with a return statement?  

      This is now corrected. The worker loop is an infinite loop and does not return anything but at each iteration pushes data to the Nodes output.

      -  p.9 (bottom right): "of the self" → "off-the-shelf"  

      Corrected.

      -  p.10 (bottom left): "second root" → "second route"  

      Corrected.

      -  Supplementary Figure 3: Green start and square seem to be swapped (the green star on top is a camera image and the green star on the bottom is value visualization - inversely for the green square).  

      The star and square have been swapped around.

      -  Caption Supplementary Figure 4 (end): "rashes to receive" → "rushes to receive"  

      Corrected.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      These experiments are some of the first to assess the role of dopamine release and the activity of D1 and D2 MSNs in pair bond formation in Mandarin voles. This is a novel and comprehensive study that presents exciting data about how the dopamine system is involved in pair bonding. The authors provide very detailed methods and clearly presented results. Here they show dopamine release in the NAc shell is enhanced when male voles encounter their pair bonded partner 7 days after cohabitation. In addition, D2 MSN activity decreases whereas D1 MSN activity increases when sniffing the pair-bonded partner.

      The authors do not provide justification for why they only use males in the current study, without discussing sex as a biological variable these data can only inform readers about one sex (which in pair-bonded animals by definition have 2 sexes). In addition, the authors do not use an isosbestic control wavelength in photometry experiments, although they do use EGFP control mice which show no effects of these interventions, a within-subject control such as an isosbestic excitation wavelength could give more confidence in these data and rule out motion artefacts within subjects.

      We agree with your suggestion that mechanism underlying pair bonding in females should also be investigated. In general, natal philopatry among mammals is female biased in the wild(Greenwood, 1983; Brody and Armitage, 1985; Ims, 1990; Solomon and Jacquot, 2002); social mammals are rarely characterized by exclusively male natal philopatry (Solomon and Jacquot, 2002). Males often disperse from natal area to a new place. Thus, males rodents may play a dominant role in the formation and maintenance of mating relationships. This is a reason we investigate pair bonding in male firstly. Certainly, female mate selection, and sexual receptivity or refusal through olfactory cues from males, thereby affect the formation and maintenance of pair bonding (Hoglen and Manoli, 2022). This is also the reason why we should focus on the mechanisms underlying pair bonding formation in females in the future research. This has been added in the limitation in the discussion.

      In photometry experiments, rAAV-D1/D2-GCaMP6m, a D1/D2 genetically encoded fluorescent calcium sensor, was injected into the NAc shell. The changes in fluorescence signals during these social interactions were collected and digitalized. To assess the specific response to social stimulus in fluorescence signals, changes in fluorescence signals during non-social behavioral bouts (such as freezing, exploration of the environment, grooming, rearing, etc…) were also recorded and analyzed. The result showed that dopamine release or D1/D2 MSNs activity displayed no significant changes after cohabitation of 3 or 7 days upon occurring of no-social behavior such as freezing, exploring, grooming and rearing. In addition, GCaMP6m is a genetically encoded calcium indicator. Changes in its fluorescence signal reflect changes in intracellular calcium ion concentration. Using EGFP virus as a control, it can be determined whether the fluorescence signal observed in the experiment is generated by the specific response of GCaMP6m to calcium or if there are other non-specific factors leading to fluorescence changes. If there is no similar fluorescence change in the EGFP control group, it can more strongly prove that the signal detected by GCaMP6m is a calcium-related specific signal. In some research article, they also use EGFP control group in photometry experiments (Yamaguchi et al., 2020; Qu et al., 2024; Zhan et al., 2024). Therefore, changes in fluorescence signals observed in the present study reflect neuron activities upon specific social behaviors, but were not affected by motion artefacts.

      There is an existing literature (cited in this manuscript) from Aragona et al., (particularly Aragona et al., 2006) which has highlighted key differences in the roles of rostral versus caudal NAc shell dopamine in pair bond formation and maintenance. Specifically, they report that dopamine transmission promoting pair bonding only occurs in the rostral shell and not the caudal shell or core regions. Given that the authors have targeted more caudally a discussion of how these results fit with previous work and why there may be differences in these areas is warranted.

      Thanks for your professional consideration. The brain coordinates of Bilateral 26-gauge guide cannulae were NAc (1.6 mm rostral, ± 1 mm bilateral, 4.5 mm ventral (for shell), 3.5 mm ventral (for core) from bregma) in report from Aragona et al (2006). In the present study, the brain coordinates of virus injection were (AP: +1.5, ML: ±0.99, DV: −4.2 (for NAc shell)). Thus, the virus injection sites were close to rostral shell in our study. However, as the diffusive expression of the virus, part of neurons in the rostrocaudal border and caudal shell also be infected by the virus, so we did not distinguish different subregions of NAc shell. In the future, we will use AAV13, a viral strategy could target / manipulate precise local neural populations, to address this issue. NAc is a complex brain structure with distinct regions that have different functions. Previous study suggested that GABAergic substrates of positive and negative types of motivated behavior in the nucleus accumbens shell are segregated along a rostrocaudal gradient (Reynolds and Berridge, 2001). However, a study found that food intake is significantly enhanced by administering μ-selective opioid agonists into the NAc, especially its shell region (Znamensky et al., 2001). Also, μ-opioid stimulation increases the motivation to eat (“wanting”) both in the NAc shell and throughout the entire NAc, as well as in several limbic or striatal structures beyond. For DAMGO stimulation of eating, the “wanting” substrates anatomically extend additionally beyond the rostrodorsal shell and throughout the entire shell (the caudal shell). Furthermore, DAMGO stimulates eating at NAc shell and core, as well as the neostriatum, amygdala…(Gosnell et al., 1986; Gosnell and Majchrzak, 1989; Peciña and Berridge, 2000; Zhang and Kelley, 2000; Echo et al., 2002; Peciña and Berridge, 2005, 2013; Castro and Berridge, 2014). In pair bond formation and maintenance, the rostral shell is the specific subregion of the NAc important for DA regulation of partner preference (Aragona et al., 2006). In conclusion, it appears that the changes in real time dopamine release and activities and electrophysiological properties of D1R, D2R MSNs in the NAc shell after pair bond formation may have primarily targeted to the rostral shell in our study, which is consistent with the report from Aragona et al.

      The authors could discuss the differences between pair bond formation and pair bond maintenance more deeply.

      Thanks for your suggestion. I have discussed the differences between pair bond formation and pair bond maintenance more deeply.

      The dopamine and different types of dopamine receptors in the NAc may play different roles in regulation of pair bond formation and maintenance. The chemogenetic manipulation revealed that VP-projecting D2 MSNs are necessary and more important in pair bond formation compared to VPprojecting D1 MSNs. It is consistent with previous pharmacological experiments that blocking of D2R with its specific antagonist, while D1R was not blocked, can prevent the formation of a pair bond in prairie voles (Gingrich et al., 2000). This indicates that D2R is crucial for the initial formation of the pair bond. D2R is involved in the reward aspects related to mating. In female prairie voles, D2R in the NAc is important for partner preference formation. The activation of D2R may help to condition the brain to assign a positive valence to the partner's cues during mating, facilitating the development of a preference for a particular mate. In addition, the cohabitation caused the DA release, the high affinity Gi-coupled D2R was activated first, which inhibited D2 MSNs activity and promoted the pair bond formation. And then, after 7 days of cohabitation, the pair bonding was already established, the significantly increased release of dopamine significantly activated Gs-coupled D1R with the low affinity to dopamine, which increased D1 MSNs activity and maintained the formation of partner preference. While D1R is also present and involved in the overall process, its role in the initial formation of the pair bond is not as dominant as D2R (Aragona et al., 2006). However, it still participates in the neurobiological processes related to pair bond formation. For example, in male mandarin voles, after 7 days of cohabitation with females, D1R activity in the NAc shell was affected during pair bond formation. The extracellular DA concentration was higher when sniffing their partner compared to a stranger, and this increase in DA release led to an increase in D1R activity in the NAc shell. In prairie voles, dopamine D1 receptors seem to be essential for pair bond maintenance. Neonatal treatment with D1 agonists can impair partner preference formation later in life, suggesting an organizational role for D1 in maintaining the bond (Aragona et al., 2006). In pair-bonded male prairie voles, D1R is involved in inducing aggressive behavior toward strangers, which helps to maintain the pair bond by protecting it from potential rivals. In the NAc shell, D1 agonist decreases the latency to attack same-sex conspecifics, while D1 antagonism increases it (Aragona et al., 2006). In summary, D2R is more crucial for pair bond formation, being involved in reward association and necessary for the initial development of the pair bond. D1R, on the other hand, is more important for pair bond maintenance, being involved in aggression and mate guarding behaviors and having an organizational role in maintaining the pair bond over time. We therefore suggest that D2 MSNs are more predominantly involved in the formation of a pair bond compared with D1 MSNs.

      The authors have successfully characterised the involvement of dopamine release, changes in D1 and D2 MSNs, and projections to the VP in pair bonding voles. Their conclusions are supported by their data and they make a number of very reasonable discussion points acknowledging various limitations

      Reviewer #2 (Public review):

      Summary:

      Using in vivo fiber-photometry the authors first establish that DA release when contacting their partner mouse increases with days of cohabitation while this increase is not observed when contacting a stranger mouse. Similar effects are found in D1-MSNs and D2-MSNs with the D1MSN responses increasing and D2-MSN responses decreasing with days of cohabitation. They then use slice physiology to identify underlying plasticity/adaptation mechanisms that could contribute to the changes in D1/D2-MSN responses. Last, to address causality the authors use chemogenetic tools to selectively inhibit or activate NAc shell D1 or D2 neurons that project to the ventral pallidum. They found that D2 inhibition facilitates bond formation while D2 excitation inhibits bond formation. In contrast, both D1-MSN activation and inhibition inhibit bond formation.

      Strengths:

      The strength of the manuscript lies in combining in vivo physiology to demonstrate circuit engagement and chemogenetic manipulation studies to address circuit involvement in pair bond formation in a monogamous vole.

      Weaknesses:

      Comment: Weaknesses include that a large set of experiments within the manuscript are dependent on using short promoters for D1 and D2 receptors in viral vectors. As the authors acknowledge this approach can lead to ectopic expression and the presented immunohistochemistry supports this notion. It seems to me that the presented quantification underestimates the degree of ectopic expression that is observed by eye when looking at the presented immunohistochemistry. However, given that Cre transgenic animals are not available for Microtus mandarinus and given the distinct physiological and behavioral outcomes when imaging and manipulating both viral-targeted populations this concern is minor.

      Thanks for your professional comment. The virus used in the present study were purchased from brainVTA company. D1/D2 receptor promoter genes were predicted and amplified for validation by the company. The promoter gene was constructed and packaged by aav virus vector (taking rAAV-D2-mCherry-WPRE-bGH_polyA virus as an example, Author response image 1A). The D1/D2 promoter sequence is shown in the Author response image 1B-C. In addition, the D1 receptor gene promoter and D2 receptor gene promoter viruses used in this paper have been used in several published papers with high specificity (Zhao et al., 2019; Ying et al., 2022). In our paper, a high proportion of virus and mRNA co-localization was found through FISH verification and also showed high specificity of virus (Figure S15, S16).

      Author response image 1.

      (A)   Gene carrier of rAAV-D2-mCherry-WPRE-bGH_polyA. (B-C) Gene sequence of D1 promoter and D2 promoter.

      The slice physiology experiments provide some interesting outcomes but it is unclear how they can be linked to the in vivo physiological outcomes and some of the outcomes don't match intuitively (e.g. cohabitation enhances excitatory/inhibitory balance in D2-MSNs but the degree of contact-induced inhibition is enhanced in D2-MSN).

      Thanks for your comment. The present study found that the frequencies of sEPSC and sIPSC were significantly enhanced after the formation of a pair bond in NAc shell D2 MSNs. The excitatory/inhibitory balance of D2 MSNs was enhanced after cohabitation.These results are not consistent with the findings from fiber photometry of calcium signals. One study showed that NAc D2 MSNs was linked to both ‘liking’ (food consumption) and ‘wanting’ (food approach) but with opposing actions; high D2 MSNs activity signaled ‘wanting’, and low D2 MSNs activity enhanced ‘liking’. D2 MSNs are faced with a tradeoff between increasing ‘wanting’ by being more active or allowing ‘liking’ by remaining silent (Guillaumin et al., 2023). Therefore, the increase in frequencies of sEPSC and sIPSC in D2 MSNs may reflect two processes, liking and wanting, respectively. We thought that hedonia and motivation might influence D2 MSNs activity differently during cohabitation and contribute to the processing of pair bond formation in a more dynamic and complex way than previously expected.

      Moreover, the frequencies of sEPSC and sIPSC were significantly reduced in the NAc shell D1 MSNs after pair bonding, whereas the intrinsic excitability increased after cohabitation with females.

      The bidirectional modifications (reduced synaptic inputs vs. increased excitability) observed in D1 MSNs might result from homeostatic regulation. The overall synaptic transmission may produce no net changes, given that reductions in both excitatory and inhibitory synaptic transmission of D1 MSNs were observed. Also, increases in the intrinsic excitability of D1 MSNs would result in an overall excitation gain on D1 MSNs.

      One interesting finding is that the relationship between D2-MSN and pair bond formation is quite clear (inhibition facilitates while excitation inhibits pair bond formation). In contrast, the role of D1-MSNs is more complicated since both excitation and inhibition disrupt pair bond formation. This is not convincingly discussed.

      Considering the reviewer’s suggestion, the discussion has been added in the revised manuscript.

      In the present study, DREADDs approaches were used to inhibit or excite NAc MSNs to VP projection and it was found that D1 and D2 NAc MSNs projecting to VP play different roles in the formation of a pair bond. Chemogenetic inhibition of VP-projecting D2 MSNs promoted partner preference formation, while activation of VP-projecting D2 MSNs inhibited it (Figure 6). Chemogenetic activation of D2 MSNs produced the opposite effect of DA on the D2 MSNs on partner preference, while inhibition of these neurons produced the same effects of DA on D2 MSNs. DA binding with D2R is coupled with Gi and produces an inhibitory effect (Lobo and Nestler, 2011). It is generally assumed that activation of D2R produces aversive and negative reinforcement. These results were consistent with the reduced D2 MSNs activity upon sniffing their partner in the fiber photometry test and the increased frequency and amplitude of sIPSC in the present study. Our results also agree with other previous studies that chemogenetic inhibition of NAc D2 MSNs is sufficient to enhance reward-oriented motivation in a motivational task (Carvalho Poyraz et al., 2016; Gallo et al., 2018). Inhibition of D2 MSNs during self-administration enhanced response and motivation to obtain cocaine (Bock et al., 2013). This also suggests that the mechanism underlying attachment to a partner and drug addiction is similar.

      Besides, in the present study, the formation of partner preference was inhibited after activation or inhibition of VP-projecting D1 MSNs, which is not consistent with conventional understanding of prairie vole behavior. Alternatively, DA binding with D1R is coupled with Gs and produces an excitatory effect (Lobo and Nestler, 2011), while activation of D1R produces reward and positive reinforcement (Hikida et al., 2010; Tai et al., 2012; Kwak and Jung, 2019). For example, activation of D1 MSNs enhances the cocaine-induced conditioned place preference (Lobo et al., 2010). In addition, D1R activation by DA promotes D1 MSNs activation, which promotes reinforcement. However, a recent study found that NAc-ventral mesencephalon D1 MSNs promote reward and positive reinforcement learning; in contrast, NAc-VP D1 MSNs led to aversion and negative reinforcement learning (Liu et al., 2022). It is consistent with our results that activation of NAc-VP D1 MSNs pathway reduced time spent side-by-side and impaired partner preference after 7 days of cohabitation. In contrast to inhibition of D2 MSNs, we found that inhibition of the D1 MSNs did not elicit corresponding increases in partner preference. One possible explanation is that almost all D1 MSNs projecting to the VTA/ substantia nigra (SN) send collaterals to the VP (Pardo-Garcia et al., 2019). For example, optogenetically stimulating VP axons may inadvertently cause effects in the VTA/SN through the antidromic activation of axon collaterals (Yizhar et al., 2011). Therefore, chemogenetic inhibition of D1 MSNs may also inhibit DA neurons in VTA, subsequently inhibiting the formation of a pair bond.

      The dopamine and different types of dopamine receptors in the NAc may play different roles in regulation of pair bond formation and maintenance. The chemogenetic manipulation revealed that VP-projecting D2 MSNs are necessary and more important in pair bond formation compared to VPprojecting D1 MSNs. It is consistent with previous pharmacological experiments that blocking of D2R with its specific antagonist, while D1R was not blocked, can prevent the formation of a pair bond in prairie voles (Gingrich et al., 2000). This indicates that D2R is crucial for the initial formation of the pair bond. D2R is involved in the reward aspects related to mating. In female prairie voles, D2R in the NAc is important for partner preference formation. The activation of D2R may help to condition the brain to assign a positive valence to the partner's cues during mating, facilitating the development of a preference for a particular mate. In addition, the cohabitation caused the DA release, the high affinity Gi-coupled D2R was activated first, which inhibited D2 MSNs activity and promoted the pair bond formation. And then, after 7 days of cohabitation, the pair bonding was already established, the significantly increased release of dopamine significantly activated Gs-coupled D1R with the low affinity to dopamine, which increased D1 MSNs activity and maintained the formation of partner preference. While D1R is also present and involved in the overall process, its role in the initial formation of the pair bond is not as dominant as D2R (Aragona et al., 2006). However, it still participates in the neurobiological processes related to pair bond formation. For example, in male mandarin voles, after 7 days of cohabitation with females, D1R activity in the NAc shell was affected during pair bond formation. The extracellular DA concentration was higher when sniffing their partner compared to a stranger, and this increase in DA release led to an increase in D1R activity in the NAc shell. In prairie voles, dopamine D1 receptors seem to be essential for pair bond maintenance. Neonatal treatment with D1 agonists can impair partner preference formation later in life, suggesting an organizational role for D1 in maintaining the bond (Aragona et al., 2006). In pair-bonded male prairie voles, D1R is involved in inducing aggressive behavior toward strangers, which helps to maintain the pair bond by protecting it from potential rivals. In the NAc shell, D1 agonist decreases the latency to attack same-sex conspecifics, while D1 antagonism increases it (Aragona et al., 2006). In summary, D2R is more crucial for pair bond formation, being involved in reward association and necessary for the initial development of the bond. D1R, on the other hand, is more important for pair bond maintenance, being involved in aggression and mate guarding behaviors and having an organizational role in maintaining the bond over time. We therefore suggest that D2 MSNs are more predominantly involved in the formation of a pair bond compared with D1 MSNs.

      It seemed a missed opportunity that physiological readout is limited to males. I understand though that adding females may be beyond the scope of this manuscript.

      We gratefully appreciate for your valuable comment. The reviewer 1 also concerned this issue. We made a following response.

      In general, natal philopatry among mammals is female biased in the wild(Greenwood, 1983; Brody and Armitage, 1985; Ims, 1990; Solomon and Jacquot, 2002); social mammals are rarely characterized by exclusively male natal philopatry (Solomon and Jacquot, 2002). Males often disperse from natal area to a new place. Thus, male rodents may play a dominant role in the formation and maintenance of mating relationships. This is a reason we investigate pair bonding in male firstly. Certainly, female mate selection, and sexual receptivity or refusal through olfactory cues from males, thereby affect the formation and maintenance of pair bonding (Hoglen and Manoli, 2022). This is also the reason why we should focus on the mechanisms underlying pair bonding formation in females in the future research. This has been added in the limitation in the discussion.

      Reviewer #3 (Public review):

      Summary:

      The manuscript is evaluating changes in dopamine signaling in the nucleus accumbens following pair bonding and exposure to various stimuli in mandarin voles. In addition, the authors present chemogenetic data that demonstrate excitation and inhibition of D1 and D2 MSN affect pair bond formation.

      Strengths:

      The experimental designs are strong. The approaches are innovative and use cutting-edge methods.

      The manuscript is well written.

      Weaknesses:

      The statistical results are not presented, and not all statistical analyses are appropriate.

      Additionally, some details of methods are absent.

      As you suggested, we added the detailed information in the revised manuscript.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Remove references to 'extreme significance' - p is set as a threshold and the test is either significant or not.

      Thanks for your suggestion. We have removed 'extreme significance' in the revised manuscript.

      (2) The second half of the abstract is a little confusing the use of activation/inhibition makes it difficult to read and follow, this could be re-worded for clarity.

      Sorry for the confusing. We reorganized the sentence as following.

      In addition, chemogenetic inhibition of ventral pallidum-projecting D2 MSNs in the NAc shell enhanced pair bond formation, while chemogenetic activation of VP-projecting D2 MSNs in the NAc shell inhibited pair bond formation.

      Reviewer #2 (Recommendations for the authors):

      (1) In many instances repeated measures are presented from the same mice (e.g. Figures 1F, I; S1BC). Repeated measures for each mouse should be connected with a line in the figures. This will allow the reader to visually compare the repeated measures for each animal.

      Thanks for your careful consideration. As reviewer suggested, the figures have been changed.

      (2) It is unclear to me how the time point 0 for sniffing was determined. How is the time point 0 for side-by-side contact determined?

      Sniffing is a behavior for olfactory investigation and defined as animals uses nose to inspect any portion of the stimulus mouse’s body, including the tail. The time point 0 for sniffing was the beginning of sniffing behavior occurs. The side-by-side behavior is defined as significant physical contact with a social object and huddle in a quiescent state. The time point 0 for side-byside behavior was the beginning of side-by-side behavior occurs.

      (3) Figure 1-3: For the fiber photometry data 7 events (sniffs) are shown in the heat maps. Are these the first 7 sniffs? What went into the quantification? It seems that DA and D1/D2 responses are habituating. This could be analyzed and would need to be discussed.

      In the heat maps (Figure 1-3), we showed the mean fluorescence signal changes of every subject (n = 7 voles) upon sniffing partner, stranger or an object in the experiment, but not the fluorescence signal changes of sniffing events in one vole. The quantification of changes in mean fluorescence signal of all subjects was showed in Figure 1F, 1I, Figure 2F, 2I, Figure 3F and 3I.

      (4) Generally, it is very difficult to obtain cell type selectivity using short promoters in viruses (the authors acknowledge this). Which D1 and D2 promoter sequences were used for obtaining specificity? The degree of ectopic expression looks much higher than the quantification (e.g. in Fig. 3b, 6C, 7C, S14A, C). Is this due to thresholding?

      The virus used in the present study were purchased from brainVTA company. D1/D2 receptor promoter genes were predicted and amplified for validation. The promoter gene was constructed and packaged by aav virus vector (taking rAAV-D2-mCherry-WPRE-bGH_polyA virus as an example, Author response image 1A). The D1/D2 promoter sequence is shown in the Author response image 1B-C. In addition, the D1 receptor gene promoter and D2 receptor gene promoter viruses used in this paper have been used in several published papers with high specificity (Zhao et al., 2019; Ying et al., 2022). In the Figure 6C, the first image is the merged fluorescence images that were taken under different fluorescence channels with the 20X objective. The second and the third images were taken under 40X objective from field of white box in the first image. The second and the third images were merged into fourth one. Due to the different exposure time and intensity, the fluorescence photo taken at 40X are clearer compared to image taken at the 20X. For example, in the Figure 6C, the labeled-cells were presented as following (Author response image 2). In our paper,virus infection and mRNA through FISH verification were co-localized in a high proportion displaying high specificity of virus (Figure S15, S16).Certainly, the number of positive neurons may be dependent on visuality (thresholding). Only visible cells were counted. The cell counting results at Author response image 2B and 2C are similar to the quantification in the Figure 6C.

      Author response image 2.

      (A) Immunohistological image showing co-localization of hM3Dq- mCherry-anti expression (green), D2R-mRNA (red), and DAPI (blue) in the NAc shell. Scale bar: 100 μm. (B) The cell counts and the determination of colocalization of the 20× immunohistochemistry images. The marked neurons were counted with white dots. (C) The cell counts and the determination of colocalization of the 40× immunohistochemistry images. The marked neurons were counted with white dots.

      (5) Figure 6D/7D: the time scale seems to be off for both traces (40 seconds). For the hM3D Gq experiment, only one trace is shown. It would be more convincing to provide an input-output curve from several mice and to statistically compare the curves.

      Response: Thanks for your careful consideration. As reviewer suggested, the figure of resting membrane potentials before and after drug CNO exposure from several voles was added in the revised manuscript.

      (6) The presence of GIRK channels in MSNs has been a long debate and hM4D Gi activation may mostly act at the level of terminals by inhibiting neurotransmitter release. For demonstrating hyperpolarization of the soma showing the resting membrane potential before and after drug CNO exposure would be more convincing.

      Thanks for your careful consideration. As reviewer suggested, the figure of resting membrane potential before and after drug CNO exposure was added in the revised manuscript.

      (7) It is unclear to me how far the slice physiology informs the in vivo physiology (e.g. cohabitation enhances excitatory/inhibitory balance in D2-MSNs but the degree of contact-induced inhibition is enhanced in D2-MSN; D2-MSNs become less responsive to DA in the slice yet but at the time of enhanced DA release D2-MSN activity is also strongly reduced).

      The present study found that the frequencies of sEPSC and sIPSC were significantly enhanced after the formation of a pair bond in NAc shell D2 MSNs. The excitatory/inhibitory balance of D2 MSNs was enhanced after cohabitation. These results are not consistent with the findings from fiber photometry of calcium signals. One study showed that NAc D2 MSNs was linked to both ‘liking’ (food consumption) and ‘wanting’ (food approach) but with opposing actions; high D2 MSNs activity signaled ‘wanting’, and low D2 MSNs activity enhanced ‘liking’. D2 MSNs are faced with a tradeoff between increasing ‘wanting’ by being more active or allowing ‘liking’ by remaining silent (Guillaumin et al., 2023). Therefore, the increase in frequencies of sEPSC and sIPSC in D2 MSNs may reflect two processes, liking and wanting, respectively. We thought that hedonia and motivation might different influence D2 MSNs activity during cohabitation and contribute to the processing of pair bond formation in a more dynamic and complex way than previously expected.

      Moreover, the frequencies of sEPSC and sIPSC were significantly reduced in the NAc shell D1

      MSNs after pair bonding, whereas the intrinsic excitability increased after cohabitation with females.

      The bidirectional modifications (reduced synaptic inputs vs. increased excitability) observed in D1 MSNs might result from homeostatic regulation. The overall synaptic transmission may produce no net changes, given that reductions in both excitatory and inhibitory synaptic transmission of D1 MSNs were observed. Also, increases in the intrinsic excitability of D1 MSNs would result in an overall excitation gain on D1 MSNs.

      (8) One interesting finding is that the relationship between D2-MSN and pair bond formation is quite clear (inhibition facilitates while excitation inhibits pair bond formation). In contrast, the role of D1-MSNs is more complicated since both excitation and inhibition disrupt pair bond formation.

      The discussion of this would benefit from another attempt.

      As reviewer suggested, the discussion was added in the revised manuscript.

      In the present study, DREADDs approaches were used to inhibit or excite NAc MSNs to VP projection and it was found that D1 and D2 NAc MSNs projecting to VP play different roles in the formation of a pair bond. Chemogenetic inhibition of VP-projecting D2 MSNs promoted partner preference formation, while activation of VP-projecting D2 MSNs inhibited it (Figure 6). Chemogenetic activation of D2 MSNs produced the opposite effect of DA on the D2 MSNs on partner preference, while inhibition of these neurons produced the same effects of DA on D2 MSNs. DA binding with D2R is coupled with Gi and produces an inhibitory effect (Lobo and Nestler, 2011). It is generally assumed that activation of D2R produces aversive and negative reinforcement. These results were consistent with the reduced D2 MSNs activity upon sniffing their partner in the fiber photometry test and the increased frequency and amplitude of sIPSC in the present study. Our results also agree with other previous studies, which showed that chemogenetic inhibition of NAc D2 MSNs is sufficient to enhance reward-oriented motivation in a motivational task (Carvalho Poyraz et al., 2016; Gallo et al., 2018). Inhibition of D2 MSNs during self-administration enhanced response and motivation to obtain cocaine (Bock et al., 2013). This also suggests that the mechanism underlying attachment to a partner and drug addiction is similar.

      Besides, in the present study, the formation of partner preference was inhibited after activation or inhibition of VP-projecting D1 MSNs, which is not consistent with conventional understanding of prairie vole behavior. Alternatively, DA binding with D1R is coupled with Gs and produces an excitatory effect (Lobo and Nestler, 2011), while activation of D1R produces reward and positive reinforcement (Hikida et al., 2010; Tai et al., 2012; Kwak and Jung, 2019). For example, activation of D1 MSNs enhances the cocaine-induced conditioned place preference (Lobo et al., 2010). In addition, D1R activation by DA promotes D1 MSNs activation, which promotes reinforcement. However, a recent study found that NAc-ventral mesencephalon D1 MSNs promote reward and positive reinforcement learning; in contrast, NAc-VP D1 MSNs led to aversion and negative reinforcement learning (Liu et al., 2022). It is consistent with our results that activation of NAc-VP D1 MSNs pathway reduced time spent side-by-side and impaired partner preference after 7 days of cohabitation. In contrast to inhibition of D2 MSNs, we found that inhibition of the D1 MSNs did not elicit corresponding increases in partner preference. One possible explanation is that almost all D1 MSNs projecting to the VTA/ substantia nigra (SN) send collaterals to the VP (Pardo-Garcia et al., 2019). For example, optogenetically stimulating VP axons may inadvertently cause effects in the VTA/SN through the antidromic activation of axon collaterals (Yizhar et al., 2011). Therefore, chemogenetic inhibition of D1 MSNs may also inhibit DA neurons in VTA, subsequently inhibiting the formation of a pair bond.

      The dopamine and different types of dopamine receptors in the NAc may play different roles in regulation of pair bond formation and maintenance. The chemogenetic manipulation revealed that VP-projecting D2 MSNs are necessary and more important in pair bond formation compared to VPprojecting D1 MSNs. It is consistent with previous pharmacological experiments that blocking of D2R with its specific antagonist, while D1R was not blocked, can prevent the formation of a pair bond in prairie voles (Gingrich et al., 2000). This indicates that D2R is crucial for the initial formation of the pair bond. D2R is involved in the reward aspects related to mating. In female prairie voles, D2R in the NAc is important for partner preference formation. The activation of D2R may help to condition the brain to assign a positive valence to the partner's cues during mating, facilitating the development of a preference for a particular mate. In addition, the cohabitation caused the DA release, the high affinity Gi-coupled D2R was activated first, which inhibited D2 MSNs activity and promoted the pair bond formation. And then, after 7 days of cohabitation, the pair bonding was already established, the significantly increased release of dopamine significantly activated Gs-coupled D1R with the low affinity to dopamine, which increased D1 MSNs activity and maintained the formation of partner preference. While D1R is also present and involved in the overall process, its role in the initial formation of the pair bond is not as dominant as D2R (Aragona et al., 2006). However, it still participates in the neurobiological processes related to pair bond formation. For example, in male mandarin voles, after 7 days of cohabitation with females, D1R activity in the NAc shell was affected during pair bond formation. The extracellular DA concentration was higher when sniffing their partner compared to a stranger, and this increase in DA release led to an increase in D1R activity in the NAc shell. In prairie voles, dopamine D1 receptors seem to be essential for pair bond maintenance. Neonatal treatment with D1 agonists can impair partner preference formation later in life, suggesting an organizational role for D1 in maintaining the bond (Aragona et al., 2006). In pair-bonded male prairie voles, D1R is involved in inducing aggressive behavior toward strangers, which helps to maintain the pair bond by protecting it from potential rivals. In the NAc shell, D1 agonist decreases the latency to attack same-sex conspecifics, while D1 antagonism increases it (Aragona et al., 2006). In summary, D2R is more crucial for pair bond formation, being involved in reward association and necessary for the initial development of the bond. D1R, on the other hand, is more important for pair bond maintenance, being involved in aggression and mate guarding behaviors and having an organizational role in maintaining the bond over time. We therefore suggest that D2 MSNs are more predominantly involved in the formation of a pair bond compared with D1 MSNs.

      (9) For the chemogenetic inhibition/excitation experiment please specify the temporal relationship between CNO injection and the behavioral testing. Are the DREADDs activated during the preference testing or are we only looking at the consequences of DREADD activation during cohabitation? This would impact the interpretation of the results.

      Considering the reviewer’s suggestion, we have clarified the time of CNO injection and the behavioral testing. In chemogenetic experiments, male voles were injected with CNO (1 mg/kg, i.p. injection) or saline once per day during 7-days cohabitation period. On day 3 and day 7 of cohabitation, the partner preference tests (3 h) were conducted after 3h of injection. Anton Pekcec (Jendryka et al., 2019) found that, in mice, after 60 min of CNO injection (i.p.), free CNO levels had dropped surprisingly sharply in CSF and cortex tissue, CNO could not be detected after 60 min. However, associated biological effects are reported to endure 6 - 24 h after CNO treatment (Farzi et al., 2018; Desloovere et al., 2019; Paretkar and Dimitrov, 2019). For example, René He et al. (Anacker et al., 2018) showed that chemogenetic inhibition of adult-born neurons in the vDG promotes susceptibility to social defeat stress by using of DREADDs for 10 days, whereas increasing neurogenesis confers resilience to chronic stress. Moreover, Ming-Ming Zhang et al. (Zhang et al., 2022) revealed that the selective activation or inhibition of the IC-BLA projection pathway strengthens or weakens the intensity of observational pain while the CNO (1 mg/kg) was i.p. injected into the infected mice on days 1, 3, 5, and 7 after virus expression. Furthermore, in study of James P Herman et al. (Nawreen et al., 2020) chronic inhibition of IL PV INs reduces passive and increases active coping behavior in FST. Therefore, we believe that 7-day CNO injections can produce chronic effects on MSNs and alters the formation of partner preferences.

      (10) Discussion: "The observed increase in DA release resulted in suppression of D2 neurons in the NAc shell". "In contrast, the rise in DA release increases D1 activity selectively in response to their partner after extended cohabitation." These statements would need to be weakened as causality is not shown here.

      Thanks for your rigorous consideration. We have reorganized the discussion in the revised manuscript.

      “The observed increase in DA release resulted in alterations in activities of D2 and D1 neurons in the NAc shell selectively in response to their partner after extended cohabitation.”

      (11) It would help if the order of supplementary figures would match their order of figures appearance in the result section.

      Thanks for your suggestion. We reorganized the order of appearance in the revised manuscript.

      (12) This may be beyond the focus of the study but it would be very interesting to know whether the physiological responses to partner contact are similarly observed in females.

      Thanks for your concern. It is regretful that we did not observe physiological responses of female to partner contact. We predict the females may show the similar response patterns to their partner. In the future, we will supplement the research on the mechanism of partner preferences in female voles.

      Reviewer #3 (Recommendations for the authors):

      The manuscript is evaluating changes in dopamine signaling in the nucleus accumbens following pair bonding and exposure to various stimuli in mandarin voles. The manuscript is generally wellwritten. The experiment designs seem strong, although there are missing details to fully evaluate them. The statistics are not completed correctly, and the statistical values are not reported making them even harder to evaluate. There are a lot of potential strengths in this research. However, my review is limited because I am limited in how to evaluate data interpretation when statistical analyses are not clear. I provide details below.

      Major

      (1) Statistics should be provided in the Results section. It is not clear how to evaluate the authors' interpretations without presenting the statistical data. What stats are being reported about viral expression in cells on lines 192-194? What posthocs? There is only one condition, so I assume the statistic was a one-sample t-test. The authors should report the t-value, df, and p-value. No post-hoc is needed. There are many issues like this, which makes reviewing this manuscript very difficult. If the statistics were not conducted properly and reported clearly, I do not have confidence that I can evaluate the author's interpretation of the results.

      Thanks for your suggestion. We report the t-value, df, and p-value in the Results section.

      (2) Statistical tests should be labeled correctly. ANOVAs (found in figure caption) for Figure 1 data are not repeated measures. Rather, they are one-way ANOVA (with stimulus as a within-subject variable).

      We used one-way ANOVA to analyze the changes in fluorescence signals in figure1-3. In the experiment, the changes in fluorescence signals of every subject were collected upon sniffing the partner, an unknown female, and an object. So, we used One-Way Repeated Measures ANOVA to analyze the data.

      (3) The protocol for behavioral assessment and stimulus presentation during fiber photometry recording is not clear. For example, the authors mention on line 662 that voles ate carrots during some of the recording sessions, but nothing else is described about the recording session. What was the order of stimulus presentation? What was the object provided? Why is eating carrots analyzed separately from object, partner, and stranger exposure?

      Response: Sorry for the confusing. The detailed description has been added. After 3 and 7 days of cohabitation, males were exposed to their partner or an unfamiliar female (each exposure lasted for 30 min) in random order in a clean social interaction cage. The changes in fluorescence signals during these social interactions with their partner, an unfamiliar vole of the opposite sex, or an object (Rubik's Cube) were collected and digitalized by CamFiberPhotometry software (ThinkerTech). To rule out that the difference in fluorescence signals was caused by the difference in virus expression at different time points, we used the same experimental strategy in new male mandarin voles and measured the fluorescence signal changes upon eating carrot after 3 and 7 days of cohabitation (The male mandarin voles were fasted for four hours before the test.). Since sniffing (object, partner, and stranger) and eating carrot were not tested in the same males, we analyzed sniffing and eating carrot separately.

      (4) Supplement figures would be better as figures instead of tables. Many effects are hard to interpret.

      As you suggested, we added the information of Supplement table1 in results.

      (5) Citations should be included to note when pair bonding occurs in mandarin voles.

      As you suggested, we added the citation in the revised manuscript.

      Minor

      (1) Add a citation for the statement that married people live longer than unmarried people (Lines 51-52).

      As you suggested, we added the citation in the revised manuscript.

      (2) There is a table labeling viral vectors, but the table is not titled properly or referenced in the methods section.

      Thanks for our careful checking. We reorganized the table title and the table was also cited in the revised manuscript.

      (3) Sentences on lines 608-610 and 610-612 seem redundant.

      This sentence was corrected.

      (4) This is a rather subjective statement "Carrots are voles' favorite food."

      We reorganized the sentence in the revised manuscript.

      "Carrots are voles' daily food."

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    1. Author response:

      The following is the authors’ response to the current reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      The authors demonstrated that NINJ1 promotes TF-positive MV release during pyroptosis and thereby triggers coagulation. Coagulation is one of the risk factors that can cause secondary complications in various inflammatory diseases, making it a highly important therapeutic target in clinical treatment. This paper effectively explains the connection between pyroptosis and MV release with Ninj1, which is a significant strength. It provides valuable insight into the potential of targeting Ninj1 as a therapeutic strategy.

      Although the advances in this paper are valuable, several aspects need to be clarified. Some comments are discussed below. 

      (1) Since it is not Ninj1 directly regulating coagulation but rather the MV released by Ninj1 playing a role, the title should include that. The current title makes it seem like Ninj1 directly regulates inflammation and coagulation. It would be better to revise the title.

      Thanks for the thoughtful comments. We show that the release of procoagulant MVs by plasma membrane rupture (PMR) is a critical step in the activation of coagulation. In addition, the release of cytokines and danger molecules by PMR may also contribute to coagulation. In choosing the title, we are trying to emphasize NINJ1-dependent PMR as a common trigger for these biological processes.

      (2) Ninj1 is known to be an induced protein that is barely expressed in normal conditions. As you showed in "Fig1G" data, control samples showed no detection of Ninj1. However, in "Figure S1", all tissues (liver, lung, kidney and spleen) expressed Ninj1 protein. If the authors stimulated the mice with fla injection, it should be mentioned in the figure legend. 

      We respectfully disagree with the comment that “Ninj1 is known to be an induced protein that is barely expressed in normal conditions”. NINJ1 protein is abundantly expressed (without induction) in tissues including liver, lung, kidney, and spleen, as shown in Fig S1. Consistently, other groups have shown abundant NINJ1 expression at baseline in tissues and cells such as liver (Kayagaki et.al. Nature 2023) and BMDM (Kayagaki et.al. Nature 2021; Borges et.al. eLife 2023). Fig 1G shows fibrin deposition as an indicator of coagulation, not NINJ1 protein.

      (3) In "Fig3A", the Ninj1 protein expression was increased in the control of BMDM +/- cell lysate rather than fla stimulation. However, in MV, Ninj1 was not detected at all in +/- control but was only observed with Fla injection. The authors need to provide an explanation for this observation. Additionally, looking at the MV β-actin lane, the band thicknesses appear to be very different between groups. It seems necessary to equalize the protein amounts. If that is difficult, at least between the +/+ and +/- controls. 

      Thanks for the valuable comments. In Fla-stimulated Ninj1+/- BMDMs, most of the NINJ1 is released in MVs, therefore, not in the cell lysate, as shown in Fig 3A. The difference in beta-actin band intensity correlated with MV numbers shown in Fig 3B. We ensure consistency by using the same number of cells.

      (4) Since the authors focused Ninj1-dependent microvesicle (MV) release, they need to show MV characterizations (EM, NTA, Western for MV markers, etc...). 

      Thanks for the suggestion. We now add NTA analysis of MV for BMDMs in Fig S4C.

      (5) To clarify whether Ninj1-dependent MV induces coagulation, the authors need to determine whether platelet aggregation is reduced with isolated +/- MVs compared to +/+ MVs. 

      Thanks for the suggestion. We agree that platelet aggregation is closely linked to blood coagulation but would argue that one does not directly cause the other. While it would be interesting to examine whether MVs induce platelet aggregation, we hope the reviewer would agree that the outcome of this experiment would neither significantly support nor challenge our statement that NINJ1-dependent PMR promotes coagulation.

      (6) Even with the authors well established experiments with haploid mice, it is a critical limitation of this paper. To improve the quality of this paper, the authors should consider confirming the findings using mouse macrophage cell lines, such as generating Ninj1-/- Raw264.7 cell lines, to examine the homozygous effect. 

      Thanks for the valuable comments. We acknowledge the limitation of using haploid mice in this study. However, our data provides strong evidence supporting the role of NINJ1-dependent plasma membrane rupture in blood coagulation using primary macrophages.

      (7) There was a paper reported in 2023 (Zhou, X. et al., NINJ1 Regulates Platelet Activation and PANoptosis in Septic Disseminated Intravascular Coagulation. Int. J. Mol. Sci. 2023) that revealed the relationship between Ninj1 and coagulation. According to this paper, inhibition of Ninj1 in platelets prevents pyroptosis, leading to reduced platelet activation and, consequently, the suppression of thrombosis. How about the activation of platelets in Ninj1 +/- mice? The author should add this paper in the reference section and discuss the platelet functions in their mice.

      Thanks for the valuable comments. We examine PT time, plasma TAT, and tissue fibrin deposition as direct evidence of blood coagulation in this manuscript. We acknowledge that platelets play a key role in thrombosis; however, we hope the reviewer would agree that tissue factor-induced blood coagulation and platelet aggregation are linked yet distinct processes. Therefore, the role of NINJ1 in platelet aggregation falls beyond the scope of this manuscript.


      The following is the authors’ response to the original reviews.

      Public Reviews: 

      Reviewer #1 (Public Review): 

      Referring to previous research findings, the authors explain the connection between NINJ1 and MVs. Additional experiments and clarifications will strengthen the conclusions of this study.

      Below are some comments I feel could strengthen the manuscript: 

      (1) The authors mentioned their choice of using heterozygous NINJ1+/- mice on page 4, because of lethality and hydrocephalus. Nonetheless, there is a substantial number of references that use homozygous NINJ1-/- mice. Could there be any other specific reasons for using heterozygous mice in this study? 

      Thanks for the thoughtful comments. We are aware that a few homozygous NINJ1-/- mouse strains were used in several publications by different groups, including Drs. Kayagaki and Dixit (Genentech), from whom we obtained the heterozygous NINJ1+/- breeders. We do not have experience with the homozygous NINJ1-/- mice used by other groups. It’s reasonable to assume that homozygous NINJ1-/-, if healthy, would have even stronger protection against coagulopathy than heterozygous NINJ1+/-. The only reason for not using homozygous mice in this study is that a majority of our homozygous NINJ1-/- develops hydrocephalus around weaning and these mice are required to be euthanized by the rules of our DLAR facility. Although our homozygous NINJ1-/- mice develop hydrocephalus (the same reported by Drs. Kayagaki and Dixit, PMID: 37196676, PMCID: PMC10307625), heterozygous NINJ1+/- mice remain healthy.

      (2) Figure S2 clearly shows the method of pyroptosis induction by flagellin. It is also necessary as a prerequisite for this paper to show the changes in flagellin-induced pyroptosis in heterozygous NINJ1+/- mice.

      Thanks for the valuable suggestions. We agree that a plasma LDH measurement as an indicator of pyroptosis in vivo would add to the manuscript. Therefore, we have made several attempts to measure plasma LDH in flagellin-challenged WT and NINJ1+/- mice using CytoTox96 Non-Radioactive Cytotoxicity Assay (a Promega kit commonly used for LDH, Promega#G1780). Flagellin-challenged WT and NINJ1+/- mice develops hemolysis, which renders plasma red. Because plasma coloring interferes with the assay, we could not get a meaningful reading to make an accurate comparison. We also tried LHD-Glo Cytotoxicity Assay (Luciferase based, Promega#J2380) with no luck on both plasma and serum. We hope the reviewer would agree that reduced plasma MV count (Fig 3C) would serve as an alternative indictor for reduced pyroptosis.

      (3) IL-1ß levels controlled by GSDMD were not affected by NINJ1 expression according to previous studies (Ref 37, 29, Nature volume 618, pages 1065-1071 (2023)). GSDMD also plays an important role in TF release in pyroptosis. Are GSDMD levels not altered in heterozygous NINJ1 +/- mice?  

      Thanks for raising these great points. It’s been reported that IL-1β secretion in cell culture supernatant were not affected by NINJ1 deficiency or inhibition when BMDMs were stimulated by LPS (Ref 29, 37, now Ref 29, 35) or nigericin (Ref 29). As GSDMD pore has been shown to facilitate the release of mature IL-1β, these in vitro observations are reasonable given that NINJ1-mediated PMR is a later event than GSDMD pore-forming. However, we observed that plasma IL-1β (also TNFα and IL-6) in Ninj1+/- mice were significantly lower. There are a few differences in the experimental condition that might contribute to the discrepancy: 1, there was no priming in our in vivo experiment, while priming in BMDMs were performed in both in vitro observations before stimulating with LPS or nigericin; 2, the flagellin in our study engages different inflammasome than either LPS or nigericin. Priming might change the expression and dynamics of IL-1β. More importantly, there might be unrecognized mechanisms in IL-1β secretion in vivo. We now add discussion on this in the main text.

      We examined GSDMD protein levels in liver, lung, kidney, and spleen from WT and NINJ1+/- mice by Western blotting. The data is now presented in the updated Fig S1, we did not observe apparent difference in GSDMD expression between the two genotypes.

      (4) In Fig 1 F, the authors used a fibrin-specific monoclonal antibody for staining fibrin, but it's not clearly defined. There may be some problem with the quality of antibody or technical issues. Considering this, exploring alternative methods to visualize fibrin might be beneficial. Fibrin is an acidophil material, so attempting H&E staining or Movat's pentachrome staining might help for identify fibrin areas.

      Thanks for the valuable suggestions. The fibrin-specific monoclonal antibody in our study is mouse anti-fibrin monoclonal antibody (59D8). This antibody has been shown to bind to fibrin even in the presence of human fibrinogen at the concentration found in plasma [Hui et al. (1983). Science. 222 (4628); 1129-1132]. We apologize that we did not cite the reference in our initial submission. We obtained this antibody from Dr. Hartmut Weiler at Medical College of Wisconsin and Dr. Rodney M. Camire at the University of Pennsylvania, who were acknowledged in our initial submission.

      We performed H&E staining on serial sections of the same tissues for Figure 1F. The data is now presented as Fig S3.

      Reviewer #2 (Public Review): 

      Summary: 

      The author's main goal is to understand the mechanism by which pyroptosis (through the formation of Gasdermin D (GSDMD) pores in the plasma membrane) contributes to increased release of procoagulant Tissue Factor-containing microvesicles (MV). Their previous data demonstrate that GSDMD is critical for the release of MV that contains Tissue Factor (TF), thus making a link between pyroptosis and hypercoagulation. Given the recent identification of NINJ1 being responsible for plasma membrane rupture (Kayagaki et al. Nature 2011), the authors wanted to determine if NINJ1 is responsible for TF-containing MV release. Given the constitutive ninj1 KO mouse leads to partial embryonic lethality, the authors decided to use a heterozygous ninj1 KO mouse (ninj1+/-). While the data are well controlled, there is limited understanding of the mechanism of action. Also, given that the GSDMD pores have an ~18 nm inner diameter enough to release IL-1β, while larger molecules like LDH (140 kDa) and other DAMPs require plasma membrane rupture (likely mediated by NINJ1), it s not unexpected that large MVs require NINJ1-mediated plasma cell rupture. 

      Strengths: 

      The authors convincingly demonstrate that ninj1 haploinsufficiency leads to decreased prothrombin time, plasma TAT and plasma cytokines 90 minutes post-treatment in mice, which leads to partial protection from lethality. 

      Weaknesses: 

      - In the abstract, the authors say "...cytokines and protected against blood coagulation and lethality triggered by bacterial flagellin". This conclusion is not substantiated by the data, as you still see 70% mortality at 24 hours in the ninj1+/- mice. 

      Thanks for the thoughtful comments. We corrected the text to “partially protected against blood coagulation and lethality triggered by bacterial flagellin”.

      - The previous publication by the authors (Wu et al. Immunity 2019) clearly shows that GSDMDdependent pyroptosis is required for inflammasome-induced coagulation and mouse lethality. However, as it is not possible for the authors to use the homozygous ninj1 KO mouse due to partial embryonic lethality, it becomes challenging to compare these two studies and the contributions of GSDMD vs. NINJ1. Comparing the contributions of GSDMD and NINJ1 in human blood-derived monocytes/macrophages where you can delete both genes and assess their relevant contributions to TF-containing MV release within the same background would be crucial in comparing how much contribution NINJ1 has versus what has been published for GSDMD? This would help support the in vivo findings and further corroborate the proposed conclusions made in this manuscript.  

      Thanks for the valuable question. We have shown that plasma MV TF activity was reduced in both GSDMD deficient mice (Ref 23) and Ninj1+/- mice (present manuscript). Given that TF is a plasma membrane protein, MV TF most likely comes from ruptured plasma membrane. In flagellin-induced pyroptosis, both GSDMD and NINJ1 deficiency equally blocked LDH release (plasma membrane rupture) in BMDMs (Ref 29). Further, in pyroptosis glycine acts downstream of GSDMD pore formation for its effect against NINJ1 activation (Ref 35). Therefore, GSDMD pore-forming should be upstream of NINJ1 activation in pyroptosis (which may not be the case in other forms of cell death) and there are likely equal effects of GSDMD and NINJ1 on MV release in flagellin-induced pyroptosis. As the reviewer suggested, experiments using human blood-derived monocytes/macrophages will enable a direct comparison to determine the relative contribution. However, this approach presents a few technical difficulties: it’s not easy to manipulate gene expression on primary human monocytes/macrophages (in our experience); variable efficiency in gene manipulation of GSDMD and NINJ1 will complicate the comparison. I hope the reviewer would agree that a direct comparison between GSDMD and NINJ1 is not required to support our conclusion that NINJ1-dependent membrane rupture is involved in inflammasome-pyroptosis induced coagulation and inflammation.

      - What are the levels of plasma TAT, PT, and inflammatory cytokines if you collect plasma after 90 minutes? Given the majority (~70%) of the ninj+/- mice are dead by 24 hours, it is imperative to determine whether the 90-minute timeframe data (in Fig 1A-G) is also representative of later time points. The question is whether ninj1+/- just delays the increases in prothrombin time, plasma TAT, and plasma cytokines. 

      Thank for the valuable question. The time point (90 min) was chosen based on our in vitro observation that flagellin-induced pyroptosis in BMDMs largely occurs within 60-90 min. 

      Because our focus on the primary effect of flagellin in vivo, potential secondary effects at later points may complicate the results and are hard to interpret. As the reviewer suggested, we have measured plasma PT, TAT at 6 hours post-flagellin challenge. The significant difference in PT sustained between Ninj1+/+ and Ninj1+/- (Fig A), suggesting coagulation proteins remained more depleted in Ninj1+/+ mice than in Ninj1+/- mice. However, plasma TAT levels were diminished to baseline level (refer to Fig 1B in main text) in both groups and showed no significant difference between groups (Fig B), which could be explained by the short half-life (less than 30 min) in the blood. Since flagellin challenge is a one-time hit, there might not a second episode of coagulation after the 90-minute time point, at least not triggered by flagellin, supported by the plasma TAT levels at 6 hours. We now comment on this limitation at the end of the main text.

      Based on our previous studies, plasma IL-1β and TNFα peaked at early time point and diminished over time, but plasma IL-6 levels maintained. As shown below, plasma IL-6 appeared higher in Ninj1+/+ compared with Ninj1+/-, but not statistically significant (partly because one missing sample, n = 4 not 5, in Ninj1+/+ group decreased the statistical power of detecting a difference).

      Author response image 1.

      Mice were injected with Fla (500 ng lFn-Fla plug3 ugPA). Blood was collected 6 hours after Fla injection. Prothrombin time (A), plasma TAT (B), and plasma IL-6 (C) were measured. Mann-Whitney test were performed.

      Recommendations for the authors:  

      Reviewer #1 (Recommendations For The Authors): 

      - Fig 1F: are there lower magnification images that capture the fibrin deposition? The IHC data seems at odds with the WB data in Fig. 1G where there is still significant fibrin detected in the heterozygous lungs and liver. Quantitating the Fig. 1G Western blot would also be helpful.

      IHC surveys a thin layer of tissue section while WB surveys a piece of tissue, therefore fibrin deposition may be missing from IHC and but found in WB. That is why we used two methods. Below we provide lower mag images of fibrin deposition (about 2 x 1.6 mm area).

      Author response image 2.

      - Fig1H - lethality study uses 5x dose of Fla used in earlier studies. In the lethality data where there is a delay in ninj1+/- mortality, are the parameters (prothrombin time, plasma TAT, and plasma cytokines) measured at 90 minutes different between WT and ninj+/- mice? This would be critical to confirm that this is not merely due to a delayed release of TF-containing MVs.

      We used 5x lower dose of Fla in coagulation study than lethality study because it’s not as easy to draw blood from septic mouse with higher dose of flagellin. We need to terminate the mice to collect blood for plasma measurement and therefore the parameters were not measured for mice in lethality study.

      - What is the effect of ninj+/- on E. coli-induced lethality in mice? How do these data compare to E. coli infection of GSDMD-/- mice? 

      We did not examine the effect of Ninj1+/- on E. coli-induced lethality. After the initial submission of our manuscript, we have focused on Ninj1 flox/flox mice instead of Ninj1+/- for NINJ1 deficiency. We are using induced global Ninj1 deficient mice for polymicrobial infectioninduced lethality in our new studies.

      - Fig 2 - in the E. coli model, the prothrombin time, plasma TAT, and plasma cytokines are measured 6 hours post-infection. How were these time points chosen? Did the authors measure prothrombin time, plasma TAT, and plasma cytokines at different time points?  

      The in vivo time point for flagellin and E.coli were chosen based on our in vitro observation of the timelines on BMDM pyroptosis induced by flagellin and bacteria. This disparity probably arises from distinct dynamics between purified protein and bacterial infections. Purified proteins can swiftly translocate into cells and take effect immediately after injection. Conversely, during bacterial infection, macrophages engulf and digest the bacteria to expose their antigens. Subsequently, these antigens initiate further effects, a process that takes some time to unfold. 

      Our focus is on the primary effect of flagellin in vivo, potential secondary effects at later points may complicate the results and are hard to interpret. As the reviewer suggested, we have measured plasma PT, TAT at 6 hours post-flagellin challenge. The significant difference in PT sustained between Ninj1+/+ and Ninj1+/- (Fig A), suggesting coagulation proteins remained more depleted in Ninj1+/+ mice than in Ninj1+/- mice. However, plasma TAT levels were diminished to baseline level (refer to Fig 1B in main text) in both groups and showed no significant difference between groups (Fig B), which could be explained by the short half-life (less than 30 min) in the blood. Since flagellin challenge is a one-time hit, there might not a second episode of coagulation after the 90-minute time point, at least not triggered by flagellin, supported by the plasma TAT levels at 6 hours. We now comment on this limitation at the end of the main text.

      Based on our previous studies, plasma IL-1β and TNFα peaked at early time point and diminished over time, but plasma IL-6 levels maintained. As shown below, plasma IL-6 appeared higher in Ninj1+/+ compared with Ninj1+/-, but not statistically significant (partly because one missing sample, n = 4 not 5, in Ninj1+/+ group decreased the statistical power of detecting a difference).

      - Fig 3 - the sequence of figure panels listed in the legend needs to be corrected. Fig 3A requires quantitation of NINJ1 levels compared to beta-actin. Fig 3C - needs a control for equal MV loading. 

      Thanks for the recommendations. The figure sequence has been corrected. There remain no common markers or loading controls for MV, so we use equal plasma volume for loading control.

      Additional comments: 

      (1) In Fig 3A, the size of NINJ1 appears to be increased in the NINJ+/- group.  

      This discrepancy is likely attributed to a technical issue when running the protein gel and protein transfer, which makes the image tilt to one side.

      (2) Describe the method of BMDM isolation.

      Thanks for the recommendations. We now include the method of BMDM isolation. In brief, mouse femur and tibia from one leg are harvested and rinsed in ice-cold PBS, followed by a brief rinse in 70% ethanol for 10-15 seconds. Both ends of the bones are then cut open, and the bone marrow is flushed out using a 10 ml syringe with a 26-gauge needle. The marrow is passed through a 19-gauge needle once to disperse the cells. After filtering through a 70-μm cell strainer, the cells are collected by centrifugation at 250 g for 5 minutes at 4 °C, then suspended in two 150 mm petri dish, each containing 25 ml of L-cell conditioned medium (RPMI-1640 supplemented with 10% FBS, 2mM L-Glutamine, 10mM HEPES, 15% LCM, and penicillin/streptomycin). After 3 days, 15 mL of LCM medium is added to each dish cells. The cells typically reach full confluency by days 5-7.

      (3) According to this method, BMDMs are seeded without any M-CSF or L929-cell conditioned medium. How many macrophages survive under this condition? 

      BMDMs are cultured and differentiated in medium supplemented with 15% L929-cell conditioned medium. For the experiment, the cells were seeded in Opti-MEM medium (Thermo Fisher Scientific, Cat# 51985034) without M-CSF or L929-cell conditioned medium. BMDMs can survive under this condition, as evidenced by low LDH and high ATP measurement (Fig S5).

      Reviewer #2 (Recommendations For The Authors): 

      - There is significant information missing in the methods and this makes it unclear how to interpret how some of the experiments were performed. For example, there is no detailed description or references in the methods on how the in vivo experiments were performed. The methods section needs significantly more details so that any reader is able to follow the protocols in this manuscript. References to previous work should also be included as needed.

      Thanks for the recommendations. We had some of the details in the figure legend. We now add details in the methods for better interpretation of our data. 

      - Line numbers in the manuscript would be helpful when resubmitting the manuscript so that the reviewer can easily point to the main text when making comments. 

      Thanks for the recommendations. We now add line numbers in the manuscript.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      (1) This is a valuable manuscript that successfully integrates several data sets to determine genomic interactions with nuclear bodies.

      In this paper we both challenge and/or revise multiple long-standing “textbook” models of nuclear genome organization while also revealing new features of nuclear genome organization. Therefore, we argue that the contributions of this paper extend well beyond “valuable”. Specifically, these contributions include:

      a. We challenge a several decades focus on the correlation of gene positioning relative to the nuclear lamina. Instead, through comparison of cell lines, we show a strong correlation of di4erences in gene activity with di4erences in relative distance to nuclear speckles in contrast to a very weak correlation with di4erences in relative distance to the nuclear lamina. This inference of little correlation of gene expression with nuclear lamina association was supported by direct experimental manipulation of genome positioning relative to the nuclear lamina. Despite pronounced changes in relative distances to the nuclear lamina there was little change relative to nuclear speckles and little change in gene expression.

      b. We similarly challenge the long-standing proposed functional correlation between the radial positioning of genes and gene expression. Here, and in a now published companion paper (doi.org/10.1038/s42003-024-06838-7), we demonstrate how nuclear speckle positioning relative to nucleoli and the nuclear lamina varies among cell types, as does the inverse relationship between genome positioning relative to nuclear speckles and the nuclear lamina. Again, this is consistent with the primary correlation of gene activity being the positioning of genes relative to nuclear speckles and also explains previous observations showing a strong relationship between radial position and gene expression only in some cell types.

      c. We identified a new partially repressed, middle to late DNA replicating type of chromosome domain- “p-w-v fILADs”- by their weak interaction with the nuclear lamina, which, based on our LMNA/LBR KO experimental results, compete with LADs for nuclear lamina association. Moreover, we show that when fLADs convert to iLADs, most conversions are to this p-w-v fiLAD state, although ~ one third are to a normal, active, early replicating iLAD state. Thus, fLADs can convert between repressed, partially repressed, and active states, challenging the prevailing assumption of the division of the genome into two states – active, early replicating A compartment/iLAD regions versus inactive, late replicating, B compartment/LAD regions.

      d. We identified nuclear speckle associated domains as DNA replication initiation zones, with the domains showing strongest nuclear speckle attachment initiating DNA replication earliest in S-phase.

      e. We describe for the first time an overall polarization of nuclear genome organization in adherent cells with the most active, earliest replicating genomic regions located towards the equatorial plane and less expressed genomic regions towards the nuclear top or bottom surfaces. This includes polarization of some LAD regions to the nuclear lamina at the equatorial plane and other LAD regions to the top or bottom nuclear surfaces.

      We have now rewritten the text to make the significance of these new findings clearer.

      (2) Strength of evidence: The evidence supporting the central claims is varied in its strength ranging from solid to incomplete. Orthogonal evidence validating the novel methodologies with alternative approaches would better support the central claims.

      We argue that our work exploited methods, data, and analyses equal to or more rigorous than the current state-of-the-art. This indeed includes orthogonal evidence using alternative methods which both supported our novel methodologies as well as demonstrating their robustness relative to more conventional approaches. This explains how we were able to challenge/revise long-standing models and discover new features of nuclear genome organization. More specifically:

      a. Unlike most previous analyses, we have integrated both genomic and imaging approaches to examine the nuclear genome organization relative to not one, but several di4erent nuclear locales and we have done this across several cell types. To our knowledge, this is the first such integrated approach and has been key to our success in appreciating new features of nuclear genome organization.

      b. The 16-fraction DNA replication Repli-seq data we developed and applied to this project represents the highest temporal mapping of DNA replication timing to date.

      c. The TSA-seq approach that we used remains the most accurate sequence-based method for estimating microscopic distance of chromosome regions to di4erent nuclear locales. As implemented, this method is unusually robust and direct as it exploits the exponential micron-scale gradient established by the di4usion of the free-radicals generated by peroxidase labeling to measure relative distances of chromosome regions to labeled nuclear locales. We had previously demonstrated that TSA-seq was able to estimate the average distances of genomic regions to nuclear speckles with an accuracy of ~50 nm, as validated by light microscopy. The TSA-seq 2.0 protocol we developed and applied to this project maintained the original resolution of TSA-seq to estimate to an accuracy of ~50 nm the average distances of genomic regions from nuclear speckles, as validated by light microscopy, while achieving more than a 10-fold reduction in the required number of cells.

      We have rewritten the text to address the reviewer concerns that led them to their initial characterization of the TSA-seq as novel and not yet validated.

      First, we have added a discussion of how the use of nuclear speckle TSA-seq as a “cytological ruler” was based on an extensive initial characterization of TSA-seq as described in previous published literature. In that previous literature we showed how the conventional molecular proximity method, ChIP-seq, instead showed local accumulation of the same marker proteins over short DNA regions unrelated to speckle distances. Second, we reference our companion paper, now published, and describe how the extension of TSA-seq to measure relative distances to nucleoli was further validated and shown to be robust by comparison to NAD-seq and extensive multiplexed immuno-FISH data. We further discuss how in the same companion paper we show how nucleolar DamID instead was inconsistent with both the NAD-seq and multiplexed immuno-FISH data as well as the nucleolar TSA-seq.

      Third, we have added scatterplots showing exactly how highly the estimated microscopic distances to all three nuclear locales, measured in IMR90 fibroblasts, correlate with the TSA-seq measurements in HFF fibroblasts. This addresses the concern that we were not using the exact same fibroblast cell line for the TSA-seq versus microscopic measurements. The strong correlation already observed would only be expected to become even stronger with use of the exact same fibroblast cell lines for both measurements.

      Fourth, we have addressed the reviewer concern that the nuclear lamin TSA-seq was not properly validated because it did not match nuclear lamin Dam-ID. We have now added to the text a more complete explanation of how microscopic proximity assays such as TSA-seq measure something di4erent from molecular proximity assays such as DamID or NAD-seq. We have added further explanation of how TSA-seq complements molecular proximity assays such as DamID and NAD-seq, allowing us to extract further information than either measurement alone. We also briefly discuss why TSA-seq succeeds for certain nuclear locales using multiple independent markers whereas molecular proximity assays may fail against the same nuclear locales using the same markers. This includes brief discussion from our own experience attempting unsuccessfully to use DamID against nucleoli and nuclear speckles.

      Reviewer #1 (Public Review):

      (1) The weakness of this study lies in the fact that many of the genomic datasets originated from novel methods that were not validated with orthogonal approaches, such as DNAFISH. Therefore, the detailed correlations described in this work are based on methodologies whose efficacy is not clearly established. Specifically, the authors utilized two modified protocols of TSA-seq for the detection of NADs (MKI67IP TSA-seq) and LADs (LMNB1-TSA-seq).

      We disagree with the statement that the TSA-seq approach and data has not been validated by orthogonal approaches. We have now addressed this point in the revised manuscript text:

      a) We added text to describe how previously FISH was used to validate speckle TSA-seq by demonstrating a residual of ~50 nm between the TSA-seq predicted distance to speckles and the distance measured by light microscopy using FISH:

      "In contrast, TSA-seq measures relative distances to targets on a microscopic scale corresponding to 100s of nm to ~ 1 micron based on the measured diffusion radius of tyramide-biotin free-radicals (Chen et al., 2018). Exploiting the measured exponential decay of the tyramide-biotin free-radical concentration, we showed how the mean distance of chromosomes to nuclear speckles could be estimated from the TSA-seq data to an accuracy of ~50 nm, as validated by FISH (Chen et al., 2018)."

      b) We note that we also previously have validated lamina (Chen et al, JCB 2018) and nucleolar (Kumar et al, 2024) TSA-seq and further validated speckle TSA-seq (Zhang et al, Genome Research 2021) by traditional immuno-FISH and/or immunostaining. The overall high correlation between lamina TSA-seq and the orthogonal lamina DamID method was also extensively discussed in the first TSA-seq paper (Chen et al, JCB 2018). Included in this discussion was description of how the di4erences between lamina TSA-seq and DamID were expected, given that DamID produces a signal more proportional to contact frequency, and independent of distance from the nuclear lamina, whereas TSA-seq produces a signal that is a function of microscopic distance from the lamina, as validated by traditional FISH.

      c) We added text to describe how the nucleolar TSA-seq previously was validated by two orthogonal methods- NAD-seq and multiplexed DNA immuno-FISH:

      "We successfully developed nucleolar TSA-seq, which we extensively validated using comparisons with two different orthogonal genome-wide approaches (Kumar et al., 2024)- NAD-seq, based on the biochemical isolation of nucleoli, and previously published direct microscopic measurements using highly multiplexed immuno-FISH (Su et al., 2020)."

      d) We have now added panels A&B to Fig. 7 and a new Supplementary Fig. 7 demonstrating further validation of TSA-seq based on showing the high correlation between the microscopically measured distances of many hundreds of genomic sites across the genome from di4erent nuclear locales and TSA-seq scores. As discussed in response #2 below, we have used comparison of distances measured in IMR90 fibroblasts with TSA-seq scores measured in HFF fibroblasts. We would argue therefore that these correlations are a lower estimate and therefore the correlation between microscopic distances and TSAseq scores would likely have been still higher if we had performed both assays in the exact same cell line.

      (2) Although these methods have been described in a bioRxiv manuscript by Kumar et al., they have not yet been published. Moreover, and surprisingly, Kumar et al., work is not cited in the current manuscript, despite its use of all TSA-seq data for NADs and LADs across the four cell lines.

      The Kumar et al, Communications Biology, 2024 paper is now published and is cited properly in our revision. We apologize for this oversight and confusion our initial omission of this citation may have created. We had been writing this manuscript and the Kumar et al manuscript in parallel and had intended to co-submit. We planned to cross-reference the two at the time we co-submitted, adding the Kumar et al reference to the first version of this manuscript once we obtained a doi from bioRxiv. But we then submitted the Kumar et al manuscript several months earlier, but meanwhile forgot that we had not added the reference to our first manuscript version.

      (3) Moreover, Kumar et al. did not provide any DNA-FISH validation for their methods.

      As we described in our response to Reviewer 1's comment #1, we had previously provided traditional FISH validation of lamina TSA-seq in our first TSA- seq paper as well as validation by comparison with lamina DamID (Chen et al, 2018).

      We also described how the nucleolar TSA-seq was extensively cross-validated in the Kumar et al, 2024 paper by both NAD-seq and the highly multiplexed immuno-FISH data from Su et al, 2020).

      We note additionally that in the Kumar et al, 2024 paper the nucleolar TSA-seq was additionally validated by correlating the predicted variations in centromeric association with nucleoli across the four cell lines predicted by nucleolar TSA-seq with the variations observed by traditional immunofluorescence microscopy.

      (4) Therefore, the interesting correlations described in this work are not based on robust technologies.

      This comment was made in reference to the Kumar et al paper not having been published, and, as noted in responses to points #2 and #3, the paper is now published.

      But we wanted to specifically note, however, that our experience is that TSA-seq has proven remarkably robust in comparison to molecular proximity assays. We've described in our responses to the previous points how TSA-seq has been cross-validated by both microscopy and by comparison with lamina DamID and nucleolar NAD-seq. We note also that in every application of TSA-seq to date, all antibodies that produced good immunostaining showed good TSA-seq results. Moreover, we obtained nearly identical results in every case in which we performed TSA-seq with different antibodies against the same target. Thus anti-SON and antiSC35 staining produced very similar speckle TSA-seq data (Chen et al, 2018), anti-lamin A and anti-lamin B staining produced very similar lamina TSA-seq data (Chen et al, 2018), antinucleolin and anti-POL1RE staining produced very similar DFC/FC nucleolar TSA-seq data (Kumar et al, 2024), and anti-MKI67IP and anti-DDX18 staining produced very similar GC nucleolar TSA-seq data (Kumar et al, 2024).

      This independence of results with TSA-seq to the particular antibody chosen to label a target differs from experience with methods such as ChIP, DamID, and Cut and Run/Tag in which results can differ or be skewed based on variable distance and therefore reactivity of target proteins from the DNA or due to other factors such as non-specific binding during pulldown (ChIP) or differential extraction by salt washes (Cut and Tag).

      Our experience in every case to date is that antibodies that produce similar immunofluorescence staining produce similar TSA-seq results. We attribute this robustness to the fact that TSA-seq is based only on the original immunostaining specificity provided by the primary and secondary antibodies plus the diffusion properties of the tyramide-free radical.

      We've now added the following text to our revised manuscript:

      "As previously demonstrated for both SON and lamin TSA-seq (Chen et al., 2018), nucleolar TSA-seq was also robust in the sense that multiple target proteins showing similar nucleolar staining showed similar TSA-seq results (Kumar et al., 2024); this robustness is intrinsic to TSA-seq being a microscopic rather than molecular proximity assay, and therefore not sensitive to the exact molecular binding partners and molecular distance of the target proteins to the DNA."

      (5) An attempt to validate the data was made for SON-TSA-seq of human foreskin fibroblasts (HFF) using multiplexed FISH data from IMR90 fibroblasts (from the lung) by the Zhuang lab (Su et al., 2020). However, the comparability of these datasets is questionable. It might have been more reasonable for the authors to conduct their analyses in IMR90 cells, thereby allowing them to utilize MERFISH data for validating the TSA-seq method and also for mapping NADs and LADs.

      We disagree with the reviewer's overall assessment that that the use of the IMR90 data to further validate the TSA-seq is questionable because the TSA-seq data from HFF fibroblasts is not necessarily comparable with multiplexed immuno-FISH microscopic distances measured in IMR90 fibroblasts.

      In response we have now added panels to Fig. 7 and Supplementary Fig. 7, showing:

      a) There is very little di4erence in correlation between speckle TSA-seq and measured distances from speckles in IMR90 cells whether we use IMR90 or HFF cells SON TSA-seq data (R<sup>2</sup> = 0.81 versus 0.76) (new Fig. 7A).

      b) There is also a high correlation between lamina (R<sup>2</sup> = 0.62) and nucleolar (R<sup>2</sup> = 0.73) HFF TSA-seq and measured distances in IMR90 cells. Thus, we conclude that this high correlation shows that the multiplexed data from ~1000 genomic locations does validate the TSA-seq. These correlations should be considered lower bounds on what we would have measured using IMR90 TSA-seq data. Thus, the true correlation between distances of loci from nuclear locales and TSA-seq would be expected to be either comparable or even stronger than what we are seeing with the IMR90 versus HFF fibroblast comparisons.

      c) This correlation is cell-type specific (Fig. 7B, new SFig. 7). Thus, even for speckle TSAseq, highly conserved between cell types, the highest correlation of IMR90 distances with speckle TSA-seq is with IMR90 and HFF fibroblast data. For lamina and nucleolar TSA-seq, which show much lower conservation between cell types, the correlation of IMR90 distances is high for HFF data but much lower for data from the other cell types. This further justifies the use of IMR90 fibroblast distance measurements as a proxy for HFF fibroblast measurements.

      Thus, we have added the following text to the revised manuscript:

      "We reasoned that the nuclear genome organization in the two human fibroblast cell lines would be sufficiently similar to justify using IMR90 multiplexed FISH data [43] as a proxy for our analysis of HFF TSA-seq data. Indeed, the high inverse correlation (R= -0.86) of distances to speckles measured by MERFISH in IMR90 cells with HFF SON TSA-seq scores is nearly identical to the inverse correlation (R= -0.89) measured instead using IMR90 SON TSA-seq scores (Fig. 7A). Similarly, distances to the nuclear lamina and nucleoli show high inverse correlations with lamina and nucleolar TSA-seq, respectively (Fig. 7A). These correlations were cell type specific, particularly for the lamina and nucleolar distance correlations, as these correlations were reduced if we used TSA-seq data from other cell types (SFig. 7A). Therefore, the high correlation between IMR90 microscopic distances and HFF TSA-seq scores can be considered a lower bound on the likely true correlation, justifying the use of IMR90 as a proxy for HFF for testing our predictions."

      Reviewer #2 (Public Review):

      Weaknesses:

      (1) The experiments are largely descriptive, and it is difficult to draw many cause-andeffect relationships...The study would benefit from a clear and specific hypothesis.

      This study was hypothesis-generating rather than hypothesis-testing in its goal. Our research was funded through the NIH 4D-Nucleome Consortium, which had as its initial goal the development, benchmarking, and validation of new genomic technologies. Our Center focused on the mapping of the genome relative to different nuclear locales and the correlation of this intranuclear positioning of the genome with functions- specifically gene expression and DNA replication timing. By its very nature, this project took a discovery-driven versus hypothesis-driven scientific approach. Our question fundamentally was whether we could gain new insights into nuclear genome organization through the integration of genomic and microscopic measurements of chromosome positioning relative to multiple different nuclear compartments/bodies and their correlation with functional assays such as RNA-seq and Repliseq.

      Indeed, this study resulted in multiple new insights into nuclear genome organization as summarized in our last main figure. We believe our work and conclusions will be of general interest to scientists working in the fields of 3D genome organization and nuclear cell biology. We anticipate that each of these new insights will prompt future hypothesis-driven science focused on specific questions and the testing of cause-and-effect relationships.

      However, we do want to point out that our comparison of wild-type K562 cells with the LMNA/LBR double knockout was designed to test the long-standing model that nuclear lamina association of genomic loci contributes to gene silencing. This experiment was motivated by our surprising result that gene expression differences between cell lines correlated strongly with differences in positioning relative to nuclear speckles rather than the nuclear lamina. Despite documenting in these double knockout cells a decreased nuclear lamina association of most LADs, and an increased nuclear lamina association of the “p-w-v” fiLADs identified in this manuscript, we saw no significant change in gene expression in any of these regions as compared to wild-type K562 cells. Meanwhile, distances to nuclear speckles as measured by TSA-seq remained nearly constant.

      We would argue that this represents a specific example in which new insights generated by our genomics comparison of cell lines led to a clear and specific hypothesis and the experimental testing of this hypothesis.

      (2) Similarly, the paper would be very much strengthened if the authors provided additional summary statements and interpretation of their results (especially for those not as familiar with 3D genome organization).

      We appreciate this feedback and agree with the reviewer that this would be useful, especially for those not familiar with previous work in the field of 3D genome organization. In an earlier draft, we had included additional summary and interpretation statements in both the Introduction and Results sections. At the start of each Results section, we had also previously included brief discussion of what was known before and the context for the subsequent analysis contained in that section. However, we had thought we might be submitting to a journal with specific word limits and had significantly cut out that text.

      We have now restored this text and, in certain cases, added additional explanations and context.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Figures 1C and D. Please add the units at the values of each y-axis.

      We have done that.

      The representation of Figure 2C lacks clarity and is diJicult to understand. The x-axis labeling regarding the gene fraction number needs clarification.

      We've modified the text to the Fig. 2C legend: "Fraction of genes showing significant di=erence in relative positioning to nuclear speckles (gene fraction, x-axis) versus log2 (HFF FKPM / H1 FKPM) (y-axis);"

      "We next used live-cell imaging to corroborate that chromosome regions close to nuclear speckles, primarily Type I peaks, would show the earliest DNA replication timing." This sentence requires modification as Supplementary Figure 3F does not demonstrate that Type I peaks exhibit the earliest DNA replication timing; it only indicates that the first PCNA foci in S-phase are in proximity to nuclear speckles.

      We've modified the text to: "We next used live-cell imaging to show that chromosome regions close to nuclear speckles show the earliest DNA replication timing; this is consistent with the earliest firing DNA replication IZs, as determined by Repli-seq, aligning with Type 1 peaks that are closely associated with nuclear speckles."

      In Figure 5, the authors employed LaminB1-DamID to quantify LADs in LBR-KO and LMNA/LBR-DKO K562 cells. These are interesting results. However, for these experiments, it is crucial to assess LMNB1 signal at the nuclear periphery via immunofluorescence (IF) to confirm the absence of changes, ensuring that the DamID signal solely reflects contacts with the nuclear lamina. Furthermore, in this instance, their findings should be validated through DNA-FISH.

      Immunostaining of LMNB1 was performed and showed a normal staining pattern as a ring adjacent to the nuclear periphery. Images of this staining were included in the metadata tied to the sequencing data sets deposited on the 4D Nucleome Data portal. We thank the reviewer for bringing up this point, and have added a sentence mentioning this result in the Results Section:

      "Immunostaining against LMNB1 revealed the normal ring of staining around the nuclear periphery seen in wt cells (images deposited as metadata in the deposited sequencing data sets)."

      Because both TSA-seq and DamID have been extensively validated by FISH, as detailed in our previous responses to the public reviewer comments, we feel it is unnecessary to validate these findings by FISH.

      p-w-v-fiLADs should be labelled in Figure 5B.

      We've added labeling as suggested.

      "The consistent trend of slightly later DNA replication timing for regions (primarily p-w-v fiLADs) moving closer to the lamina" is not visible in the representation of the data of Figure 5G.

      We did not make a change as we believed this trend was apparent in the Figure.

      To reduce the descriptive nature of the data, it would be pertinent to conduct H3K9me3 and H3K27me3 ChIP-seq analyses in both the parental and DKO mutant cells. This would elucidate whether p-w-v-fiLADs and NADs anchoring to the nuclear lamina undergo changes in their histone modification profile.

      We believe further analysis of the reasons underlying these shifts in positioning, including such ChIP-seq or equivalent analysis, is of interest but beyond the scope of this publication. We see such measurements as the beginning of a new story but insuJicient alone to determine mechanism. Therefore we believe such experiments should be part of that future study.

      The description of Figure 7 lacks clarity. Additionally, it appears that TSA-seq for NADs and LADs may not be universally applicable across all cell types, particularly in flat cells, whereas DamID scores demonstrate less variation across cell lines, as also stated by the authors.

      TSA-seq is a complement to rather than a replacement for either DamID or NAD-seq. TSAseq reports on microscopic distances whereas both DamID and NAD-seq instead are more proportional to contact frequency with the nuclear lamina or nucleoli, respectively, and insensitive to distances of loci away from the lamina or nucleoli. Thus, TSA-seq provides additional information based on the intrinsic diJerences in what TSA-seq measures relative to molecular proximity methods such as DamID or NAD-seq. The entire point is that the convolution of the exponential point-spread-function of the TSA-seq with the shape of the nuclear periphery allows us to distinguish genomic regions in the equatorial plane versus the top and bottom of the nuclei. The TSA-seq is therefore highly "applicable" when properly interpreted in discerning new features of genome organization. As we stated in the revised manuscript, the lamina DamID and TSA-seq are complementary and provide more information together then either method along. The same is true for the NAD-seq and nucleolar TSA-seq comparison, as described in more detail in the Kumar, et al, 2024 paper.

      Introduction:

      The list of methodologies for mapping genomic contacts with nucleoli (NADs) should also include recent technologies, such as Nucleolar-DamID (Bersaglieri et al., PMID: 35304483), which has been validated through DNA-FISH.

      We did not include nucleolar DamID in the mention in the Introduction of methods for identifying diJerential lamina versus nucleolar interactions of heterochromatin- either from our own collaborative group or from the cited reference- because we did not have confidence in the accuracy of this method in identifying NADs. In the case of the published nucleolar DamID from our collaborative group, published in Wang et al, 2021, we later discovered that despite apparent agreement of the nucleolar DamID with a small number of published FISH localization the overall correlation of the nucleolar DamID with nucleolar localization was poor. As described in detail in the Kumar et al, 2024 publication, this poor correlation of the nucleolar DamID was established using three orthogonal methods- nucleolar TSA-seq, NAD-seq, and multiplexed immuno-FISH measurements from ~1000 genomic locations. Instead, we found that this nucleolar DamID showed high correlation with lamina DamID. We note that many strong NADs are also LADs, which we think is why validation with only several FISH probes is inadequate to demonstrate overall validation of the approach.

      We could not compare our nucleolar-DamID data in human cells with the alternative nucleolar-DamID results cited by the reviewer which were performed in mouse cells. We note that in this paper the nucleolar DamID FISH validation only included several putative NAD chromosome regions and, I believe, one LAD region. However, our initial comparison of the nucleolar DamID cited by the reviewer with unpublished TSA-seq data from mouse ESCs produced by the Belmont laboratory and with NAD-seq data from the Kaufman laboratory shows a similar lack of correlation of the nucleolar DamID signal with nucleolar TSA-seq and NAD-seq, as well as multiplexed immuno-FISH data from the Long Cai laboratory, as we saw in our analysis of own nucleolar DamID data in human cells.

      We have added explanation concerning the lack of correlation of our nucleolar DamID with orthogonal measurements of nucleolar proximity in the added text (below) to our revised manuscript:

      "Nucleolar DamID instead showed broad positive peaks over large chromatin domains, largely overlapping with LADs mapped by LMNB1 DamID (Wang et al., 2021). However, this nucleolar DamID signal, while strongly correlated with lamin DamID, showed poor correlation with either NAD-seq or nucleolar distances mapped by multiplexed immunoFISH (Kumar et al., 2024). We suspect the problem is that with molecular proximity assays the output signals are disproportionally dominated by the small fraction of target proteins juxtaposed in su=icient proximity to the DNA to produce a signal rather than the amount of protein concentrated in the target nuclear body. "

      Our mention of nucleolar TSA-seq was in the context of why we focused on nucleolar TSAseq and excluded our own nucleolar DamID. We chose not to discuss the second nucleolar DamID method cited above 1) because it was not appropriate to our discussion of our own experimental approach and 2) also because we cannot yet make a definitive statement of its accuracy for nucleolar mapping.

      Reviewer #2 (Recommendations For The Authors):

      (1) The authors start the manuscript by describing the 'radial genome organization' model and contrast it with the 'binary model' of genome organization. It would be helpful for the authors to contextualize their results a bit more with regard to these two diJerent models in the discussion.

      We have added several sentences in the first paragraph of the Discussion to accomplish this contextualization. The new paragraph reads:

      "Here we integrated imaging with both spatial (DamID, TSA-seq) and functional (Repli-seq, RNA-seq) genomic readouts across four human cell lines. Overall, our results significantly extend previous nuclear genome organization models, while also demonstrating a cell-type dependent complexity of nuclear genome organization. Briefly, in contrast to the previous radial model of genome organization, we reveal a primary correlation of gene expression with relative distances to nuclear speckles rather than radial position. Additionally, beyond a correlation of nuclear genome organization with radial position, in cells with flat nuclei we show a pronounced correlation of nuclear genome organization with distance from the equatorial plane. In contrast to previous binary models of genome organization, we describe how both iLAD / A compartment and LAD / B compartment contain within them smaller chromosome regions with distinct biochemical and/or functional properties that segregate di=erentially with respect to relative distances to nuclear locales and geometry."

      (2) Data should be provided demonstrating KO of LBR and LMNA - immunoblotting for both proteins would be one approach. In addition, it would be helpful to provide additional nuclear morphology measurements of the DKO cells (volume, surface area, volume of speckles/nucleoli, number of speckles/nucleoli).

      We've added additional description describing the generation and validation of the KO lines:

      "To create LMNA and LBR knockout (KO) lines and the LMNA/LBR double knockout (DKO) line, we started with a parental "wt" K562 cell line, clone #17, expressing an inducible form of Cas9 (Brinkman et al., 2018). The single KO and DKO were generated by CRISPR-mediated frameshift mutation according to the procedure described previously (Schep et al., 2021). The "wt" K562 clone #17 was used for comparison with the KO clones.

      The LBR KO clone, K562 LBR-KO #19, was generated, using the LBR2 oligonucleotide GCCGATGGTGAAGTGGTAAG to produce the gRNA, and validated previously, using TIDE (Brinkman et al., 2014) to check for frameshifts in all alleles as described elsewhere (Schep et al., 2021). The LMNA/LBR DKO, K562 LBR-LMNA DKO #14, was made similarly, starting with the LBR KO line and using the combination of two oligonucleotides to produce gRNAs:

      LMNA-KO1: ACTGAGAGCAGTGCTCAGTG, LMNA-KO2: TCTCAGTGAGAAGCGCACGC.

      Additionally, the LMNA KO line, K562 LMNA-KO #14, was made the same way but starting with the "wt" K562 cell line. Validation was as described above; additionally, for the new LMNA KO and LMNA/LBR DKO lines, immunostaining showed the absence of anti-LMNA antibody signal under confocal imaging conditions used to visualize the wt LMNA staining while the RNA-seq from these clones revealed an ~20-fold reduction in LMNA RNA reads relative to the wt K562 clone."

      As suggested, we also added morphological data for the DKO line in a modified SFig.5.

      (3) The rationale for using LMNB1 TSA-seq and LMNB1 DAMID is not immediately clear. The LMNB1 TSA-seq is more variable across cell types and replicates than the DAMID. Could the authors please compare the datasets a bit more to understand the diJerences? For example, the authors demonstrate that "40-70% of the genome shows statistically significant diJerences in Lamina TSA-seq over regions 100 kb or larger, with most of these regions showing little or no diJerences in speckle TSA-seq scores." If the LMNB1 DAMID data is used for this analysis or Figure 2D, is the same conclusion reached? Also, in Figure 6, the authors conclude that C1 and C3 LAD regions are enriched for constitutive LADs, while C2 and C4 LAD regions are fLADs. This is a bit surprising because the authors and others have previously shown that constitutive LADs have higher LMNB1 contact frequency than facultative LADs (Kind, et al Cell 2015, Figure 3C).

      Indeed, in the first TSA-seq paper (Chen et al, 2018) we did observe that cLADs had the highest LMNB TSA-seq scores; this was for K562 cells with round nuclei in which there is therefore no diJerence in lamina TSA-seq scores produced by nuclear shape over the entire nucleus.

      However, there are diJerences between TSA-seq and DamID in terms of what they measure and we refer the reviewer to the first TSA-seq paper (Chen et al, 2018) that explains in greater depth these diJerences. This first paper explains how DamID is indeed related to contact frequency but how the TSA-seq instead estimates mean distances from the target, in this case the nuclear lamina. This is because the diJusion of tyramide free radicals from the site of their constant HRP production produces an exponential decay gradient of tyramide free radical concentration at steady state.

      We have summarized these diJerences in in text we have added to introduce both DamID and TSA-seq in the second Results section:

      "DamID is a well-established molecular proximity assay; DamID applied to the nuclear lamina divides the genome into lamina-associated domains (LADs) versus nonassociated “inter-LADs” or “iLADs” (Guelen et al., 2008; van Steensel and Belmont, 2017). In contrast, TSA-seq measures relative distances to targets on a microscopic scale corresponding to 100s of nm to ~ 1 micron based on the measured diJusion radius of tyramide-biotin free-radicals (Chen et al., 2018)... While LMNB1 DamID segments LADs most accurately, lamin TSA-seq provides distance information not provided by DamID- for example, variations in relative distances to the nuclear lamina of diJerent iLADs and iLAD regions. These diJerences between the LMNB1 DamID and LMNB TSA-seq signals are also crucial to a computational approach, SPIN, that segments the genome into multiple states based on their varying nuclear localization, including biochemically and functionally distinct lamina-associated versus near-lamina states (Consortium et al., 2024; Wang et al., 2021).

      Thus, lamin DamID and TSA-seq complement each other, providing more information together than either one separately."

      We note that these diJerences in lamina DamID and TSA-seq are crucial to being able to gain additional information by comparing variations in the lamina TSA-seq for LADs in Figs. 6&7. See our response to point (4) below, for further explanation.

      (4) In 7B/C, the authors show that the highest LMNB1 regions in HFF are equator of IMR90s. However, in Figure 7G, their cLAD score indicates that constitutive LADs are not at the equator. This is a bit surprising given the point above and raises the possibility that SON signals (as opposed to LMNB1 signals) might be more responsible for correlation to localization relative to the equator. Hence, it might be helpful if the authors repeat the analyses in Figures 7B/C in regions with diJering LMNB1 signals but similar SON signals (and vice versa).

      Again, this is based on the apparent assumption by the reviewer that DamID and TSA-seq work the same way and measure the same thing. But as explained above in the previous point, this is not true.

      In our first TSA-seq paper (Chen et al, 2018) we showed how we could use the exponential decay point-spread-function produced by TSA, measured directly by light microscopy, to convert sequencing reads from the TSA-seq into a predicted mean distance from nuclear speckles, approximated as point sources. These mean distances predicted from the SON TSA-seq data agreed with measured FISH distances to nuclear speckles to within ~50 nm for a set of DNA probes from diJerent chromosome regions. Moreover, varying TSA staining conditions changed the decay constants of this exponential decay, thus producing diJerences in the SON TSA-seq signals. By using these diJerent exponential decay functions to convert the TSA-seq scores from these independent data sets to estimated distances from nuclear speckles, we again observed a distance residual of ~50 nm; in this case though this distance residual of ~50 nm represented the mean residual observed genome-wide. This gives us great confidence that the TSA-seq is working as we have modeled it.

      As we mentioned in our response to point 3 above, we did see the highest LMNB TSA-seq signal for cLADs in K562 cells with round nuclei (Chen et al, 2018).

      But as we now show in our simulation performed in this paper for Fig. 7, the observed tyramide free radical exponential decay gradient convolved with the flat nuclear lamina shape produces a higher equatorial LMNB1 TSA-seq signal for LADs at the equatorial plane. We confirmed that LADs with this higher TSA-seq signal were enriched at the equatorial plane by mining the multiplexed IMR90 imaging data. Similar mining of the multiplexed FISH IMR90 data showed localization of cLADs away from the equatorial plane.

      We are not clear about the rationale for what the reviewer is suggesting about SON signals "being more responsible for correlation to localization to the equator". We have provided an explanation for the higher lamina TSA-seq scores for LADs near the equator based on the measured spreading of the tyramide free radicals convolved with the eJect of the nuclear shape. This makes a prediction that the observed variation in lamina TSA-seq scores for LADs with similar DamID scores is related to their positioning relative to the equatorial plane as we then validated through our mining of the IMR90 multiplexed FISH data.

      (5) FISH of individual LADs, v-fiLADs, and p-w-v-fiLADs relative to the lamina and speckle would be helpful to understand their relative positioning in control and LBR/LMNA double KO cells. This would significantly bolster the claim that "histone mark enrichments..more precisely revealed the diJerential spatial distribution of LAD regions...".

      Adequately testing these predictions made from the lamina/SON TSA-seq scatterplots by direct FISH measurements would require measurements from large numbers of diJerent chromosome regions through a highly multiplexed immuno-FISH approach. We are not equipped currently in any of our laboratories to do such measurements and we leave this therefore for future studies.

      Rather our statement is based on our use of TSA-seq analyzed through these 2D scatterplots and should be valid to the degree that our TSA-seq measurements do indeed correlate with microscopy derived distances.

      However, we do now include demonstration of a high correlation of speckle, lamina, and nucleolar TSA-seq with highly multiplexed immuno-FISH measurement of distances to these locales in a revised Fig. 7. The high correlation shown between the TSA-seq scores and measured distances does therefore add additional support to our claim that the reviewer is discussing, even without our own multiplexed FISH validation.

      (6) "In contrast, genes within genomic regions which in pair-wise comparisons of cell lines show a statistically significant diJerence in lamina TSA-seq show no obvious trend in their expression diJerences (Figure 2C).". This appears to be an overstatement based on the left panel of 2D.

      We do not follow the reviewer's point. In Fig. 2C we show little bias in the diJerences in gene expression between the two cell types for regions that showed diJerences in lamina TSA-seq. The reviewer is suggesting something otherwise based on their impression, not explicitly stated, of the left panel of Fig. 2D. But we see similar shades of blue extending vertically at low SON values and similar shades of red extending vertically at high SON values, suggesting a correlation of gene expression only with the SON TSA-seq score but not with the LMNB1 TSA-seq score displayed on the y-axis. This is also consistent with the very small and/or insignificant correlation coeJicients measured in our linear model relating diJerences in LMNB1 TSA-seq to diJerences in expression but the large correlation coeJicient observed for SON TSA-seq (Fig. 2E). Thus, we see Fig. 2C-E as self-consistent.

      (7) In the section on "Polarity of Nuclear Genome Organization" - "....Using the IMR90 multiplexed FISH data set [43]...." - The references are not numbered.

      We thank the reviewer for this correction.

      (8) I believe there is an error in the Figure 7B legend. The descriptions of Cluster 1 and 2 do not match those indicated in the figure.

      We again thank the reviewer for this correction.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The drug Ivermectin is used to effectively treat a variety of worm parasites in the world, however resistance to Ivermectin poses a rising challenge for this treatment strategy. In this study, the authors found that loss of the E3 ubiquitin ligase UBR-1 in the worm C. elegans results in resistance to Ivermectin. In particular, the authors found that ubr-1 mutants are resistant to the effects of Ivermectin on worm viability, body size, pharyngeal pumping, and locomotion. The authors previously showed that loss of UBR-1 disrupts homeostasis of the amino acid and neurotransmitter glutamate resulting in increased levels of glutamate in C. elegans. Here, the authors found that the sensitivity of ubr-1 mutants to Ivermectin can be restored if glutamate levels are reduced using a variety of different methods. Conversely, treating worms with exogenous glutamate to increase glutamate levels also results in resistance to Ivermectin supporting the idea that increased glutamate promotes resistance to Ivermectin. The authors found that the primary known targets of Ivermectin, glutamate-gated chloride channels (GluCls), are downregulated in ubr-1 mutants providing a plausible mechanism for why ubr-1 mutants are resistant to Ivermectin. Although it is clear that loss of GluCls can lead to resistance to Ivermectin, this study suggests that one potential mechanism to decrease GluCl expression is via disruption of glutamate homeostasis that leads to increased glutamate. This study suggests that if parasitic worms become resistant to Ivermectin due to increased glutamate, their sensitivity to Ivermectin could be restored by reducing glutamate levels using drugs such as Ceftriaxone in a combination drug treatment strategy.

      Strengths:

      (1) The use of multiple independent assays (i.e., viability, body size, pharyngeal pumping, locomotion, and serotonin-stimulated pharyngeal muscle activity) to monitor the effects of Ivermectin

      (2) The use of multiple independent approaches (got-1, eat-4, ceftriaxone drug, exogenous glutamate treatment) to alter glutamate levels to support the conclusion that increased glutamate in ubr-1 mutants contributes to Ivermectin resistance.

      Weaknesses:

      (1) The primary target of Ivermectin is GluCls so it is not surprising that alteration of GluCl expression or function would lead to Ivermectin resistance.

      (2) It remains to be seen what percent of Ivermectin-resistant parasites in the wild have disrupted glutamate homeostasis as opposed to mutations that more directly decrease GluCl expression or function.

      Thank you for your thoughtful and constructive comments. We completely agree with your observation that alterations in GluCl expression or function can lead to Ivermectin resistance. However, we would like to emphasize that our study highlights an additional mechanism: disruptions in glutamate homeostasis can also lead to decreased GluCl expression, thereby contributing to Ivermectin resistance. This mechanism, which has not been fully explored previously, offers new insights into the complexity of drug resistance and could have important implications for understanding the development of Ivermectin resistance in parasitic nematodes.

      As you pointed out, the role of disrupted glutamate homeostasis in wild parasitic populations and the proportion of resistant parasites with this mechanism remain unknown. We believe this uncertainty underlines the significance of our findings, as they suggest a novel avenue for studying Ivermectin resistance and for developing potential strategies to counteract it.

      We have incorporated this discussion into the revised manuscript to further enrich the context of our findings.

      Reviewer #2 (Public review):

      Summary:

      The authors provide a very thorough investigation of the role of UBR-1 in anthelmintic resistance using the non-parasitic nematode, C. elegans. Anthelmintic resistance to macrocyclic lactones is a major problem in veterinary medicine and likely just a matter of time until resistance emerges in human parasites too. Therefore, this study providing novel insight into the mechanisms of ivermectin resistance is particularly important and significant.

      Strengths:

      The authors use very diverse technologies (behavior, genetics, pharmacology, genetically encoded reporters) to dissect the role of UBR-1 in ivermectin resistance. Deploying such a comprehensive suite of tools and approaches provides exceptional insight into the mechanism of how UBR-1 functions in terms of ivermectin resistance.

      Weaknesses:

      I do not see any major weaknesses in this study. My only concern is whether the observations made by the authors would translate to any of the important parasitic helminthes in which resistance has naturally emerged in the field. This is always a concern when leveraging a non-parasitic nematode to shed light on a potential mechanism of resistance of parasitic nematodes, and I understand that it is likely beyond the scope of this paper to test some of their results in parasitic nematodes.

      Thank you for your kind words and positive feedback on our work. We greatly appreciate your acknowledgment of the diverse technologies and comprehensive approaches we utilized to uncover the role of UBR-1 in ivermectin resistance.

      Your concern about whether our findings in C. elegans translate to parasitic helminthes in which ivermectin resistance has naturally emerged is both valid and critical. This is indeed a key question we expect to figure out in future studies. Collaborating with parasitologists to investigate whether naturally occurring mutations in ubr-1 exist in parasitic and non-parasitic nematodes is a priority for us. We hope that these efforts will lead to meaningful discoveries that have a significant impact on both livestock management and medicine.

      Reviewer #3 (Public review):

      Summary:

      Li et al propose to better understand the mechanisms of drug resistance in nematode parasites by studying mutants of the model roundworm C. elegans that are resistant to the deworming drug ivermectin. They provide compelling evidence that loss-of-function mutations in the E3 ubiquitin ligase encoded by the UBR-1 gene make worms resistant to the effects of ivermectin (and related compounds) on viability, body size, pharyngeal pumping rate, and locomotion and that these mutant phenotypes are rescued by a UBR-1 transgene. They propose that the mechanism is resistance is indirect, via the effects of UBR-1 on glutamate production. They show mutations (vesicular glutamate transporter eat-4, glutamate synthase got-1) and drugs (glutamate, glutamate uptake enhancer ceftriaxone) affecting glutamate metabolism/transport modulate sensitivity to ivermectin in wild-type and ubr-1 mutants. The data are generally consistent with greater glutamate tone equating to ivermectin resistance. Finally, they show that manipulations that are expected to increase glutamate tone appear to reduce expression of the targets of ivermectin, the glutamate-gated chloride channels, which is known to increase resistance.

      There is a need for genetic markers of ivermectin resistance in livestock parasites that can be used to better track resistance and to tailor drug treatment. The discovery of UBR-1 as a resistance gene in C. elegans will provide a candidate marker that can be followed up in parasites. The data suggest Ceftriaxone would be a candidate compound to reverse resistance.

      Strengths:

      The strength of the study is the thoroughness of the analysis and the quality of the data. There can be little doubt that ubr-1 mutations do indeed confer ivermectin resistance. The use of both rescue constructs and RNAi to validate mutant phenotypes is notable. Further, the variety of manipulations they use to affect glutamate metabolism/transport makes a compelling argument for some kind of role for glutamate in resistance.

      Weaknesses:

      The proposed mechanism of ubr-1 resistance i.e.: UBR-1 E3 ligase regulates glutamate tone which regulates ivermectin receptor expression, is broadly consistent with the data but somewhat difficult to reconcile with the specific functions of the genes regulating glutamatergic tone. Ceftriaxone and eat-4 mutants reduce extracellular/synaptic glutamate concentrations by sequestering available glutamate in neurons, suggesting that it is extracellular glutamate that is important. But then why does rescuing ubr-1 specifically in the pharyngeal muscle have such a strong effect on ivermectin sensitivity? Is glutamate leaking out of the pharyngeal muscle into the extracellular space/synapse? Is it possible that UBR-1 acts directly on the avr-15 subunit, both of which are expressed in the muscle, perhaps as part of a glutamate sensing/homeostasis mechanism?

      Thank you for your insightful feedback and thought-provoking questions. These are excellent points that have prompted us to critically reconsider our findings and the proposed mechanism.

      Several potential explanations could be considered, although we currently lack direct evidence to support this hypothesis: (1) The pharynx likely plays a dominant role in ivermectin resistance, as previously reported (Dent et al., 1997; Dent et al., 2000), and overexpression of UBR-1 in the pharyngeal muscle may exhibit a strong effect on ivermectin sensitivity. (2) It is also possible that pharyngeal muscle cells have the capacity to release glutamate into the extracellular space, which could contribute to the observed effect. (3) Alternatively, UBR-1 expression in the pharyngeal muscle may regulate other indirect pathways affecting extracellular or synaptic glutamate concentrations.

      We also appreciate your suggestion that UBR-1 may act directly on AVR-15 in the pharynx. While this is an interesting possibility, UBR-1 is an E3 ubiquitin ligase, and if AVR-15 were a direct target, we would expect UBR-1 to ubiquitinate AVR-15 and promote its degradation. In this case, loss of UBR-1 should inhibit AVR-15 ubiquitination, reduce its degradation, and lead to increased AVR-15 protein levels in the pharynx. However, our experimental data show a reduction, rather than an increase, in AVR-15::GFP levels in ubr-1 mutants (Figure 4A). This observation suggests that AVR-15 is less likely to be a direct target of UBR-1. To definitively address this hypothesis, a direct assessment of AVR-15 ubiquitination levels in wild-type and ubr-1 mutant backgrounds would be needed. We agree that this is an important avenue for future investigation.

      The use of single ivermectin dose assays can be misleading. A response change at a single dose shows that the dose-response curve has shifted, but the response is not linear with dose, so the degree of that shift may be difficult to discern and may result from a change in slope but not EC50. Similarly, in Figure 3C, the reader is meant to understand that eat-4 mutant is epistatic to ubr-1 because the double mutant has a wild-type response to ivermectin. But eat-4 alone is more sensitive, so (eyeballing it and interpolating) the shift in EC50 caused by the ubr-1 mutant in a wild type background appears to be the same as in an eat-4 background, so arguably you are seeing an additive effect, not epistasis. For the above reasons, it would be desirable to have results for rescuing constructs in a wild-type background, in addition to the mutant background.

      Thank you for your detailed feedback and observations.

      The potential additive effect you noted in Figure 3C appears to be specific to the body length analysis. In our other three ivermectin resistance assays (viability, pumping rate, and locomotion velocity), this additive effect was not observed. A possible explanation for this is that eat-4 and got-1 single mutants inherently exhibit reduced body length compared to wild-type worms (Mörck and Pilon 2006; Greer et al. 2008; Chitturi et al. 2018), which may give the appearance of an additive effect in this particular assay.

      Regarding the use of rescuing constructs, we performed these experiments in the ubr-1;got-1 and ubr-1;eat-4 double mutant backgrounds. This was designed to test whether the suppression of ubr-1-mediated ivermectin resistance by got-1 or eat-4 mutations is indeed due to the functional activity of GOT-1 and EAT-4, respectively. The choice of this setup was to ensure that the double mutant phenotype was fully addressed. In contrast, rescuing constructs of GOT-1 and/or EAT-4 in a wild-type background might not sufficiently reveal the relationship between GOT-1, EAT-4, and UBR-1. However, we are open to further testing your suggestion in the future.

      To aid in the interpretation and clarify the apparent effects, we have revised Figure 3 annotation to clearly represent the data and the comparisons being made. We hope this adjustment makes the results more straightforward and easier for readers to understand.

      The added value of the pumping data in Figure 5 (using calcium imaging) over the pump counts (from video) in Figure 1G, Figure 2E, F, K, & Figure 3D, H is not clearly explained. It may have to do with the use of "dissected" pharynxes, the nature/advantage of which is not sufficiently documented in the Methods/Results.

      Thank you for pointing this out. The behavioral pumping data in Figure 1G, Figure 2E, F, K, & Figure 3D and calcium imaging data in Figure 5 were obtained under different experimental conditions. Specifically, the behavioral assays (pumping rate) were conducted on standard culture plates with freely moving worms, whereas the calcium imaging experiments were performed in a liquid environment with immobilized worms. In the calcium imaging setup, the dissection refers to gently puncturing the epidermis behind head of the worm with a glass electrode to relieve internal pressure, which aids in stabilizing the calcium imaging process and ensures better visualization of pharyngeal muscle activity.

      We compared the pharyngeal muscle activity of worms that were not subjected to puncturing the epidermis and found no significant difference when activated by 20 mM serotonin. Therefore, we speculate that there is no direct interaction between the bath solution and the pharynx or head neurons. To avoid confusion, we have removed the term "dissected" from the manuscript and added additional experimental details in the Methods section.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) The authors propose that ubr-1 mutants are resistant to ivermectin due to persistent elevation of glutamate that leads to a compensatory reduction in GluCl levels and thus resistance to Ivermectin. This model would be strengthened by experiments more directly connecting glutamate, GluCls and Ivermectin sensitivity. For example, does overexpression of a relevant GluCl such as AVR-15 restore Ivermectin sensitivity to ubr-1 mutants? Does Ceftriaxone treatment affect the Ivermectin resistance of worms lacking the relevant GluCls (i.e., avr-15, avr-14 and glc-1)? - The model suggests that Ceftriaxone treatment would have no effect in the latter case.

      Thank you for your valuable suggestion. Based on your recommendation, we have performed two additional experiments to strengthen our model. First, we conducted an overexpression experiment of AVR-15 and found that it significantly, though partially, restored ivermectin sensitivity in ubr-1 mutants (p < 0.01, Supplemental Figure S5D). Second, we tested the effect of Ceftriaxone treatment on the IVM resistance of avr-15; avr-14; glc-1 triple mutants, which encode the most critical glutamate receptors involved in IVM sensitivity. As expected, we found that Ceftriaxone treatment did not alter the IVM resistance in these triple mutants (Supplemental Figure S5E), supporting the idea that these specific GluCls are key to the observed resistance.

      These two experiments provide further support for our proposed model. We have integrated the results into the manuscript, updating the Results section and Supplemental Figure S5D, E, as well as the corresponding Figure Legends.

      (2) Line 211 - Ceftriaxone is known to upregulate EAAT2 expression in mammals. Do the authors know if the drug also increases EAAT expression in C. elegans?

      Thank you for raising this point. To our knowledge, this is the first study to demonstrate the antagonistic effect of ceftriaxone on ivermectin resistance in C. elegans, particularly in the context of ubr-1-mediated resistance. Ceftriaxone enhances glutamate uptake by increasing the expression of excitatory amino acid transporter-2 (EAAT2) in mammals (Rothstein et al., 2005, Lee et al., 2008). C. elegans has six glutamate transporters encoded by glt-1 and glt-3–7 (Mano et al. 2007).

      Compared to testing whether ceftriaxone increases the expression of these EAATs in C. elegans, identifying which specific glt gene targeted by ceftriaxone may better reveal its mechanism of action. To investigate this, we performed a genetic analysis. In the ubr-1 mutant, we individually deleted the six glt genes and found that ceftriaxone’s ability to restore ivermectin sensitivity was specifically suppressed in the ubr-1; glt-1 and ubr-1; glt-5 double mutants (Author response image 1A). This suggests that glt-1 and glt-5 may be the targets of ceftriaxone in C. elegans. In contrast,  ivermectin sensitivity was unaffected in the individual glt mutants (Author response image 1B), indicating that a single glt deletion may not be sufficient to alter glutamate level or induce GluRs downregulation. Further studies are needed to determine whether ceftriaxone directly increases GLT-1 and GLT-5 expression in C. elegans and to explore the underlying mechanisms.

      Author response image 1.

      Glutamate transporter removal inhibits ceftriaxone-mediated restoration of ivermectin sensitivity in ubr-1. (A) Compared to the ubr-1 mutants, the ubr-1; glt-1 and ubr-1; glt-5 double mutants show enhanced ivermectin resistance under ceftriaxone treatment. (B) The glt mutants do not show resistance to ivermectin. ****p < 0.0001; one-way ANOVA test.

      (3) Line 64 - as part of the rationale for the study, the authors state that "...increasing reports of unknown causes of IVM resistance continue to emerge...suggesting that additional unknown mechanisms are awaiting investigation." While this may be true, the ultimate conclusion from this study is that decreasing expression of Ivermectin-targeted GluCls causes Ivermectin resistance, which is a known mechanism. The field already knows that Ivermectin targets GluCls and thus decreasing GluCl expression or function would lead to Ivermectin resistance. The authors may want to edit the sentence mentioned above for clarity.

      Thanks for the suggestion. We have revised the sentence for clarity: “…, suggesting that previously unrecognized or additional mechanisms regulating GluCls expression may await further investigation.” This revision better reflects the focus on GluCl regulation and clarifies the potential for additional mechanisms to be explored.

      (4) The introduction to the serotonin-stimulated pharyngeal Calcium imaging section is a little confusing. The role of the various GluCls in pharyngeal pumping should be defined/clarified in the introduction to the last section (lines 337-341).

      Thanks. We have revised and clarified the introduction as follows: “GluCls downregulation was functionally validated by the diminished IVM-mediated inhibition of serotonin-activated pharyngeal Ca2+ activity observed in ubr-1 mutants. ”

      Additionally, the role of the various GluCls in pharyngeal pumping has been clarified:

      “Using translational reporters, we found that IVM resistance in ubr-1 mutants is caused by the functional downregulation of IVM-targeted GluCls, including AVR-15, AVR-14, and GLC-1. These receptors are activated by glutamate to facilitate chloride ion influx into pharyngeal muscle cells, resulting in the inhibition of muscle contractions and the suppression of food intake in C. elegans. ”

      We hope these revisions address the concerns raised and improve the clarity of this section.

      (5) The color code key on the right-hand side of the Raster Plots in Figure 1H should be made larger for clarity.

      Revised.

      (6) In Figure S3, a legend should be included to define the black and blue box plots.

      Thank you for your comment. We have added the following clarification to the figure legend: “Black plots: wild-type, blue plots: ubr-1 mutants.” This should now make the distinction between the two groups clear.

      (7) Figure S4, the brackets above the graphs are misleading. It is not clear which comparisons are being made.

      Thank you for your feedback. We have clarified the figure by updating the legend to include the statement: “All statistical analyses were performed against the ubr-1 mutant.” This clarification is now also included in Figure 3F-I to ensure consistency and avoid any confusion regarding the comparisons being made.

      Reviewer #2 (Recommendations for the authors):

      (1) In Figure 1A: the "trails" table needs more clarification to orient the reader.

      To improve clarity and better orient the reader, we have updated Figure 1A by explicitly adding the number of trials and including a statistical analysis of the viability of wild-type and ubr-1 mutants under different ML conditions. In Figure 1A legend, we have added “we used shades of red to represent worm viability on each experimental plate (n = 50 animals per plate), with darker shades indicating lower survival rates. The viability test was repeated at least 5 times (5 trials).”. These modifications aim to provide a clearer understanding of the data presentation and its significance.

      (2) In Figure S2: it would benefit the reader to include the major human parasitic nematodes in the phylogeny and include a discussion of the conservation.

      Thank you for your insightful comment. In Figure S2A, we have included the human parasitic nematodes Onchocerca volvulus, Brugia malayi, and Toxocara canis. Unfortunately, other major human parasitic nematodes, such as Ascaris lumbricoides (roundworm), Ancylostoma duodenale (hookworm), and Trichuris trichiura (whipworm), currently lack reported homologs of the ubr-1 gene.

      To provide some context, Onchocerca volvulus is a leading cause of infectious blindness globally, affecting millions of people, while Brugia malayi causes lymphatic filariasis, a significant tropical disease. Toxocara canis is a zoonotic parasite responsible for serious human syndromes such as visceral and ocular larval migration. Ivermectin remains a primary treatment for these parasitic infections.

      Interestingly, while we have identified relevant sequences in Onchocerca volvulus, Brugia malayi, and Toxocara canis, potential mutations in ubr-1-like genes in these parasitic nematodes may lead to ivermectin resistance. Sequence comparison analysis could shed light on the risks of such mutations and their relevance to ivermectin treatment failure, warranting further attention. We have added a discussion of this potential risk in the manuscript.

      Reviewer #3 (Recommendations for the authors):

      Minor corrections/suggestions:

      (1) The level of resistance in ubr-1 is similar to dyf genes. Should double-check ubr-1 mutant is not dyf.

      Thank you for your insightful suggestion. We are also interested in this point and designed the following experiments. We first directly tested the Dyf phenotype of ubr-1 using standard DIO dye staining (Author response image 2A) and found that ubr-1 clearly show a "dye filling defective" phenotype (Author response image 2B). This raises an interesting question: Could the IVM resistance observed in ubr-1 be due to its Dyf defect? To address this, we further performed experiment by using Ceftriaxone to test ubr-1’s Dyf phenotype. Ceftriaxone can fully rescue the sensitivity of ubr-1 to IVM (Figure 2). If IVM resistance observed in ubr-1 is due to its Dyf defect, we should observe same rescued Dyf defect. After treating ubr-1 mutants with Ceftriaxone (50 μg/mL) until L4 stage, we again performed DIO dye staining and found that while Ceftriaxone fully rescued IVM resistance in ubr-1, it did not rescue the Dyf defect (Author response image 2C). These results suggest that while ubr-1 has a Dyf defect, it is unlikely the primary cause of the IVM resistance in ubr-1 mutant.

      Author response image 2.

      ubr-1 mutant is not dyf. (A) Depiction of the DIO dye-staining assays. Diagram is adapted from (Power et al. 2020). (B) ubr-1 mutant exhibits obvious Dyf phenotype. (C) Cef treatment (50 μg/mL) does not alter the ubr-1 Dyf defect phenotype. Scale bar, 20 µm.

      (2) 367 "in IVM" superscript.

      (3) 429 ubr-1 italics.

      Thanks, revised.

      (4) Methods: Need more info on dissection: if there is direct interaction of bath with pharynx, as suggested by bath solution, then 5HT concentrations are too high. Direct exposure to 20mM 5HT will kill a pharynx. 20uM 5HT?

      Thank you for your comment. We have reviewed our experimental records and confirmed that the concentration mentioned in the manuscript is correct. In our experiment, the dissection refers to gently puncturing the epidermis behind head of the worm with a glass electrode to relieve internal pressure, which helps stabilize the calcium imaging process. In fact, there is no direct interaction between the bath solution and the pharynx or head neurons. We have revised the Methods section to clarify this point.

      (5) Figure 2: Meaning of "Trials" arrow on grid y-axis is not immediately obvious to me. Would prefer you just label/number individual trials.

      Sure, we have labeled the trails accordingly in revised Figure 1, 2, and Figure S1.

      (6) Figure 3: Legend should include [IVM]. Meaning of +EAT-4, +GOT-1 should be described in the legend.

      Thank you for your suggestion. We have updated the figure legend to include the IVM concentration (5 ng/mL). Additionally, we have clarified the meaning of +EAT-4 and +GOT-1 in the legend with the description: “…whereas the re-expression of GOT-1 (+GOT-1) and EAT-4 (+EAT-4) partially reinstated IVM resistance in the respective double mutants.” This ensures the figure is more informative and accessible to the reader.

      (7) 784 signalling pathway should just be pathway.

      Thanks, revised.

      (8) Line 811 " Both types of motor neurons are innervated by serotonin (5 -HT)." Innervated by serotonergic "neurons"? However, even that is misleading because serotonin is not necessarily synaptic.

      Thank you for your comment. We have revised the sentence to: “Both types of motor neurons could be activated by serotonin (5-HT).” This clarification better reflects the role of serotonin in modulating motor neuron activity.

      (9) Line 814 puffing or perfusion. Perfusion seems more accurate. Make the figure consistent.

      Thanks, revised.

      (10) Figure S1 requires an x axis label with better explanation.

      Thank you for your feedback. We have revised Figure S1 and added "x-axis" to clarify that it represents the trail number. Additionally, we have updated the figure legend to include the experimental conditions: “The shades of red represent worm viability, with darker shades indicating lower survival rates, based on 100 animals per plate and at least 5 trials.”

      (11) Figure S2 C-F needs ivermectin concentration.

      (12) Line 865 plants -> plates?

      Thanks, revised.

      (13) Figure S4. 875 "Rescue of IVM sensitivity of the ubr-1 mutant by the UBR-1 genomic fragment." Wrong title? Describes GFP expression and RNAi experiments.

      Thank you for pointing out the mistake in the title. We have revised the title to: “Knockdown of UBR-1 induces IVM resistance phenotypes.” Additionally, we have updated the figure description to include details about GFP expression and RNAi experiments. The GFP expression is now described as: “Expression of functional UBR-1::GFP, driven by its endogenous promoter, was observed predominantly in the pharynx, head neurons, and body wall muscles with weaker expression detected in vulval muscles and the intestine.” The RNAi experiments are described as: “Double-stranded RNA (dsRNA) interference was employed to suppress gene expression in specific tissues (Methods).”

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1:

      The entire study is based on only 2 adult animals, that were used for both the single cell dataset and the HCR. Additionally, the animals were caught from the ocean preventing information about their age or their life history. This makes the n extremely small and reduces the confidence of the conclusions. 

      This statement is incorrect.  While the scRNAseq was indeed performed in two animals (n=2), the HCR-FISH was performed in 3-5 animals (depending on the probe used).  These were different animals from those used for the scRNAseq.  The number of animals used has now been included in the manuscript.

      All the fluorescent pictures present in this manuscript present red nuclei and green signals being not color-blind friendly. Additionally, many of the images lack sufficient quality to determine if the signal is real. Additional images of a control animal (not eviscerated) and of a negative control would help data interpretation. Finally, in many occasions a zoomed out image would help the reader to provide context and have a better understanding of where the signal is localized. 

      Fluorescent photos have been changed to color-blind friendly colors.  Diagrams, arrows and new photos have been included as to guide readers to the signal or labeling in cells. Controls for HCR-FISH and labeling in normal intestines have been included.  

      Reviewer #2:

      The spatial context of the RNA localization images is not well represented, making it difficult to understand how the schematic model was generated from the data. In addition, multiple strong statements in the conclusion should be better justified and connected to the data provided.

      As explained above we have made an effort to provide a better understanding of the cellular/tissue localization of the labeled cells. Similarly, we have revised the conclusions so that the statements made are well justified.

      Reviewer #3:

      Possible theoretical advances regarding lineage trajectories of cells during sea cucumber gut regeneration, but the claims that can be made with this data alone are still predictive.

      We are conscious that the results from these lineage trajectories are still predictive and have emphasized this in the text. Nonetheless, they are important part of our analyses that provide the theoretical basis for future experiments.

      Better microscopy is needed for many figures to be convincing. Some minor additions to the figures will help readers understand the data more clearly.

      As explained above we have made an effort to provide a better understanding of the cellular/tissue localization of the labeled cells.  

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      -  Page 4, line 70-81: if the reader is not familiar with holothurian anatomy and regeneration process, this section can be complicated to fully understand. An illustration, together with clear definitions of mesothelium, coelomic epithelium, celothelium and luminal cells would help the reader. 

      A figure (now Figure 1) detailing the holothurian anatomy of normal and regenerating animals has been added. A figure detailing the intestinal regeneration process has also been included (S1).

      -  Page 5 line 92-104: this paragraph could be shortened. It would be more important to explain what the main question is the Authors would like to answer and why single cell would be the best technique to answer it, than listing previous studies that used scRNA-Seq. 

      The paragraph has been shortened and the focus has been shifted to the question of cellular components of regenerative tissues in holothurians.

      -  Page 6, line 125-127 and line 129-132: this belongs to the method section. 

      This information is now provided in the Materials and Methods section.

      -  Page 11, line 210-217: this belongs to the discussion. 

      This section has now been included in the Discussion.

      -  How many mesenteries are present in one animal? 

      This has now been included as part of Figure S1.

      -  In the methods there are no information about the quality of the dataset and the sequencing and the difference between the 2 samples coming from the 2 animals. How many cells from each sample and which is the coverage? The Authors provided this info only between mesentery and anlage but not between animals. 

      We have added additional information about the sequencing statistics in S4 Fig and S15 Table. Description has also been added in the methods in lines 922-926 under Single Cell RNA Sequencing and Data Analysis section.

      -  The result section "An in-depth analysis of the various cluster..." is particularly long and very repetitive. I would encourage to Authors to remove a lot of the details (list of genes and GO terms) that can be found in the figures and stressed only the most important elements that they will need to support their conclusions. Having full and abbreviated gene names and the long list of references makes the text difficult to read and it is challenging to identify the main point that the Authors are trying to highlight. 

      This section has been abbreviated.

      -  Figure 1: I would suggest adding a graph of holothurian anatomy before and after the evisceration to provide more context of the process we are looking at and remove 1C. 

      Information on the holothurian anatomy has been included in a new Fig 1 and in supplementary figure S1

      -  Figure 2: I would suggest removing this figure that is redundant with Figure 3 and several genes are not cluster specific. Figure 3 is doing a better job in showing similar concepts. 

      Figure 2 was removed and placed in the Supplement section. 

      - In figure 3 how were the 3 cell types defined? Was this done manually or through a bioinformatic analysis? 

      The cell definition was done following the analysis of the highly expressed transcripts and comparisons to what has been shown in the scientific literature.

      -  Figure 2O shows that one of the supra-cluster is made of C2, C7, C6 and C10. This contradicts the text page 9, line 195. 

      The transcript chosen for this figure gives the wrong idea that these 4 clusters are similar. We have now addressed this in the manuscript.

      -  Figure 4A and 4C: if these are representing a subset of Figure 3, they should be removed in one or the other. The same comment is valid also for Figures 5, 6 and 7. In general the manuscript is very redundant both in terms of Figures and text. 

      These are indeed subsets of Fig 3 that were added with the purpose of clarifying the findings, however, in view of the reviewer’s comment we have deleted the redundant information from all figures.

      -  Figure 9: since the panels are not in order, it is difficult to follow the flow of the figure.  - All UMAP should have the number of the cluster on the UMAP itself instead of counting only on the color code in order to be color-blind friendly. 

      The figure has been modified and clusters are now identified in the UMAP by their number.

      -  Figure S1F seems acquired in very different conditions compared to the other images in the same figure. 

      Fig S1F (now S2 Fig) is an overlay of fluorescent immune-histochemistry (UV light detected) with “classical” toluidine blue labeling (visible light detected).  This has now been explained in the figure legend.

      -  Table S7 is lacking some product numbers. 

      The toluidine blue product number has now been added to the table.  The antibodies that lack product number correspond to antibodies generated in our lab  and described in the references provided.

      -  The discussion is pretty long and partially redundant with the result section. I would encourage the Authors to shorten the text and shorten paragraphs that have repeating information.  - It might be out of the scope of the Authors but the readers would benefit from having a manuscript that focuses more on the novel aspects discovered with the single-cell RNA-Seq and then have a review that will bring together all the literature published on this topic and integrating the single-cell data with everything that is known so far. 

      We have tried to shorten the discussion by eliminating redundant text.

      Reviewer #2 (Recommendations For The Authors): 

      -  An intriguing finding is the lack of significant difference in the cell clusters between the anlage and mesentery during regeneration. This discovery raises important questions about the regenerative process. The authors should provide a more detailed explanation of the implications of this finding. For example, does it suggest that both organs contribute equally to the regenerated tissues? 

      The lack of significant differences in the cell clusters between the anlage and the mesentery is somewhat surprising but can be explained by two different facts. First, we have previously shown that many of the cellular processes that take place in the anlage, including cell proliferation, apoptosis, dedifferentiation and ECM remodeling occur in a gradient that begins at the tip of the mesentery where the anlage forms and extends to various degrees into the mesentery.  Similarly, migrating cells move along the connective tissue of the mesentery to the anlage.  Thus, there is no clear partition of the two regions that would account for distinct cell populations associated with the regenerative stage.  Second, the two cell populations that would have been found in the mesentery but not in the regenerating anlage, mature muscle and neurons, were not dissociated by our experimental protocol as to allow for their sequencing.  Our current experiments are being done using single nuclei RNA sequencing to overcome this hurdle. This has now been included in the discussion.

      -  Proliferating cells are obviously important to the study of regeneration as it is assumed these form the regenerating tissue. The authors describe cluster 8 as the proliferative cells. Is there evidence of proliferation in other cell types or are these truly the only dividing cells? Is c8 of multiple cell types but the clustering algorithm picks up on the markers of cell division i.e. what happens if you mask cell division markers - does this cluster collapse into other cluster types? This is important as if there is only one truly proliferating cell type then this may be the origin of the regenerative tissues and is important for this study to know this. 

      As the reviewer highlights, we also believe this to be an important aspect to discuss. We have addressed this in the manuscript discussion with the following: “Our data suggest that there appears to be a specific population of only proliferative cells (C8) characterized by a large number of cell proliferation genes, which can be visualized by the top genes shown in Fig 3. These cell proliferation genes are specific to C8, with minimum representation in other populations. Interestingly, as mentioned before C8 expresses at lower levels many of the genes of other coelomic epithelium populations. Nevertheless, even if we mask the top 38 proliferation genes (not shown), this cluster is maintained as an independent cluster, suggesting that its identity is conferred by a complex transcriptomic profile rather than only a few proliferation-related genes. Therefore, the identity and potential role of C8 could be further described by two distinct alternatives: (1) cells of C8 could be an intermediate state between the anlage precursor cells (discussed below) and the specialized cell populations or (2) cells of C8 are the source of the anlage precursor populations from which all other populations arise. The pseudotime data is certainly complex and challenging to interpret with our current dataset, yet the RNA velocity analysis showed in Fig 11B would suggests that cells of C8 transition into the anlage precursor populations, rather than being an intermediate state. This is also supported by the Slingshot pseudotime analysis that incorporates C8 (S13 Fig).

      Nevertheless, additional experiments are needed to confirm this hypothesis.”

      -  The schematic model presented in Fig 10 is essential for clarifying the paper's findings and will provide a crucial baseline model for future research. However, the comparison of the data shown in the HCR figures with the schematic is challenging due to the lack of spatial context in the HCR figures. The authors should find a way to provide better context in the figures, such as providing two-color in situ images to compare spatial relationships of cell types and/or including lower resolution and side-by-side fluorescent and bright field images if possible. 

      The figure has been modified to explain the spatial arrangement of the tissues.

      The authors make several strong statements in the discussion that weren't well connected to the findings in the data. Specifically: 

      “Regardless of which cell population is responsible for giving rise to the cells of the regenerating intestine, our study reveals that the coelomic epithelium, as a tissue layer, is pluripotent.” 

      This has now been expanded to better explain the statement.

      738 “…we postulate that cells from C1 stand as the precursor cell population from which the rest of the cells in the coelomic epithelium arise”. 

      This has now been expanded to better explain the statement.

      748 “differentiation: muscle, neuroepithelium, and coelomic epithelium cells. We also propose the presence of undifferentiated and proliferating cell populations in the coelomic epithelia, which give rise to the cells in this layer…”

      This has now been expanded to better explain the statement.

      777 “amphibians, the cells of the holothurian anlage coelomic epithelium are proliferative undifferentiated cells and originated via a dedifferentiation process…”

      This has now been expanded to better explain the statement.

      Reviewer #3 (Recommendations For The Authors): 

      Specific questions: 

      - Is there any way to systematically compare these cells to evolutionarily-diverged cells in distant relatives to sea cucumbers? Or even on a case-by-case basis? For example, is there evidence for any of these transitory cell types to have correlate(s) in vertebrate gut regeneration? 

      This is a most interesting question but one that is perhaps a bit premature to answer due to multiple reasons.  First, most of the studies in vertebrates focus on the regeneration of the luminal epithelium, a layer that we are not studying in our system since it appears later in the regeneration process.  Second, there is still too little data from adult echinoderms to fully comprehend which cells are cell orthologues to vertebrates. Third, we are only analyzing one regenerative stage.  It is our hope that this is just the start of a full description of what cell types/stages are found and how they function in regeneration and that this will lead us to identify the cellular orthologues among animal species.

      Major revisions: 

      - If lineage tracing is within the scope of this paper, it would provide more definitive evidence to the conclusions made about the precursor populations of the regenerating anlage. 

      Response:  This is certainly one of the next steps, however at present, it is not possible due to technical limitations.

      Minor revisions: 

      - Line 47: "for decades" even longer! Could the authors also cite some other amphibians, such as other salamanders (newts) and larval frogs? 

      References have been added.

      - Line 85: "specially"-could authors potentially change to "specifically" 

      Corrected

      - Line 122: Authors should add the full words of what these abbreviations stand for in the caption for Figure 1 or in Figure 1A itself. 

      Corrected

      - Lines 153: What conclusions are the authors trying to make from one type of tubulin presence compared to the others? It's unclear from the text. 

      The authors are not trying to reach any particular conclusion.  They are just stating what was found using several markers, and the possibility that what might be viewed first hand as a single cell population might be more heterogenous.  Although the tubulin-type information might not be relevant for the conclusions in the present manuscript, it might be important for future work on the cell types involved in the regeneration process.

      - Line 226: Could the authors clarify if "WNT9" is "WNT9a". Figure 3 lists WNT9a but authors refer to WNT9 in the text. 

      The gene names in Fig 3 are based on the human identifiers. H. glaberrima only has one sequence of Wnt9 (Auger et al. 2023) and this sequence shares the highest similarity to human Wnt9a, thus the name in the list. We have now identified the gene as Wnt9 to avoid confusion.

      - Lines 236-237: Can authors rule out that some immune cells might infiltrate the mesenchymal population? 

      No, this cannot be ruled out.  In fact, we believe that most of the immune cells found in our scRNA-seq are indeed cells that have infiltrated the anlage and are part of the mesenchyma.  This has been reported by us previously (see Garcia-Arraras et al. 2006). We have now included this in the text.

      - Line 452-453: The over-representation of ribosomal genes not shown. Would it be possible to show this information in the supplementary figures? 

      The sentence has been modified, the data is being prepared as part of a separate publication that focuses on the ribosomal genes.

      - Line 480: Could authors clarify if it's WNT9a or just WNT9?

      It is indeed Wnt9. See previous response above.

      - Line 500: In future experiments, it would be interesting to compare to populations at different timepoints in order see how the populations are changing or if certain precursors are activated at different times. 

      We fully agree with the reviewer. These are ongoing experiments or are part of new grant proposals.

      - Line 567-568: Choosing 9-dpe allowed for 13 clusters, but do authors expect a different number of clusters at different timepoints as things become more terminally differentiated? 

      Definitely, we believe that clusters related to the different regenerative stages of cells can be found by looking at earlier or later regeneration stages of the organ.  A clear example is that if the experiment is done at 14-dpe, when the lumen is forming, cells related to luminal epithelium populations will appear. It is also possible that different immune cells will be associated with the different regeneration stages.

      - Line 653: References Figure 10D (not in this manuscript). Are authors referring to only 1D or 9D or an old draft figure number? 

      As the reviewer correctly points out, this was a mistake where the reference is to a previous draft. It has now been corrected.

      - Line 701: "our study reveals that the coelomic epithelium, as a tissue layer, is pluripotent." Phrasing may be better as referring to the cell population making up the tissue layer as pluripotent/multipotent or that the cells it contains would likely be pluripotent or multipotent. Additionally, lineage tracing may be needed to definitively demonstrate this. 

      This has been modified.

      - Line 808: The authors may make a more accurate conclusion by saying that the characteristics are similar to blastemas or behave like a blastema rather than it is blastema. There is ambiguity about the meaning of this term in the field, but most researchers seem to currently have in mind that the "blastema" definition includes a discrete spatial organization of cells, and here these cells are much more spread out. This could be a good opportunity for the authors to engage in this dialogue, perhaps parsing out the nuances of what a "blastema" is, what the term has traditionally referred to, and how we might consider updating this term or at least re-framing the terminology to be inclusive of functions that "blastemas" have traditionally had in the literature and how they may be dispersed over geographical space in an organism more so than the more rigid, geographically-restricted definition many researchers have in mind. However, if the authors choose to elaborate on these issues, those elaborations do belong in the discussion, and the more provisional terminology we mention here could be used throughout the paper until that element of the revised discussion is presented. We would welcome the authors to do this as a way to point the field in this direction as this is also how we view the matter. For example, some of the genes whose expression has been observed to be enriched following removal of brain tissue in axolotls (such as kazald2, Lust et al.), are also upregulated in traditional blastemas, for instance, in the limb, but we appreciate that the expression domain may not be as localized as in a limb blastema. Additionally, since there is now evidence that some aspects of progenitor cell activation even in limb regeneration extend far beyond the local site of amputation injury (Johnson et al., Payzin-Dogru et al.), there is an opportunity to connect the dots and make the claim that there could be more dispersion of "blastema function" than previously appreciated in the field. Diving a bit more into these nuances may also enable better conceptual framework of how blastema function may evolve across vast evolutionary time and between different injury contexts in super-regenerative organisms. 

      We have followed the reviewer’s suggestion and stated that the holothurian anlage behaves as a blastema. Though we would love to elaborate on the blastema topic, as suggested by the reviewer, we believe that it would extend the discussion too much and that the topic might be better served in a different publication.

      - In the discussion, it would be important not to leave the reader with the impression that all amphibian blastema cells originate via dedifferentiation. This is not the case. For example, in axolotls (Sandoval-Guzman et al.) and in larval/juvenile newts, muscle progenitors within the blastema structure have been shown to originate from muscle satellite cells, a kind of stem cell, in stump tissues (while adult newts use dedifferentiation of myofibers to generate muscle progenitors in the blastema). Most cell lineages simply have not been evaluated in the level of detail that would be required to definitively conclude one way or the other, and the door is open for a more substantial contribution from stem cell populations than previously appreciated especially because new tools exist to detect and study them. Providing the reader with a more nuanced view of this situation will not negatively impact the findings in this paper, but it will show that there is biological complexity still waiting to be discovered and that we don't have all the answers at this point. 

      This has now been corrected. 

      Figures: Overall, the figures need minor work. 

      - Figure 1A: Can the authors draw a smaller, full-body cartoon and feature the current high-mag cartoon as an inset to that? Can they label the axes and make it clear how the geometry works here?

      Fig 1 has been re-done and now is split into Fig 1 and Fig 2.

      - Figure 1B: Can the authors label the UMAP with cluster identities on the map itself? This will make it easier to identify each cluster (especially to make sure cluster 11 is easier to find). 

      This has been corrected.

      - Figure 2: Could the authors put boxes/clearly distinguish panel labels around each cluster (AO), so that there are clear boundaries? 

      Fig 2 has been moved to Supplement, following another reviewer recommendation.

      - "Gene identifiers starting with "g" correspond to uncharacterized gene models of H. glaberrima." - The sentence is from another figure caption but this figure would benefit from having this sentence in the figure caption as well. 

      This has been added to other figures as suggested.

      - Figure 3A: Can the authors potentially bold, highlight, or underline genes you discuss in text, so it's easier for the reader to reference? 

      This has been added as suggested.

      - Figure 3C: Can the authors please label the cell types directly on the UMAP here as well? 

      The changes were made following the reviewer’s recommendation.

      - Figure 4D-E: There's not much context here to determine if this HCR-FISH validation can tell us anything about these cells besides some of them appear to be there. Do authors expect the coelomocyte morphology to look different in regenerating/injured tissue versus normal animals? Can the authors provide some double in situs, as well as some lower-magnification views showing where the higher-magnification insets are located? Is there any spatial pattern to where these cells are found? Counter stains would be helpful. 

      - Figure 6C: If clusters C5, C8, C9 are part of the coelomic epithelium, then authors could show a smaller diagram above with blue and grey to show types and then show clusters separately to help get their point across better. 

      - Figure 6G: This image appears to have high background- would it be possible for authors to repeat phalloidin stain or reimage with a lower exposure/gain. Additionally, imaging with Zstacks would help to obtain maximum intensity projections. It would greatly aid the reader if each image was labeled with HCR probes/antibodies that have been applied to the sample. 

      - Figure 7E: The cells appear to be out of focus and have high background. Additionally, they are lacking the speckled appearance expected to be seen with HCR-FISH. Would it be possible for authors to collect another image utilizing z-stacks? 

      HCR-FISH figures identifying the gene expression characteristic of cell clusters have been modified following the reviewer’s concerns.  The changes include:

      (1) Additional clusters have been verified with probes to gene identifiers. These include clusters 8, 9 and 12.

      (2) Redundant information has been removed.

      (3) Colors have been changed to make figures friendlier to color-impaired readers.

      (4) Spatial context has been added or identified.

      (5) In some cases, improved photos have been added

      (6) Better labels have been included

      (7) When necessary individual photos used for the overlay have been included.

      - Figure 9A: Could authors add cluster labels onto UMAP directly? 

      This change was made to Fig 2A. UMAP in Fig 9A is the same and used just as reference of the subset.

      - Figure 10: It could be useful if authors put a small map of the sea cucumber like in other images so that readers know where in the anlage this zoomed in model represents. 

      Added as suggested by the reviewer.

      - Supplementary figure 1F: Could authors add an arrow to the dark cell that's being pointed out? 

      Changed made as suggested by the reviewer.

      - Supplementary figure 1: Could authors label clearly what color is labeled with what marker? 

      Changed made as suggested by the reviewer.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      The authors present valuable findings on trends in hind limb morphology throughout the evolution of titanosaurian sauropod dinosaurs, the land animals that reached the most remarkable gigantic sizes. The solid results include the use of 3D geometric morphometrics to examine the femur, tibia, and fibula to provide new information on the evolution of this clade and understand the evolutionary trends between morphology and allometry. Further justification of the ontogenetic stages of the sampled individuals would help strengthen the manuscript's conclusions, and the inclusion of additional large-body mass taxa could provide expanded insights into the proposed trends.

      Most of the analyzed specimens, especially from the smaller taxa, come from adult or subadult specimens. None exhibit features that may indicate juvenile status. However, we lack information of the paleohistology that may be a stronger indicator on the ontogenetic status of the individual, and some of operative taxonomic units used in the study come from mean shape of all the sampled specimens.

      Current information on morphological differences between adult and subadult or juvenile specimens indicates that even early juvenile specimens may share same morphological features and overall morphology as the adult (e.g., see Curry-Rogers et al., 2016; Appendix S3). We included a comprehensive analysis of the impact of juvenile specimens as one of the aspects of the intraspecific variability that may alter our results in Appendix S3.

      Public Reviews:

      Reviewer #1:

      Weaknesses:

      Several sentences throughout the manuscript could benefit from citations. For example, the discussion of using hind limb centroid size as a proxy for body mass has no citations attributed. This should be cited or described as a new method for estimating body mass with data from extant taxa presented in support of this relationship. This particular instance is a very important point to include supporting documentation because the authors' conclusions about evolutionary trends in body size are predicated on this relationship.

      We address this issue in the text (Line 32 & 64). Centroid size seems a good indication as it’s the overall size of the entire hind limb, and the length of the femur and tibia is well correlated independently with the body size/mass. Also, as we use few landmarks and only those that are purely type I or II landmarks, with curves of semilandmarks bounded or limited by them, centroid size is not sensible to landmark number differences across the sample in our study (as the centroid size is dependent of the number of landmarks of the current study as well as the physical dimensions of the specimens).

      We have sampled and repeated all the analyses using other proxies like the femoral length and the body mass estimated from the Campione & Evans (2020) and Mazzeta et al. (2004) methods. The comprehensive description of the method is in Appendix S2, the alternative analyses can be accessed in the Appendix S3 and S4; and the code for the alternative analyses can be accessed in the modified Appendix S5. All offer similar results than the ones obtained in our analyses with the body size proxied with the hind limb landmark configuration centroid size.

      An additional area of concern is the lack of any discussion of taphonomic deformation in Section 3.3 Caveats of This Study, the results, or the methods. The authors provide a long and detailed discussion of taphonomic loss and how this study does a good job of addressing it; however, taphonomic deformation to specimens and its potential effects on the ensuing results were not addressed at all. Hedrick and Dodson (2013) highlight that, with fossils, a PCA typically includes the effects of taphonomic deformation in addition to differences in morphology, which results in morphometric graphs representing taphomorphospaces. For example, in this study, the extreme negative positioning of Dreadnoughtus on PC 2 (which the authors highlight as "remarkable") is almost certainly the result of taphonomic deformation to the distal end of the holotype femur, as noted by Ullmann and Lacovara (2016).

      We included a brief commentary in the Caveats of This Study (Line 467) and greatly expanded this issue in the Appendix S3. We followed the methodology proposed by Lefebvre et al. (2020) to discuss the effects of taphonomic deformation in the shape analyses.

      Our shape variables (PCs obtained from the shape PCA) should be viewed as taphomorphospaces as Hedrick and Dodson, as well as the reviewer, points in such cases.

      The analysis of the effects of taphonomy or errors induced by the landmark estimation method indicate that Dreadnoughtus schrani is one of the few sampled taxa that may have a noticeable impact on our analyses due lithostatic deformation. Other taxa like Mendozasaurus neguyelap or Ampelosaurus atacis may also induce some alterations to the PCs. In general, the trends of those PCs slightly altered by taphonomy, where D. scharni is the only sauropod that may alter an entire PC like PC2, did not exhibit phylogenetic signal and are a small proportion of the sample variance.

      The authors investigated 17 taxa and divided them into 9 clades, with only Titanosauria and Lithostrotia including more than two taxa (and four clades are only represented by one taxon). While some of these clades represent the average of multiple individuals, the small number of plotted taxa can only weakly support trends within Titanosauria. If similar general trends could be found when the taxa are parsed into fewer, more inclusive clades, it would support and strengthen their claims. Of course, the authors can only study what is preserved in the fossil record, and titanosaurian remains are often highly fragmentary; these deficiencies should therefore not be held against the authors. They clearly put effort and thought into their choices of taxa to include in this study, but there are limitations arising from this low sample size that inherently limit the confidence that can be placed on their conclusions, and this caveat should be more clearly discussed. Specifically, the authors note that their dataset contains many lithostrotians, but they do not discuss unevenness in body size sampling. As neither their size-category boundaries nor the taxa which fall into each of them are clearly stated, the reader must parse the discussion to glean which taxa are in each size category. It should be noted that the authors include both Jainosaurus and Dreadnoughtus as 'large' taxa even though the latter is estimated to have been roughly five times the body mass of the former, making Dreadnoughtus the only taxon included in this extreme size category. The effects that this may have on body size trends are not discussed. Additionally, few taxa between the body masses of Jainosaurus and Dreadnoughtus have been included even though the hind limbs of several such macronarians have been digitized in prior studies (such as Diamantinasaurus and Giraffititan; Klinkhamer et al. 2018). Also, several members of Colossosauria are more similar in general body size to Dreadnoughtus than Jainosaurus, but unfortunately, they do not preserve a known femur, tibia, and fibula, so the authors could not include them in this study. Exclusion of these taxa may bias inferences about body size evolution, and this is a sampling caveat that could have been discussed more clearly. Future studies including these and other taxa will be important for further evaluating the hypotheses about macronarian evolution advanced by Páramo et al. in this study.

      Sadly, we could not include some larger sized titanosaurians sauropods. As the reviewers points out, the lack of larger sauropods among the sampled taxa may hinder our results, as the “large-bodied” category is filled with some mid-sized taxa and the former Dreadnoughtus schrani which is five times larger than some of them. We tried to include Elaltitan lilloi, digitized for this study and included in preliminary analyses, but the fragmentary status increased greatly the error by the estimation method as there is only a proximal third or mid femur preserved from this taxon. Therefore we opted to exclude it from our database.

      Other taxa considered, as the reviewer suggest, was not readily available for the authors as the time of this study was conducted and including now may have increased the possible bias of our study. Giraffatitan brancai is an Late Jurassic brachiosaurid, which may again increase the number of early-branching titanosauriforms with large body masses while most of the smaller taxa sampled are recovered in deeply-branching macronarians (including Diamantinasaurus matildae if we would have also included it). Future analyses may include a wider sample of the mid to large-bodied titanosaurians, especially lithostrotians, as well as some colossosaurs like Patagotitan mayorum.

      Reviewer #1 (Recommendations For The Authors):

      These are all minor comments that would improve the manuscript.

      - There are a few typos throughout the manuscript such as: line 70 should be 2016 and line 242 should be forelimb.

      Corrected.

      - To me, the most interesting aspect of your study is the diversity and trends recovered in titanosaurian subclades and I would highlight this, not gigantism, in the title if you choose to revise the title.

      It has been addressed. The specificality of some of the tests and the implication to the acquisition of the spread limb posture and gigantism in early-branching taxa is important nonetheless, so we think that it may remain in the title.

      - The abstract should provide more details on the results such as none of the listed trends were statistically significant.

      Many of the trends exhibit phylogenetic signal, but not the allometric components. We have briefly addressed them.

      - Several sentences in the manuscript need citations such as: line 48 the reference to other megaherbivores, line 66 the discussion of poor understanding of the relationship of wide gauge posture and gigantism, and the use of centroid size as an estimate of body mass (see Public Review).

      We changed the line 66 to improve the focus on the current state of the art in the hypothesis of a relationship between arched limbs and in the increase of body size. We included a section relating centroid size as a proxy (due the good correlation between the femur and tibia length and the body mass) and the caveats of using it. We also expanded in the Appendix S2 the use of centroid size and the alternative models.

      - With titanosaur evolution, you mention that they are adapting to new niches and topography (line 64). What support is there for this versus they are adapting to be more successful in their current environment?

      Noted, we have changed the phrase to improved efficiency exploiting of inland environments, as thy can be either opening new inland niches or adapting better to current inland niches that were already exploited for less deeply branching sauropods. However, its testing is beyond the scope of the current work.

      - Line 384-385: the discussion of Rapetosaurus should mention that it is a juvenile and some studies have suggested that titanosaur limbs grow allometrically.

      We have included a small line. Whether Rapetosaurus krausei exhibit allometric growth or not may not change greatly the discussion, maybe only excluding it as morphologically convergent to Lirainosaurus and Muyelensaurus. But if that so, it will be further proof that small-sized titanosaurs exhibit the robust skeleton expected in the giant titanosaurs.

      - I would consider addressing the question of if we are certain enough in our understanding of titanosaurian phylogeny to rule out homology, especially when you discuss the uncertainty of the placement of specific taxa. Also, Diamantinasaurus is not the only titanosaur that has been proposed as a member of both basal and more derived subclades (e.g., Dreadnoughtus).

      We tried to assume a more conservative approach. We could not fully rule out that some of the features observed in the sampled deeply branching lithostrotians, especially saltasauroids, cannot be present in the entire somphospondylan lineage. However, none of the less deeply-branching or early-branching titanosaurs exhibit this kind of morphology. Recent studies propose the possibility that entire groups, included in this study like the Colossosauria, change its position in the phylogeny. However, despite the debated phylogenetic position of Diamantinasaurus or Dreadnoughtus, or even the inclusion of Colossosauria within the saltasauroids and the inclusion of the Ibero-Armorican lithostrotians as putative saltasaurids (Mocho et al. 2024). However, even considering these changes we did not notice any relevant differences in our conclusions about hind limb arched morphology nor about size. Distal hind limb overall robustness should indeed be addressed in the light of shifts in phylogenetic position and include some interesting sauropods like Diamantinasaurus or expand the large-sized Colossosauria or early-branching somphospondyls as it may have profound implications on the morphofunctional adaptations to specific feeding niches, e.g., see current hypotheses about rearing as mentioned in Bates et al. (2016), Ullmann et al. (2017) or Vidal et al. (2020). We had not enough information to conclude the presence of any plesiomorphic condition or analogous feature with our current sample and the debated titanosaurian phylogeny.

      - I understand this is not standard in the field, but your study provides the opportunity to conduct sensitivity testing of the effects of cartilage thickness and user articulation of the bones on PCA results. This would be an inciteful addition to the field of GMM.

      We are currently developing such a comprehensive analysis and several other implications on our past results. However, we feel that it is beyond the scope of the current study. We appreciate the suggestion nonetheless, as it would be a sensitivity test of the impact of several of our assumptions in the final results that is often not considered.

      - In Figure 1, if all the limbs were arranged the same way it would be easier to interpret. Consider flipping panels B and D to match A and C.

      Accepted.

      - In Figures 2-4, the views in C should be labeled in the figure or caption. Oceanotitan is also in the PCA plot but not included in the figure caption. Also, consider changing the names to represent the paraphyletic groupings you are using instead of formal clade names. For example, change 'Titanosauria' to 'Basal Titanosaurs' to reflect that it is not including all titanosaurs in the sample.

      Changes accepted for the shape PCA results. The informal (i.e., paraphyletic) terms such as “Basal Titanosaurs” were only used in the shape analyses as in the RMA, the Titanosauria (and other more inclusive groups) were used as natural groups. Each partial RMA model is based on a sample of all the taxa that are included within that particular clade (e.g., Titanosauria includes both Dreadnoughtus and Saltasaurus; Lithostrotia excludes the former).

      - I am concerned that centroid size does not scale evenly across the wide-ranging body mass of titanosaurs. I do not know if this affects your size trends or their significance, but as I mentioned above Dreadnoughtus is much bigger than most of the taxa included and that isn't as drastically apparent in centroid size (in Figure 5) as it is when taxa are plotted by body mass.

      Main problematic with centroid size of the hind limb is the shift in the body plan of deeply-branching titanosaurs as the Center of Masses is displaced toward the anterior portion of the body and it has been proposed due a large development of the forelimb region (e.g., Bates et al. 2016). However, it would only increase the effects of the phyletic body size reduction, as smaller taxa tend to have a 1:1 fore limb and hind limb ratio, e.g., from our past analyses as in Páramo et al. (2019), and the sacrum is not as beveled as in earlier somphospondyls, e.g., Vidal et al. (2020). The role of the low-browsing feeding habits of deeply-branching lithostrotians shall be explored elsewhere, as it may be the main driving force of this effect. Our point is, the proxy used may have some slight offset due some high-browsing giant early-branching titanosaurs which has a greater cranial region development which increase its body size and mass beyond our bare-minimum estimation based on the hind limb region. But, overall, this offset is assumed to be low. We repeated the analyses with the femoral length as proxy of body size and a mass estimation, including the quadratic equation based on both humeral and femoral lengths, and the results remain similar. Another problem that arises with the use of centroid size is the way it shall be calculated, but as we used an even number of landmarks and curve semilandmarks, and all of them bounded to anatomical features, it remains equal at least for our sample (but cannot be extrapolated to other geometric morphometric studies that do not use the same configurations)

      We appreciate the reviewer concerns nonetheless, as it was on of our own when designing this study, and we in the future will try to expand the analyses, or advise anyone expanding on this study, using total body size/volume estimations following Bates et al. (2016). Which also includes test of the effects of the different whole-body estimation models.

      Cites:

      Bates KT, Mannion PD, Falkingham PL, Brusatte SL, Hutchinson JR, Otero A, Sellers WI, Sullivan C, Stevens KA, Allen V. 2016. Temporal and phylogenetic evolution of the sauropod dinosaur body plan. Royal Society Open Science 3:150636. doi:10.1098/rsos.150636

      Mocho P, Escaso F, Marcos-Fernández F, Páramo A, Sanz JL, Vidal D, Ortega F. 2024. A Spanish saltasauroid titanosaur reveals Europe as a melting pot of endemic and immigrant sauropods in the Late Cretaceous. Commun Biol 7:1016. doi:10.1038/s42003-024-06653-0

      Páramo A, Ortega F, Sanz JL. 2019. A Niche Partitioning Scenario for the Titanosaurs of Lo Hueco (Upper Cretaceous, Spain). International Congress of Vertebrate Morphology (ICVM) - Abstract Volume, Journal of Morphology. Prague. p. S197.

      Ullmann PV, Bonnan MF, Lacovara KJ. 2017. Characterizing the Evolution of Wide-Gauge Features in Stylopodial Limb Elements of Titanosauriform Sauropods via Geometric Morphometrics. The Anatomical Record 300:1618–1635. doi:10.1002/ar.23607

      Vidal D, Mocho P, Aberasturi A, Sanz JL, Ortega F. 2020. High browsing skeletal adaptations in Spinophorosaurus reveal an evolutionary innovation in sauropod dinosaurs. Sci Rep 10:6638. doi:10.1038/s41598-020-63439-0

      Reviewer #2:

      The authors report a quantitative comparative study regarding hind limb evolution among titanosaurs. I find the conclusions and findings of the manuscript interesting and relevant. The strength of the paper would be increased if the authors were to improve their reporting of taxon sampling and their discussion of age estimation and the potential implications that uncertainty in these estimates would have for their conclusions regarding gigantism (vs. ontogenetic patterns).

      Considering the observations made by reviewer #1, we included a data about the impact of ontogenetic patterns and other intraspecific variability in the Appendix S3. We considered to increase the sample but it has not been possible at the time of this study was carried out.

      Reviewer #2 (Recommendations For The Authors):

      I have a few concerns/requests for the authors, that I hope can be easily resolved.

      Comments:

      - What drove taxon sampling?

      Random sampling of somphospondylan sauropods focused on the Lithostrotia clade for the thesis project of one of the authors, APB. Logistics were also one of the bias on our sample, and based on the available titanosaurian material we left out several macronarians that has been already sampled but would further induce a early-branching large sauropod, deeply-branching small sauropod that may alter our results.

      - Which phylogenies were used to create the supertree applied to the analyses? What references were used to time-calibrate the tips and deeper nodes? I couldn't find any reference to this. Additionally, more information regarding the R packages and analytical pipeline would be appreciated: e.g. were measurements used in the analyses log-transformed?

      A comprehensive description of the methodology is provided in Appendix S2.

      - Age estimate: can the author confirm the skeletal maturity of the sampled individuals? If this is not the case, how can the author be sure that the patterns towards gigantism are not reflecting different ontogenetic stages? I believe this should be part of both methods and discussion.

      As commented before, we excluded small, probable juvenile specimens from our sample. We have no paleohistological sample backing the claims of the ontogenetic status of some of the specimens that were included or excluded were calculating the mean shape for the operative taxonomic units. However, we followed a criteria to identify the relative ontogenetic status and it has been included in Appendix S3.

      - The authors used the centroid size for regressions in Figure 6. Although I believe that this is a good variable, would the author be willing to use body mass and log-transformed femur length in addition to what was done? These would be very useful considering that these variables are (relatively) independent from shape/morphology.

      Accepted, we tested our hypotheses with three alternative models based on femoral length, combined femoral and humeral lengths for body mass estimations. Methodology can be found in Appendix S2, results on Appendix S4, code for the alternative methods in Appendix S5.

      - Data access: will stl. Files of the limb elements be shared and freely available? In this case, where the files will be deposited?

      At the time of the current study, some of the sampled specimens cannot be available (material under study) but the mean shapes can be generated after the landmarks and semilandmark curves and the “atlas” mesh.

      - Additionally, outstanding references regarding limb evolution, GMM, role of ontogeny, and evolution of columnar gait are missing. The authors should reinforce the literature review with the following (alphabetical order):

      Bonnan, M. F. (2003). The evolution of manus shape in sauropod dinosaurs: implications for functional morphology, forelimb orientation, and phylogeny. Journal of Vertebrate Paleontology, 23(3), 595-613.

      Botha, J., Choiniere, J. N., & Benson, R. B. (2022). Rapid growth preceded gigantism in sauropodomorph evolution. Current Biology, 32(20), 4501-4507.

      Curry Rogers, K., Whitney, M., D'Emic, M., & Bagley, B. (2016). Precocity in a tiny titanosaur from the Cretaceous of Madagascar. Science, 352(6284), 450-453.

      Day, J. J., Upchurch, P., Norman, D. B., Gale, A. S., & Powell, H. P. (2002). Sauropod trackways, evolution, and behavior. Science, 296(5573), 1659-1659.

      Fabbri, M., Navalón, G., Benson, R. B., Pol, D., O'Connor, J., Bhullar, B. A. S., ... & Ibrahim, N. (2022). Subaqueous foraging among carnivorous dinosaurs. Nature, 603(7903), 852-857.

      Fabbri, M., Navalón, G., Mongiardino Koch, N., Hanson, M., Petermann, H., & Bhullar, B. A. (2021). A shift in ontogenetic timing produced the unique sauropod skull. Evolution, 75(4), 819-831.

      González Riga, B. J., Lamanna, M. C., Ortiz David, L. D., Calvo, J. O., & Coria, J. P. (2016). A gigantic new dinosaur from Argentina and the evolution of the sauropod hind foot. Scientific Reports, 6(1), 19165.

      Lefebvre, R., Allain, R., & Houssaye, A. (2023). What's inside a sauropod limb? First three‐dimensional investigation of the limb long bone microanatomy of a sauropod dinosaur, Nigersaurus taqueti (Neosauropoda, Rebbachisauridae), and implications for the weight‐bearing function. Palaeontology, 66(4), e12670.

      McPhee, B. W., Benson, R. B., Botha-Brink, J., Bordy, E. M., & Choiniere, J. N. (2018). A giant dinosaur from the earliest Jurassic of South Africa and the transition to quadrupedality in early sauropodomorphs. Current Biology, 28(19), 3143-3151.

      Martin Sander, P., Mateus, O., Laven, T., & Knötschke, N. (2006). Bone histology indicates insular dwarfism in a new Late Jurassic sauropod dinosaur. Nature, 441(7094), 739-741.

      Remes, K. (2008). Evolution of the pectoral girdle and forelimb in Sauropodomorpha (Dinosauria, Saurischia): osteology, myology and function (Doctoral dissertation, München, Univ., Diss., 2008).

      Sander, P. M., & Clauss, M. (2008). Sauropod gigantism. Science, 322(5899), 200-201.

      Yates, A. M., & Kitching, J. W. (2003). The earliest known sauropod dinosaur and the first steps towards sauropod locomotion. Proceedings of the Royal Society of London. Series B: Biological Sciences, 270(1525), 1753-1758.

      We appreciate this suggestion and we already used some of the articles in our study but the selection of cites were based also in the available manuscript space enforced by the edition guidelines. We would have like to include several of these works but we had opted to include some of the works that summarize some of them, whereas excluding others.

    1. Author response:

      The following is the authors’ response to the original reviews.

      We thank the reviewers for their constructive criticism. It is rare and gratifying to receive such thoughtful feedback, and the result is a much stronger paper. We made significant changes to our statistical analyses and figures to better differentiate the effects of sex and dominance rank on food-cleaning behaviors. These revisions uphold our original conclusion––that rank-related variation overwhelms any sex difference in cleaning behavior. We hope that these edits, together with the rest of our responses, provide a convincing demonstration of the tradeoffs of eliminating quartz from food surfaces.

      Reviewer #1 (Public Review):

      Summary

      We have no objections to Reviewer 1’s summary of our manuscript.

      Strengths

      Reviewer 1 is extremely gracious, and we are grateful for the kind words.

      Weaknesses

      Reviewer 1 identified several weaknesses, enumerating three types: (1) statistics, (2) insufficient links to foraging theory, and (3) interpretation and validity of the model. The present response is organized around these same categories.

      (1) Statistics

      We put all of our data and code into the Zenodo repository prior to submission. This content should have been accessible to Reviewer 1 from the outset. But in any event, we are very sorry for the mixup. To ensure access to our data and code during the present stage of review, we included the URL in the main mainscript and here: https://doi.org/10.5281/zenodo.14002737

      (a) AIC and outcome distributions

      Reviewer 1 criticized our use of AIC for determining model selection. We agree and this aspect of our manuscript is now removed. In lieu of AIC, we produced two data sets consisting of whole number counts (seconds) with means <5. The data were right-skewed due to high concentrations of biologically-meaningful zeros (i.e., bouts of food handling without any cleaning effort). Following the recommendations of Bolker et al. (2008) and others (Brooks et al. 2017, 2019), we chose an outcome distribution (zero-inflated Poisson, see response below) that best matched this data distribution. In addition, we evaluated the post-hoc performance of each of our models using the standardized residual diagnostic tools for hierarchical regression models available in the DHARMa package (Hartig, 2022). To further evaluate our choice of outcome distribution, we generated QQ-plots and residual vs. predicted plots for each model and included them in our revision as Figures S3-S5.

      (b) zeros

      Reviewer 1 expressed concern over our treatment of biologically-meaningful zeros, and recommended use of a zero-inflated GLMM with either a Poisson or negative binomial outcome distribution. We agree that such models are best for our two data sets. Accordingly, we fit a series of zero-inflated generalized linear mixed models (ZIGLMM) using the glmmTMB package in R, each with a logit-link function, a single zero-inflation parameter applying to all observations, and a Poisson error distribution. For the food-brushing model, we fit a zero-inflated Poisson (ZIP), which produced favorable standardized residual diagnostic plots with no major patterns of deviation (Figure S3) and minor, but non-significant underdispersion (DHARMa dispersion statistic = 0.99, p = 0.80). For our two food-washing models, we used zero-inflated models with Conway-Maxwell Poisson (ZICMP) distributions, an error distribution chosen for its ability to handle data that are more underdispersed (DHARMa dispersion statistic = 8.2E-09, p = 0.74) than the standard zero-inflated Poisson (Brooks et al. 2019). Using this error distribution improved residual diagnostic plots over a standard ZIP model and we view any deviations in the standardized residuals as minor and attributable to the smaller sample size of our food-washing data set (see Figures S4 and S5) (Hartig, 2022). We reported the summarized fixed effects tests for each GLMM in Tables S1-S3 as Analysis of Deviance Tables (Type II Wald chi square tests, one-sided) along with 𝜒2 values, degrees of freedom, and p-values (one-sided tests). Full model summaries with standard errors and confidence intervals are also included in Tables S4-S6. For all statistical analyses, we set 𝛼 = 0.05.

      (2) Absence of Links to Foraging Theory

      This critique has three components. The first revisits the absence of code for the optimal cleaning time model. This omission was an unfortunate error at the moment of submission, but our code is available now as a Mathematica notebook in Zenodo (https://doi.org/10.5281/zenodo.14002737). The second pivots around our scholarship, admonishing us for failing to acknowledge the marginal value theorem of Charnov (1976). It is a fair point and we have corrected the oversight with a citation to this classic paper. The third criticism is also rooted in scholarship, with Reviewer 1 asking for greater connection to the existing literature on optimal foraging theory, a point echoed in the summary assessment of the editors at eLife. This comment and the weight given to it by eLife’s editors put us in a difficult spot, as our paper is focused on the optimization of delayed gratification, not food acquisition per se. So, we are in the awkward position of gently resisting this recommendation while simultaneously agreeing with Reviewer 1 that we need to better situate our findings in the landscape of existing literature. To thread this needle, we produced Box 2 with a photograph and 410 words. This display box puts our findings into direct conversation with recent research focused on the sunk cost fallacy.

      (3) Interpretation and validity of model relative to data

      This critique is focused on the simulated brushing and washing results reported in Figure S1, along with its captioning, which was inadequate. We edited the caption to identify the author (JER) who simulated the brushing and washing behaviors of the monkeys. In addition, we clarified the number of brushing replicates (3) and washing replicates (3) for each of three treatments, for a total of 18 simulations.

      We followed Reviewer 1’s suggestion, incorporating the experimental uncertainty of grit removal into our optimal cleaning time model. We drew % grit removed values the % grit removed is used to estimate the cleaning inefficiency≥ 100%parameter 𝑐 for from a distribution, discounting the rare event when values were drawn. As brushing and washing, the included uncertainty now allows us to evaluate these parameters as distributions; and, in turn, obtain a distribution for our predicted brushing and washing optimal cleaning times. As we now describe in the main text, the optimal cleaning time for brushing and washing are 𝑡* \= 0. 98 ± 0. 19 s and * = 2. 40 ± 0. 74 s, respectively. We are grateful for Reviewer 1’s suggestion, for it added𝑡 valuable context to our model predictions. Notably, the inclusion of experimental uncertainty did not change the qualitative nature of our results, or the interpretations of our model predictions compared to observed cleaning behaviors.

      We choose to exclude variability in handling time h to generate predicted cleaning time optima, at least in the main text. Our reasoning stems from the observation that handling time variability is long-tailed, with the longer handling times associated with behaviors that we do not account for in our analysis. For example, individuals carrying multiple cucumber slices to the ocean were apt to drop them, struggling at times to re-grasp so many at once. Such moments increased handling times substantially. Still, we acted on Reviewer 1’s suggestion, accounting for the tandem effects of handling time variability and uncertainty in % grit removed (see Figure S6). Drawing handling time estimates from a log-normal distribution fitted to the handling time data, we found that these dual sources of uncertainty did not qualitatively change our results. They added further uncertainty to the predicted washing time, but the mean remains roughly equivalent. (We note that brushing is assumed to have a constant handling time––composed of only assessment time and no travel––such that the results for brushing do not change.) Both analyses are included in the Mathematica notebook at (https://doi.org/10.5281/zenodo.14002737).

      Reviewer #2 (Public Review):

      Summary

      We have no objections to Reviewer 2’s summary of our manuscript.

      Strengths

      Reviewer 2 is extremely gracious, and we are grateful for the kind words.

      Weaknesses

      Reviewer 2 noted that our manuscript failed to provide “sufficient background on [our study] population of animals and their prior demonstrations of food-cleaning behavior or other object-handling behaviors (e.g., stone handling).” To address this comment, we edited the introduction (lines 56-58) to alert readers to the onset of regular food-cleaning behaviors sometime after December 26, 2004. In addition, we edited our methods text (lines 155-160) to highlight the onset and limited scope of prior research with this study population:

      “The animals are well habituated to human observers due to regular tourism and sustained study since 2013 (Tan et al., 2018). Most of this research has revolved around stone tool-mediated foraging on mollusks, the only activity known to elicit stone handling (Malaivijitnond et al., 2007; Gumert and Malaivijitnond, 2012, 2013; Tan et al., 2015), although infants and juveniles will sometimes use stones during object play (Tan, 2017). There has been no prior examination of food-cleaning behaviors.”

      Reviewer #3 (Public Review):

      Reviewer 3 identified three weaknesses, which we address in three paragraphs.

      Reviewer 3 questioned our methods for determining rank-dependent differences in cleaning behavior, arguing that our conclusions were unsupported. It is a fair point, and it compelled us to combine males and females into a single standardized ordinal rank of 24 individuals. This unified ranking is now reflected in the x-axes of Figure 2 and Figure S2. Plotting the data this way––see Figure S2––underscores Reviewer 3’s concern that sex and dominance rank are confounding variables. To address this problem, our GLMM included rank and sex as predictor variables, which controls for the effect of sex when assessing the relationship between rank and cleaning time across the three treatments. Reported in Tables S1-S3, these findings show that the effect of sex on either brushing or washing time was not significant. This result bolsters our original contention that rank-related variation in cleaning time overwhelms any sex differences.

      Relatedly, Reviewer 3 questioned our conclusions on the effects of rank because our study was focused on a single social group. In other words, it is plausible that our results were heavily influenced by the idiosyncrasies of select individuals, not dominance rank per se. It is a fair point, and it compelled us to include individual ID as a random effect in each of our GLMMs. Including individual ID as a random intercept allowed us to control for inter-individual variation in cleaning duration while assessing the effects of rank. An analysis based on additional social groups or longitudinal data are certainly desirable, but also well beyond the scope of a Short Report for eLife.

      Finally, Reviewer 3 objected to fragments of sentences in our abstract, introduction, and discussion, combining them into a criticism of claims that we did not and do not make. It probably wasn’t intentional, but it puts us in the awkward position of deconstructing a strawman:

      ● Review 3 begins, “there is no evidence presented on the actual fitness-related costs of tooth wear or the benefits of slightly faster food consumption”. This statement is true while insinuating that collecting such evidence was our intent. To be clear, our experiment was never designed to measure tooth wear or reproductive fitness, nor do we make any claims of having done so.

      ● Reviewer 3 adds, “Support for these arguments is provided based on other papers, some of which come from highly resource-limited populations (and different species). But this is a population that is supplemented by tourists with melons, cucumbers, and pineapples!” We were puzzled over these sentences. The first fails to mention that the citations exist in our discussion. Citing relevant work in a discussion is a basic convention of scientific writing. But it seems the underlying intent of these words is to denigrate the value of our study population because two dozen tourists visit Koram Island once a day. Exclamations to the contrary, the amount of tourist-provisioned food in the diet of any one monkey is negligible.

      ● Last, Reviewer 3 commented on matters of style, objecting to “overly strong claims.” We puzzled over this criticism because the claims in question are broader points of introduction or discussion, not results. The root problem appears to be the final sentence of our abstract:

      “Dominant monkeys abstained from washing, balancing the long-term benefits of mitigating tooth wear against immediate energetic requirements, an essential predictor of reproductive fitness.”

      This sentence has three clauses. The first is a statement of results, whereas the second and third are meant to mirror our discussion on the importance of our findings. We combined the concepts into a single concluding sentence for the sake of concision, but we can appreciate how a reader could feel deceived, expecting to see data on tooth wear and fitness. So, our impression is that we are dealing with a simple misunderstanding of our own making, and that this single sentence explains Reviewer 3’s criticism and tone––it cast a long shadow over the substance of our paper. To resolve this problem, we edited the sentence:

      “Dominant monkeys abstained from washing, a choice consistent with the impulses of dominant monkeys elsewhere: to prioritize rapid food intake and greater reproductive fitness over the long-term benefits of prolonging tooth function.”

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      In their manuscript, Gomez-Frittelli and colleagues characterize the expression of cadherin6 (and -8) in colonic IPANs of mice. Moreover, they found that these cdh6-expressing IPANs are capable of initiating colonic motor complexes in the distal colon, but not proximal and midcolon. They support their claim by morphological, electrophysiological, optogenetic, and pharmacological experiments.

      Strengths:

      The work is very impressive and involves several genetic models and state-of-the-art physiological setups including respective controls. It is a very well-written manuscript that truly contributes to our understanding of GI-motility and its anatomical and physiological basis. The authors were able to convincingly answer their research questions with a wide range of methods without overselling their results.

      We greatly appreciate the reviewer’s time, careful reading and support of our study.

      Weaknesses:

      The authors put quite some emphasis on stating that cdh6 is a synaptic protein (in the title and throughout the text), which interacts in a homophilic fashion. They deduct that cdh6 might be involved in IPAN-IPAN synapses (line 247ff.). However, Cdh6 does not only interact in synapses and is expressed by non-neuronal cells as well (see e.g., expression in the proximal tubuli of the kidney). Moreover, cdh6 does not only build homodimers, but also heterodimers with Chd9 as well as Cdh7, -10, and -14 (see e.g., Shimoyama et al. 2000, DOI: 10.1042/02646021:3490159). It would therefore be interesting to assess the expression pattern of cdh6proteins using immunostainings in combination with synaptic markers to substantiate the authors' claim or at least add the possibility of cell-cell-interactions other than synapses to the discussion. Additionally, an immunostaining of cdh6 would confirm if the expression of tdTomato in smooth muscle cells of the cdh6-creERT model is valid or a leaky expression (false positive).

      We agree with the reviewer that Cdh6 could be mediating some other cell-cell interaction besides synapses between IPANs, and we noted it in the discussion. Cdh6 primarily forms homodimers but, as the reviewer points out, has been known to also form heterodimers with some other cadherins. We performed RNAscope in the colonic myenteric plexus with Cdh7 and found no expression (data not shown). Cdh10 is suggested to have very low expression (Drokhlyansky et al., 2020), possibly in putative secretomotor vasodilator neurons, and Cdh14 has not been assayed in any RNAseq screens. We attempted to visualize Cdh6 protein via antibody staining (Duan et al., 2018) but our efforts did not result in sufficient signal or resolution to identify synapses in the ENS, which remain broadly challenging to assay. Similarly, immunostaining with Cdh6 antibody was unable to confirm Cdh6 protein in tdT-expressing muscle cells, or by RNAscope. We have addressed these caveats in the discussion section.

      (1) E. Drokhlyansky, C. S. Smillie, N. V. Wittenberghe, M. Ericsson, G. K. Griffin, G. Eraslan, D. Dionne, M. S. Cuoco, M. N. Goder-Reiser, T. Sharova, O. Kuksenko, A. J. Aguirre, G. M. Boland, D. Graham, O. Rozenblatt-Rosen, R. J. Xavier, A. Regev, The Human and Mouse Enteric Nervous System at Single-Cell Resolution. Cell 182, 1606-1622.e23 (2020).

      (2) X. Duan, A. Krishnaswamy, M. A. Laboulaye, J. Liu, Y.-R. Peng, M. Yamagata, K. Toma, J. R. Sanes, Cadherin Combinations Recruit Dendrites of Distinct Retinal Neurons to a Shared Interneuronal Scaffold. Neuron 99, 1145-1154.e6 (2018).

      Reviewer #2 (Public review):

      Summary:

      Intrinsic primary afferent neurons are an interesting population of enteric neurons that transduce stimuli from the mucosa, initiate reflexive neurocircuitry involved in motor and secretory functions, and modulate gut immune responses. The morphology, neurochemical coding, and electrophysiological properties of these cells have been relatively well described in a long literature dating back to the late 1800's but questions remain regarding their roles in enteric neurocircuitry, potential subsets with unique functions, and contributions to disease. Here, the authors provide RNAscope, immunolabeling, electrophysiological, and organ function data characterizing IPANs in mice and suggest that Cdh6 is an additional marker of these cells.

      Strengths:

      This paper would likely be of interest to a focused enteric neuroscience audience and increase information regarding the properties of IPANs in mice. These data are useful and suggest that prior data from studies of IPANs in other species are likely translatable to mice.

      We appreciate the reviewer’s support of our study and insightful critiques for its improvement.

      Weaknesses:

      The advance presented here beyond what is already known is minimal. Some of the core conclusions are overstated and there are multiple other major issues that limit enthusiasm. Key control experiments are lacking and data do not specifically address the properties of the proposed Cdh6+ population.

      Major weaknesses:

      (1) The novelty of this study is relatively low. The main point of novelty suggests an additional marker of IPANs (Cdh6) that would add to the known list of markers for these cells. How useful this would be is unclear. Other main findings basically confirm that IPANs in mice display the same classical characteristics that have been known for many years from studies in guinea pigs, rats, mice and humans.

      We appreciate the already existing markers for IPANs in the ENS and the existing literature characterizing these neurons. The primary intent of this study was to use these well-established characteristics of IPANs in both mice and other species to characterize Cdh6-expressing neurons in the mouse myenteric plexus and confirm their classification as IPANs.

      (2) Some of the main conclusions of this study are overstated and claims of priority are made that are not true. For example, the authors state in lines 27-28 of the abstract that their findings provide the "first demonstration of selective activation of a single neurochemical and functional class of enteric neurons". This is certainly not true since Gould et al (AJP-GIL 2019) expressed ChR2 in nitrergic enteric neurons and showed that activating those cells disrupted CMC activity. In fact, prior work by the authors themselves (Hibberd et al., Gastro 2018) showed that activating calretinin neurons with ChR2 evoked motor responses. Work by other groups has used chemogenetics and optogenetics to show the effects of activating multiple other classes of neurons in the gut.

      We thank the reviewer for bringing up this important point and apologize if our wording was not clear. Whilst single neurochemical classes of enteric neurons have been manipulated to alter gut functions, all such instances to date do not represent manipulation of a single functional class of enteric neurons. In the given examples, multiple functional classes are activated utilizing the same neurotransmitter, as NOS and calretinin are each expressed to varying degrees across putative motor neurons, interneurons and IPANs. In contrast, Chd6 is restricted to IPANs and therefore this study is the first optogenetic investigation of enteric neurons from a single putative functional class. Our abstract and discussion emphasizes this point and differentiates this study from those previous.

      (3) Critical controls are needed to support the optogenetic experiments. Control experiments are needed to show that ChR2 expression a) does not change the baseline properties of the neurons, b) that stimulation with the chosen intensity of light elicits physiologically relevant responses in those neurons, and c) that stimulation via ChR2 elicits comparable responses in IPANs in the different gut regions focused on here.

      We completely agree controls are essential. However, our paper is not the first to express ChR2 in enteric neurons. Authors of our paper have shown in Hibberd et al. 2018 that expression of ChR2 in a heterogeneous population of myenteric neurons did not change network properties of the myenteric plexus. This was demonstrated in the lack of change in control CMC characteristics in mice expressing ChR2 under basal conditions (without blue light exposure). Regarding question (b), that it should be shown that stimulation with the chosen intensity of light elicits physiologically relevant responses in those neurons. We show the restricted expression of ChR2 in IPANs and that motor responses (to blue light) are blocked by selective nerve conduction blockade.

      Regarding question (c), that our study should demonstrate that stimulation via ChR2 elicits comparable responses in IPANs in the different gut regions. We would not expect each region of the gut to behave comparably. This is because the different gut regions (i.e. proximal, mid, distal) are very different anatomically, as is anatomy of the myenteric plexus and myenteric ganglia between each region, including the density of IPANs within each ganglia, in addition to the presence of different patterns of electrical and mechanical activity [Spencer et al., 2020]. Hence, it is difficult to expect that between regions stimulation of ChR2 should induce similar physiological responses. The motor output we record in our study (CMCs) is a unified motor program that involves the temporal coordination of hundreds of thousands of enteric neurons and a complex neural circuit that we have previously characterized [Spencer et al., 2018]. But, never has any study until now been able to selectively stimulate a single functional class of enteric neurons (with light) to avoid indiscriminate activation of other classes of neurons.

      (1) T. J. Hibberd, J. Feng, J. Luo, P. Yang, V. K. Samineni, R. W. Gereau, N. Kelley, H. Hu, N. J. Spencer, Optogenetic Induction of Colonic Motility in Mice. Gastroenterology 155, 514-528.e6 (2018).

      (2) N. J. Spencer, L. Travis, L. Wiklendt, T. J. Hibberd, M. Costa, P. Dinning, H. Hu, Diversity of neurogenic smooth muscle electrical rhythmicity in mouse proximal colon. American Journal of Physiology-Gastrointestinal and Liver Physiology 318, G244–G253 (2020).

      (3) N. J. Spencer, T. J. Hibberd, L. Travis, L. Wiklendt, M. Costa, H. Hu, S. J. Brookes, D. A. Wattchow, P. G. Dinning, D. J. Keating, J. Sorensen, Identification of a Rhythmic Firing Pattern in the Enteric Nervous System That Generates Rhythmic Electrical Activity in Smooth Muscle. The Journal of Neuroscience 38, 5507–5522 (2018).

      (4) The electrophysiological characterization of mouse IPANs is useful but this is a basic characterization of any IPAN and really says nothing specifically about Cdh6+ neurons. The electrophysiological characterization was also only done in a small fraction of colonic IPANs, and it is not clear if these represent cell properties in the distal colon or proximal colon, and whether these properties might be extrapolated to IPANs in the different regions. Similarly, blocking IH with ZD7288 affects all IPANs and does not add specific information regarding the role of the proposed Cdh6+ subtype.

      Our electrophysiological characterization was guided to be within a subset of Cdh6+ neurons by Hb9:GFP expression. As in the prior comment (1) above, we used these experiments to confirm classification of Cdh6+ (Hb9:GFP+) neurons in the distal colon as IPANs. We have clarified in the results and methods that these experiments were performed in the distal colon and agree that we cannot extrapolate that these properties are also representative of IPANs in the proximal colon. We apologize that this was confusing. Finally, we agree with the reviewer that ZD7288 affects all IPANs in the ENS and have clarified this in the text.

      (5) Why SMP IPANs were not included in the analysis of Cdh6 expression is a little puzzling. IPANs are present in the SMP of the small intestine and colon, and it would be useful to know if this proposed marker is also present in these cells.

      We agree with the reviewer. In addition to characterizing Cdh6 in the myenteric plexus, it would be interesting to query if sensory neurons located within the SMP also express Cdh6. Our preliminary data (n=2) show ~6-12% tdT/Hu neurons in Cdh6-tdT ileum and colon (data not shown). We have added a sentence to the discussion.

      (6) The emphasis on IH being a rhythmicity indicator seems a bit premature. There is no evidence to suggest that IH and IT are rhythm-generating currents in the ENS.

      Regarding the statement there is no evidence to suggest that IH and IT are rhythm-generating currents in the ENS. We agree with the reviewer that evidence of rhythm generation by IH and IT in the ENS has not been explicitly confirmed. We are confident the reviewer agrees that an absence of evidence is not evidence of absence, although the presence of IH has been well described in enteric neurons. We have modified the text in the results to indicate more clearly that IH and IT are known to participate in rhythm generation in thalamocortical circuits, though their roles in the ENS remain unknown. Our discussion of the potential role of IH or IT in rhythm generation or oscillatory firing of the ENS is constrained to speculation in the discussion section of the text.

      (7) As the authors point out in the introduction and discuss later on, Type II Cadherins such as Cdh6 bind homophillically to the same cadherin at both pre- and post-synapse. The apparent enrichment of Cdh6 in IPANs would suggest extensive expression in synaptic terminals that would also suggest extensive IPAN-IPAN connections unless other subtypes of neurons express this protein. Such synaptic connections are not typical of IPANs and raise the question of whether or not IPANs actually express the functional protein and if so, what might be its role. Not having this information limits the usefulness of this as a proposed marker.

      We agree with the reviewer that the proposed IPAN-IPAN connection is novel although it has been proposed before (Kunze et al., 1993). As detailed in our response to Reviewer #1, we attempted to confirm Cdh6 protein expression, but were unsuccessful, due to insufficient signal and resolution. We therefore discuss potential IPAN interconnectivity in the discussion, in the context of contrasting literature.

      (1) W. A. A. Kunze, J. B. Furness, J. C. Bornstein, Simultaneous intracellular recordings from enteric neurons reveal that myenteric ah neurons transmit via slow excitatory postsynaptic potentials. Neuroscience 55, 685–694 (1993).

      (8) Experiments shown in Figures 6J and K use a tethered pellet to drive motor responses. By definition, these are not CMCs as stated by the authors.

      The reviewer makes a valid criticism as to the terminology, since tethered pellet experiments do not record propagation. We believe the periodic bouts of propulsive force on the pellet is triggered by the same activity underlying the CMC. In our experience, these activities have similar periodicity, force and identical pharmacological properties. Consistent with this, we also tested full colons (n = 2) set up for typical CMC recordings by multiple force transducers, finding that CMCs were abolished by ZD7288, similar to fixed pellet recordings (data not shown).

      (9) The data from the optogenetic experiments are difficult to understand. How would stimulating IPANs in the distal colon generate retrograde CMCs and stimulating IPANs in the proximal colon do nothing? Additional characterization of the Cdh6+ population of cells is needed to understand the mechanisms underlying these effects.

      We agree that the different optogenetic responses in the proximal and distal colon are challenging to interpret, but perhaps not surprising in the wider context. It is not only possible that the different optogenetic responses in this study reflect regional differences in the Chd6+ neuronal populations, but also differences in neural circuits within these gut regions. A study some time ago by the authors showed that electrical stimulation of the proximal mouse colon was unable to evoke a retrograde (aborally) propagating CMC (Spencer, Bywater, 2002), but stimulation of the distal colon was readily able to. We concluded that at the oral lesion site there is a preferential bias of descending inhibitory nerve projections, since the ascending excitatory pathways have been cut off. In contrast, stimulation of the distal colon was readily able to activate an ascending excitatory neural pathway, and hence induce the complex CMC circuits required to generate an orally propagating CMC. Indeed, other recent studies have added to a growing body of evidence for significant differences in the behaviors and neural circuits of the two regions (Li et al., 2019, Costa et al., 2021a, Costa et al., 2021b, Nestor-Kalinoski et al., 2022). We have expanded this discussion.

      (1) N. J. Spencer, R. A. Bywater, Enteric nerve stimulation evokes a premature colonic migrating motor complex in mouse. Neurogastroenterology & Motility 14, 657–665 (2002).

      (2) Li Z, Hao MM, Van den Haute C, Baekelandt V, Boesmans W, Vanden Berghe P, Regional complexity in enteric neuron wiring reflects diversity of motility patterns in the mouse large intestine. Elife 8:e42914 (2019).

      (3) Costa M, Keightley LJ, Hibberd TJ, Wiklendt L, Dinning PG, Brookes SJ, Spencer NJ, Motor patterns in the proximal and distal mouse colon which underlie formation and propulsion of feces. Neurogastroenterology & Motility e14098 (2021a).

      (4) Costa M, Keightley LJ, Hibberd TJ, Wiklendt L, Smolilo DJ, Dinning PG, Brookes SJ, Spencer NJ, Characterization of alternating neurogenic motor patterns in mouse colon. Neurogastroenterology & Motility 33:e14047 (2021b).

      (5) Nestor-Kalinoski A, Smith-Edwards KM, Meerschaert K, Margiotta JF, Rajwa B, Davis BM, Howard MJ, Unique Neural Circuit Connectivity of Mouse Proximal, Middle, and Distal Colon Defines Regional Colonic Motor Patterns. Cellular and Molecular Gastroenterology and Hepatology 13:309-337.e303 (2022).

      Recommendations for the Authors:

      Reviewer #1 (Recommendations for the authors):

      As mentioned above, immunolocalization of cdh6 would be helpful to substantiate the claims regarding IPAN-IPAN synapses.

      As mentioned in our response to both reviewers’ public reviews, we attempted to visualize Cdh6 protein via antibody staining (Duan et al., 2018), but our efforts did not result in sufficient signal or resolution to identify Cdh6+ synapses.

      (1) X. Duan, A. Krishnaswamy, M. A. Laboulaye, J. Liu, Y.-R. Peng, M. Yamagata, K. Toma, J. R. Sanes, Cadherin Combinations Recruit Dendrites of Distinct Retinal Neurons to a Shared Interneuronal Scaffold. Neuron 99, 1145-1154.e6 (2018).

      Reviewer #2 (Recommendations for the authors):

      (1) The authors repeatedly refer to IPANs as "sensory" neurons (e.g. in title, abstract, and introduction) but there is some debate regarding whether these cells are truly "sensory" because the information they convey never reaches sensory perception. This is why they have classically been referred to as intrinsic primary afferent (IPAN) neurons. It would be more appropriate to stick with this terminology unless the authors have compelling data showing that information detected by IPANs reaches the sensory cortex.

      We thank the reviewer for their comment, but respectfully disagree. The term “sensory neuron” is well established in the ENS. The first definitive proof that “sensory neurons” exist in the ENS was published in Kunze et al., 1995. We note that this paper did not use the word “IPAN” but used the term “sensory neuron”. Furthermore, mechanosensory neurons were published in Spencer and Smith (2004).

      Regarding the reviewer’s comment that the authors would need compelling data showing that information detected by IPANs reaches the sensory cortex before the term “sensory neuron” should be valid, it is important to note that many sensory neurons do not provide direct information to the cortex.

      (1) W. A. A. Kunze, J. C. Bornstein, J. B. Furness, Identification of sensory nerve cells in a peripheral organ (the intestine) of a mammal. Neuroscience 66, 1–4 (1995).

      (2) N. J. Spencer, T. K. Smith, Mechanosensory S-neurons rather than AH-neurons appear to generate a rhythmic motor pattern in guinea-pig distal colon. The Journal of Physiology 558, 577–596 (2004).

      (2) Important information regarding the gut region shown and other details are absent from many figure legends.

      We apologize for this omission. We have updated the figure legends to include information on gut regions.

    1. Author response:

      Thank you for the constructive feedback from the reviewers. We are grateful for their insights and are committed to addressing the key concerns raised in the public reviews through the following revisions:

      (1) Validating Axoneme Stability Claims

      We have procured new antibodies for DRC11, as well as marker proteins for ODA, IDA, and RS. We will conduct quantitative immunofluorescence staining to validate our claims regarding axoneme stability.

      (2) Investigating ANKRD5 Expression in Other Ciliated Cells

      We plan to examine the expression of ANKRD5 in mouse respiratory cilia to determine whether it is also expressed in these cells.

      (3) Supplementing Key Citations for N-DRC Components

      We will add references to published studies on N-DRC components (e.g., DRC1, DRC2, DRC3, DRC5) associated with male infertility in the Introduction to strengthen the background context.

      (4) Further Analysis and Validation of ANKRD5 Interactome

      We will conduct additional analyses and validation of the interactome of ANKRD5 detected by LC-MS.

      (5) Elucidating the Function of ANKRD5 in Mitochondria

      We will further investigate the role of ANKRD5 in mitochondrial function.

      (6) Investigating Mitochondrial Function and Energy Metabolism

      We will further explore the role of ANKRD5 in mitochondrial function and energy metabolism.

      (7) Improving Cryo-ET Data Quality and Interpretation

      We will attempt to further improve the quality of the STA results and try to calculate the DMT structure with a period of 96 nm. We will also use the WT density map with the same period to generate a difference map.

      (8) Expanding Discussion and Correcting Terminology

      The Discussion section will be revised to elaborate on the implications of ANKRD5 for male contraceptive research, particularly in targeting sperm motility. We will also correct terminology inaccuracies (e.g., changing "9+2 microtubule doublet" to "9+2 structure") and address formatting issues (e.g., capitalizing "Control").

      Response to Reviewer #2 Comment 4:

      We appreciate the reviewer's careful consideration of our proteomic data. However, our Gene Set Enrichment Analysis (GSEA) of glycolysis/gluconeogenesis pathways showed no significant enrichment (p-value=0.089, NES=0.708; Fig.6D), which does not meet the statistical thresholds for biological significance (|NES|>1, pvalue<0.05). This observation is further corroborated by our direct ATP measurements showing no difference between genotypes (Fig.6E). We agree that further studies on metabolic regulation could be valuable, but current evidence does not support glycolysis disruption as a primary mechanism for the motility defects observed in Ankrd5-null sperm. This misinterpretation likely arose from the reviewer's overinterpretation of non-significant proteomic trends. We request that this specific claim be excluded from the assessment to avoid misleading readers.

      We will provide a comprehensive point-by-point response, along with detailed experimental data and revised figures, in the resubmitted manuscript. Thank you once again for the opportunity to address the reviewers' concerns. We are confident that these revisions will strengthen our manuscript and contribute to the scientific community.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this study, the authors examined the role of Afadin, a key adaptor protein associated with cell-adhesion molecules, in retinal development. Using a conditional knockout mouse line (Six3-Cre; AfadinF/F), the authors successfully characterized a disorganized pattern of various neuron types in the mutant retinae. Despite these altered distributions, the retinal neurons maintained normal cell numbers and seemingly preserved some synaptic connections. Notably, tracing results indicated mistargeting of retinal ganglion cell (RGC) axon projections to the superior colliculus, and electroretinography (ERG) analyses suggested deficits in visual functions.

      Thank you for the summary and highlights of our study. We appreciate the input from Reviewer 1 and the Editor on this study, with focus on laminar choices, synaptic choices and axonal projections.

      Strengths:

      This compelling study provides solid evidence addressing the important question of how cell-adhesion molecules influence neuronal development. Compared to previous research conducted in other parts of the central nervous system (CNS), the clearly defined lamination of cell types in the retina serves as a unique model for studying the aberrant neuronal localizations caused by Afadin knockout. The data suggest that cell-cell interactions are critical for retinal cellular organization and proper axon pathfinding, while aspects of cell fate determination and synaptogenesis remain less understood. This work has broad implications not only for retinal studies but also for developmental biology and regenerative medicine.

      Weaknesses:

      While the phenotypes observed in the Afadin knockout (cKO) mice are intriguing, I would expect to see evidence confirming that Afadin is indeed knocked out in the retina through immunostaining. Specifically, is Afadin knocked out only in certain retinal regions and not others, as suggested by Figures 4A-B? Are Afadin levels different among distinct neuron types, which could mean that its knockout may have a more pronounced impact on certain cell types, such as rods compared to others?

      The authors suggest that synapses may form between canonical synaptic partners, based on the proximity of their processes (Figure 2). However, more solid evidence is needed to verify these synapses through the use of synaptic marker staining or transsynaptic labeling before drawing further conclusions.

      Although the Afadin cKO mice displayed dramatic phenotypes, additional experiments are necessary to clarify the details of this process. By manipulating Afadin levels in specific cell types or at different developmental time points, we could gain a better understanding of how Afadin regulates accurate retinal lamination and axonal projection.

      Regarding the antibody confirming the Knockout, we tested the commercially available antibody from Sigma but weren’t able to confirm its specificity. There was a homemade antibody from another Japan-based laboratory, but it was not available to share at the moment when the study was conducted. Nonetheless, the original allele was derived for hippocampal and cortical studies by Louis Reichardt’s Lab (UCSF), with verified efficacies of the KO allele.

      Regarding phenotypical penetrance, this may likely come from the mosaicism of the clone and the symmetric cell division, leading to a rosette-like structure. At this moment, we reason that Afadin KO does NOT lead to direct neuronal loss, and the selective rod loss may derive from other issues, but we lack direct evidence to validate this point.

      In regards to the specific neuronal types and synaptic pairs, we acknowledge the limitations of the current Figure 2 in linking the mutant phenotypes to circuit changes. However, the current genetic reagents (Six3Cre) are not compatible with neuron-type specific labeling of synaptic labeling – i.e., cell type-specific Cre and additional Cre-dependent AAV tools might be desired. To do so, we will need to initiate cell-type-specific breeding of transgenic markers such as Hb9GFP for ooDSGCs, or Chat-Cre, VGlut3-Cre for starburst amacrine cells, vG3 amacrine cells, followed by retinal physiology. These experiments take multi-allelic genetic crosses for a very low breeding yield (1/16 or 1/32 Mendelian ratio). These extensive genetic tests are beyond the scope of the current manuscript.

      Reviewer #2 (Public review):

      Summary:

      This study by Lum and colleagues reports on the role of Afadin, a cytosolic adapter protein that organizes multiple cell adhesion molecule families, in the generation and maintenance of complex cellular layers in the mouse retina. They used a conditional deletion approach, removing Afadin in retinal progenitors, and allowing them to analyze broad effects on retinal neuron development.

      The study presents high-quality and extensive characterization of the cellular phenotypes, supporting the main conclusions of the paper. They show that Afadin loss results in significant disorganization of the retinal cellular layers and the neuropil, producing rosettes and displacement of cells away from their resident layers. The major classes of neurons in the inner retina are affected, and some neurons are, remarkably, displaced to the other side of the inner plexiform layer. Nevertheless, they mostly target their synaptic partners, including the RGCs to distant retinorecipient targets in the brain. The main conclusions are as follows. Afadin is necessary for establishing and maintaining the retinal architecture. It is not necessary for the generation of the correct numbers/densities of retinal neuron subtypes. Moreover, Afadin loss preserves associations between known synaptic partners and preserves axonal targeting to retinorecipient layers. The consequences on photoreceptor viability and visual processing are also interesting, underscoring the essential function for maintaining retinal structure and function. Overall, the main conclusions describing the consequences are supported by the results.

      Strengths:

      The study provides new knowledge on the requirement of Afadin in retinal development. The introduction and discussion effectively set up the rationale for this work, and place it in the context of previous studies of Afadin in other regions of the CNS.

      The study presents high-quality and extensive characterizations of the cellular phenotypes resulting from Afadin loss. By analyzing various aspects of retinal organization - from cellular densities to axon targeting to brain - the study narrows down the role of a structure for promoting the establishment of the layers, or maintenance. The data are straightforward and convincing, and the interpretations are bounded by the data shown (though minor weakness re. survival). Another important finding is that the targeting of retinal neuron processes to synaptic partners, including retinorecipient targets in the brain, are intact.

      The study is important as it establishes a focused requirement for Afadin to set up and preserve the overall cellular organizations within the retinal tissue. The demonstration that Afadin is needed for photoreceptor viability and overall visual function enhances impact by establishing its functional importance.

      The manuscript is well well-written and presented. The images are attractive and compelling, and the figures are well organized.

      Thank you for your high praise on the logic, data presentation, and significance of the current manuscript. We appreciate your comments on the novelty and impact of our study using retinal circuits as a model.

      Weaknesses:

      (1) Expanding on the developmental mechanism is beyond the scope of the study, and would not add to the main conclusions. However, the manuscript would be improved by providing more clarity on the developmental emergence of the defects. The study left me questioning whether the rosettes and cell displacements occur during earlier stages of retina development, or are progressive. For instance, do the RGCs migrate and establish within the GCL correctly at first, and then are displaced with the progressive disorganization? Or are they disorganized and delaminate en route? Images of RGC staining at P0, or earlier during their migration, would be informative. Data in Figure 1 is limited to DAPI staining at P7. Figure 4 shows an image of rod photoreceptors at P7, with their displacement in the GCL layer (and not contained within a rosette). Are the progenitors mislocalized due to delamination?  A few additional thoughts on how these defects compare to other mutants with rosettes might give us more context for understanding the results.

      We chose P7 as our focus due to the lamination in controls. In the revised manuscript, we plan to include earlier time points, as suggested by the reviewer. The data in Figure 1 at P7 utilizes well-established cell type markers (RBPMS, Chx10, Ap2α) and is not limited only to DAPI. Additionally, we will revise the discussion section and place our mutant analyses in the context of other mutants with rosettes (beta-catenin, etc.) in the retina. Finally, we will address the comment on progenitor lamination by exploring earlier developmental time points.

      (2) The manuscript reports that the densities of major inner retinal classes are unaffected. There are a few details missing for this point. How were the cell densities quantified (in terms of ROI size), and normalized? This information is lacking in the methods. There is a striking thickening of the GCL in the DAPI-labeled images shown in Figure 1. What are these cells?

      We will revise the manuscript, particularly the methods section, to address these comments. Additionally, we will tackle ROI units and normalization. The cells in the thickened GCL were identified as displaced amacrine cells and bipolar cells.

    1. Author response:

      Reviewer #1:

      Summary:

      The authors address the role of the centromere histone core in force transduction by the kinetochore.

      Strengths:

      They use a hybrid DNA sequence that combines CDEII and CDEIII as well as Widom 601 so they can make stable histones for biophysical studies (provided by the Widom sequence) and maintain features of the centromere (CDE II and III).

      Weaknesses:

      The main results are shown in one figure (Figure 2). Indeed the Centromere core of Widom and CDE II and III contribute to strengthening the binding force for the OA-beads. The data are very nicely done and convincingly demonstrate the point. The weakness is that this is the entire paper. It is certainly of interest to investigators in kinetochore biology, but beyond that, the impact is fairly limited in scope.

      This reviewer might have missed that this is a Research Advance, not an article.  Research Advances are limited in scope by definition and provide a new development that builds on research reported in a prior paper.  They can be of any length.  Our Research Advance builds on our prior work, Hamilton et al., 2020 and provides the new result that native centromere sequences strengthen the attachment of the kinetochore to the nucleosome.

      Reviewer #2:

      Summary:

      This paper provides a valuable addendum to the findings described in Hamilton et al. 2020 (https://doi.org/ 10.7554/eLife.56582). In the earlier paper, the authors reconstituted the budding yeast centromeric nucleosome together with parts of the budding yeast kinetochore and tested which elements are required and sufficient for force transmission from microtubules to the nucleosome. Although budding yeast centromeres are defined by specific DNA sequences, this earlier paper did not use centromeric DNA but instead the generic Widom 601 DNA. The reason is that it has so far been impossible to stably reconstitute a budding yeast centromeric nucleosome using centromeric DNA.

      In this new study, the authors now report that they were able to replace part of the Widom 601 DNA with centromeric DNA from chromosome 3. This makes the assay more closely resemble the in vivo situation. Interestingly, the presence of the centromeric DNA fragment makes one type of minimal kinetochore assembly, but not the other, withstand stronger forces.

      We thank the reviewer for their careful and positive assessment of our work.

      Which kinetochore assembly turned out to be affected was somewhat unexpected, and can currently not be reconciled with structural knowledge of the budding yeast centromere/kinetochore. This highlights that, despite recent advances (e.g. Guan et al., 2021; Dendooven et al., 2023), aspects of budding yeast kinetochore architecture and function remain to be understood and that it will be important to dissect the contributions of the centromeric DNA sequence.

      We couldn’t agree more.

      Given the unexpected result, the study would become yet more informative if the authors were able to pinpoint which interactions contribute to the enhanced force resistance in the presence of centromeric DNA.

      Strength:

      The paper demonstrates that centromeric DNA can increase the attachment strength between budding yeast microtubules and centromeric nucleosomes.

      Weakness:

      How centromeric DNA exerts this effect remains unclear.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      eLife Assessment

      In this work, the authors use a Drosophila adult ventral nerve cord injury model extending and confirming previous observations; this important study reveals key aspects of adult neural plasticity. Taking advantage of several genetic reporter and fate tracing tools, the authors provide solid evidence for different forms of glial plasticity, that are increased upon injury. The data on detected plasticity under physiologic conditions and especially the extent of cell divisions and cell fate changes upon injury would benefit from validation by additional markers. The experimental part would improve if strengthened and accompanied by a more comprehensive integration of results regarding glial reactivity in the adult CNS.

      Thank you very much for your thoughtful comments and constructive feedback regarding our manuscript. We appreciate all the positive remarks on the significance of our findings on neural plasticity in this Drosophila adult ventral nerve cord injury model.

      In response to your suggestion, we fully agree that the continuation of this project should address in detail cell fate changes with additional markers if available, or an “omic” approach such as scRNAseq. Unfortunately, these further experiments are beyond the scope of this paper to describe the in vivo phenomena of cell reprogramming, and the cellular events that take glial cells to convert into neurons or neuronal precursors.

      Additionally, we agree that the experimental part can be further improved by providing a more comprehensive integration of our results with current knowledge on glial reactivity in the adult CNS. We will revise the manuscript accordingly to include a deeper discussion of the broader implications of our findings and their alignment with existing literature.

      Thank you again for your valuable input, which will undoubtedly enhance the quality of our work. We look forward to submitting the revised manuscript for your consideration.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Casas-Tinto et al. present convincing data that injury of the adult Drosophila CNS triggers transdifferentiation of glial cell and even the generation of neurons from glial cells. This observation opens up the possibility to get an handle on the molecular basis of neuronal and glial generation in the vertebrate CNS after traumatic injury caused by Stroke or Crush injury. The authors use an array of sophisticated tools to follow the development of glial cells at the injury site in very young and mature adults. The results in mature adults reveal a remarkable plasticity in the fly CNS and dispels the notion that repair after injury may be only possible in nerve cords which are still developing. The observation of so called VC cells which do not express the glial marker repo could point to the generation of neurons by former glial cells.

      Conclusion:

      The authors present an interesting story which is technically sound and could form the basis for an in depth analysis of the molecular mechanism driving repair after brain injury in Drosophila and vertebrates.

      Strengths:

      The evidence for transdifferentiation of glial cells is convincing. In addition, the injury to the adult CNS shows an inherent plasticity of the mature ventral nerve cord which is unexpected.

      Weaknesses:

      Traumatic brain injury in Drosophila has been previously reported to trigger mitosis of glial cells and generation of neural stem cells in the larval CNS and the adult brain hemispheres. Therefore this report adds to but does not significantly change our current understanding. The origin and identity of VC cells is still unclear. The authors show that VC cells are not GABA- or glutamergic. Yet, there are many other neurotransmitter or neuropetides. It would have been nice to see a staining with another general neuronal marker such as anti-Syt1 to confirm the neuronal identity of Syt1.

      We thank the reviewer for the constructive comments and positive feedback. We concur that previous studies have demonstrated glial cell proliferation in response to CNS injury. In contrast, our study focuses on glial transdifferentiation that emerges as a novel phenomenon, particularly in response to injury. We found that neuropile glia lose their glial identity and express the pan-neuronal marker Elav. To investigate the identity of these newly observed elav-positive cells, we employed anti-ChAT, antiGABA and anti-GluRIIA antibodies to determine the functional identity of these cells, besides we stained them with other neuronal markers such Enabled, Gigas or Dac (not shown); however, our attempts yielded limited success. To address this, we have now included a discussion section exploring the potential identity of these cells, considering the possibility that they may represent immature neurons.

      Reviewer #2 (Public review):

      Summary:

      Casas-Tinto et al., provide new insight into glial plasticity using a crush injury paradigm in the ventral nerve cord (VNC) of adult Drosophila. The authors find that both astrocyte-like glia (ALG) and ensheating glia (EG) divide under homeostatic conditions in the adult VNC and identify ALG as the glial population that specifically ramps up proliferation in response to injury, whereas the number of EGs decreases following the insult. Using lineage-tracing tools, the authors interestingly observe interconversion of glial subtypes, especially of EGs into ALGs, which occurs independent of injury and is dependent on the availability of the transcription factor Prospero in EGs, adding to the plasticity observed in the system. Finally, when tracing the progeny of glia, Casas-Tinto and colleagues detect cells of neuronal identity and provide evidence that such gliaderived neurogenesis is specifically favoured following ventral nerve cord injury, which puts forward a remarkable way in which glia can respond to neuronal damage.

      Strengths:

      This study highlights a new facet of adult nervous system plasticity at the level of the ventral nerve cord, supporting the view that proliferative capacity is maintained in the mature CNS and stimulated upon injury.

      The injury paradigm is well chosen, as the organization of the neuromeres allows specific targeting of one segment, compared to the remaining intact and with the potential to later link observed plasticity to behaviour such as locomotion.

      Numerous experiments have been carried out in 7-day old flies, showing that the observed plasticity is not due to residual developmental remodelling or a still immature VNC.

      By elegantly combining different methods, the authors show glial divisions including with mitotic-dependent tracing and find that the number of generated glia is refined by apoptosis later on.

      The work identifies prospero in glia as an important coordinator of glial cell fate, from development to the adult context, which draws further attention to the upstream regulatory mechanisms.

      We would like to thank the reviewer for his/her comments and the positive analysis of this work.

      Weaknesses:

      The authors observe consistent inter-conversion of EG to ALG glial subtypes that is further stimulated upon injury. The authors conclude that these findings have important consequences for CNS regeneration and potentially for memory and learning. However, it remains somewhat unclear how glial transformation could contribute to regeneration and functional recovery.

      This is an ongoing question in the laboratory and in the field. We know that glial cells contribute to the regenerative program in the nervous system, and molecular signalling in glial cells is determinant for the functional recovery (Losada-Perez et al 2021). Therefore, we include this concept in the discussion as the evidence indicates that glial cells participate in these programs. However, further investigation is required to clarify and determine the mechanisms underlying this glial contribution. To determine if glial to neuron transformation contributes to functional recovery, we would need to compare the recovery of animals with new VC to animals without VC, however, the  molecular mechanism that produces this change of identity is still unknown, and therefore we are not able to generate injured flies with no new VC

      The signal of the Fucci cell cycle reporter seems more complex to interpret based on the panels provided compared to the other methods employed by the authors to assess cell divisions.

      We agree that Fly Fucci is a genetic reporter that might be more complex to interpret than EdU staining or other markers. However, glial cells proliferation is a milestone of this manuscript, and we used different available tools to confirm our results. We have revised this specific section to ensure that the text is clear and straightforward.

      Elav+ cells originating from glia do not express markers for mature neurons at the analysed time-point. If they will eventually differentiate or what type of structure is formed by them will have to be followed up in future studies.

      We fully agree with the reviewer, and we will analyze later days to study neuronal fate and contribution to VNC function.

      Context/Discussion

      There is some lack of connecting or later comparing the observed forms of glial plasticity in the VNC with respect to plasticity described in the fly brain.

      Highlighting some differences in the reactiveness of glia in the VNC compared to the brain could point to relevant differences in repair capacity in different areas of the CNS.

      Based on the assays employed, the study points to a significant amount of glial "identity" changes or interconversions under homeostatic conditions. The potential significance of this rather unexpected "baseline" plasticity in adult tissues is not explicitly pointed out and could improve the understanding of the findings.

      Some speculations if "interconversion" of glia is driven by the needs in the tissue could enrich the discussion.

      We would like to thank the reviewer for these suggestions. We have changed the discussion to introduce these concepts.

      Reviewer #3 (Public review):

      In this manuscript, Casas-Tintó et al. explore the role of glial cell in the response to a neurodegenerative injury in the adult brain. They used Drosophila melanogaster as a

      model organism, and found that glial cells are able to generate new neurons through the mechanism of transdifferentiation in response to injury. This paper provides a new mechanism in regeneration, and gives an understanding to the role of glial cells in the process.

      Comments on revisions:

      In the previous version of the manuscript, I had suggested several recommendations for the authors. Unfortunately, none of these were addressed in the author's revision.

      We are sorry for this error. We apologize but we never received these comments. We have now found them, and we have incorporated these comments in the new version of the manuscript.

      (1) Have you tried screening for other markers for the EdU+ Repo+ Pros- cells?

      We have identified these cells as glial cells (Repo +), and not astrocyte-like glia (pros-). But we have not further characterized  the identity of these cells. Our aim was to identify these proliferating glial cells as NPG (Neuropile glia), which are Astrocyte-Like Glia (ALG), as previous works suggest in larvae (Kato et al., 2020; Losada-Perez et al., 2016), or Ensheathing Glia (EG). To discard the ALG identity, we used prospero as the best marker. The results indicate that there are ALG among the proliferating population, but in addition, we also found pros- glial cells that were EdU positive. These cells are located in the interface between cortex and neuropile, where the neuropile glia position is described. The anti-pros staining indicated they were no ALG which suggest that they are EG.

      There is no specific nuclear marker for EG cells, therefore we used FLY_FUCCI under the control of a EG specific promoter (R56F03-Gal4) to determine if the other dividing cells were EG. These results indicate that EG glia divide although their proliferation does not increase upon injury.

      The R56F03 Gal4 construct is described as ensheathing glia specific by previous publications, including:

      (1) Kremer M. C., Jung C., Batelli S., Rubin G. M. and Gaul U. (2017). The glia of the adult Drosophila nervous system. Glia 65, 606-638. 10.1002/glia.23115

      (2) Qingzhong Ren, Takeshi Awasaki, Yu-Chun Wang, Yu-Fen Huang, Tzumin Lee. Lineage-guided Notch-dependent gliogenesis by Drosophila multi-potent progenitors. Development. 2018 Jun 11;145(11):dev160127. doi: 10.1242/dev.160127   

      To summarize, our results suggest that part of these proliferating glial cells are ALG and EG. Our results can not discard that a residual part of these proliferating cells are not AG nor EG.

      (2) You mentioned that ALG are heterogenous in size and shape, does that mean that you may have different subpopulations of ALG? Would that also mean that only a portion of them responds to injury?

      Yes, as in Astrocytes in vertebrates this population is highly heterogeneous. Currently there are no molecular tools to specifically identify these subpopulations and characterize their distinct roles. However, emerging research suggests that differences in size, shape, and potentially molecular markers could correlate with functional diversity. This implies that certain subpopulations of ALG may be more specialized or primed to respond to injury, while others may play roles in homeostasis or other processes. Understanding this heterogeneity will require advanced techniques such as single-cell RNA sequencing, spatial transcriptomics, or live imaging to unravel how these subpopulations contribute to injury responses and overall tissue dynamics.

      (3) You mentioned that NP-like cells have similar nuclear shape and size to ALG and EG, while Ventral cortex cells have larger nuclei. Can you please show a quantification of the NP-like cells and Ventral cortex cells size, and show a direct comparison with ALG and EG cells to support those claims (images, quantification and analysis)?

      We added a new supplementary figure with a graph showing nuclei size differences between VC and NP-like cells, and a diagram showing VC cell localization. Images in figure 2A-A’ and 2B-B’ show both types of cells with the same scale, additionally, NPG cells are shown in red (current expression of the specific Gal4 line). A direct comparison between EG and NP-like glia can be observed in Figure 3 as well.

      Besides of size and localization, we conclude  that VC and N-like cells present different molecular markers as VC are elav-positive and reponegative whereas NP-like cells are repo-positive elav-negative

      (4) In Figure 2B, the repo expression is not very clear. I suggest using a different example to support the claim that NP cells are Repo+.

      We have changed the color of anti-elav staining to facilitate visualisation

      (5) Again, in Figure 2C, you need quantification and analysis to support the claim that you used nuclear shape and size to identify VC vs. NP like cells.

      Quantification in point 3, criteria in Figure S1

      (6) What is the identity of the newly formed neurons? Other than Elav, have you tried using other markers of neurons that are typically found in this area?

      This question is of great interest and relevance. We have done great efforts to solve this open question and so far, our data suggest that these neurons might be in an immature state. In this last version of the manuscript, we included the results (Figure S1) with several different markers. 

      The molecular identity of these cell populations, glia and neurons, is currently under investigation.

      Minor comments:

      (1) In the abstract, EG and ALG abbreviations are not introduced properly.

      Thank you very much for noticing this missing information, we have now included it in the abstract.

      (2) Please include a representation of the NPG somata location in Figure 1A.

      We have included this information in the figure

      (3) A schematic showing the differences between ALG and EG cells would be helpful as well.

      We have included in the introduction references and reviews where other authors describe in detail the differences.

      (4) In Figure 1 E, G, H- please indicated the genotype of the fly used in the panel as well as the cell type studied.

      The complete genotype is included in the corresponding figure legend. We have added a simplified genotype in the figure for clarity.

      (5) Please show the genotype used for images in Figure 2: ALG or EG specific drivers.

      This information is included in the corresponding figure legend. We believe that it is better to keep the figure clean so we decided to keep the complete genotype, which is considerably long, only in the figure legend.

    1. Author response:

      We appreciate the constructive feedback provided by the reviewers and the editorial board. We are delighted by the positive reception of our work and the thoughtful insights shared.

      Regarding the validation of our predicted interactions, we are currently conducting yeast two-hybrid (Y2H) assays using a commercially available Arabidopsis thaliana cDNA library to screen for interacting partners of the ANK putative effector PBTT_00818 from Plasmodiophora brassicae. Following this initial screening, we will validate positive interactions through targeted 1-to-1 Y2H assays. In particular, we aim to confirm the AlphaFold Multimer-predicted interaction between PBTT_00818 and MPK3, a key immunity-related kinase in Arabidopsis

      We are grateful for the reviewers’ thoughtful suggestions regarding clustering visualization, sequence vs. structure-based motif alignments, and structural confidence assessments. We will carefully incorporate these improvements in our planned revisions.

      Once again, we thank the editors and reviewers for their rigorous and constructive assessment. We look forward to implementing these refinements and submitting an updated version that further enhances the impact of our study.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Recommendations for the authors):

      Minor Points:

      • HEK293T cells are not typically Type 1 IFN-producing cells; it is recommended to use other immune cell lines to validate results obtained with ORMDL3 overexpression in 293T cells. The same applies to A549 alveolar basal epithelial cells.

      Thanks for the reviewer’s insightful comment. In Figure 1C, we overexpressed ORMDL3 in mouse primary BMDM cell and stimulated it with poly(I:C) or poly(dG:dC), which suggests that ORMDL3 inhibits IFN expression in primary cell BMDM.

      • Clarify whether TLR3 is expressed in the cell lines used in Figure 1 and whether TLR3 is present in mouse BMDMs.

      Thanks for your suggestions. We identified whether TLR3 is expressed in HEK293T, A549 and BMDM. We designed primers of human TLR3 and murine Tlr3, and the results showed that Tlr3 is expressed in BMDM but not in HEK293T and A549. As it shown in Author response image 1.

      Author response image 1.

      PCR amplification of human TLR3 was conducted on cDNA derived from HEK293T and A549 cells (lanes 1 and 2, respectively), and PCR amplification of murine Tlr3 was performed on cDNA from BMDM (lane 3). Human spleen cDNA (lane 4, TAKARA Human MTCTM Panel I, Cat# 636742) served as a positive control, and 18s rRNA was used as an internal control.

      primer sequences:

      human TLR3: forward TTGCCTTGTATCTACTTTTGGGG   reverse TCAACACTGTTATGTTTGTGGGT

      murine Tlr3: forward GTGAGATACAACGTAGCTGACTG   reverse TCCTGCATCCAAGATAGCAAGT

      18s (human/mice): forward GTAACCCGTTGAACCCCATT   reverse CCATCCAATCGGTAGTAGCG

      • Specify the type of luciferase reporter assay used in Figure 1E.

      Thanks for the reviewer’s insightful comment. The Dual-Luciferase® Reporter (DLR™) Assay System efficiently measures two luciferase signals. In brief, the IFN-reporter luciferase is derived from firefly (Photinus pyralis), while the internal control luciferase is from Renilla (Renilla reniformis or sea pansy). These dual luciferases are measured sequentially from a single sample. In Figure 1E, we measured the luciferase activity of IFN (firefly) and internal control gene TK (Renilla), and their ratio is shown in Figure 1E.

      • Clarify what was knocked down in the A549 stable KD cell line and whether HSV-1 infects and replicates in A549 cells.

      We sincerely appreciate the reviewer’s concern and apologize for any ambiguous descriptions. In Figure 1H, we knocked down ORMDL3 and infected the cell with HSV-1, which shows that ORMDL3 does not affect the infection and replication of HSV-1 in A549.

      • In Figure 2E, provide the rationale for using the same tag (Flag) in overexpression experiments with different molecules such as Flag-ORDML3 and Flag-RIG-I.

      We thank the reviewer’s concern. We tried to co-express different tags of ORMDL3 and innate immunity proteins, and we got the same results as before. ORMDL3-Myc overexpression can only promote the degradation of Flag-RIG-I-N, as shown in current Figure 2E.

      • Address the low knockdown efficiency shown in Figure 2D and consider whether it is sufficient for drawing conclusions.

      Thanks for the reviewer’s concern. Because ORMDL3 antibody (Abcam 107639) can recognize all ORMDL family members (ORMDL1, 2 and 3), this may explain why the knockdown efficiency of ORMDL3 is not apparent in Figure2D. We also detect the knockdown efficiency of ORMDL3 at mRNA level, which showed that ORMDL3 was silenced efficiently and specifically (Figure S2C).

      • Replace the Tubulin/β-Actin WB control with a more distinguishable band.

      Thanks for the suggestion. Owing to different gel concentration, sometimes the protein bands appear fused, but it is distinguishable that the internal controls are consistent.

      • In Figures 3D/E, the expression level of the Lysine mutant of RIG-I-N is too low. Please provide an explanation or repeat the experiment to achieve comparable expression levels and update the figure accordingly.

      Thanks for the question. The expression of lysine mutant of RIG-I-N is low, we have increased the amount of plasmid in transfection, but this still hasn't increased its expression level. Though its abundance is low, we provided evidence to show that it would not be degraded by ORMDL3. In some literatures (for example: RNF122 suppresses antiviral type I interferon production by targeting RIG-I CARDs to mediate RIG-I degradation. Proc Natl Acad Sci U S A. 2016 Aug 23;113(34):9581-6; TRIM4 modulates type I interferon induction and cellular antiviral response by targeting RIG-I for K63-linked ubiquitination. J Mol Cell Biol. 2014 Apr;6(2):154-63.), it has also been reported that lysine mutant can affect RIG-I stability. In addition, we speculate that the 4KR mutant (K146R, K154R, K164R, K172R) may change RIG-I conformation, so its expression is lower.

      • Explain why there is no difference in MAVS expression levels despite binding with MAVS.

      Thanks for the question. In our experiment, ORMDL3 has no effect on MAVS expression. Our results showed that ORMDL3 interacts with MAVS and promotes the degradation of RIG-I, so only RIG-I level has a significant difference.

      • Verify if Flag-tagged ORMDL3 is present in the IP sample in Figure 3G.

      Thanks for the comment. We reloaded the samples and blot flag, and we found that ORMDL3 cannot be pulled down by RIG-I. We have added the results in Figure 3G.

      • Reload the samples in Figure 4C to clearly identify the correct band for GFP-tagged ORMDL3.

      Thanks for the question. As ORMDL3 is small molecular protein, we fused it and its fragments to GFP to increase its molecular weight. In our GFP vector, for some unknown reason, the 26kDa band always exists. This is actually a technical difficulty. Although the GFP-fused protein and GFP band are very close, they can still be distinguished as two bands.

      • Rerun the Western blot for Actin IB in Figure 4E, as the ORMDL3-GFP (1-153) full-length appears abnormal.

      Thanks for the question. As we first blot GFP and then blot actin on the same membrane, so it appears abnormal. We reloaded the previous sample and blotted the actin again.

      • Clarify in which figure RIG-I ubiquitination is shown and whether ORMDL3 has E3 ubiquitin ligase activity. Explain how ORMDL3 facilitates USP10 transfer to RIG-I despite no direct interaction.

      Thank you for your question. In Figure 3B we showed the ubiquitination of RIG-I and ORMDL3 does not have an E3 ubiquitin ligase activity. Our results showed that although ORMDL3 does not directly interacted with RIG-I, it forms complex with USP10 (Figure 5B, 5C) and disrupt USP10 induced RIG-I stabilization by decreasing the interaction between USP10 and RIG-I (Figure 6A). The detailed mechanism needs further investigation.

      • Provide quantification for Figure 5D. Explain why the bands are not degraded by RIG-I and USP10.

      Thanks for the concern. We quantified the bands and found that overexpression of USP10 increased RIG-I protein abundance. The quantitative gray values are added into the image. USP10 functions to stabilize RIG-I rather than promoting its degradation.

      • Explain the decrease in RIG-I levels in Figure 5E when USP10 levels decrease.

      Thanks for the concern. As shown in the working model (Supplementary Figure 8), USP10 is a deubiquitinase that stabilizes RIG-I by decreasing its K48-linked ubiquitination. So, in Figure 5E, we knocked down USP10 and found a decrease in RIG-I levels, which is consistent with Figure 5D.

      • Clarify whether K48 ubiquitination on RIG-I has decreased in Figure 5F, as this is not clear from the image.

      Thanks for the question. In Figure 5F it is shown that the K48 ubiquitination level of RIG-I significantly decreased (please see the density of the bands in the IP samples).

      • Address whether ORMDL3 reduces RIG-I-N degradation in Figure 5H, as the results do not clearly support this claim.

      Thanks for the concern. We quantified the bands and the results showed that ORMDL3 promotes the degradation of RIG-I-N. The quantitative gray values are added into the image.

      • Reload Flag-ORMDL3 in Figure 6C to determine whether RIG-I-N is restored in the MG132-treated samples.

      Thank you for your question. We quantified the bands and the results showed that RIG-I-N is restored in the MG132-treated samples. The quantitative gray values are added into the image.

      • Correct numerous typos and errors, especially in the Discussion section, to improve readability

      Thanks for the suggestion. We have revised the manuscript carefully to correct these errors.

      Reviewer #2 (Recommendations for the authors):

      (1) In Figure 1G and H, The number of virus-infected cells was observed using a fluorescence microscope. In addition, can the author use other techniques to detect the impact of ORMDL3 on virus replication?

      Thanks for the question. Except for using a fluorescence microscope, we also used RT-PCR to quantify the amount of viral mRNA, and results were added in Figure 1G and H.

      (2) In Figure 3C, ORMDL3 overexpression promotes the degradation of RIG-I-N. ORMDL3 is one of three ORMDL proteins with similar amino acid sequences, does ORMDL1/2 also have this function?

      Thanks for the suggestion. We compared the function between ORMDLs and found that only ORMDL3 overexpression facilitated RIG-I-N degradation. The results were shown in Figure S2D.

      (3) In Figure 5A, USP10 is not the top protein in the Mass spec assay. Does the author verified the interaction between ORMDL3 and other protein (for example CAND1)?

      Thanks for your suggestion. We verified that ORMDL3 has no interaction with CAND1 and UFL1 but only interacts with USP10, as Figure S5 shows.

      (4) A scale bar to be added to the images in Figure 1 G, H and Figure 7K.

      Thanks for the suggestion. We have added the scale bars.

      (5) The annotations in Figure 4B, C and E should be aligned.

      Thanks for the suggestion. We have aligned the annotations.

      (6) Provide Statistical methods

      Thanks for the suggestion. We have provided the statistical methods in the materials and methods part.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This important study identifies the "H-state" as a potential conformational marker distinguishing amyloidogenic from non-amyloidogenic light chains, addressing a critical problem in protein misfolding and amyloidosis. By combining advanced techniques such as small-angle X-ray scattering, molecular dynamics simulations, and H-D exchange mass spectrometry, the authors provide convincing evidence for their novel findings. However, incomplete experimental descriptions, limitations in SAXS data interpretation, and the way HDX MS data is presented aHect the strength and generalizability of the conclusions. Strengthening these aspects would enhance the impact of this work for researchers in amyloidosis and protein misfolding.

      We thank eLife editors and reviewers for their constructive feedback. The manuscript has been improved to provide a more complete description of the experiments and to strengthen the interpretation and presentation of all data. Updated Figures (Figure 2 and Figure 5) and a new Table (Table 2) in the main text provide a more complete and clearer comparison of the SAXS data with MD simulations as well as a clearer representation of the HDX MS data. Additional figures have been added in SI. The text has been extended accordingly and complete materials and methods are now included in the main text. Abstract, introduction and discussion have been revised to improve the overall readability of the manuscript.

      Public Reviews:

      Reviewer #1 (Public review):

      The study investigates light chains (LCs) using three distinct approaches, with a focus on identifying a conformational fingerprint to diHerentiate amyloidogenic light chains from multiple myeloma light chains. The study's major contribution is identifying a low-populated "H state," which the authors propose as a unique marker for AL-LCs. While this finding is promising, the review highlights several strengths and weaknesses. Strengths include the valuable contribution of identifying the H state and using multiple approaches, which provide a comprehensive understanding of LC structural dynamics. However, the study suHers from weaknesses, particularly in interpreting SAXS data, lack of clarity in presentation, and methodological inconsistencies. Critical concerns include high error margins between SAXS profiles and MD fits, unclear validation of oligomeric species in SAXS measurements, and insuHicient quantitative cross-validation between experimental (HDX) and computational data (MD). This reviewer calls for major revisions including clearer definitions, improved methodology, and additional validation, to strengthen the conclusions.

      We thank the reviewer for the supportive comments, in the revised version of the manuscript we have focused on improving the clarity and completeness of our work. We are sorry for example to not have made previously clear enough that the comparison of SAXS with MD simulation was not that shown in the main text in Figure 1 and Table 1 (this is the comparison with single structures) but that reported in the SI (previously Figure S1 and Table S2, showing very good fits). These data have been moved in the main text in the reworked Figure 2 and new Table 2.  We have also improved the presentation of the HDX MS data in Figure 5 and in the text adding also additional analysis in SI. Materials and methods are now completely moved in the main text. We generally revised the manuscript for clarity.

      Reviewer #2 (Public review):

      Summary:

      This well-written manuscript addresses an important but recalcitrant problem - the molecular mechanism of protein misfolding in Ig light chain (LC) amyloidosis (AL), a major life-threatening form of systemic human amyloidosis. The authors use expertly recorded and analyzed smallangle X-ray scattering (SAXS) data as a restraint for molecular dynamics simulations (called M&M) and to explore six patient-based LC proteins. The authors report that a highly populated "H-state" determined computationally, wherein the two domains in an LC molecule acquire a straight rather than bent conformation, is what distinguishes AL from non-AL LCs. They then use H-D exchange mass spectrometry to verify this conclusion. If confirmed, this is a novel and interesting finding with potentially important translational implications.

      We thank the reviewer for the supportive comments.

      Strengths:

      Expertly recorded and analyzed SAXS data combined with clever M&M simulations lead to a novel and interesting conclusion. Regardless of whether or not the CL-CL domain interface is destabilized in AL LCs explored in this (Figure 6) and other studies, stabilization of this interface is an excellent idea that may help protect at least a subset of AL LCs from misfolding in amyloid. This idea increases the potential impact of this interesting study.

      We thank the reviewer for the supportive comments.

      Weaknesses:

      The HDX analysis could be strengthened.

      We have extended the analysis and improved the presentation of the HDX data. Figure 5 has been reworked, text has been improved accordingly and additional analysis have been reported in SI.

      Reviewer #3 (Public review):

      Summary:

      This study identifies conformational fingerprints of amyloidogenic light chains, that set them apart from the non-amyloidogenic ones.

      We thank the reviewer for the supportive comments.

      Strengths:

      The research employs a comprehensive combination of structural and dynamic analysis techniques, providing evidence that conformational dynamics at the VL-CL interface and structural expansion are distinguished features of amyloidogenic LCs.

      We thank the reviewer for the supportive comments.

      Weaknesses:

      The sample size is limited, which may aHect the generalizability of the findings. Additionally, the study could benefit from deeper analysis of specific mutations driving this unique conformation to further strengthen therapeutic relevance.

      We agree, we tried to maximise the size of the sample and this was the best we could do. With respect to the analysis of the mutations, while we tried to discuss some of them also in view of previous works, because our set covers multiple germlines instead than focusing on a single one, this limit our ability to discuss single point mutations systematically, at the same time the discussion of single points mutations has been the focus of many recent works, while our approach provide a diNerent point of view.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      This study provides an investigation of light chains (LCs) using three distinct approaches, focusing primarily on identifying a conformational fingerprint to distinguish amyloidogenic light chains (AL-LCs) from multiple myeloma light chains (MM-LCs). The authors propose that the presence of a low-populated "H state," characterized by an extended quaternary structure and a perturbed CL-CL interface, is unique to AL-LCs. This finding is validated through hydrogendeuterium exchange mass spectrometry (HDX-MS). The study makes a valuable contribution to understanding the structural dynamics of light chains, particularly with the identification of the H state in AL-LCs. However, significant concerns regarding the interpretation of the SAXS data, clarity in presentation, and methodological rigor must be addressed. I recommend major revisions and resubmission of the work.

      Major concerns:

      (1) A critical concern is how the authors ensure that the SAXS profiles represent only dimeric species, given the high propensity of LCs to aggregate. If higher-order aggregates or monomers were present, this would significantly impact the SAXS data and SAXS-MD integration. Some measurements are bulk SAXS, while others are SEC-SAXS, making the study questionable. The authors need to clarify how only dimeric species were measured for the SEC-SAXS analysis, and all assessments of the dimeric state should be shown in the SI. Additionally, complementary techniques such as DLS or SEC-MALS should be used to verify the oligomeric state of the samples. Without this validation, the SAXS profiles may not be reliable.

      We added SEC-MALS and SEC-SAXS data in the SI (Figures S20 and S21) as well the SAXS curves shown in log-log plot (Figure S1) that display a flat trend at low q that exclude aggregation. SAXS is very sensitive to oligomers and aggregates and our data do not indicate the presence of those species. When we had indication of possible aggregation in the sample we used SEC-SAXS.

      (2) A major problem with the paper is that the claim of the "H state," which is the novelty of the study and serves as a marker of aggregation, is derived from samples where the error between the SAXS profiles and MD fits is extremely high. This casts doubt on whether the structure is indeed resolved by MD. The main conclusion of the paper is derived from weak consistency between experiment and simulation. In AL55, the error between experiment and simulation is greater than 5; for H7, it is higher than 2.8. The residuals show significant error at mid-q values, suggesting that long-range distance correlations (20-10 Å, CL, VL positioning) are not consistent between simulation and experiment. Furthermore, the FES plots of two independent replicas show deviation in the existence of the H state. One shows a minimum in that region, while the other does not. So, how robust is this conclusion? What is the chi-squared value if each replica is used independently? A separate experimental cross-validation is necessary to claim the existence of the H state.

      We apologise for the misunderstanding underlying this reviewer comment. The poor agreement mentioned is not between the SAXS and MD simulations, but with the individual structures, and this disagreement led us to perform MD simulations that are in much better agreement with the data (previously Fig. S1 and Table S2). To avoid this misunderstanding, which would indeed weaken our work, we have now moved both the figure and the table in the main text to the updated Figure 2 and the new Table 2.

      Regarding the robustness of the sampling, we believe that Table 3 (previously Table 2) clearly shows the statistical convergence of the data, diNerences in the presentation of the free energy are purely interpolation issues. The chi-squares of each replicate are reported in Table 2 (previously Table S2).

      (3) There is insuHicient discussion about SAXS computations from MD trajectories. The accuracy of these calculations is crucial to deriving the existing conclusions, and the study's reliance on the PLUMED plugin, which is known to give inaccurate results for SAXS computations, raises concerns. How the solvent is treated in the SAXS computations needs to be explained. Alternative methods like WAXSiS or Crysol should be explored to check whether the SAXS profiles derived from the MD trajectory are consistent across other SAXS computation methods for the major conformers of the proteins.

      We have now clarified that while the SAXS calculation to perform Metainference MD were done using PLUMED (that to our knowledge is as accurate as crysol) SAXS curves used for analysis were calculated using crysol.

      (4) The HDX and MD results do not seem to correlate well, and there is a disconnect between Figure 2 (SAXS profiles) and Figure 5 (HDX structural interpretation). The authors should quantitatively assess residue-level dynamics by comparing HDX signals with MD-derived HDX signals for each protein. This would provide a cross-validation between the experimental and computational data.

      In our opinion our SAXS, MD and HDX MS data provide a consistent picture. Our HDX-MS do not provide per residue data, making a quantitative comparison out of scope. RMSF data do not necessarily need to correlate with the deuterium uptake.

      (5) MD simulations are only used to refine the structure of AlphaFold predictions, but the trajectories could help explain why these structures diHer, what stabilizes the dimer, or what leads to the conformational transition of the H state. A lack of analysis regarding the physical mechanism behind these structural changes is a weakness of the study. The authors should dedicate more eHort to analyzing their data and provide physical insights into why these changes are observed.

      Our aim was to identify a property that could discriminate between AL and MM LCs. We used MD simulations, not to refine structures, but to explore the conformational dynamics of LCs (starting from either X-ray structures, homology or AlphaFold models), because SAXS data suggested that conformational dynamics could discriminate between AL- and MM-LCs. Simulations allowed us to propose a hypothesis, which we tested by HDX MS. While more insight is always welcome, we believe that we have achieved our goal for now. In the discussion, we present additional analysis of the simulations to connect with previous literature, we agree that more analysis can be done, and also for this reason, all our data are publicly available.

      Minor concerns

      (6) The abstract leans heavily on describing the problem and methods but lacks a clear presentation of key results. Providing a concise summary of the main findings (e.g., the identification of the H state) would better balance the abstract.

      We agree with the reviewer and we rewrote the abstract.

      (7) In the abstract, the term "experimental structure" is used ambiguously. Since SAXS also provides an experimental structure, it is unclear what the authors are referring to. This should be clarified.

      We agree with the reviewer and we rewrote the abstract.

      (8) Abbreviations such as VL (variable domain) and CL (constant domain) are not defined, making it harder for readers unfamiliar with the field to follow. Abbreviations should be defined when first mentioned.

      We agree with the reviewer and we rewrote the abstract.

      (9) The introduction provides a good general context but fails to explicitly define the knowledge gap. Specifically, the structural and dynamic determinants of LC amyloidogenicity are not well established, and this study could be framed as addressing that gap.

      We thank the reviewer and we agree this could be better framed, we improved the introduction accordingly.

      (10) The introduction does not present the novel discovery of the H state early enough. The unique contribution of identifying this state as a marker for AL-LCs should be mentioned upfront to guide the reader through the significance of the study.

      We thank the reviewer and we have now made more explicit what we found.

      (11) The therapeutic implications of this research should be highlighted more clearly in the discussion. Examples of how these findings could be utilized in drug design or therapeutic approaches would enhance the study's impact.

      We thank the reviewer, but while we think that the H-state could be targeted for drug design, since we do not have data yet we do not want to stress this point more than what we are already doing.

      (12) There is an overwhelming use of abbreviations such as H3, H7, H18, M7, and M10 without proper introduction. This makes it diHicult for readers to follow the results, and the average reader may become lost in the details. An introductory figure summarizing the sequences under study, along with a schematic of the dimeric structure defining VL and CL domains, would significantly aid comprehension.

      We agree and we tried to better introduce the systems and simplify the language without adding a figure that we think would be redundant.

      (13) In Figure 1, add labels to each SAXS curve to indicate which protein they correspond to. Also, what does online SEC-SAXS mean?

      Done

      (14) The caption of Figure 3 is unclear, particularly with abbreviations like Lb, Ls, G, and H, which are not mentioned in the captions. The authors should define these terms for clarity.

      Done

      (15) The study claims that the dominant structure of the dimer changes between diHerent LCs. However, Figure 5 shows identical structures for all proteins, raising questions about the consistency between the SAXS and HDX data. This inconsistency is a general problem between the MD and HDX sections, where cross-communication and comparisons are not properly addressed.

      We do not claim that the dominant structure of the dimer changes between diNerent LCs, this would also be in contradiction with current literature. We claim a diNerence in a low-populated state. From this point of view using always the same structure is consistent and should simplify the representation of the results. We agree that the manuscript may be not always easy to follow and we thank the reviewer in helping us improving it.

      (16) The authors show I(q) vs q and residuals for each protein. The Kratky plots are not suHicient to compare the SAXS computations with the measured profile.

      Showing Kratky and residuals is a standard and complementary way to present and compare SAXS data to structures. Chi-square values are also reported. Log-log plots have been added to SI in response to previous comments.

      (17) The authors need to explain how they estimate the Rg values (from simulation or SAXS profiles). If they are using simulations, they should compute the Rg values from the simulations for comparison.

      Rg values reported in Table 1 are derived from SAXS. Rg from simulations have been added in Table 2.

      (18) The evolution of the sampling is unclear. The authors need to show the initial starting conformation in each case and the most likely conformation after M&M in the SI, to demonstrate that their approach indeed caused changes in the initial predictions.

      Our approach is not structure refinement and as such the proposed analysis would be misleading. Metainference is meant to generate a statistical ensemble representing the equilibrium conformations that as whole reproduce the data. DiNerences (or not) between initial and selected configurations will not be particularly informative in this context.

      (19) The authors should also provide a running average of chi-squared values over time to demonstrate that the conformational ensemble converged toward the SAXS profile.

      Our simulations are not driven to improve the agreement with SAXS over time, this is not structure refinement. Metainference is meant to generate a statistical ensemble representing the equilibrium conformations that as whole reproduce the data. The suggested analysis would be a misinterpretation of our simulations. The comparison with SAXS is provided in Figure 2 and Table 2 as mentioned above.

      (20) The aggregate simulation time of 120 microseconds is misleading, as each replica was only run for 2-3 microseconds. This should be clarified.

      The number reported in the text is accurate and represent the aggregated sampling. The number of replicas for each metainference simulation and their length is reported in Table 2 now moved for clarity from the SI to main text.

      (21) It is not clear how the replicas were weighted to compute the SAXS profiles and FES. There are two independent runs in each case, and each run has about 30 replicas. How these replicas are weighted needs to be discussed in the SI.

      Done

      (22) The methods section is unevenly distributed, with detailed explanations of LC production and purification, while other key methodologies like SAXS+MD integration and HDX are not even mentioned in the main text (they are in the Supporting Information). The authors should provide a brief overview of all methodologies in the main text or move everything to the SI for consistency.

      We agree with the reviewer, all methods are now in main text. 

      Reviewer #2 (Recommendations for the authors):

      (1) Computational M&M evidence is strong (Figure 3) and is supported by SAXS (used as restraints). However, Kratky plots reported in the main MS Figure 1 show significant diHerences between the data and the structural model only for one protein, AL-55. It is hard for the general reader to see how these SAXS data support a clear diHerence between AL and non-AL proteins. If possible, please strengthen the evidence; if not, soften the conclusions.

      We thank the reviewer for the comments. The chi-square (Table 1) and the residuals (Figure 1) are a strong indication of the diNerence. To strengthen the evidence, following also the comment from reviewer 3 we calculated the p-value (<10<sup>-5</sup>) on the significance of the radius of gyration to discriminate AL and MM LCs. We agree that SAXS alone was not enough and this is indeed what prompted us to perform MD simulations.

      (2) HDX MS results are cursory and not very convincing as presented. The butterfly plots in Figure 5 are too small to read and are unlabeled so it is unclear which protein is which.  

      Figure 5 has been reworked for readability. More data have been added in SI. 

      (3) What labeling time was selected to construct these plots and why?

      The deuterium uptakes at 30 min HDX time showed the most pronounced diNerences between diNerent proteins, which were chosen to illustrate the key structural features in the main figure panel (Figure 5).

      How diHerent are the results at other labeling times? Showing uptake curves (with errors) for more than just two peptides in the supplement Figure S12 might be helpful. 

      We found a continuous increase in deuterium uptake as we increased the exchange time from 0.5 to 240 min, which reached saturation at 120 min. Therefore, the exchange follows the same pattern at all time points. Butterfly plots at diNerent HDX times of 0.5 to 240 min are shown in gradient of light blue to dark blue which clearly shows the pattern of deuterium uptake at increasing incubation times (Figure 5). The HDX uptake kinetics of selected peptides with corresponding error bars are shown in Figure S12.

      How redundant are the data, i.e. how good is the peptide coverage/resolution in key regions at the domain-domain interface that the authors deem important? Mapping the maximal deuterium uptake on the structures in Figure 5 is not very helpful. Perhaps mapping the whole range of uptake using a gradient color scheme would be more informative.

      Overall coverage and redundancy for all four proteins are> 90% and > 4.0, respectively, with an average error margin in fractional uptake among all peptides is 0.04-0.05 Da, which suggests that our data is reliable (Table S3). We modified the main panel figures showing the gradient of deuterium uptake in blue-white-red for 0 to 30% of deuterium uptake on the chain A of the dimeric LCs.

      (3) Is the conformational heterogeneity depicted in M&M simulations consistent with HDX results? The authors may want to address this by looking at the EX1/EX2 exchange kinetics for AL vs. non-AL proteins. Do AL proteins show more EX1?

      No, we don’t see any EX1 exchange kinetics in our analysis. This is compatible with the prediction of the H-state that is a native like state and not an unfolded/partially folded state. 

      (4) Perhaps the main conclusion could be softened given the small number of proteins (six), esp. since only four (3 AL and 1 non-AL) could be explored by HDX. Are other HDX MS data of AL LCs from the same Lambda6 family (e.g. PMID: 34678302) consistent with the conclusions that a particular domain-domain interface is weakened in AL vs. non-AL LCs?

      We thank the reviewer for this suggestions. A diNerence in HDX MS data is indeed visible between AL and MM proteins for peptide 33-47 in the suggested paper (Figures 4, S5 and S8). The diNerence is reduced by the mutation identified in the paper as driving the aggregation in that specific case. We now mention this in the discussion.

      (5) Please clarify if the H* state is the same for a covalent vs. non-covalent LC dimer.

      We do not know because our data are only for covalent dimers. But, interestingly, the state is very similar to what was observed for a model kappa light-chain in Weber, et al., we have better highlighted this point in the discussion.

      (6) Please try and better explain why a smaller distance between CL domains in H7 protein and a larger distance in other AL proteins both promote protein misfolding.

      We do not have elements to discuss this point in more detail.

      (7) Please comment on the Kratky plots data vs. model agreement (see comments above).

      Done.

      (8) Please find a better way to display, describe, and interpret the HD exchange MS data.

      We have generated new main text (new Figure 5) and SI figures that we think allow the reader to better appreciated our observations. Corresponding results sections have been also improved.

      Minor points:

      (9) Is the population of the H-state with perturbed CL-CL domain interface, which was obtained in M&M simulations, suHicient to be observable by HDX MS?

      While populations alone are not enough to determine what is observable by HDX MS, a 10% population correspond roughly to 6 kJ/mol of ΔG and is compatible with EX2 kinetics. Previous works suggested that HDX-MS data should be sensitive to subpopulations of the order of 10%, (https://doi.org/10.1016/j.bpj.2020.02.005, https://doi.org/10.1021/jacs.2c06148)

      (10) Typically, an excited intermediate in protein unfolding is a monomer, while here it is an LC dimer. Is this unusual?

      This is a good point, we think that intermediates have mostly been studied on monomeric proteins because these are more commonly used as model systems, but we do not feel like discussing this point.

      (11) Low deuterium uptake is consistent with a rigid structure but may also reflect buried structure and/or structure that moves on a time scale greater than the labeling time.

      We agree.

      Reviewer #3 (Recommendations for the authors):

      (1) The p-value (statistical significance) of Rg diHerence should be computed.

      We thank the reviewer for the suggestion, we calculated the p-value that resulted quite significant.

      (2) The significance of mutations (SHM?) at the interface, such as A40G should be compared with previous observations. (Garrofalo et al., 2021).

      We thank the reviewer for the suggestion, a sentence has been added in the discussion.

    2. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This important study identifies the "H-state" as a potential conformational marker distinguishing amyloidogenic from non-amyloidogenic light chains, addressing a critical problem in protein misfolding and amyloidosis. By combining advanced techniques such as small-angle X-ray scattering, molecular dynamics simulations, and H-D exchange mass spectrometry, the authors provide convincing evidence for their novel findings. However, incomplete experimental descriptions, limitations in SAXS data interpretation, and the way HDX MS data is presented aHect the strength and generalizability of the conclusions. Strengthening these aspects would enhance the impact of this work for researchers in amyloidosis and protein misfolding.

      We thank eLife editors and reviewers for their constructive feedback. The manuscript has been improved to provide a more complete description of the experiments and to strengthen the interpretation and presentation of all data. Updated Figures (Figure 2 and Figure 5) and a new Table (Table 2) in the main text provide a more complete and clearer comparison of the SAXS data with MD simulations as well as a clearer representation of the HDX MS data. Additional figures have been added in SI. The text has been extended accordingly and complete materials and methods are now included in the main text. Abstract, introduction and discussion have been revised to improve the overall readability of the manuscript.

      Public Reviews:

      Reviewer #1 (Public review):

      The study investigates light chains (LCs) using three distinct approaches, with a focus on identifying a conformational fingerprint to diHerentiate amyloidogenic light chains from multiple myeloma light chains. The study's major contribution is identifying a low-populated "H state," which the authors propose as a unique marker for AL-LCs. While this finding is promising, the review highlights several strengths and weaknesses. Strengths include the valuable contribution of identifying the H state and using multiple approaches, which provide a comprehensive understanding of LC structural dynamics. However, the study suHers from weaknesses, particularly in interpreting SAXS data, lack of clarity in presentation, and methodological inconsistencies. Critical concerns include high error margins between SAXS profiles and MD fits, unclear validation of oligomeric species in SAXS measurements, and insuHicient quantitative cross-validation between experimental (HDX) and computational data (MD). This reviewer calls for major revisions including clearer definitions, improved methodology, and additional validation, to strengthen the conclusions.

      We thank the reviewer for the supportive comments, in the revised version of the manuscript we have focused on improving the clarity and completeness of our work. We are sorry for example to not have made previously clear enough that the comparison of SAXS with MD simulation was not that shown in the main text in Figure 1 and Table 1 (this is the comparison with single structures) but that reported in the SI (previously Figure S1 and Table S2, showing very good fits). These data have been moved in the main text in the reworked Figure 2 and new Table 2. We have also improved the presentation of the HDX MS data in Figure 5 and in the text adding also additional analysis in SI. Materials and methods are now completely moved in the main text. We generally revised the manuscript for clarity.

      Reviewer #2 (Public review):

      Summary:

      This well-written manuscript addresses an important but recalcitrant problem - the molecular mechanism of protein misfolding in Ig light chain (LC) amyloidosis (AL), a major life-threatening form of systemic human amyloidosis. The authors use expertly recorded and analyzed smallangle X-ray scattering (SAXS) data as a restraint for molecular dynamics simulations (called M&M) and to explore six patient-based LC proteins. The authors report that a highly populated "H-state" determined computationally, wherein the two domains in an LC molecule acquire a straight rather than bent conformation, is what distinguishes AL from non-AL LCs. They then use H-D exchange mass spectrometry to verify this conclusion. If confirmed, this is a novel and interesting finding with potentially important translational implications.

      We thank the reviewer for the supportive comments.

      Strengths:

      Expertly recorded and analyzed SAXS data combined with clever M&M simulations lead to a novel and interesting conclusion. Regardless of whether or not the CL-CL domain interface is destabilized in AL LCs explored in this (Figure 6) and other studies, stabilization of this interface is an excellent idea that may help protect at least a subset of AL LCs from misfolding in amyloid. This idea increases the potential impact of this interesting study.

      We thank the reviewer for the supportive comments.

      Weaknesses:

      The HDX analysis could be strengthened.

      We have extended the analysis and improved the presentation of the HDX data. Figure 5 has been reworked, text has been improved accordingly and additional analysis have been reported in SI.

      Reviewer #3 (Public review):

      Summary:

      This study identifies conformational fingerprints of amyloidogenic light chains, that set them apart from the non-amyloidogenic ones.

      We thank the reviewer for the supportive comments.

      Strengths:

      The research employs a comprehensive combination of structural and dynamic analysis techniques, providing evidence that conformational dynamics at the VL-CL interface and structural expansion are distinguished features of amyloidogenic LCs.

      We thank the reviewer for the supportive comments.

      Weaknesses:

      The sample size is limited, which may aHect the generalizability of the findings. Additionally, the study could benefit from deeper analysis of specific mutations driving this unique conformation to further strengthen therapeutic relevance.

      We agree, we tried to maximise the size of the sample and this was the best we could do. With respect to the analysis of the mutations, while we tried to discuss some of them also in view of previous works, because our set covers multiple germlines instead than focusing on a single one, this limit our ability to discuss single point mutations systematically, at the same time the discussion of single points mutations has been the focus of many recent works, while our approach provide a diNerent point of view.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      This study provides an investigation of light chains (LCs) using three distinct approaches, focusing primarily on identifying a conformational fingerprint to distinguish amyloidogenic light chains (AL-LCs) from multiple myeloma light chains (MM-LCs). The authors propose that the presence of a low-populated "H state," characterized by an extended quaternary structure and a perturbed CL-CL interface, is unique to AL-LCs. This finding is validated through hydrogendeuterium exchange mass spectrometry (HDX-MS). The study makes a valuable contribution to understanding the structural dynamics of light chains, particularly with the identification of the H state in AL-LCs. However, significant concerns regarding the interpretation of the SAXS data, clarity in presentation, and methodological rigor must be addressed. I recommend major revisions and resubmission of the work.

      Major concerns:

      (1) A critical concern is how the authors ensure that the SAXS profiles represent only dimeric species, given the high propensity of LCs to aggregate. If higher-order aggregates or monomers were present, this would significantly impact the SAXS data and SAXS-MD integration. Some measurements are bulk SAXS, while others are SEC-SAXS, making the study questionable. The authors need to clarify how only dimeric species were measured for the SEC-SAXS analysis, and all assessments of the dimeric state should be shown in the SI. Additionally, complementary techniques such as DLS or SEC-MALS should be used to verify the oligomeric state of the samples. Without this validation, the SAXS profiles may not be reliable.

      We added SEC-MALS and SEC-SAXS data in the SI (Figures S20 and S21) as well the SAXS curves shown in log-log plot (Figure S1) that display a flat trend at low q that exclude aggregation. SAXS is very sensitive to oligomers and aggregates and our data do not indicate the presence of those species. When we had indication of possible aggregation in the sample we used SEC-SAXS.

      (2) A major problem with the paper is that the claim of the "H state," which is the novelty of the study and serves as a marker of aggregation, is derived from samples where the error between the SAXS profiles and MD fits is extremely high. This casts doubt on whether the structure is indeed resolved by MD. The main conclusion of the paper is derived from weak consistency between experiment and simulation. In AL55, the error between experiment and simulation is greater than 5; for H7, it is higher than 2.8. The residuals show significant error at mid-q values, suggesting that long-range distance correlations (20-10 Å, CL, VL positioning) are not consistent between simulation and experiment. Furthermore, the FES plots of two independent replicas show deviation in the existence of the H state. One shows a minimum in that region, while the other does not. So, how robust is this conclusion? What is the chi-squared value if each replica is used independently? A separate experimental cross-validation is necessary to claim the existence of the H state.

      We apologise for the misunderstanding underlying this reviewer comment. The poor agreement mentioned is not between the SAXS and MD simulations, but with the individual structures, and this disagreement led us to perform MD simulations that are in much better agreement with the data (previously Fig. S1 and Table S2). To avoid this misunderstanding, which would indeed weaken our work, we have now moved both the figure and the table in the main text to the updated Figure 2 and the new Table 2.

      Regarding the robustness of the sampling, we believe that Table 3 (previously Table 2) clearly shows the statistical convergence of the data, diNerences in the presentation of the free energy are purely interpolation issues. The chi-squares of each replicate are reported in Table 2 (previously Table S2).

      (3) There is insuHicient discussion about SAXS computations from MD trajectories. The accuracy of these calculations is crucial to deriving the existing conclusions, and the study's reliance on the PLUMED plugin, which is known to give inaccurate results for SAXS computations, raises concerns. How the solvent is treated in the SAXS computations needs to be explained. Alternative methods like WAXSiS or Crysol should be explored to check whether the SAXS profiles derived from the MD trajectory are consistent across other SAXS computation methods for the major conformers of the proteins.

      We have now clarified that while the SAXS calculation to perform Metainference MD were done using PLUMED (that to our knowledge is as accurate as crysol) SAXS curves used for analysis were calculated using crysol.

      (4) The HDX and MD results do not seem to correlate well, and there is a disconnect between Figure 2 (SAXS profiles) and Figure 5 (HDX structural interpretation). The authors should quantitatively assess residue-level dynamics by comparing HDX signals with MD-derived HDX signals for each protein. This would provide a cross-validation between the experimental and computational data.

      In our opinion our SAXS, MD and HDX MS data provide a consistent picture. Our HDX-MS do not provide per residue data, making a quantitative comparison out of scope. RMSF data do not necessarily need to correlate with the deuterium uptake.

      (5) MD simulations are only used to refine the structure of AlphaFold predictions, but the trajectories could help explain why these structures diHer, what stabilizes the dimer, or what leads to the conformational transition of the H state. A lack of analysis regarding the physical mechanism behind these structural changes is a weakness of the study. The authors should dedicate more eHort to analyzing their data and provide physical insights into why these changes are observed.

      Our aim was to identify a property that could discriminate between AL and MM LCs. We used MD simulations, not to refine structures, but to explore the conformational dynamics of LCs (starting from either X-ray structures, homology or AlphaFold models), because SAXS data suggested that conformational dynamics could discriminate between AL- and MM-LCs. Simulations allowed us to propose a hypothesis, which we tested by HDX MS. While more insight is always welcome, we believe that we have achieved our goal for now. In the discussion, we present additional analysis of the simulations to connect with previous literature, we agree that more analysis can be done, and also for this reason, all our data are publicly available.

      Minor concerns

      (6) The abstract leans heavily on describing the problem and methods but lacks a clear presentation of key results. Providing a concise summary of the main findings (e.g., the identification of the H state) would better balance the abstract.

      We agree with the reviewer and we rewrote the abstract.

      (7) In the abstract, the term "experimental structure" is used ambiguously. Since SAXS also provides an experimental structure, it is unclear what the authors are referring to. This should be clarified.

      We agree with the reviewer and we rewrote the abstract.

      (8) Abbreviations such as VL (variable domain) and CL (constant domain) are not defined, making it harder for readers unfamiliar with the field to follow. Abbreviations should be defined when first mentioned.

      We agree with the reviewer and we rewrote the abstract.

      (9) The introduction provides a good general context but fails to explicitly define the knowledge gap. Specifically, the structural and dynamic determinants of LC amyloidogenicity are not well established, and this study could be framed as addressing that gap.

      We thank the reviewer and we agree this could be better framed, we improved the introduction accordingly.

      (10) The introduction does not present the novel discovery of the H state early enough. The unique contribution of identifying this state as a marker for AL-LCs should be mentioned upfront to guide the reader through the significance of the study.

      We thank the reviewer and we have now made more explicit what we found.

      (11) The therapeutic implications of this research should be highlighted more clearly in the discussion. Examples of how these findings could be utilized in drug design or therapeutic approaches would enhance the study's impact.

      We thank the reviewer, but while we think that the H-state could be targeted for drug design, since we do not have data yet we do not want to stress this point more than what we are already doing.

      (12) There is an overwhelming use of abbreviations such as H3, H7, H18, M7, and M10 without proper introduction. This makes it diHicult for readers to follow the results, and the average reader may become lost in the details. An introductory figure summarizing the sequences under study, along with a schematic of the dimeric structure defining VL and CL domains, would significantly aid comprehension.

      We agree and we tried to better introduce the systems and simplify the language without adding a figure that we think would be redundant.

      (13) In Figure 1, add labels to each SAXS curve to indicate which protein they correspond to. Also, what does online SEC-SAXS mean?

      Done

      (14) The caption of Figure 3 is unclear, particularly with abbreviations like Lb, Ls, G, and H, which are not mentioned in the captions. The authors should define these terms for clarity.

      Done

      (15) The study claims that the dominant structure of the dimer changes between diHerent LCs. However, Figure 5 shows identical structures for all proteins, raising questions about the consistency between the SAXS and HDX data. This inconsistency is a general problem between the MD and HDX sections, where cross-communication and comparisons are not properly addressed.

      We do not claim that the dominant structure of the dimer changes between diNerent LCs, this would also be in contradiction with current literature. We claim a diNerence in a low-populated state. From this point of view using always the same structure is consistent and should simplify the representation of the results. We agree that the manuscript may be not always easy to follow and we thank the reviewer in helping us improving it.

      (16) The authors show I(q) vs q and residuals for each protein. The Kratky plots are not suHicient to compare the SAXS computations with the measured profile.

      Showing Kratky and residuals is a standard and complementary way to present and compare SAXS data to structures. Chi-square values are also reported. Log-log plots have been added to SI in response to previous comments.

      (17) The authors need to explain how they estimate the Rg values (from simulation or SAXS profiles). If they are using simulations, they should compute the Rg values from the simulations for comparison.

      Rg values reported in Table 1 are derived from SAXS. Rg from simulations have been added in Table 2.

      (18) The evolution of the sampling is unclear. The authors need to show the initial starting conformation in each case and the most likely conformation after M&M in the SI, to demonstrate that their approach indeed caused changes in the initial predictions.

      Our approach is not structure refinement and as such the proposed analysis would be misleading. Metainference is meant to generate a statistical ensemble representing the equilibrium conformations that as whole reproduce the data. DiNerences (or not) between initial and selected configurations will not be particularly informative in this context.

      (19) The authors should also provide a running average of chi-squared values over time to demonstrate that the conformational ensemble converged toward the SAXS profile.

      Our simulations are not driven to improve the agreement with SAXS over time, this is not structure refinement. Metainference is meant to generate a statistical ensemble representing the equilibrium conformations that as whole reproduce the data. The suggested analysis would be a misinterpretation of our simulations. The comparison with SAXS is provided in Figure 2 and Table 2 as mentioned above.

      (20) The aggregate simulation time of 120 microseconds is misleading, as each replica was only run for 2-3 microseconds. This should be clarified.

      The number reported in the text is accurate and represent the aggregated sampling. The number of replicas for each metainference simulation and their length is reported in Table 2 now moved for clarity from the SI to main text.

      (21) It is not clear how the replicas were weighted to compute the SAXS profiles and FES. There are two independent runs in each case, and each run has about 30 replicas. How these replicas are weighted needs to be discussed in the SI.

      Done

      (22) The methods section is unevenly distributed, with detailed explanations of LC production and purification, while other key methodologies like SAXS+MD integration and HDX are not even mentioned in the main text (they are in the Supporting Information). The authors should provide a brief overview of all methodologies in the main text or move everything to the SI for consistency.

      We agree with the reviewer, all methods are now in main text.

      Reviewer #2 (Recommendations for the authors):

      (1) Computational M&M evidence is strong (Figure 3) and is supported by SAXS (used as restraints). However, Kratky plots reported in the main MS Figure 1 show significant diHerences between the data and the structural model only for one protein, AL-55. It is hard for the general reader to see how these SAXS data support a clear diHerence between AL and non-AL proteins. If possible, please strengthen the evidence; if not, soften the conclusions.

      We thank the reviewer for the comments. The chi-square (Table 1) and the residuals (Figure 1) are a strong indication of the diNerence. To strengthen the evidence, following also the comment from reviewer 3 we calculated the p-value (<10<sup>-5</sup>) on the significance of the radius of gyration to discriminate AL and MM LCs. We agree that SAXS alone was not enough and this is indeed what prompted us to perform MD simulations.

      (2) HDX MS results are cursory and not very convincing as presented. The butterfly plots in Figure 5 are too small to read and are unlabeled so it is unclear which protein is which.

      Figure 5 has been reworked for readability. More data have been added in SI.

      (3) What labeling time was selected to construct these plots and why?

      The deuterium uptakes at 30 min HDX time showed the most pronounced diNerences between diNerent proteins, which were chosen to illustrate the key structural features in the main figure panel (Figure 5).

      How diHerent are the results at other labeling times? Showing uptake curves (with errors) for more than just two peptides in the supplement Figure S12 might be helpful.

      We found a continuous increase in deuterium uptake as we increased the exchange time from 0.5 to 240 min, which reached saturation at 120 min. Therefore, the exchange follows the same pattern at all time points. Butterfly plots at diNerent HDX times of 0.5 to 240 min are shown in gradient of light blue to dark blue which clearly shows the pattern of deuterium uptake at increasing incubation times (Figure 5). The HDX uptake kinetics of selected peptides with corresponding error bars are shown in Figure S12.

      How redundant are the data, i.e. how good is the peptide coverage/resolution in key regions at the domain-domain interface that the authors deem important? Mapping the maximal deuterium uptake on the structures in Figure 5 is not very helpful. Perhaps mapping the whole range of uptake using a gradient color scheme would be more informative.

      Overall coverage and redundancy for all four proteins are> 90% and > 4.0, respectively, with an average error margin in fractional uptake among all peptides is 0.04-0.05 Da, which suggests that our data is reliable (Table S3). We modified the main panel figures showing the gradient of deuterium uptake in blue-white-red for 0 to 30% of deuterium uptake on the chain A of the dimeric LCs.

      (3) Is the conformational heterogeneity depicted in M&M simulations consistent with HDX results? The authors may want to address this by looking at the EX1/EX2 exchange kinetics for AL vs. non-AL proteins. Do AL proteins show more EX1?

      No, we don’t see any EX1 exchange kinetics in our analysis. This is compatible with the prediction of the H-state that is a native like state and not an unfolded/partially folded state.

      (4) Perhaps the main conclusion could be softened given the small number of proteins (six), esp. since only four (3 AL and 1 non-AL) could be explored by HDX. Are other HDX MS data of AL LCs from the same Lambda6 family (e.g. PMID: 34678302) consistent with the conclusions that a particular domain-domain interface is weakened in AL vs. non-AL LCs?

      We thank the reviewer for this suggestions. A diNerence in HDX MS data is indeed visible between AL and MM proteins for peptide 33-47 in the suggested paper (Figures 4, S5 and S8). The diNerence is reduced by the mutation identified in the paper as driving the aggregation in that specific case. We now mention this in the discussion.

      (5) Please clarify if the H* state is the same for a covalent vs. non-covalent LC dimer.

      We do not know because our data are only for covalent dimers. But, interestingly, the state is very similar to what was observed for a model kappa light-chain in Weber, et al., we have better highlighted this point in the discussion.

      (6) Please try and better explain why a smaller distance between CL domains in H7 protein and a larger distance in other AL proteins both promote protein misfolding.

      We do not have elements to discuss this point in more detail.

      (7) Please comment on the Kratky plots data vs. model agreement (see comments above).

      Done.

      (8) Please find a better way to display, describe, and interpret the HD exchange MS data.

      We have generated new main text (new Figure 5) and SI figures that we think allow the reader to better appreciated our observations. Corresponding results sections have been also improved.

      Minor points:

      (9) Is the population of the H-state with perturbed CL-CL domain interface, which was obtained in M&M simulations, suHicient to be observable by HDX MS?

      While populations alone are not enough to determine what is observable by HDX MS, a 10% population correspond roughly to 6 kJ/mol of ΔG and is compatible with EX2 kinetics. Previous works suggested that HDX-MS data should be sensitive to subpopulations of the order of 10%, (https://doi.org/10.1016/j.bpj.2020.02.005, https://doi.org/10.1021/jacs.2c06148)

      (10) Typically, an excited intermediate in protein unfolding is a monomer, while here it is an LC dimer. Is this unusual?

      This is a good point, we think that intermediates have mostly been studied on monomeric proteins because these are more commonly used as model systems, but we do not feel like discussing this point.

      (11) Low deuterium uptake is consistent with a rigid structure but may also reflect buried structure and/or structure that moves on a time scale greater than the labeling time.

      We agree.

      Reviewer #3 (Recommendations for the authors):

      (1) The p-value (statistical significance) of Rg diHerence should be computed.

      We thank the reviewer for the suggestion, we calculated the p-value that resulted quite significant.

      (2) The significance of mutations (SHM?) at the interface, such as A40G should be compared with previous observations. (Garrofalo et al., 2021).

      We thank the reviewer for the suggestion, a sentence has been added in the discussion.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this article the authors described mouse models presenting with backer muscular dystrophy, they created three transgenic models carrying three representative exon deletions: ex45-48 del., ex45-47 19 del., and ex45-49 del. This article is well written but needs improvement in some points.

      Strengths:

      This article is well written. The evidence supporting the authors' claims is robust, though further implementation is necessary. The experiments conducted align with the current state-of-the-art methodologies.

      Weaknesses:

      This article does not analyze atrophy in the various mouse models. Implementing this point would improve the impact of the work

      We thank the reviewer for their constructive suggestions and comments on this work. Muscle hypertrophy is shown with growth in dystrophin-deficient skeletal muscle in mdx mice; thus, we did not pay attention to the factors associated with muscle atrophy in BMD mice. As the reviewer suggested, the examination of the association between type IIa fiber reduction and muscle atrophy is important, and the result is considered to be helpful in resolving the cause of type IIa fiber reduction in BMD mice.

      In response, we reviewed the following.

      (1) The cross-sectional areas (CSAs) of muscles. We confirmed that the CSAs in BMD and mdx mice were rather high at 3 months, in accordance with muscle hypertrophy, compared with those of WT mice. The data is presented in Fig. 4–figure supplement 1B.

      (2) The mRNA expression levels of Murf1 and atrogin-1. We confirmed that these muscle atrophy inducing factors did not differ among WT, BMD, and mdx mice. The data is presented in Fig. 4–figure supplements 1C and 1D.

      Reviewer #2 (Public review):

      Summary:

      Miyazaki et al. established three distinct BMD mouse models by deleting different exon regions of the dystrophin gene, observed in human BMD. The authors demonstrated that these models exhibit pathophysiological changes, including variations in body weight, muscle force, muscle degeneration, and levels of fibrosis, alongside underlying molecular alterations such as changes in dystrophin and nNOS levels. Notably, these molecular and pathological changes progress at different rates depending on the specific exon deletions in the dystrophin gene. Additionally, the authors conducted extensive fiber typing, revealing a site-specific decline in type IIa fibers in BMD mice, which they suggest may be due to muscle degeneration and reduced capillary formation around these fibers.

      Strengths:

      The manuscript introduces three novel BMD mouse models with different dystrophin exon deletions, each demonstrating varying rates of disease progression similar to the human BMD phenotype. The authors also conducted extensive fiber typing across different muscles and regions within the muscles, effectively highlighting a site-specific decline in type IIa muscle fibers in BMD mice.

      Weaknesses:

      The authors have inadequate experiments to support their hypothesis that the decay of type IIa muscle fibers is likely due to muscle degeneration and reduced capillary formation. Further investigation into capillary density and histopathological changes across different muscle fibers is needed, which could clarify the mechanisms behind these observations.

      We thank the reviewer for these positive comments and the very important suggestion about type IIa fiber reduction and capillary change around muscle fibers in BMD mice. From the results of the cardiotoxin-induced muscle degeneration and regeneration model, type IIa and IIx fibers showed delayed recovery compared with that of type-IIb fibers. However, this delayed recovery of type IIa and IIx could not explain the cause of the selective muscle fiber reduction limited to type IIa fibers in BMD mice. Therefore, we considered vascular dysfunction as the reason for the selective type IIa fiber reduction, and we found morphological capillary changes from a “ring pattern” to a “dot pattern” around type IIa fibers in BMD mice. However, the association between selective type IIa fiber reduction and the capillary change around muscle fibers in BMD mice remains unclear due to the lack of information about capillaries around type IIx and IIb fibers. The reviewer pointed out this insufficient evaluation of capillaries around other muscle fibers (except for type IIa fibers), and this suggestion is very helpful for explaining the association between selective type IIa fiber reduction and vascular dysfunction in BMD mice.

      In response, we reviewed the following.

      (1) The capillary formation around type IIx, IIb, and I fibers, in addition to that around type IIa fibers. We found that capillaries contacting around type IIx, IIb, and I fibers were poor in WT mice compared with that around type IIa fibers, with ‘incomplete ring-patterns’ around type IIx fibers, and ‘dot-patterns’ around type IIb and I fibers in WT mice. Morphological capillary changes around muscle fibers from WT to d45-49 and mdx mice were ‘incomplete dot-pattern’ to ‘dot-pattern’ around type IIx fibers, and ‘dot-pattern’ to ‘dot-pattern’ around type IIb and I fibers. This was in contrast to those around type IIa fibers: remarkable ‘ring-pattern’ to ‘dot-pattern’. These data are presented in Fig. 6B.

      (2) The endothelial area in contact with type IIx, IIb, and I fibers, and additionally that in contact with type IIa fibers. The endothelial area in contact with both type IIa and IIx fibers was less in d45-49 and mdx mice than in WT mice, but the reduction was larger around type IIa fibers than around type IIx fibers, reflecting the difference between the ‘ring-pattern’ around the former and the ‘incomplete ring-pattern’ around the latter in WT mice. These data are presented in Fig. 6C.

      (3) Transversely interconnected branches and capillary loops, using longitudinal muscle sections. We confirmed that there were fewer interconnected capillaries in BMD and mdx mice than in WT mice. These data are presented in Fig. 6E.

      (4) The mRNA expression levels of neuronal nitric oxide synthase (nNOS). We confirmed that nNOS protein expression levels were decreased in BMD and mdx mice in spite of adequate levels of nNOS mRNA expression. The data on nNOS mRNA expression levels is presented in Fig. 3–figure supplement 1C.

      (5) We added a sentence in the Abstract about the potential utility of BMD mice in developing vascular targeted therapies.

      Recommendation for the authors:

      Reviewer #1 (Recommendation for the authors):

      Abstract:

      Abstract: more emphasis should be on the pathological implications of Becker muscular dystrophy (BMD). Furthermore, should be emphasized the findings made in this article and the conclusions. Abbreviations such as DMD and MDX should be written in full and only then with the acronym.

      We appreciate the reviewers’ comments, and we apologize for the confusion over abbreviations. DMD is the gene name encoding dystrophin, and mdx is the strain name of mouse lacking dystrophin.

      In the Abstract and the Figure legends we changed:

      (1) DMD to DMD;

      (2) mdx mice to mdx mice.

      Results:

      Line 95: in this line, authors evaluated serum creatinine kinase (CK) levels at 1, 3, 6 and 12 months in WT mice and mdx mice. Why did you decide to study it? This part should be described in more detail. Serum CK is one of the main markers of muscle necrosis; therefore, I would report this data alongside the description of the muscle histology and necrotic fibers.

      We thank the reviewers for the important remarks. In this study, serum creatine kinase (CK) levels were two-fold to four-fold higher in BMD mice than in WT mice, but its rate of increase was less than that of mdx mice. We consider that the lesser changes in serum CK levels in BMD mice may be due to the smaller area of muscle degeneration because of focal and uneven muscle degeneration compared with that in mdx mice, which showed diffuse muscle degeneration.

      In response, we have moved the description of serum CK levels in the Results, from the section about the establishment of BMD mice to the section about site-specific muscle degeneration in BMD mice.

      In addition, we added a description in the Discussion about the possible association between the lesser changes in serum CK levels in BMD mice and its uneven distribution of muscle degeneration.

      Line 192-202: In these lines, authors observed a decrease in type IIa fibers after 3 months in BMD mice. I suggest evaluating also atrophy through evaluating cross-sectional areas (CSA) and expression of Murf1 and Atrogin1

      We thank the reviewer for the point about the association between type IIa fiber reduction and muscle atrophy. We evaluated the CSAs and the mRNA expression levels of Murf1 and atrogin-1. We confirmed that the CSAs in BMD and mdx mice were rather high at 3 months, in accordance with muscle hypertrophy, compared with those of WT mice, and that Murf1 and atrogin-1 mRNA expression levels did not differ among WT, BMD, and mdx mice. These data are presented in Fig. 4–figure supplements 1B, 1C, and 1D. We added a sentence about the changes in CSA and muscle atrophy inducing factors in the Discussion.

      Methods and material

      Line 342-348: authors have described animals, but not specified sex and number of mice in each group. This part should be improved.

      We apologize for our insufficient information about the sex and number of mice in the Materials and methods.

      We added a sentence specifying the sex, number, and evaluation period of each mouse group in the section on the generation of BMD mice.

      Line 426-433: authors described qPCR. It is necessary that the authors also describe primer sequences.

      We apologize for any lack of information about the primer sequences used in qPCR analysis. Supplemental Table 1 lists the primer sequences.

      We also added a sentence about the information in the primer list in the section on RNA isolation and RT-PCR in the Materials and methods.

      Reviewer #2 (Recommendation for the authors):

      Miyazaki et al. established three distinct BMD mouse models by removing different exon regions of the dystrophin gene. The authors demonstrated that the pathophysiological and molecular changes in these models progress at varying rates. Additionally, they observed a site-specific decline in type IIa fibers in BMD mice, while the proportions of other fiber types, such as type I and type IIx, remained consistent with those in wild-type mice. They proposed that the selective decay of type IIa fibers in BMD mice could be due to two primary factors: 1) muscle degeneration and regeneration, supported by their findings in cardiotoxin-treated mouse models, and 2) reduced capillary formation around type IIa fibers. However, the authors also presented evidence that type IIx fibers exhibited delayed recovery, similar to type IIa fibers, as demonstrated in cardiotoxin-induced regeneration models. Additionally, dot-patterned capillary formations were observed around both type IIa and type IIx fibers. Despite these findings, BMD mice did not show any changes in the proportion of type IIx fibers in inner BMD muscles. The authors should consider adding further analysis to strengthen their hypothesis and to disclose any possible mechanisms that led to these discrepancies.

      If the authors hypothesize that reduced capillary density around type IIa fibers contribute to their site-specific decay in BMD mice, they should consider measuring and statistically analyzing the endothelial area around all fiber types. By plotting and comparing these measurements across different fiber types between wild-type, BMD, and mdx mice, the authors could provide more robust evidence to support their hypothesis. This approach would help clarify whether reduced capillary density is a contributing factor to the site-specific decay of type IIa fibers in BMD mice and the more diffuse, non-specific muscle changes observed in mdx mice.

      The authors reported in the first part of the manuscript that histopathological changes, including muscle degeneration in BMD mice, are predominantly restricted to the inner part of the muscles. In the second part, they noted a decline in type IIa fibers specifically in the inner muscle region. To strengthen the hypothesis that the decay of type IIa fibers in the inner muscle is linked to muscle degeneration, the authors should consider performing histopathological measurements across different fiber types within the inner muscle. Reporting the correlations between these measurements would provide more compelling evidence to support their hypothesis.

      We thank the reviewer for these important suggestions about the association between type IIa fiber reduction and capillary change around muscle fibers in BMD mice. We prepared an additional evaluation about the capillary formation (in Fig. 6B) and endothelial area (in Fig. 6C) around type IIx, IIb, and I fibers. We found that capillaries contacting around type IIx, IIb, and I fibers were poor in WT mice compared with those around type IIa fibers, and showed an ‘incomplete ring-pattern’ around type IIx fibers and a ‘dot-pattern’ around type IIb and I fibers in WT mice, in contrast with type IIa fibers, which showed remarkable ‘ring-pattern’ capillaries. Reflecting this, the changes in endothelial area around type IIx, IIb, and I fibers between WT and BMD mice were less than those around type IIa fibers. These results suggest that type IIa fibers may require numerous capillaries and maintained blood flow compared with type IIx, IIb, and I fibers, and this high requirement for blood flow might be associated with the type IIa fiber-specific decay in BMD mice.

      We added the following.

      (1) Sentences in the Results about the capillary changes around type IIx, IIb, and I fibers in WT, d45-49, and mdx mice.

      (2) Sentences in the Results about the changes in endothelial area around type IIx, IIb, and I fibers in WT, d45-49, and mdx mice.

      (3) Sentences in the Discussion about the association between the type IIa fiber-specific decay in BMD mice and the differences in capillary changes of each muscle fiber from WT to BMD mice.

      We changed a sentence in the Discussion about the delayed recovery of type IIa and IIx fibers after CTX injection, to make it clear that the recovery of type IIx fibers was slower than that of type IIa fibers after CTX injection, and that therefore the type IIa fiber-specific decay in BMD mice might not be explained by this vulnerability and delayed recovery during muscle degeneration and regeneration.

      Minor Issues:

      Line 103: The word "mice" is duplicated and should be corrected.

      We apologize that “mice” was duplicated. We have corrected it.

      Line 120: Revise for clarity: "The proportion of opaque fibers is significantly different between d45-48 mice and WT at 3 months, with an increased tendency observed only in 1-month-old mice."

      We apologize for the confusion about the proportion of opaque fibers. We revised this sentence as follows.

      “Opaque fibers, which are thought to be precursors of necrotic fibers, increased at an earlier age of 1 month in d45–49 mice compared with WT mice; in contrast, the proportion of opaque fibers differs significantly between d45–47 and WT mice at 3 months, with an increased tendency only in 1-month-old mice (Fig. 2C).”

      Line 152: Clarify the statement regarding utrophin levels, as it currently contradicts the Western blot data. The sentence reads: "The increased levels of utrophin are 8-fold higher at 1 month and 30-fold higher at 3 months." This should be verified against the data, as the band densities in the Western blots suggest otherwise.

      We apologize for the confusion about utrophin expression levels. We revised this sentence as follows.

      “By western blot analysis, the utrophin expression levels showed only an increased tendency in all BMD mice at 3 months, whereas there was a significant increase in mdx mice (8-fold at 1 month, and 30-fold at 3 months) compared to WT mice (Figs. 3C and F).”

      Line 235: Correct the sentence to accurately reflect the findings: "BMD mice showed reduced muscle weakness."

      We apologize for our incorrect wording. We have removed the word “reduced” in this sentence.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      The manuscript by Dr. Shinkai and colleagues is about the posttranslational modification of a highly important protein, MT3, also known as the growth inhibitory factor. Authors postulate that MT3, or generally all MT isoforms, are sulfane sulfur binding proteins. The presence of sulfane sulfur at each Cys residue has, according to the authors, a critical impact on redox protein properties and almost does not affect zinc binding. They show a model in which 20 Cys residues with sulfane sulfur atoms can still bind seven zinc ions in the same clusters as unmodified protein. They also show that recombinant MT3 (but also MT1 and MT2) protein can react with HPE-IAM, an efficient trapping reagent of persulfides/polysulfides. This reaction performed in a new approach (high temperature and high reagent concentration) resulted in the formation of bis-S-HPE-AM product, which was quantitatively analyzed using LC-MS/MS. This analysis indicated that all Cys residues of MT proteins are modified by sulfane sulfur atoms. The authors performed a series of experiments showing that such protein can bind zinc, which dissociates in the reaction with hydrogen peroxide or SNAP. They also show that oxidized MT3 is reduced by thioredoxin. It gives a story about a new redox-dependent switching mechanism of zinc/persulfide cluster involving the formation of cystine tetrasulfide bridge.

      The whole story is hard to follow due to the lack of many essential explanations or full discussion. What needs to be clarified is the conclusion (or its lack) about MT3 modification proven by mass spectrometry. Figure 1B shows the FT-ICR-MALDI-TOF/MS spectrum of recombinant MT3. It clearly shows the presence of unmodified MT3 protein without zinc ions. Ions dissociate in acidic conditions used for MALDI sample preparation. If the protein contained all Cys residues modified, its molecular weight would be significantly higher. Then, they show the MS spectrum (low quality) of oxidized protein (Fig. 1C), in which new signals (besides reduced apo-MT3) are observed. They conclude that new signals come from protein oxidation and modification with one or two sulfur atoms. If the conclusion on Cys residue oxidation is reasonable, how this protein contains sulfur is unclear. What is the origin of the sulfur if apo-MT does not contain it? Oxidized protein was obtained by acidification of the protein, leading to zinc dissociation and subsequent neutralization and air oxidation. Authors should perform a detailed isotope analysis of the isotopic envelope to prove that sulfur is bound to the protein. They say that the +32 mass increase is not due to the appearance of two oxygen donors. They do not provide evidence. This protein is not a sulfane sulfur binding protein, or its minority is modified. Moreover, it is unacceptable to write that during MT3 oxidation are "released nine molecules of H2". How is hydrogen molecule produced? Moreover, zinc is not "released", it dissociates from protein in a chemical process.

      Thank you for your comment. According to your suggestion, we have rewritten the corresponding sentences below, together with addition of new Fig.1D.

      First, the sentence “which corresponded to the mass of zinc-free apo-GIF/MT3 and indicated that zinc was removed during MS analysis.” was changed to “which corresponded to the mass of zinc-free apo-GIF/MT3 and indicated that zinc dissociates from protein in acidic conditions used for MALDI sample preparation.” in the introduction section. Second, we have added the following sentence “However, FT-ICR-MALDI-TOF/MS analysis failed to detect sulfur modifications in GIF/MT-3 (Fig. 1B), suggesting that sulfur modifications in the protein were dissociated during laser desorption/ionization. Therefore, we postulate that the small amount of sulfur detected in oxidized apo-GIF/MT-3 is derived from the effect of laser desorption/ionization rather than any actual modification of the minority component.” in the discussion section. Third, we have added new Fig. 1D and the corresponding citation in the introduction. Fourth, the sentence “An increase in mass of 32 Da can also result from addition of two oxygen atoms, but we attributed it to one sulfur atom for reasons described later.” was changed to “Note that an increase in mass of 32 Da can also result from addition of two oxygen atoms.”.

      Another important point is a new approach to the HPE-IAM application. Zinc-binding MT3 was incubated with 5 mM reagent at 60°C for 36 h. Authors claim that high concentration was required because apoMT3 has stable conformation. Figure 2B shows that product concentration increases with higher temperature, but it is unclear why such a high temperature was used. Figure 1D shows that at 37°C, there is almost no reaction at 5 mM reagent. Changing parameters sounds reasonable only when the reaction is monitored by mass spectrometry. In conclusion, about 20 sulfane sulfur atoms present in MT3 would be clearly visible. Such evidence was not provided. Increased temperature and reagent concentration could cause modification of cysteinyl thiol/thiolates as well, not only persulfides/polysulfides. Therefore, it is highly possible that non-modified MT3 protein could react with HPE-IAM, giving false results. Besides mass spectrometry, which would clearly prove modifications of 20 Cys, authors should use very important control, which could be chemically synthesized beta- or alfa-domain of MT3 reconstituted with zinc (many protocols are present in the literature). Such models are commonly used to test any kind of chemistry of MTs. If a non-modified chemically obtained domain would undergo a reaction with HPE-IAM under such rigorous conditions, then my expectation would be right.

      Thank you for your comments. Although we have already confirmed that no false-positive results were observed using this method in Fig. 5 (previously Fig. 4), we have conducted additional experiments by preparing chemically synthesized α- and β-domains of GIF/MT-3, as well as recombinant α- and β-domains of GIF/MT-3. As shown in the new Fig. S2A, the chemically synthesized α- and β-domains of GIF/MT-3 detected almost no sulfane sulfur (less than 1 molecule per protein), whereas the recombinant α- and β-domains detected several molecules of sulfane sulfur (more than 5 molecules per protein) (Fig. S2A). Therefore, I would like to emphasize here that the cysteine residue itself cannot be the source of the bis-S-HPE-AM product (sulfane sulfur derivative).

      Accordingly, we have added the following sentence in the results section: “Because this assay was performed at relatively high temperatures (60°C), we also examined the sulfane sulfur levels of several mutant proteins using chemically synthesized α- and β-domains of GIF/MT-3 to eliminate false-positive results. As shown in Fig. S2A, sulfane sulfur (less than 1 molecule per protein) was undetectable in chemically synthesized α- and β-domains of GIF/MT-3, whereas several molecules of sulfane sulfur per protein were detected in recombinant α- and β-domains exhibited (Fig. S2B, left panel). These findings indicated that the sulfane sulfur detected in our assay was derived from biological processes executed during the production of GIF/MT-3 protein. We further analyzed mutant proteins with β-Cys-to-Ala and α-Cys-to-Ala substitutions and found that their sulfane sulfur levels were comparable with those of the α- and β-domains of GIF/MT-3, respectively (Fig. S2B, left panel). Additionally, Ser-to-Ala mutation did not affect the sulfane sulfur levels of GIF/MT-3. The zinc content of each mutant protein was also determined under these conditions (Fig. S2B, right panel).”

      - The remaining experiments provided in the manuscript can also be applied for non-modified protein (without sulfane sulfur modification) and do not provide worthwhile evidence. For instance, hydrogen peroxide or SNAP may interact with non-modified MTs. Zinc ions dissociate due to cysteine residue modification, and TCEP may reduce oxidized residue to rescue zinc binding. Again, mass spectrometry would provide nice evidence.

      Thank you for your comment. We understand that such experiments can also be applied to non-modified proteins (without sulfane sulfur modification). However, the experiments shown in Fig. 4 and Fig. 6 were conducted to investigate the role of sulfane sulfur under oxidative stress conditions, rather than to examine sulfur modification in the protein itself. As mentioned previously, it is difficult to detect sulfur modifications directly in the protein using MALDI-TOF/MS (Fig. 1), as sulfur modifications appear to dissociate during the laser desorption/ionization process.

      - The same is thioredoxin (Fig. 7) and its reaction with oxidized MT3. Nonmodified and oxidized MT3 would react as well.

      Thank you for your comment. We understand that such experiments can also be applied to non-modified MT-3 protein. However, to the best of our knowledge, this is the first report demonstrating that apo-MT-3 can serve as a good substrate for the Trx system. In fact, this experiment is not intended to prove that MT-3 is sulfane sulfur-binding protein. Rather, it demonstrates the novel finding that apo-MT3 serves as an excellent substrate for Trx and that the sulfane sulfur (persulfide structure) remains intact throughout the reduction process.

      - If HPE-IAM reacts with Cys residues with unmodified MT3, which is more likely the case under used conditions, the protein product of such reaction will not bind zinc. It could be an explanation of the cyanolysis experiment (Fig. 6).

      Thank you for your comment. As you pointed out, HPE-IAM reacts with cysteine residues in unmodified MT-3, thereby preventing zinc from binding to the protein. However, we did not use HPE-IAM prior to measuring zinc binding. Instead, HPE-IAM was used solely for determining the sulfane sulfur content in the protein, and thus it cannot explain the results of the cyanolysis experiment.

      - Figure 4 shows the reactivity of (pol)sulfides with TCEP and HPE-IAM. What are redox potentials? Do they correlate with the obtained results?

      Thank you for your comment. However, we must apologize as we do not fully understand the rationale behind determining redox potentials in this experiment. We believe the data itself to be very clear and presenting convincing results.

      - Raman spectroscopy experiments would illustrate the presence of sulfane sulfur in MT3 only if all Cys were modified.

      Yes, that is correct. Since approximately 20 sulfane sulfur atoms are detected in the protein with 20 cysteine residues, we believe that nearly all cysteine residues are modified by sulfane sulfur. Therefore, Raman spectroscopy is considered applicable to our current study.

      - The modeling presented in this study is very interesting and confirms the flexibility of metallothioneins. MT domains are known to bind various metal ions of different diameters. They adopt in this way to larger size the ions. The same mechanism could be present from the protein site. The presence of 9 or 11 sulfur atoms in the beta or alfa domain would increase the size of the domains without changing the cluster structure.

      We truly appreciate your positive evaluation of this work.

      - Comment to authors. Apo-MT is not present in the cell. It exists as a partially metallated species. The term "apo-MT" was introduced to explain that MTs are not fully saturated by metals and function as a metal buffer system. Apo-MT comes from old ages when MT was considered to be present only in two forms: apo-form and fully saturated forms.

      Thank you for your insightful comments. We find it reasonable to understand that apo-MT exists as a partially metallated species within the cell.

      Reviewer #2 (Public Review):

      Summary:

      In this manuscript, the authors reveal that GIF/MT-3 regulates zinc homeostasis depending on the cellular redox status. The manuscript technically sounds, and their data concretely suggest that the recombinant MTs, not only GIF/MT-3 but also canonical MTs such as MT-1 and MT-2, contain sulfane sulfur atoms for the Zn-binding. The scenario proposed by the authors seems to be reasonable to explain the Zn homeostasis by the cellular redox balance.

      Strengths:

      The data presented in the manuscript solidly reveal that recombinant GIF/MT-3 contains sulfane sulfur.

      Weaknesses:

      It is still unclear whether native MTs, in particular, induced MTs in vivo contain sulfane sulfur or not.

      Thank you for pointing out the strengths and weaknesses of this manuscript. Based on your suggestions, we have determined the sulfane sulfur content in the native GIF/MT-3 protein, as explained in our response to "Recommendations for the Authors #2."

      Reviewer #3 (Public Review):

      Summary:

      The authors were trying to show that a novel neuronal metallothionein of poorly defined function, GIF/MT3, is actually heavily persulfidated in both the Zn-bound and apo (metal-free) forms of the molecule as purified from a heterologous or native host. Evidence in support of this conclusion is compelling, with both spectroscopic and mass spectrometry evidence strongly consistent with this general conclusion. The authors would appear to have achieved their aims.

      Strengths:

      The analytical data are compelling in support of the author's primary conclusions are strong. The authors also provide some modeling evidence that strongly supports the contention that MT3 (and other MTs) can readily accommodate sulfane sulfur on each of the 20 cysteines in the Zn-bound structure, with little perturbation of the structure. This is not the case with Cys trisulfides, which suggests that the persulfide-metallated state is clearly positioned at lower energy relative to the immediately adjacent thiolate- or trisulfidated metal coordination complexes.

      Weaknesses:

      The biological significance of the findings is not entirely clear. On the one hand, the analytical data are clearly solid (albeit using a protein derived from a bacterial over-expression experiment), and yes, it's true that sulfane S can protect Cys from overoxidation, but everything shown in the summary figure (Fig. 8D) can be done with Zn release from a thiol by ROS, and subsequent reduction by the Trx/TR system. In addition, it's long been known that Zn itself can protect Cys from oxidation. I view this as a minor weakness that will motivate follow-up studies. Fig. 1 was incomplete in its discussion and only suggests that a few S atoms may be covalently bound to MT3 as isolated. This is in contrast to the sulfate S "release" experiment, which I find quite compelling.

      Impact:

      The impact will be high since the finding is potentially disruptive to the metals in the biology field in general and the MT field for sure. The sulfane sulfur counting experiment (the HPE-IAM electrophile trapping experiment) may well be widely adopted by the field. Those of us in the metals field always knew that this was a possibility, and it will interesting to see the extent to which metal-binding thiolates broadly incorporate sulfate sulfur into their first coordination shells.

      Thank you for pointing out the strengths and weaknesses of this manuscript. As you noted, the explanations and discussions regarding Fig. 1 were missing. To address this, we have added the following sentences to the discission section: “However, FT-ICR-MALDI-TOF/MS analysis failed to detect sulfur modifications in GIF/MT-3 (Fig. 1B), suggesting that sulfur modifications in the protein were dissociated during laser desorption/ionization. Therefore, we postulate that the small amount of sulfur detected in oxidized apo-GIF/MT-3 is derived from the effect of laser desorption/ionization rather than any actual modification of the minority component.”

      Reviewer #1 (Recommendations For The Authors):

      Overall, the topic of the study is interesting, but the provided evidence is insufficient to claim that MT3 is a sulfane sulfur-binding protein. Indeed, some recent studies showed that natural and recombinant MT proteins can be modified, but only one or a few cysteine residues were modified. Authors should follow my suggestion and apply mass spectrometry to all performed reactions and, first of all, to freshly obtained protein. I strongly suggest using chemically synthesized and reconstituted domains to test whether the home-developed approach is appropriate. Moreover, native MS and ICP-MS analysis of MT3 would support their claims.

      Thank you for your insightful comments. Following your suggestions, we have prepared chemically synthesized proteins of the α- and β-domains of GIF/MT-3 and conducted additional experiments, as explained in response comments to “Public Review #1”. Regarding the MS analysis, we have also added a discussion on the difficulty of detecting sulfur modifications in the protein.

      Reviewer #2 (Recommendations For The Authors):

      I have some minor points which should be considered by the authors.

      (1) Table 1: In the simulation by MOE, the authors speculated 7 atoms of metal bound to GIF/MT-3. Although a total of 7 atoms of Zn or Cd are actually bound to MTs as a divalent ion, the number of Cu and Hg bound to MTs as a monovalent ion is scientifically controversial. Several ideas have been proposed in the literature, however, "7 atoms of Cu or Hg" could be inappropriate as far as I know. The authors should simulate again using a more appropriate number of Cu or Hg in MTs.

      Thank you for providing this valuable information. We reviewed several papers by the Stillman group and found that the relative binding constants of Cu4-MT, Cu6-MT, and Cu10-MT were determined after the addition of Cu(I) to apo MT-1A, MT-2, and MT-3 (Melenbacher and Stillman, Metallomics, 2024). However, incorporating these copper numbers into our GIF/MT-3 simulation model proved challenging. Therefore, we decided to omit the score value for copper in Table 1.

      On the other hand, some researchers have reported that mercury binds to MT as a divalent ion, and the formation of Hg<sub>7</sub>MT is possible (not just other forms). Therefore, we decided to continue using the score value for mercury shown in Table 1.

      (2) If possible, native MT samples isolated from an experimental animal should be evaluated for the sulfane sulfur content. Canonical MTs, MT-1 and MT-2, are highly inducible by not only heavy metals but also oxidative stress. Under the oxidative stress condition such as the exposure of hydrogen peroxide, it is questionable whether the induced Zn-MTs contain sulfane sulfur or not.

      According to your suggestion, we evaluated the sulfane sulfur content in native GIF/MT-3 samples isolated from mouse brain cytosol (Fig. 10). The measured amount was 3.3 per protein. This suggests that sulfane sulfur in GIF/MT-3 could be consumed under oxidative conditions, as you anticipated. Another possible explanation for the discrepancy between the native form and recombinant protein is likely related to metal binding in the protein. It is generally understood that both zinc and copper bind to GIF/MT-3 in approximately equal proportions in vivo. When we prepared recombinant copper-binding GIF/MT-3 protein, the sulfane sulfur content in the protein was significantly different (approximately 4.0 per protein) compared to the Zn<sub>7</sub>GIF/MT-3 form. Further studies are needed to clarify the relationship between sulfane sulfur binding and the types of metals in the future.

      (3) The biological significance of sulfane sulfur in MTs is still unclear to me.

      Thank you for your comments. To address this question, we have added the following sentence to the discussion section: “The biological significance of sulfane sulfur in MTs lies in its ability to 1) contribute to metal binding affinity, 2) provide a sensing mechanism against oxidative stress, and 3) aid in the regeneration of the protein.”

      (4) According to the widely accepted nomenclature of MT, "MT3" should be amended to "MT-3".

      According to your suggestion, we have amended from MT3 to MT-3 throughout the manuscript.

      Reviewer #3 (Recommendations For The Authors):

      Most of my comments are editorial in nature, largely focused on what I perceive as overinterpretation or unnecessary speculation.

      The authors state in the abstract that the intersection of sulfane sulfur and Zn enzymes "has been overlooked." This is not actually true - please tone down to "under investigated" or something like this.

      Based on your suggestion, we have replaced the term “has been overlooked” with “has been under investigated” in the abstract.

      Line 228: The discussion of Fig. 6C involved too much speculation. I cannot see a quantitative experiment that supports this.

      Based on your suggestion, we have removed Fig. 6C (currently referred to as Fig. 7C). Additionally, we have revised the sentence from “implying that the sulfane sulfur is an essential zinc ligand in apo-GIF/MT3 and that an asymmetric SSH or SH ligand is insufficient for native zinc binding (Fig. 6C)” to “implying the contribution of sulfane sulfur to zinc binding in GIF/MT-3”.

      Line 247 "persulfide in apo-GIF/MT3 seems.." I think the authors mean that the Zn form of the protein is resistant to Trx or TCEP.

      Thank you for pointing this out. We realized that the term “persulfide in apo-GIF/MT3” might be confusing. Therefore, we have replaced it with “persulfide formation derived from apo-GIF/MT3” in the corresponding sentence.

      Molecular modeling: We need more details- were these structures energy-minimized in any way? Can the authors comment on the plethora of S-S dihedral angles in these structures, and whether they are consistent with expectations of covalent geometry? Please add text to explain or even a table that compiles these data.

      Thank you for your comment. Yes, energy minimization calculations for structural optimization were conducted during homology modeling in MOE. In fact, we have already stated in the Methods section that “Refinement of the model with the lowest generalized Born/volume integral (GBVI) score was achieved through energy minimization of outlier residues in Ramachandran plots generated within MOE.” In this model, covalent geometry, including the S-S dihedral angles, is also taken into consideration.

      What is a thermostability score? Perhaps a bit more discussion here and what relationship this has to an apparent (or macroscopic) metal affinity constant.

      The thermostability score is used to compare the thermal stability between the wild-type and mutant proteins. As shown in Equation (1) in the method section, it is calculated by subtracting the energy of the hypothetical unfolded state from the energy of the folded state. Since obtaining the structure of the unfolded state requires extensive computational effort, MOE employs an empirical formula based on two-dimensional structural features to estimate it. The ΔΔG values represent the difference between ΔGf(WT) and ΔGf(Mut). However, because it is difficult to directly determine ΔGf(Mut) and ΔGf(WT), MOE calculates ΔΔG using the thermodynamic cycle equivalence: ΔΔGs =ΔGsf (WT→Mut) - ΔGsu (WT→Mut), as expressed in Equation (1).

      On the other hand, the affinity score represents the interaction energy between the target ligand and the protein. In this study, we calculated the affinity score by selecting metal atoms as the ligands. The interaction energy (E int) is defined as:

      E int = E complex − E receptor − E ligand

      where each term is as follows:

      E complex : Potential energy of the complex.

      E receptor : Potential energy of the receptor alone.

      E ligand : Potential energy of the ligand alone.

      Each potential energy term includes contributions from bonded interactions such as bond lengths and bond angles. However, since there is no structural difference among E receptor, and E ligand, the bonded energy components cancel out. Consequently, E int is determined as:

      E int = ΔEele +ΔEvdW +ΔE sol

      Here, a negative E int indicates that the complex is more stable, while a positive E int implies that the receptor and ligand are more stable in their dissociated states.

      We have revised the sentence "The affinity score was also calculated using MOE software as the difference between the ΔΔGs values of the protein, free zinc, and metal–protein complex” to "The affinity score was also calculated using MOE software as the difference between the potential energy values of the protein, free zinc, and metal–protein complex” to correct the misdescription.

      Lines 278-280: The authors state that they observe a "marked enhancement of metal binding affinity, and rearrangement of zinc ions." I don't see support for this rather provocative conclusion. This is the expectation of course. I would love to see actual experimental data on this point, direct binding titrations with metals performed before and after the release of the sulfate sulfur atoms.

      Thank you for your comments. Although this statement is based on the 3D modeling simulation, we have also experimentally observed that the diminishment of sulfane sulfur in GIF/MT-3 resulted in a decrease in zinc binding levels, as shown in Fig. 7. However, conducting direct binding titration experiments was difficult for us due to the difficulty in preparing pure GIF/MT-3 protein with or without sulfane sulfur. Therefore, we have revised the sentence "marked enhancement of metal binding affinity, and rearrangement of zinc ions" to simply "enhancement of metal binding affinity" to avoid over-speculation.

      Table I- quantitatively lower stability for the Cu complex- the stoichiometry is clearly wrong in this simulation- please redo this simulation with the right stoichiometry or Cu to MT3- consult a Stillman paper.

      Thank you for providing this valuable information. We reviewed several papers by the Stillman group and found that the relative binding constants of Cu4-MT, Cu6-MT, and Cu10-MT were determined after the addition of Cu(I) to apo MT-1A, MT-2, and MT-3 (Melenbacher and Stillman, Metallomics, 2024). However, incorporating these copper numbers into our GIF/MT-3 simulation model proved challenging. Therefore, we decided to omit the score value for copper in Table 1.

      I like the model for reversible metal release mediated by the thioredoxin system (Fig. 8D)- but you can also do this with thiols- nothing really novel here. Has it been generally established that tetraulfides are better substrates for the Trx/TR system? The data shown in Fig. 7B seems to suggest this, but is this broadly true, from the literature?

      There are reports describing that persulfides and polysulfides are reduced by the thioredoxin system. However, it is not well-established that tetraulfides are better substrates for the Trx/TR system. To the best of our knowledge, this is the first report demonstrating that apo-MT-3 can serve as a good substrate for the Trx/TR system. Further research is required to compare the catalytic efficiency between proteins containing disulfide and those with tetraulfide moieties.

      Line 380: Many groups have reported that many proteins are per- or polysulfidated in a whole host of cells using mass spectrometry workflows, and that terminal persulfides can be readily reduced by general or specific Trx/TR systems. This work could be better acknowledged in the context of the authors' demonstration of the reduction of the tetrasulfides, which itself would appear to be novel (and exciting!).

      We truly appreciate your positive evaluation of this work.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This manuscript uses the eye lens as a model to investigate basic mechanisms in the Fgf signaling pathway. Understanding Fgf signaling is of broad importance to biologists as it is involved in the regulation of various developmental processes in different tissues/organs and is often misregulated in disease states. The Fgf pathway has been studied in embryonic lens development, namely with regards to its involvement in controlling events such as tissue invagination, vesicle formation, epithelium proliferation, and cellular differentiation, thus making the lens a good system to uncover the mechanistic basis of how the modulation of this pathway drives specific outcomes. Previous work has suggested that proteins, other than the ones currently known (e.g., the adaptor protein Frs2), are likely involved in Fgfr signaling. The present study focuses on the role of Shp2 and Shc1 proteins in the recruitment of Grb2 in the events downstream of Fgfr activation.

      Strengths:

      The findings reveal that the juxtamembrane region of the Fgf receptor is necessary for proper control of downstream events such as facilitating key changes in transcription and cytoskeleton during tissue morphogenesis. The authors conditionally deleted all four Fgfrs in the mouse lens that resulted in molecular and morphological lens defects, most importantly, preventing the upregulation of the lens induction markers Sox2 and Foxe3 and the apical localization of F-actin, thus demonstrating the importance of Fgfrs in early lens development, i.e. during lens induction. They also examined the impact of deleting Fgfr1 and 2, on the following stage, i.e. lens vesicle development, which could be rescued by expressing constitutively active KrasG12D. By using specific mutations (e.g. Fgfr1ΔFrs lacking the Frs2 binding domain and Fgfr2LR harboring mutations that prevent binding of Frs2), it is demonstrated that the Frs2 binding site on Fgfr is necessary for specific events such as morphogenesis of lens vesicle. Further, by studying Shp2 mutations and deletions, the authors present a case for Shp2 protein to function in a context-specific manner in the role of an adaptor protein and a phosphatase enzyme. Finally, the key surprising finding from this study is that downstream of Fgfr signaling, Shc1 is an important alternative pathway - in addition to Shp2 - involved in the recruitment of Grb2 and in the subsequent activation of Ras. The methodologies, namely, mouse genetics and state-of-the-art cell/molecular/biochemical assays are appropriately used to collect the data, which are soundly interpreted to reach these important conclusions. Overall, these findings reveal the flexibility of the Fgf signaling pathway and its downstream mediators in regulating cellular events. This work is expected to be of broad interest to molecular and developmental biologists.

      Weaknesses:

      A weakness that needs to be discussed is that Le-Cre depends on Pax6 activation, and hence its use in specific gene deletion will not allow evaluation of the requirement of Fgfrs in the expression of Pax6 itself. But since this is the earliest Cre available for deletion in the lens, mentioning this in the discussion would make the readers aware of this issue. Referring to Jag1 among "lens-specific markers" (page 5) is debatable, suggesting changing to the lines of "the expected upregulation of Jag1 in lens vesicle". The Abstract could be modified to clearly convey the existing knowledge gap and the key findings of the present study. As it stands now, it is a bit all over the place. Some typos in the manuscript need to be fixed, e.g. "...yet its molecular mechanism remains largely resolved" - unresolved? "...in the development lens" - in the developing lens? In Figure 4 legend, "(B) Grb2 mutants Grb2 mutants displayed...", etc.

      We thank the reviewer for the thoughtful and constructive feedback. We have added the caveat regarding the Le-Cre dependency on Pax6 expression to the discussion, removed the reference to Jag1 as a “lens-specific marker” and corrected the typographical errors noted by the reviewer.

      Reviewer #2 (Public review):

      Summary:

      I have reviewed a manuscript submitted by Wang et al., which is entitled "Shc1 cooperates with Frs2 and Shp2 to recruit Grb2 in FGF-induced lens development". In this paper, the authors first examined lens phenotypes in mice with Le-Cre-mediated knockdown (KD) of all four FGFR (FGFR1-4), and found that pERK signals, Jag1, and foxe3 expression are absent or drastically reduced, indicating that FGF signaling is essential for lens induction. Next, the authors examined lens phenotypes of FGFR1/2-KD mice and found that lens fiber differentiation is compromised and that proliferative activity and cell survival are also compromised in lens epithelium. Interestingly, Kras activation rescues defects in lens growth and lens fiber differentiation in FGFR1/2-KD mice, indicating that Ras activation is a key step for lens development. Next, the authors examined the role of Frs2, Shp2, and Grb2 in FGF signaling for lens development. They confirmed that lens fiber differentiation is compromised in FGFR1/3-KD mice combined with Frs2-dysfunctional FGFR2 mutants, which is similar to lens phenotypes of Grb2-KD mice. However, lens defects are milder in mice with Shp2YF/YF and Shp2CS mutant alleles, indicating that the involvement of Shp2 is limited for the Grb2 recruitment for lens fiber differentiation. Lastly, the authors showed new evidence on the possibility that another adapter protein, Shc1, promotes Grb2 recruitment independent of Frs2/Shp2-mediated Grb2 recruitment.

      Strengths:

      Overall, the manuscript provides valuable data on how FGFR activation leads to Ras activation through the adapter platform of Frs2/Shp2/Grb2, which advances our understanding of complex modification of the FGF signaling pathway. The authors applied a genetic approach using mice, whose methods and results are valid to support the conclusion. The discussion also well summarizes the significance of their findings.

      Weaknesses:

      The authors eventually found that the new adaptor protein Shc1 is involved in Grb2 recruitments in response to FGF receptor activation. however, the main data for Shc1 are histological sections and statistical evaluation of lens size. So, my major concern is that the authors need to provide more detailed data to support the involvement of Shc1 in Grb2 recruitment of FGF signaling for lens development.

      We thank the reviewer for the positive comments and valuable suggestions. We have addressed the concerns in detail in the response to the recommendation outlined below.

      Reviewer #3 (Public review):

      Summary:

      The manuscript entitled "Shc1 cooperates with Frs2 and Shp2 to recruit Grb2 in FGF-induced lens development" by Wang et al., investigates the molecular mechanism used by FGFR signaling to support lens development. The lens has long been known to depend on FGFR signaling for proper development. Previous investigations have demonstrated that FGFR signaling is required for embryonic lens cell survival and for lens fiber cell differentiation. The requirement of FGFR signaling for lens induction has remained more controversial as deletion of both Fgfr1 and Fgfr2 during lens placode formation does not prevent the induction of definitive lens markers such as FOXE3 or αA-crystallin. Here the authors have used the Le-Cre driver to delete all four FGFR genes from the developing lens placode demonstrating a definitive failure of lens induction in the absence of FGFR signaling. The authors focused on FGFR1 and FGFR2, the two primary FGFRs present during early lens development, and demonstrated that lens development could be significantly rescued in lenses lacking both FGFR1 and FGFR2 by expressing a constitutively active allele of KRAS. They also showed that the removal of pro-apoptotic genes Bax and Bak could also lead to a substantial rescue of lens development in lenses lacking both FGFR1 and FGFR2. In both cases, the lens rescue included both increased lens size and the expression of genes characteristic of lens cells.

      Significantly the authors concentrated on the juxtamembrane domain, a portion of the FGFRs associated with FRS2. Previous investigations have demonstrated the importance of FRS2 activation for mediating a sustained level of ERK activation. FRS2 is known to associate both with GRB2 and SHP2 to activate RAS. The authors utilized a mutant allele of Fgfr1, lacking the entire juxtamembrane domain (Fgfr1ΔFrs), and an allele of Fgfr2 containing two-point mutations essential for Frs2 binding (Fgfr2LR). When combining three floxed alleles and leaving only one functional allele (Fgfr1ΔFrs or Fgfr2LR) the authors got strikingly different phenotypes. When only the Fgfr1ΔFrs allele was retained, the lens phenotype matched that of deleting both Fgfr1 and Fgfr2. However, when only the Fgfr2LR allele was retained the phenotype was significantly milder, primarily affecting lens fiber cell differentiation, suggesting that something other than FRS2 might be interacting with the juxtamembrane domain to support FGFR signaling in the lens. The authors also deleted Grb2 in the lens and showed that the phenotype was similar to that of the lenses only retaining the Fgfr2LR allele, resulting in a failure of lens fiber cell differentiation and decreased lens cell survival. However, mutating the major tyrosine phosphorylation site of GRB2 did not affect lens development. The author additionally investigated the role of SHP2 lens development by making by either deleting SHP2 or by making mutations in the SHP2 catalytic domain. The deletion of the SHP2 phosphatase activity did not affect lens development as severely as the total loss of SHP2 protein, suggesting a function for SHP2 outside of its catalytic activity. Although the loss of Shc1 alone has only a slight effect on lens size and pERK activation in the lens, the authors showed that the loss of Shc1 exacerbated the lens phenotype in lenses lacking both Frs2 and Shp2. The authors suggest that SHC1 binds to the FGFR juxtamembrane domain allowing for the recruitment of GRB2 independently of FRS2.

      Strengths:

      (1) The authors used a variety of genetic tools to carefully dissect the essential signals downstream of FGFR signaling during lens development.

      (2) The authors made a convincing case that something other than FRS2 binding mediates FGFR signaling in the juxtamembrane domain.

      (3) The authors demonstrated that despite the requirement of both the adaptor function and phosphatase activity of SHP2 are required for embryonic survival, neither of these activities is absolutely required for lens development.

      (4) The authors provide more information as to why FGFR loss has a phenotype much more severe than the loss of FRS2 alone during lens development.

      (5) The authors followed up their work analyzing various signaling molecules in the context of lens development with biochemical analyses of FGF-induced phosphorylation in murine embryonic fibroblasts (MEFs).

      (6) In general, this manuscript represents a Herculean effort to dissect FGFR signaling in vivo with biochemical backing with cell culture experiments in vitro.

      We thank the reviewer for the thorough review of our paper and positive comments.

      Weaknesses:

      (1) The authors demonstrate that the loss of FGFR1 and FGFR2 can be compensated by a constitutive active KRAS allele in the lens and suggest that FGFRs largely support lens development only by driving ERK activation. However, the authors also saw that lens development was substantially rescued by preventing apoptosis through the deletion of BAK and BAX. To my knowledge, the deletion of BAK and BAX should not independently activate ERK. The authors do not show whether ERK activation is restored in the BAK/BAX deficient lenses. Do the authors suggest the FGFR3 and/or FGFR4 provide sufficient RAS and ERK activation for lens development when apoptosis is suppressed? Alternatively, is it the survival function of FGFR-signaling as much as a direct effect on lens differentiation?

      Our interpretation is that at the lens induction stage, where FGFR1 and FGFR2 are crucial, their primary function operates through Ras signaling to promote cell survival. Thus, either constitutively active KRAS or the direct suppression of apoptosis by deleting Bak and Bax is sufficient to rescue lens induction. This rescue enables the subsequent differentiation of lens progenitor cells, a process for which FGFR3 and FGFR4 are sufficient to support.

      (2) The authors make the argument that deleting all four FGFRs prevented lens induction but that the deletion of only FGFR1 and FGFR2 did not. Part of this argument is the retention of FOXE3 expression, αA-crystallin expression, and PROX1 expression in the FGFR1/2 double mutants. However, in Figure 1E, and Figure 1F, the staining of the double mutant lens tissue with FOXE3, αA-crystallin, and PROX1 is unconvincing. However, the retention of FOXE3 expression in the FGFR1/FGFR2 double mutants was previously demonstrated in Garcia et al 2011. Also, there needs to be an enlargement or inset to demonstrate the retention of pSMAD in the quadruple FGFR mutants in Figure 1D.

      We have updated Figure 1E with a clearer image of FOXE3 staining to better illustrate FOXE3 expression in the FGFR1/2 double mutants. It seems there may have been a misunderstanding regarding our claims about αA-crystallin and PROX1. To clarify, our observation is that both αA-crystallin and PROX1 are lost in the FGFR1/2 double mutants, which we believe is clearly demonstrated in Figure 1F. Additionally, we have added inserts to Figure 1D to highlight the retention of pSMAD.

      (3) Do the authors suggest that GRB2 is required for RAS activation and ultimately ERK activation? If so, do the authors suggest that ERK activation is not required for FGFR-signaling to mediate lens induction? This would follow considering that the GRB2 deficient lenses lack a problem with lens induction.

      We do believe that GRB2 is required for RAS-ERK signaling activation; however, ERK activation is not absolutely required for lens induction. This conclusion is consistent with our previous study, which showed that deletion of ERK1/2 did not prevent lens induction (Garg et al. eLife 2020;9:e51915), as well as with our current findings demonstrating that the GRB2-deficient mutant is still capable of supporting lens induction.

      (4) The increase in p-Shc is only slightly higher in the Cre FGFR1f/f FGFR2r/LR than in the FGFR1f/Δfrs FGFR2f/f. Can the authors provide quantification?

      pShc quantification is now provided in Fig. 7B.

      (5) The authors have not shown directly that Shc1 binds to the juxtamembrane region of either Fgfr1 or Fgfr2.

      It is not yet clear whether Shc1 directly binds to the juxtamembrane region of FGFR1 or FGFR2, as it may also be recruited indirectly. We acknowledge this as an important question that warrants further investigation in future studies.

      (6) The authors have used the Le-Cre strain for all of their lens deletion experiments. Previous work has documented that the Le-Cre transgene can cause lens defects independent of any floxed alleles in both homozygous and hemizygous states on some genetic backgrounds (Dora et al., 2014 PLoS One 9:e109193 and Lam et al., Human Genomics 2019 13(1):10. Are the controls used in these experiments Le-Cre hemizygotes?

      As stated in the Method section, Le-Cre only or Le-Cre and heterozygous flox mice were used as controls.  

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Weaknesses

      There are only a few minor weaknesses that need to be addressed.

      (1) The point could be made in the Discussion that since Le-Cre depends on Pax6 placodal expression, it is challenging to evaluate the impact of deletion of the four Fgfrs on the expression of Pax6 (since Pax6 needs to be activated prior to achieving Fgfr deletion). A different Cre line (e.g. a Cre which is expressed in the surface ectoderm prior to lens placode formation) could help partially address this question, although it may not be able to comment on the requirement of the Fgfrs specifically in the lens ectoderm. Thus, it will be prudent to mention this in the discussion.

      We have added the caveat regarding the Le-Cre dependency on Pax6 expression to the discussion.

      (2) Referring to Jag1 among "lens-specific markers" (page 5) is debatable, I suggest changing it along the lines of "the expected upregulation of Jag1 in lens vesicle".

      The wording has been changed as suggested.  

      (3) The Abstract could be modified to clearly convey the existing knowledge gap and the key findings of the present study. As it stands now, it is a bit all over the place.

      The abstract has been revised.  

      (4) Some typos in the manuscript need to be fixed.

      e.g. "...yet its molecular mechanism remains largely resolved" - unresolved?, "...in the development lens" - in the developing lens?, In Fig. 4 legend, "(B) Grb2 mutants Grb2 mutants displayed...", etc.

      These typos have been corrected.

      Reviewer #2 (Recommendations for the authors):

      My specific suggestions are shown below.

      (1) The authors need to describe the role of Shc1 in FGF signaling and vertebrate lens development, by citing previous publications in the introduction.

      We have detailed previous studies on the role of Shc in FGF signaling in the Introduction and discussed its function in the vertebrate lens in the Discussion section.

      (2) Figure 1B bottom panels: Inset images seem to be missing, although frames and arrowheads are there. Please check them.

      The inset images were correctly placed.

      (3) Results (page 5, line 13): The authors mentioned "Sox2 expression remained at basal levels". Since Figure 1B indicates that Sox2 expression fails to be upregulated in FGFR1/2 mutant lens placode in contrast to Pax6, it is better to clearly mention the failure in upregulation of Sox2 expression in the FGFR1/2 mutants.

      This sentence has been rewritten as suggested.  

      (4) Results (page 6, line 8): The authors mentioned "we observed .... expression of Foxe3 in ...mutant lens cells (Figure 1E, arrows). However, Foxe3-expressing lens cells are a very small population in Figure 1E. It is important to state the decreased number of Foxe3-expressing lens cells in FGFR1/2 mutants. In addition, I would like to request the authors to show histograms indicating sample size and statistical analysis for marker expression: Foxe3 (Figure 1E), Prox1 and aA-crystallin (Fig. 1F), cyclin D1 and TUNEL (Fig. 1G) and pmTOR and pS6 (Supplementary figure 1B).

      We added a statement indicating that the number of Foxe3-expressing cells is reduced in FGFR1/2 mutants, which is now quantified in Fig. 1H. Quantifications for Cyclin D1 and TUNEL are now shown in Fig. 1I and J, respectively. However, we chose not to quantify Prox1, αA-crystallin, pmTOR, and pS6, as the FGFR1/2 mutants showed no staining for these markers.

      (5) Results (page 6, line 19- page 7, line 6): The authors showed that inducible expression of constitutive active Kras, KrasG12D, using Le-Cre, recovered lens size to the half level of wild-type control. However, in the lens of mice with Le-Cre; FGFR1/2f/f; LSL-KrasG12D, pERK was detected in the most posterior edge of the lens fiber core, whereas pERK was detected in the broader area of the lens in control. Furthermore, pMEK was detected in the whole lens of mice with Le-Cre; FGFR1/2f/f; and LSL-KrasG12D, whereas pMEK was detected only in the lens epithelial cells at the equator. So, the spatial profile of pERK and pMEK expression was different from those of wild-type, although the authors observed that Prox1 and Crystallin expression are normally induced in the lens of mice with Le-Cre; FGFR1/2f/f; LSL-KrasG12D. I wonder whether the lens normally develops in mice with Le-Cre; LSL-KrasG12D? Is the lens growth enhanced in mice with Le-Cre; LSL-KrasG12D? Please add the panels of mice with Le-Cre; LSL-KrasG12D in Figure 2B and 2C. In addition, I wonder whether apoptosis is suppressed in the lens of mice with Le-Cre; FGFR1/2f/f; LSL-KrasG12D?

      As we previously reported (Developmental Biology 355, 2011, 12–20), Le-Cre; LSL-KrasG12D did not lead to enhanced lens growth. While we agree that including images of Le-Cre; LSL-KrasG12D as controls in Fig. 2B and C and evaluating apoptosis in Le-Cre; FGFR1/2f/f; LSL-KrasG12D mutants would be appropriate, we regretfully no longer have these animals available to conduct these experiments.

      (6) Results (page 11, line 15): the PCR genotyping image of Fig. 6C seems to be missing.

      The PCR genotyping image was correctly placed below Fig. 6B. 

      (7) Results (page 11, lines 15-20): there is no citation of Figure 6D in the results section.

      The citation for Fig. 6D is added in the results section.

      (8) Figures 5H, 6H, and 7A: Western blotting of some of the pERK, ERK lanes is missing.

      These western blots all have pERK/ERK overlay images.

      (9) Figure 7A, western blotting data on pShc levels are important to suggest the involvement of Shc1 in Frs2-independent Grb2 activation by FGF stimulation. Please provide the histogram for statistical analysis.

      pShc quantification is now provided in Fig. 7B.

      (10) There is no citation of Figure 7D, E, and F in the results section. Please add them.

      These citations have been added.

      (11) Figures 7E, and 7F: The authors showed that lens morphology and lens size evaluation in genetic combinations: control, Frs2/Shc1 KD, Frs2/Shp2 KD, and Frs2/Shp2/Shc1 KD. However, I would like to request the authors to show more detailed data in these genetic combinations, for example, pERK, foxe3, Maf, Prox1, Jag1, p57, cyclin D3, g-crystallin, and TUNEL.

      Unfortunately, we no longer have these mutant mice to perform these detailed staining.  

      Reviewer #3 (Recommendations for the authors):

      (1) The figure legend for Figure 2 lists (G) twice. The second (G) should be (H). Also, in Figures 2G and H there is no indication as to what stage lenses were used for the TUNEL and size analyses. I assume that it was E13.5, but it should be explicitly stated.

      The figure labeling has been corrected and the stage added to the figure legend.

      (2) In Figure 4 A the label should be gamma-crystallin rather than r-crystallin.

      The figure labeling has been corrected.

      (3) In Figure 6 D, I believe that the immunolabeling for Maf and Foxe3 are reversed. The Maf should be red as it is in the fibers and the Foxe3 should be green as it is epithelial.

      The figure labeling has been corrected.

      (4) In Figure 6C I believe that the labels for the WT and YF alleles on the western blot are reversed.

      The YF PCR band was designed to be larger than WT, so the labeling was correct as is.

      (5) In Figure 6F I believe that the labels for WT and CS on the western blot are reversed.

      The figure labeling has been corrected.

      (6) In Supplemental figure 2 there are no genotype labels for the TUNEL bar graph.

      The figure labeling has been added.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer 3 (Public review):

      Major comments:

      (1) Can isolated mitochondria be transported to cultured cardiomyocytes, such as H9C2 cells, in vitro?

      Thank you for this insightful question. Mitochondria are highly dynamic organelles that play a crucial role in cellular energy metabolism. When cells encounter various stressors and increased energy demands, they can benefit from the incorporation of exogenous mitochondria. In 2013, Masuzawa et al. (Masuzawa, et al.,2013) were the first to demonstrate that transplanted mitochondria are internalized by cardiomyocytes 2 to 8 hours after transplantation, significantly contributing to the preservation of myocardial energetics. Ali et al. (Ali, et al.,2020) discovered that exogenous mitochondria could be internalized by H9C2 cardiomyocytes as quickly as 5 minutes after co-incubation, resulting in an acute enhancement of normal cellular bioenergetics following mitochondrial transplantation. Pacak et al. (Pacak, et al.,2015) established that the internalization of mitochondria into cardiomyocytes is time-dependent and occurs through actin-dependent endocytosis.

      Collectively, these evidences illustrate that exogenous mitochondria can be effectively internalized by H9C2 cells and other cardiomyocytes, our experiments further confirmed that mitochondrial transplantation can be incorporated by the myocardium in vivo.

      (2) The description of results in the manuscript is too simple. It lacks detail on the rationale behind the experiments and the significance of the data.

      Thank you for this suggestion. We have realized that the results in the submitted manuscript have not been adequately interpreted. We have added necessary details on the rationale behind the experiments and the significance of the data to the results section (Lines 57~59, 69~73, 81~88, 91~98, 100~102, 103~104,  10<sup>9</sup>~115, 124~129, 135~146, 149~157, 159~161, 168~169, 178~179). We would like to express our gratitude to the reviewers once again and hope that our modifications will meet their requirements.

      (3) The authors demonstrate that mitochondrial transplantation reduces cardiomyocyte apoptosis. Therefore, Western blot analysis of apoptosis-related caspases could be provided for further confirmation.

      Thank you for this constructive comment. We fully agree with the reviewer's perspective on the detection of apoptosis-related caspases and have conducted a Western blot assay to investigate the impact of mitochondria on myocardial tissue. Our new evidence indicates that rats receiving mitochondrial transplantation exhibited reduced expression of cleaved caspase-3 compared with those in the NS and Vehicle groups (Fig. 6G, 6H, Lines 168~169), suggesting that mitochondrial transplantation decreased the level of apoptosis in the myocardium.

      (4) Do donor mitochondria fuse with recipient mitochondria? Relevant experiments and data should be provided to address this question.

      This is a very helpful comment. Investigating the fate of transplanted mitochondria in myocardial cells after CA is of great significance. The internalization of exogenous mitochondria has been observed across various cell types (Liu, et al.,2021; Shanmughapriya, et al.,2020). Notably, a recent study indicated that after being incorporated into host cells, isolated mitochondria are transported to endosomes and lysosomes. Subsequently, most of these mitochondria escape from these compartments and fuse with the endogenous mitochondrial network (Cowan, et al.,2017). We have discussed this in the manuscript. (Lines 217~220)

      Oxidative stress, a pathophysiological phenomenon common to cells suffering from ischemia/reperfusion insults after CA/CPR, was implicated to promote internalization and survival of exogenous mitochondria (Aharoni-Simon, et al.,2022). In our study, we confirmed that mitochondrial transplantation can enhance the metabolism of cardiomyocytes, increase ATP level, and reduce reactive oxygen species (ROS). Our results indirectly confirm that isolated mitochondria can successfully fuse with myocardial mitochondria.

      (5) In Figure 5A, the histograms are not labeled with the specific experimental groups.

      We apologize for this oversight. We have labeled the specific experimental groups in the histograms presented in Figure 6B and 6C (originally Figure 5A).

      Reviewer #1 (Recommendations For The Authors):

      (1) The age, gender, and strain of the donor rats should be specified in the Methods section. Additionally, it is not obvious what doses of mitochondria were injected into the rats and how the dosage was initially determined.

      Thanks for your suggestion. We have included relevant information about the donor rats in the Methods section(Lines 361~362).

      In Mito group, each animal received 0.5 mL of 1× 10<sup>9</sup>/mL mitochondrial suspension. (Lines 342~345). Considerable amounts of data have demonstrated the efficacy of mitochondrial transplantation in cellular, animal, and human research (Alemany, et al.,2024; Kaza, et al.,2017; Liu, et al.,2023). However, there is currently no evidence to determine the optimal dosage for transplantation. In previous research, isolated mitochondria (1 ×  10<sup>9</sup>) were delivered to the left coronary ostium in pigs, and can be a viable treatment modality in cardiac ischemia-reperfusion injury (Blitzer, et al.,2020; Guariento, et al.,2020). Additionally, the dose of 1× 10<sup>9</sup> mitochondria achieve the maximal hyperemic effect when administered via intracoronary injection (Shin, et al.,2019). Considering that Sprague-Dawley (SD) rats are smaller than pigs and that there is a loss of mitochondria during pulmonary circulation, we adopted a mitochondrial transplantation dose of 5× 10<sup>8</sup>. We will explore the optimal dosage in our future research.

      (2) In Figure 4a, the number of transplanted mitochondria appears to be very low. Considering the high number of mitochondria present in cardiomyocytes, it is unclear whether this small amount of transplanted mitochondria can significantly impact complex II activity and ATP levels in myocardial tissues, as shown in Figures 4b-d, or improve survival post-ROSC, as shown in Figure 2d. Could the observed benefits of mitochondrial transplantation be due to the indirect effects of the injected mitochondria, such as the release of mitochondrial contents, rather than the mitochondria themselves, as discussed by Bertero et al. (2021, Circ. Research)? This issue should be addressed in the manuscript.

      Thanks for this wonderful comment. As presented in Fig. 4 (originally Figure 4A), our results indicated the internalization of mitochondria by myocardium, shown by colocalization of Mito-tracker and myocardium marker. We would like to make our points here regrading to Fig. 4:

      (1) Significant left ventricular systolic and diastolic dysfunction that occurs in the myocardium shortly after the return of ROSC is referred to post-cardiac arrest myocardial dysfunction (PAMD) (Laurent, et al.,2002). It has demonstrated the efficacy of mitochondrial transplantation for the heart following ischemia-reperfusion injury in cellular, animal, and human studies, despite inadequate mitochondrial internalization (Liu, et al.,2023). A low number of transplanted mitochondria may improve cardiac function.

      (2) Only biologically active mitochondria can be specifically labeled with Mito-tracker. Therefore, cardiomyocytes uptake mitochondria that possess complete functionality. Previous results have demonstrated that mitochondrial contents, such as nonviable mitochondria, mitochondrial fractions, mitochondrial deoxyribonucleic acid, ribonucleic acid, exogenous adenosine diphosphate and ATP, do not provide protection to the ischemic heart (McCully, et al.,2017; McCully, et al.,2009).

      (3) The specific mechanism for mitochondrial internalization has yet to be fully elucidated. We totally agree with reviewer’s opinion pertaining the presence of other mechanisms of mitochondria transplantation that play a role in cardiac protection. Multiple mechanism may involve in the cardiac protection effect of mitochondria transplantation, and we are actively seeking reasonable approach to verify these hypotheses in an underway study (Lines 236~246).

      (3) In Figure 4g, the claims regarding sarcomere length, mitochondrial structure, the number of cristae, accumulated calcium etc. seem to rely on the visual interpretation of representative images. To ensure a reliable interpretation of the data, a blinded quantification of each image in each group should be conducted. The same applies to the claims made in Figure 5E.

      Thanks for this suggestion. We have quantitatively evaluated the electron microscope images and HE images of the myocardium to ensure reliable interpretation. Corresponding supplements have been added to the methods (Lines 433~441, 494~496), results sections (Lines  10<sup>9</sup>~115, 178~179), and Figures 5C, 5D, 6K and 6H (originally Figures 4G and 5E).

      (4) In line 69, it is unclear why the authors claim that MAP and HR decrease at 1, 2, 3, and 4 hours after ROSC in all groups compared to the Sham group, despite stating in line 72 that "MAP and HR did not differ at any observational time points (P>0.05, Figure 2C)."

      We apologize for our inaccurate phrasing. In the presented study, there was no statistically significant difference between MAP and HR at any observational timepoints (P>0.05, Figure 2C). In the NS, Vehicle and Mito groups, the MAP and HR decreased at 1, 2, 3, and 4 hours after ROSC, reaching their nadir at 1 hour. Subsequently, MAP and HR increased gradually but did not show any statistically significant differences compared with the Sham group.  (Lines 69~73).

      (5) The absence of increased mitochondrial content in the mito-groups should be discussed further in the manuscript.

      Thank you for your suggestion. We discussed the reasons why the mass of isolated mitochondria did not increase in Lines 224~235.

      (6) The N in Figure 5d should be provided.

      Thanks for your suggestion. We have revised the figure legend to include N of Figure 6F (originally Figures 5D).

      (7) Figure 6 demonstrates content beyond the findings in this manuscript. This reviewer recommends limiting the graphical abstract to the findings specifically in this paper.

      Thanks for your great advice. We have revised Figure 7 (originally Figure 6) and restricted the graphical abstract to the findings presented in this paper.

      Minor issues:

      (8) The order of data in Figure 4 should be consistent with the text in the manuscript. Figures 4E-F-G are described before Figures 4B-C-D in the text. Similarly, Figure 5F was described before Figure 5E in the text.

      Thanks for your great advice. We have rearranged the order of the pictures to align with the text. Thank you for your proposal.

      (9) In Figure 4A, the locations of the epicardium, muscle, and endocardium should be indicated for clarity. Also, it is not obvious where the close-up box refers to in the actual image.

      Thank you for your suggestion. We primarily seek evidence of mitochondrial internalization within the endocardium, as injury occurs first during myocardial ischemia (Kuwada and Takenaka,2000). The close-up box in Fig. 4 refers to the endocardium.

      (10) In Figure 5A, the group annotations are missing from the MDA and SOD graphs. The standard deviation bars for the SOD vehicle and SOD mito groups (3rd and 4th columns) appear to overlap. Can the authors provide the actual p-values?

      We apologize for the mission of group annotations in the MDA and SOD graphs. The p-value between the Vehicle group and the Mito group was 0.004. The SOD activity level of myocardial samples in the groups are presented in Table 1.

      Author response table 1.

      The SOD activity levels of myocardial samples in groups (U/mgprot)

      (11) In line 58, NS abbreviation is used without defining what NS is.

      We apologize for not including the full name of NS. NS is the abbreviation of normal. It has now been marked in the manuscript. (Line 58)

      (12) In line 118, what MDA stands for is not described until line 348. MDA should be defined in the text for the general audience.

      We apologize for this. We have defined it in the manuscript. (Lines 156~157)

      (13) In line 192, the authors state that "mitochondrial transplantation... increased the expression of antioxidant enzymes after four hours of ROSC," while only SOD activity levels were assessed in the manuscript. Increased activity levels do not necessarily imply an increase in expression levels. This discrepancy should be addressed in the Discussion section.

      Sorry for confusing the ‘activity’ with ‘expression’. Although mitochondrial transplantation has been shown to be involved in the restoration of manganese superoxide dismutase levels after ischemic insults, the changes in antioxidant enzyme expression level were not evaluated at the protein level in this paper (Tashiro, et al.,2022). To avoid misunderstandings, we have replaced the term ‘expression’ with ‘activity’ as appropriate. (Lines 268~271)

      (14) Mitochondria from non-ischemic gastrocnemius muscle of health donor animals were isolated and a manner that maximized their healing potential. This sentence is not clear.

      We apologize for the confusing sentence in the original manuscript. To improve clarity, we have revised that sentence. We isolated mitochondria from allogeneic gastrocnemius muscle tissue of healthy rats and maintained optimal mitochondrial activity and therapeutic effects. (Lines 199~201)

      Minor grammar issues:

      In line 153, mitochondrial should be mitochondria.

      Figure 2D: Percent servival should be percent survival.

      There should be a blank in complex IIactivity Figure 4B, and complex IV activity in Figure 4C.

      In line 134, Four hours of ROSC, Tissue samples from. Tissue is capital.

      In line 190, Similaerly should be similarly.

      Thank you for your valuable comments. We apologize for the grammatical issues caused by our oversight. We have made the necessary corrections in the manuscript and figures. (Lines 198, 179, and 268), Figure 2D, Figure 5E (originally Figure 4B); Figure 5F (originally Figure 4C).

      Reviewer #2 (Recommendations For The Authors):

      Some details are lacking clarity, such as the rationale behind choosing certain doses or time points for interventions.

      Thank you for this valuable suggestion. We have explained the rationale behind the selection of the dosage and the timing of the intervention. (Lines 201~212)

      I would suggest verifying mitochondrial function using the seahorse experiment oxygen consumption, and to check mitochondrial oxidative stress. I would also suggest checking the mitochondrial permeability transition pore opening, using for example calcein cobalt quenching or simply a kit to examine this further.

      Thank you for your valuable advice. In our manuscript, we added results regarding mitochondrial reactive oxygen species (ROS) and the mitochondrial permeability transition pore (mPTP) opening. As anticipated, mitochondrial transplantation reduced the increase in mitochondrial ROS and the mPTP opening in ischemic myocardium. (Lines 135~146, 149~157, 442~455, 460~476, Figure 5H, 5I, 6A)

      We agree that seahorse experiment oxygen consumption would be beneficial for understanding the intricacies of their interactions and enhancements. Additionally, Ali et al. (Ali, et al.,2020) have demonstrated that introducing non-autologous mitochondria from healthy skeletal muscle cells into normal cardiomyocytes results in a short-term improvement in bioenergetics, as measured using a Seahorse Extracellular Flux Analyzer. In our results, we have not yet conducted cellular experiments, The process of isolating cells from the myocardial tissue of adult SD rats for Seahorse analysis can lead to secondary damage to the myocardial cells (Jacobson, et al.,1985). In this experiment, we measured ATP content and the activity of mitochondrial complexes to evaluate energy changes after mitochondrial transplantation. We will conduct cell experiments and utilize Seahorse measurements to further clarify the alterations in myocardial energy in future.

      For Figure 3B, it would be beneficial to include the relative quantification of the mitochondrial marker COX-IV. Additionally, if feasible, I suggest verifying the representation of the mitochondria outer membrane TOM20 or VDAC.

      Thank you for your great suggestion. As suggested, we added TOM20 to assess the purity of the isolated mitochondria and reached the same conclusion: the isolated mitochondria exhibited high purity (Figure 3B). TOM20 was expressed in both muscle lysates and isolated mitochondria, whereas GAPDH was exclusively found in the muscle lysate. (We re-validated the purity of the mitochondria by using relative quantification of TOM20 and COX VI.)

      In Figure 2C, the clarity of the graphs depicting both arterial pressure (MAP) and heart rate (HR) is lacking and could potentially confuse the reader. I recommend incorporating color coding instead of relying solely on symbols, or by presenting the data in a more comprehensible format and that aligns with graph B as well.

      Thank you for your constructive comments. We have color-coded the diagrams in Figure 2B and 2C.

      In Figure 4A, please include high-magnification of the mitochondria to provide a more detailed examination.

      Thank you for this insightful comment. We have provided a high-magnification image of the mitochondria in Figure 4.

      Regarding lines 81-82, I recommend specifying the sentence more precisely for better clarity and understanding.

      Thank you for your comments. We have revised the sentences in lines 83~86 to enhance their clarity for readers.

      In the Materials and Methods section, it is crucial to provide precise details. For instance, when staining the exogenous mitochondria with MitoTracker Red, it is important to specify the duration of staining, such as the standard 20 minutes for example. Additionally, it is advisable to mention the number of times these mitochondria were washed with the respiratory solution to ensure thorough removal of excess MitoTracker, thus preventing unintended staining of endogenous mitochondria with MitoTracker red upon injection of pre-labeled mitochondria.

      Thank you for your suggestion. We have added the necessary details regarding Mito-Tracker Red dyeing. (Lines 373~376) In addition, we also added other details in necessary (Lines 373~376, 379~382, 395~396, 397~400, 487~488). We appreciate your suggestion once again.

      The sensitivity of JC-1 dye to temperature and pH fluctuations underscores the necessity for meticulous experimental conditions. It is crucial for the authors to elucidate why they chose to maintain the samples at 4 {degree sign} C for 60 minutes, especially considering the dye's optimal operating temperature of 25 {degree sign} C. Providing a rationale behind this deviation from standard protocol would enhance the scientific rigor and reproducibility of the study. Please add more information on the objectives used in the fluorescence microscope (BX53, OLYMPUS, Tokyo, Japan) and the software used.

      We sincerely apologize for the mistake in this sentence. The purified mitochondria, which are stained with JC-1, should be stored at 4°C and examined using a fluorescence microscope within 60 minutes. Purified mitochondria were incubated with JC-1 staining solution at 37°C for 20 minutes. The fluorescence microscope used in our experiment is equipped with a WHN 10/22 eyepiece, and the software version is OLYMPUS cellSens Standard 3.2. (Lines 379~382)

      Moreover, in the context of immunoblotting, it is imperative for the authors to furnish detailed information regarding the preparation of muscle tissue homogenates. Specifically, clarification is needed regarding the solution utilized for tissue grinding. Did the authors employ ice-cold RIPA lysis buffer or an alternative lysis buffer, supplemented with a protease inhibitor cocktail? Such details are pivotal for methodological transparency.

      Thanks for this wonderful comment. In the methods section, we added detailed information about protein extraction. (Lines 383~385)

      Furthermore, it would be beneficial for the authors to specify the instrument employed for scanning the immunoblots, as well as the software utilized for subsequent analysis of the immunoblot images. Providing this information would not only enhance the reproducibility of the findings but also facilitate the evaluation of the experimental results.

      Thank you for your suggestion. We have included the instrument used for scanning the Western blot, as well as the software used for image analysis in the manuscript. (Lines 397~400)

      Authors must exercise caution against copy-pasting. In line 282, there's a query regarding how the mitochondria were isolated. It is recommended to cite a specific reference and offer more comprehensive details. Despite the authors referencing a number within the text, the absence of numbered references makes it challenging to cross-reference.

      Thank you for pointing this out; we have updated the citation accordingly (Line 361).

      Figure 5C please double check some misspelling label errors (e.g: Vehicle and not Vehucle).

      We apologize for the misspelling in Figure 6E (originally Figure 5C) and have corrected it. Additionally, we have thoroughly reviewed the text for spelling errors and sincerely apologize once again for the previous mistakes. (Lines 249~252, 322)

      References:

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      Alemany VS, Nomoto R, Saeed MY, Celik A, Regan WL, Matte GS, Recco DP, Emani SM, Del NP, McCully JD. 2024. Mitochondrial transplantation preserves myocardial function and viability in pediatric and neonatal pig hearts donated after circulatory death. J THORAC CARDIOV SUR 167: e6-e21. doi: 10.1016/j.jtcvs.2023.05.010

      Ali PP, Kenney MC, Kheradvar A. 2020. Bioenergetics Consequences of Mitochondrial Transplantation in Cardiomyocytes. J AM HEART ASSOC 9: e14501. doi:10.1161/JAHA.119.014501

      Blitzer D, Guariento A, Doulamis IP, Shin B, Moskowitzova K, Barbieri GR, Orfany A, Del NP, McCully JD. 2020. Delayed Transplantation of Autologous Mitochondria for Cardioprotection in a Porcine Model. ANN THORAC SURG  109:711-719. doi: 10.1016/j.athoracsur.2019.06.075

      Cowan DB, Yao R, Thedsanamoorthy JK, Zurakowski D, Del NP, McCully JD. 2017. Transit and integration of extracellular mitochondria in human heart cells. SCI REP-UK 7:17450. doi:10.1038/s41598-017-17813-0

      Guariento A, Blitzer D, Doulamis I, Shin B, Moskowitzova K, Orfany A, Ramirez-Barbieri G, Staffa SJ, Zurakowski D, Del NP, McCully JD. 2020. Preischemic autologous mitochondrial transplantation by intracoronary injection for myocardial protection. J THORAC CARDIOV SUR 160: e15-e29. doi: 10.1016/j.jtcvs.2019.06.111

      Jacobson SL, Banfalvi M, Schwarzfeld TA. 1985. Long-term primary cultures of adult human and rat cardiomyocytes. BASIC RES CARDIOL 80 Suppl 1:79-82. doi:10.1007/978-3-662-11041-6_15

      Kaza AK, Wamala I, Friehs I, Kuebler JD, Rathod RH, Berra I, Ericsson M, Yao R, Thedsanamoorthy JK, Zurakowski D, Levitsky S, Del NP, Cowan DB, McCully JD. 2017. Myocardial rescue with autologous mitochondrial transplantation in a porcine model of ischemia/reperfusion. J THORAC CARDIOV SUR 153:934-943. doi: 10.1016/j.jtcvs.2016.10.077

      Kuwada Y, Takenaka K. 2000. [Transmural heterogeneity of the left ventricular wall: subendocardial layer and subepicardial layer]. J CARDIOL 35:205-218.

      Laurent I, Monchi M, Chiche JD, Joly LM, Spaulding C, Bourgeois B, Cariou A, Rozenberg A, Carli P, Weber S, Dhainaut JF. 2002. Reversible myocardial dysfunction in survivors of out-of-hospital cardiac arrest. J AM COLL CARDIOL 40:2110-2116. doi:10.1016/s0735- 1097(02)02594-9

      Liu D, Gao Y, Liu J, Huang Y, Yin J, Feng Y, Shi L, Meloni BP, Zhang C, Zheng M, Gao J. 2021. Intercellular mitochondrial transfer as a means of tissue revitalization. SIGNAL TRANSDUCT TAR 6:65. doi:10.1038/s41392-020-00440-z

      Liu Q, Liu M, Yang T, Wang X, Cheng P, Zhou H. 2023. What can we do to optimize mitochondrial transplantation therapy for myocardial ischemia-reperfusion injury? MITOCHONDRION 72:72-83. doi: 10.1016/j.mito.2023.08.001

      Masuzawa A, Black KM, Pacak CA, Ericsson M, Barnett RJ, Drumm C, Seth P, Bloch DB, Levitsky S, Cowan DB, McCully JD. 2013. Transplantation of autologously derived mitochondria protects the heart from ischemia-reperfusion injury. AM J PHYSIOL-HEART C 304:H966-H982. doi:10.1152/ajpheart.00883.2012

      McCully JD, Cowan DB, Emani SM, Del NP. 2017. Mitochondrial transplantation: From animal models to clinical use in humans. MITOCHONDRION 34:127-134. doi: 10.1016/j.mito.2017.03.004

      McCully JD, Cowan DB, Pacak CA, Toumpoulis IK, Dayalan H, Levitsky S. 2009. Injection of isolated mitochondria during early reperfusion for cardioprotection. AM J PHYSIOL-HEART C 296:H94-H105. doi:10.1152/ajpheart.00567.2008

      Pacak CA, Preble JM, Kondo H, Seibel P, Levitsky S, Del NP, Cowan DB, McCully JD. 2015. Actin-dependent mitochondrial internalization in cardiomyocytes: evidence for rescue of mitochondrial function. BIOL OPEN 4:622-626. doi:10.1242/bio.201511478

      Shanmughapriya S, Langford D, Natarajaseenivasan K. 2020. Inter and Intracellular mitochondrial trafficking in health and disease. AGEING RES REV 62:101128. doi: 10.1016/j.arr.2020.101128

      Shin B, Saeed MY, Esch JJ, Guariento A, Blitzer D, Moskowitzova K, Ramirez-Barbieri G, Orfany A, Thedsanamoorthy JK, Cowan DB, Inkster JA, Snay ER, Staffa SJ, Packard AB, Zurakowski D, Del NP, McCully JD. 2019. A Novel Biological Strategy for Myocardial Protection by Intracoronary Delivery of Mitochondria: Safety and Efficacy. JACC-BASIC TRANSL SC 4:871-888. doi: 10.1016/j.jacbts.2019.08.007

      Tashiro R, Bautista-Garrido J, Ozaki D, Sun G, Obertas L, Mobley AS, Kim GS, Aronowski J, Jung JE. 2022. Transplantation of Astrocytic Mitochondria Modulates Neuronal Antioxidant Defense and Neuroplasticity and Promotes Functional Recovery after Intracerebral Hemorrhage. J NEUROSCI 42:7001-7014. doi:10.1523/JNEUROSCI.2222-21.2022

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This manuscript investigates a mechanism between the histone reader protein YEATS2 and the metabolic enzyme GCDH, particularly in regulating epithelial-to-mesenchymal transition (EMT) in head and neck cancer (HNC).

      Strengths:

      Great detailing of the mechanistic aspect of the above axis is the primary strength of the manuscript.

      Weaknesses:

      Several critical points require clarification, including the rationale behind EMT marker selection, the inclusion of metastasis data, the role of key metabolic enzymes like ECHS1, and the molecular mechanisms governing p300 and YEATS2 interactions.

      We would like to sincerely thank the reviewer for the detailed, in-depth, and positive response. We are committed to implementing constructive revisions to the manuscript to address the reviewer’s concerns effectively.

      Major Comments:

      (1) The title, "Interplay of YEATS2 and GCDH mediates histone crotonylation and drives EMT in head and neck cancer," appears somewhat misleading, as it implies that YEATS2 directly drives histone crotonylation. However, YEATS2 functions as a reader of histone crotonylation rather than a writer or mediator of this modification. It cannot itself mediate the addition of crotonyl groups onto histones. Instead, the enzyme GCDH is the one responsible for generating crotonyl-CoA, which enables histone crotonylation. Therefore, while YEATS2 plays a role in recognizing crotonylation marks and may regulate gene expression through this mechanism, it does not directly catalyse or promote the crotonylation process.

      We thank the reviewer for raising this concern. As stated by the reviewer, YEATS2 functions as a reader protein, capable of recognizing histone crotonylation marks and assisting in the addition of this mark to nearby histone residues, possibly by assisting the recruitment of the writer protein for crotonylation. Our data indicates the involvement of YEATS2 in the recruitment of writer protein p300 on the promoter of the SPARC gene, making YEATS2 a regulatory factor responsible for the addition of crotonyl marks in an indirect manner. Thus, we have decided to make changes in the title by replacing the word “mediates” with “regulates”. Therefore, the updated title can be read as: “Interplay of YEATS2 and GCDH regulates histone crotonylation and drives EMT in head and neck cancer”.

      (2) The study suggests a link between YEATS2 and metastasis due to its role in EMT, but the lack of clinical or pre-clinical evidence of metastasis is concerning. Only primary tumor (PT) data is shown, but if the hypothesis is that YEATS2 promotes metastasis via EMT, then evidence from metastatic samples or in vivo models should be included to solidify this claim.

      We appreciate the reviewer’s suggestion. Here, we would like to state that the primary aim of this study was to delineate the molecular mechanisms behind the role of YEATS2 in maintaining histone crotonylation at the promoter of genes that favour EMT in head and neck cancer. We have dissected the importance of histone crotonylation in the regulation of gene expression in head and neck cancer in great detail, having investigated the upstream and downstream molecular players involved in this process that promote EMT. Moreover, with the help of multiple phenotypic assays, such as Matrigel invasion, wound healing, and 3D invasion assays, we have shown the functional importance of YEATS2 in promoting EMT in head and neck cancer cells. Since EMT is known to be a prerequisite process for cancer cells undergoing metastasis(1), the evidence of YEATS2 being associated with EMT demonstrates a potential correlation of YEATS2 with metastasis. However, as part of the revision, we will use publicly available patient data to investigate the direct association of YEATS2 with metastasis by checking the expression of YEATS2 between different grades of head and neck cancer, as an increase in tumor grade is often correlated with the incidence of metastasis(2).

      (3) There seems to be some discrepancy in the invasion data with BICR10 control cells (Figure 2C). BICR10 control cells with mock plasmids, specifically shControl and pEGFP-C3 show an unclear distinction between invasion capacities. Normally, we would expect the control cells to invade somewhat similarly, in terms of area covered, within the same time interval (24 hours here). But we clearly see more control cells invading when the invasion is done with KD and fewer control cells invading when the invasion is done with OE. Are these just plasmid-specific significant effects on normal cell invasion? This needs to be addressed.

      We appreciate the reviewer for the thorough evaluation of the manuscript. The figure panels in question, Figure 2B and 2C, represent two different experiments performed independently, the invasion assay performed after knockdown and overexpression of YEATS2, respectively. We would like to clarify that both panels represent results that are distinct and independent of each other and that the method used to knockdown or overexpress YEATS2 is also different. As stated in the Materials and Methods section, the knockdown is performed using lentivirus-mediated transfection (transduction) of cells, on the other hand, the overexpression is done using standard method of transfection by directly mixing transfection reagent and the respective plasmids, prior to the addition of this mix to the cells. The difference in the experimental conditions in these two experiments might have attributed to the differences seen in the controls as observed previously(3). Hence, we would like to state that the results of figure panels Figure 2B and Figure 2C should be evaluated independently of each other.

      (4) In Figure 3G, the Western blot shows an unclear band for YEATS2 in shSP1 cells with YEATS2 overexpression condition. The authors need to clearly identify which band corresponds to YEATS2 in this case.

      The two bands seen in the shSP1+pEGFP-C3-YEATS2 condition correspond to the endogenous YEATS2 band (lower band, indicated by * in the shControl lane) and YEATS2-GFP band (upper band, corresponding to overexpressed YEATS2-GFP fusion protein, which has a higher molecular weight). To avoid confusion, the endogenous band will be highlighted (marked by *) in the lane representing the shSP1+pEGFP-C3-YEATS2 condition in the revised version of the manuscript.

      (5) In ChIP assays with SP1, YEATS2 and p300 which promoter regions were selected for the respective genes? Please provide data for all the different promoter regions that must have been analysed, highlighting the region where enrichment/depletion was observed. Including data from negative control regions would improve the validity of the results.

      Throughout our study, we have performed ChIP-qPCR assays to check the binding of SP1 on YEATS2 and GCDH promoter, and to check YEATS2 and p300 binding on SPARC promoter. Using transcription factor binding prediction tools and luciferase assays, we selected multiple sites on the YEATS2 and GCDH promoter to check for SP1 binding. The results corresponding to the site that showed significant enrichment were provided in the manuscript. The region of SPARC promoter in YEATS2 and p300 ChIP assay was selected on the basis of YEATS2 enrichment found in the YEATS2 ChIP-seq data. We will provide data for all the promoter regions investigated (including negative controls) in the revised version of the manuscript.

      (6) The authors establish a link between H3K27Cr marks and GCDH expression, and this is an already well-known pathway. A critical missing piece is the level of ECSH1 in patient samples. This will clearly delineate if the balance shifted towards crotonylation.

      We thank the reviewer for their valuable suggestion. To support our claim, we had checked the expression of GCDH and ECHS1 in TCGA HNC RNA-seq data (provided in Figure 4—figure supplement 1A and B) and found that GCDH showed increase while ECHS1 showed decrease in tumor as compared to normal samples. We hypothesized that higher GCDH expression and decreased ECHS1 expression might lead to an increase in the levels of crotonylation in HNC. To further substantiate our claim, we will check the abundance of ECHS1 in HNC patient samples as part of the revision.

      (7) The p300 ChIP data on the SPARC promoter is confusing. The authors report reduced p300 occupancy in YEATS2-silenced cells, on SPARC promoter. However, this is paradoxical, as p300 is a writer, a histone acetyltransferase (HAT). The absence of a reader (YEATS2) shouldn't affect the writer (p300) unless a complex relationship between p300 and YEATS2 is present. The role of p300 should be further clarified in this case. Additionally, transcriptional regulation of SPARC expression in YEATS2 silenced cells could be analysed via downstream events, like Pol-II recruitment. Assays such as Pol-II ChIP-qPCR could help explain this.

      Using RNA-seq and ChIP-seq analyses, we have shown that YEATS2 affects the expression of several genes by regulating the level of histone crotonylation at gene promoters globally. The histone writer p300 is a promiscuous acyltransferase protein that has been shown to be involved in the addition of several non-acetyl marks on histone residues, including crotonylation(4). Our data provides evidence for the dependency of the writer p300 on YEATS2 in mediating histone crotonylation, as YEATS2 downregulation led to decreased occupancy of p300 on the SPARC promoter (Figure 5F). However, the exact mechanism of cooperativity between YEATS2 and p300 in maintaining histone crotonylation remains to be investigated. To address the reviewer’s concern, we will perform various experiments to delineate the molecular mechanism pertaining to the association of YEATS2 with p300 in regulating histone crotonylation. Following are the experiments that will be performed:

      (a) Co-immunoprecipitation experiments to check the physical interaction between YEATS2 and p300.

      (b) We will check H3K27cr levels on the SPARC promoter and SPARC expression in p300-depleted HNC cells.

      (c) Rescue experiments to check if the decrease in p300 occupancy on the SPARC promoter can be compensated by overexpressing YEATS2.

      (d) As suggested by the reviewer, Pol-II ChIP-qPCR at the promoter of SPARC will be performed in YEATS2-silenced cells to explain the mode of transcriptional regulation of SPARC expression by YEATS2.

      (8) The role of GCDH in producing crotonyl-CoA is already well-established in the literature. The authors' hypothesis that GCDH is essential for crotonyl-CoA production has been proven, and it's unclear why this is presented as a novel finding. It has been shown that YEATS2 KD leads to reduced H3K27cr, however, it remains unclear how the reader is affecting crotonylation levels. Are GCDH levels also reduced in the YEATS2 KD condition? Are YEATS2 levels regulating GCDH expression? One possible mechanism is YEATS2 occupancy on GCDH promoter and therefore reduced GCDH levels upon YEATS2 KD. This aspect is crucial to the study's proposed mechanism but is not addressed thoroughly.

      The source for histone crotonylation, crotonyl-CoA, can be produced by several enzymes in the cell, such as ACSS2, GCDH, ACOX3, etc(5). Since metabolic intermediates produced during several cellular pathways in the cell can act as substrates for epigenetic factors, we wanted to investigate if such an epigenetic-metabolism crosstalk existed in the context of YEATS2. As described in the manuscript, we performed GSEA using publicly available TCGA RNA-seq data and found that patients with higher YEATS2 expression also showed a high correlation with expression levels of genes involved in the lysine degradation pathway, including GCDH. Since the preferential binding of YEATS2 with H3K27cr and the role of GCDH in producing crotonyl-CoA was known(6,7), we hypothesized that higher H3K27cr in HNC could be a result of both YEATS2 and GCDH. We found that the presence of GCDH in the nucleus of HNC cells is correlated to higher H3K27cr abundance, which could be a result of excess levels of crotonyl-CoA produced via GCDH. We also found a correlation between H3K27cr levels and YEATS2 expression, which could arise due to YEATS2-mediated preferential maintenance of crotonylation. This states that although being a reader protein, YEATS2 is affecting the promoter H3K27cr levels, possibly by helping in the recruitment of p300 (as shown in Figure 5F). Thus, YEATS2 and GCDH are both responsible for the regulation of histone crotonylation-mediated gene expression in HNC.

      We did not find any evidence of YEATS2 regulating the expression of GCDH in HNC cells. However, we found that YEATS2 downregulation reduced the nuclear pool of GCDH in head and neck cancer cells (Figure 7F). This suggests that YEATS2 not only regulates histone crotonylation by affecting promoter H3K27cr levels (with p300), but also by affecting the nuclear localization of crotonyl-CoA producing GCDH. Also, we observed that the expression of YEATS2 and GCDH are regulated by the same transcription factor SP1 in HNC. We found that the transcription factor SP1 binds to the promoter of both genes, and its downregulation led to a decrease in their expression (Figure 3 and Figure 7).

      We would like to state that the relationship between YEATS2 and the nuclear localization of GCDH, as well as the underlying molecular mechanism, remains unexplored and presents an open question for future investigation.

      (9) The authors should provide IHC analysis of YEATS2, SPARC alongside H3K27cr and GCDH staining in normal vs. tumor tissues from HNC patients.

      We thank the reviewer for their suggestion. We are consulting our clinical collaborators to assess the feasibility of including this IHC analysis in our revision and will make every effort to incorporate it.

      Reviewer #2 (Public review):

      Summary:

      The manuscript emphasises the increased invasive potential of histone reader YEATS2 in an SP1-dependent manner. They report that YEATS2 maintains high H3K27cr levels at the promoter of EMT-promoting gene SPARC. These findings assigned a novel functional implication of histone acylation, crotonylation.

      We thank the reviewer for the constructive comments. We are committed to making beneficial changes to the manuscript in order to alleviate the reviewer’s concerns.

      Concerns:

      (1) The patient cohort is very small with just 10 patients. To establish a significant result the cohort size should be increased.

      We thank the reviewer for this suggestion. We will increase the number of patient samples to assess the levels of YEATS2 and H3K27cr in normal vs. tumor samples.

      (2) Figure 4D compares H3K27Cr levels in tumor and normal tissue samples. Figure 1G shows overexpression of YEATS2 in a tumor as compared to normal samples. The loading control is missing in both. Loading control is essential to eliminate any disparity in protein concentration that is loaded.

      In Figures 1G and 4D, we have used Ponceau S staining as a control for equal loading. Ponceau S staining is frequently used as an alternative for housekeeping genes like GAPDH as a control for protein loading(8). It avoids the potential for variability in housekeeping gene expression. However, it may be less quantitative than using housekeeping proteins. To address the reviewer’s concern, we will probe with an antibody against a house keeping gene as a loading control in the revised figures, provided its expression remains stable across the conditions tested.

      (3) Figure 4D only mentions 5 patient samples checked for the increased levels of crotonylation and hence forms the basis of their hypothesis (increased crotonylation in a tumor as compared to normal). The sample size should be more and patient details should be mentioned.

      A total of 9 samples were checked for H3K27cr levels (5 of them are included in Figure 4D and rest included in Figure 4—figure supplement 1D). However, as a part of the revision, we will check the H3K27cr levels in more patient samples.

      (4) YEATS2 maintains H3K27Cr levels at the SPARC promoter. The p300 is reported to be hyper-activated (hyperautoacetylated) in oral cancer. Probably, the activated p300 causes hyper-crotonylation, and other protein factors cause the functional translation of this modification. The authors need to clarify this with a suitable experiment.

      In our study, we have shown that p300 is dependent on YEATS2 for its recruitment on the SPARC promoter. As a part of the revision, we propose the following experiments to further substantiate the role of p300 in YEATS2-mediated gene regulation:

      (a) Co-immunoprecipitation experiments to check the physical interaction between YEATS2 and p300.

      (b) We will check H3K27cr levels on the SPARC promoter and SPARC expression in p300-depleted HNC cells.

      (c) Rescue experiments to check if the decrease in p300 occupancy on the SPARC promoter can be compensated by overexpressing YEATS2.

      (d) Pol-II ChIP-qPCR at the promoter of SPARC will be performed in YEATS2-silenced cells to explain the mode of transcriptional regulation of SPARC expression by YEATS2.

      (5) I do not entirely agree with using GAPDH as a control in the western blot experiment since GAPDH has been reported to be overexpressed in oral cancer.

      We would like to clarify that GAPDH was not used as a loading control for protein expression comparisons between normal and tumor samples. GAPDH was used as a loading control only in experiments using head and neck cancer cell lines where shRNA-mediated knockdown or overexpression was employed. These manipulations specifically target the genes of interest and are not expected to alter GAPDH expression, making it a suitable loading control in these instances.

      (6) The expression of EMT markers has been checked in shControl and shYEATS2 transfected cell lines (Figure 2A). However, their expression should first be checked directly in the patients' normal vs. tumor samples.

      We thank the reviewer for the suggestion. To address this, we will check the expression of EMT markers alongside YEATS2 expression in normal vs. tumor samples.

      (7) In Figure 3G, knockdown of SP1 led to the reduced expression of YEATS2 controlled gene Twist1. Ectopic expression of YEATS2 was able to rescue Twist1 partially. In order to establish that SP1 directly regulates YEATS2, SP1 should also be re-introduced upon the knockdown background along with YEATS2 for complete rescue of Twist1 expression.

      To address the reviewer’s concern regarding the partial rescue of Twist1 in SP1 depleted-YEATS2 overexpressed cells, we will perform the experiment as suggested by the reviewer. In brief, we will overexpress both SP1 and YEATS2 in SP1-depleted cells and then assess the expression of Twist1.

      (8) In Figure 7G, the expression of EMT genes should also be checked upon rescue of SPARC expression.

      We thank the reviewer for the suggestion. We will check the expression of EMT markers on YEATS2/ GCDH rescue and update Figure 7G in the revised version of the manuscript.

      References

      (1) T. Brabletz, R. Kalluri, M. A. Nieto and R. A. Weinberg, Nat Rev Cancer, 2018, 18, 128–134.

      (2) P. Pisani, M. Airoldi, A. Allais, P. Aluffi Valletti, M. Battista, M. Benazzo, R. Briatore, S. Cacciola, S. Cocuzza, A. Colombo, B. Conti, A. Costanzo, L. Della Vecchia, N. Denaro, C. Fantozzi, D. Galizia, M. Garzaro, I. Genta, G. A. Iasi, M. Krengli, V. Landolfo, G. V. Lanza, M. Magnano, M. Mancuso, R. Maroldi, L. Masini, M. C. Merlano, M. Piemonte, S. Pisani, A. Prina-Mello, L. Prioglio, M. G. Rugiu, F. Scasso, A. Serra, G. Valente, M. Zannetti and A. Zigliani, Acta Otorhinolaryngol Ital, 2020, 40, S1–S86.

      (3) J. Lin, P. Zhang, W. Liu, G. Liu, J. Zhang, M. Yan, Y. Duan and N. Yang, Elife, 2023, 12, RP87510.

      (4) X. Liu, W. Wei, Y. Liu, X. Yang, J. Wu, Y. Zhang, Q. Zhang, T. Shi, J. X. Du, Y. Zhao, M. Lei, J.-Q. Zhou, J. Li and J. Wong, Cell Discov, 2017, 3, 17016.

      (5) G. Jiang, C. Li, M. Lu, K. Lu and H. Li, Cell Death Dis, 2021, 12, 703.

      (6) D. Zhao, H. Guan, S. Zhao, W. Mi, H. Wen, Y. Li, Y. Zhao, C. D. Allis, X. Shi and H. Li, Cell Res, 2016, 26, 629–632.

      (7) H. Yuan, X. Wu, Q. Wu, A. Chatoff, E. Megill, J. Gao, T. Huang, T. Duan, K. Yang, C. Jin, F. Yuan, S. Wang, L. Zhao, P. O. Zinn, K. G. Abdullah, Y. Zhao, N. W. Snyder and J. N. Rich, Nature, 2023, 617, 818–826.

      (8) I. Romero-Calvo, B. Ocón, P. Martínez-Moya, M. D. Suárez, A. Zarzuelo, O. Martínez-Augustin and F. S. de Medina, Anal Biochem, 2010, 401, 318–320.

    1. Author response:

      We thank the reviewers for their careful evaluation of our manuscript and appreciate the suggestions for improvement. We will outline our planned revisions in response to these reviews.

      Reviewer 2:

      “The one exception is the claim that "maintenance of respiration is the only cellular target of chalkophore mediated copper acquisition." While under the in vitro conditions tested this does appear to be the case; however, it can't be ruled out that the chalkophore is important in other situations. In particular, for maintenance of the periplasmic superoxide dismutase, SodC, which is the other M. tuberculosis enzyme known to require copper.”

      And

      Reviewer 3:

      “Because the phenotype of M. tuberculosis lacking chalkophores is similar, if not identical, to using Q203, an inhibitor of cytochrome bcc:aa3, the authors propose that the copper-containing cytochrome bcc:aa3 is the only recipient of copper-uptake by chalkophores. A minor weakness of the work is that this latter conclusion is not verified under infection conditions and other copper-enzymes might still be functionally required during one or more stages of infection.

      Both comments concern the question of whether the bcc:aa3 respiratory oxidase supercomplex is the only target of chalkophore delivered copper. In culture, our experiments suggest that bcc:aa3 is the only target. The evidence for this claim is in Figure 2E and F. In 2E, we show that M. tuberculosis DctaD (a subunit of bcc:aa3) is growth impaired, copper chelation with TTM does not exacerbate that growth defect, and that a DctaDDnrp double mutant is no more sensitive to TTM than DctaD. These data indicate that role of the chalkophore in protecting against copper deprivation is absent when the bcc:aa3 oxidase is missing. Similar results were obtained with Q203 (Figure 2F). Q203 or TTM arrest growth of M. tuberculosis Dnrp, but the combination has no additional effect, indicating that when Q203 is inhibiting the bcc:aa3 oxidase, the chalkophore has no additional role. However, we agree with the reviewers that we cannot exclude the possibility that during infection, there is an additional target of chalkophore mediated Cu acquisition. We will add this caveat to the revised version of this manuscript.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary:

      In previous work, the authors described necrosis-induced apoptosis (NiA) as a consequence of induced necrosis. Specifically, experimentally induced necrosis in the distal pouch of larval wing imaginal discs triggers NiA in the lateral pouch. In this manuscript, the authors confirmed this observation and found that while necrosis can kill all areas of the disc, NiA is limited to the pouch and to some extent to the notum, but is excluded from the hinge region. Interestingly and unexpectedly, signaling by the Jak/Stat and Wg pathways inhibits NiA. Further characterization of NiA by the authors reveals that NiA also triggers regenerative proliferation which can last up to 64 hours following necrosis induction. This regenerative response to necrosis is significantly stronger compared to discs ablated by apoptosis. Furthermore, the regenerative proliferation induced by necrosis is dependent on the apoptotic pathway because RNAi targeting the RHG genes is sufficient to block proliferation. However, NiA does not promote proliferation through the previously described apoptosis-induced proliferation (AiP) pathway, although cells at the wound edge undergo AiP. Further examination of the caspase levels in NiA cells allowed the authors to group these cells into two clusters: some cells (NiA) undergo apoptosis and are removed, while others referred to as Necrosis-induced Caspase Positive (NiCP) cells survive despite caspase activity. It is the NiCP cells that repair cellular damage including DNA damage and that promote regenerative proliferation. Caspase sensors demonstrate that both groups of cells have initiator caspase activity, while only the NiA cells contain effector caspase activity. Under certain conditions, the authors were also able to visualize effector caspase activity in NiCP cells, but the level was low, likely below the threshold for apoptosis. Finally, the authors found that loss of the initiator caspase Dronc blocks regenerative proliferation, while inhibiting effector caspases by expression of p35 does not, suggesting that Dronc can induce regenerative proliferation following necrosis in a non- apoptotic manner. This last finding is very interesting as it implies that Dronc can induce proliferation in at least two ways in addition to its requirement in AiP.

      Strengths:

      This is a very interesting manuscript. The authors demonstrate that epithelial tissue that contains a significant number of necrotic cells is able to regenerate. This regenerative response is dependent on the apoptotic pathway which is induced at a distance from the necrotic cells. Although regenerative proliferation following necrosis requires the initiator caspase Dronc, Dronc does not induce a classical AiP response for this type of regenerative response. In future work, it will be very interesting to dissect this regenerative response pathway genetically.

      Weaknesses:

      No weaknesses were identified.

      We thank the reviewer for their positive evaluation and kind words.

      Reviewer #2 (Public Review):

      Summary / Strengths:

      In this manuscript, Klemm et al., build on past published findings (Klemm et al., 2021) to characterize caspase activation in distal cells following necrotic tissue damage within the Drosophila wing imaginal disc. Previously in Klemm et al., 2021, the authors describe necrosis-induced-apoptosis (NiA) following the development of a genetic system to study necrosis that is caused by the expression of a constitutive active GluR1 (Glutamate/Ca2+ channel), and they discovered that the appearance of NiA cells were important for promoting regeneration.

      In this manuscript, the authors aim to investigate how tissues regenerate following necrotic cell death. They find that the cells of the wing pouch are more likely to have non-autonomous caspase activation than other regions within the wing imaginal disc (hinge and notum),two signaling pathways that are known to be upregulated during regeneration, Wnt (wingless) and JAK/Stat signaling, act to prevent additional NiA in pouch cells, and may explain the region specificity, the presence of NiA cells promotes regenerative proliferation in late stages of regeneration, not all caspase-positive cells are cleared from the epithelium (these cells are then referred to as Necrosis-induced Caspase Positive (NiCP) cells), these NiCP cells continue to live and promote proliferation in adjacent cells, the caspase Dronc is important for creating NiA/NiCP cells and for these cells to promote proliferation. Animals heterozygous for a Dronc null allele show a decrease in regeneration following necrotic tissue damage.

      The study has the potential to be broadly interesting due to the insights into how tissues differentially respond to necrosis as compared to apoptosis to promote regeneration.

      Weaknesses:

      However, here are some of my current concerns for the manuscript in its current version:

      The presence of cells with activated caspase that don't die (NiCP cells) is an interesting biological phenomenon but is not described until Figure 5. How does the existence of NiCP cells impact the earlier findings presented? Is late proliferation due to NiA, NiCP, or both? Does Wg and JAK/STAT signaling act to prevent the formation of both NiA and NiCP cells or only NiA cells? Moreover, the authors are able to specifically manipulate the wound edge (WE) and lateral pouch cells (LP), but don't show how these manipulations within these distinct populations impact regeneration. The authors provide evidence that driving UAS-mir(RHG) throughout the pouch, in the LP or the WE all decrease the amount of NiA/NiCP in Figure 3G-O, but no data on final regenerative outcomes for these manipulations is presented (such as those presented for Dronc-/+ in Fig 7M). The manuscript would be greatly enhanced by quantification of more of the findings, especially in describing if the specific manipulations that impacted NiA /NiCP cells disrupt end-point regeneration phenotypes.

      We have added a line to the results to clarify that we believe the finding that some NiA likely persist as NiCP does not affect our conclusions up to this point.

      We have added a statement emphasizing the results from our first paper, which demonstrate that LP>miRHG expression reduces the overall capacity to regenerate.

      Quantification of the change in posterior NiA number have been added to Figure 2L to strengthen the evidence. Likewise, we have included quantification of the E2F time course presented in Figure 3A (Figure 3 – Figure supplement 1C), and quantification of the change in GC3Ai signal over time has been added to Figure 5 - Figure supplement 1D) to emphasize the perdurance of GC3Ai-positive NiA/NiCP.

      How fast does apoptosis take within the wing disc epithelium? How many of the caspase(+) cells are present for the whole 48 hours of regeneration? Are new cells also induced to activate caspase during this time window? The author presented a number of interesting experiments characterizing the NiCP cells. For the caspase sensor GC3Ai experiments in Figure 5, is there a way to differentiate between cells that have maintained fluorescent CG3Ai from cells that have newly activated caspase? What is the timeline for when NiA and NiCP are specified? In addition, what fraction of NiCP cells contribute to the regenerated epithelium? Additional information about the temporal dynamics of NiA and NiCP specification/commitment would be greatly appreciated.

      We have included more information concerning the kinetics of apoptotic cell removal, and how this compares to the observations we have made with NiA/NiCP in our GC3Ai experiments. Additionally, we have included a quantification of the percent of the whole wing pouch with GC3Ai signal over time (Figure 5F) as well as the distal wing pouch with GC3Ai signal over time (Figure 5 – Figure supplement 1D) to further support the idea that NiCP persist over time.

      We acknowledge that our GC3Ai time course unfortunately cannot confirm whether the increase in GC3Ai signal over time is due to cells with new caspase activity or proliferating NiCP and have included this point in the discussion.

      We attempted to track the lineage of NiA/NiCP into the pupal and adult wings with CasExpress and DBS, however the results of these experiments were inconsistent, and therefore we did not feel confident to include these data or draw conclusions in either direction. We are currently designing variations of these lineage trace tools in order to better track the lineage of these cells that we hope to include in a future paper.

      The notum also does not express developmental JAK/STAT, yet little NiA was observed within the notum. Do the authors have any additional insights into the differential response between the pouch and notum? What makes the pouch unique? Are NiA/NiCP cells created within other imaginal discs and other tissues? Are they similarly important for regenerative responses in other contexts?

      We have added a brief mention of these points to the appropriate results section to avoid further increasing the length of the discussion.

      Data on the necrosis of other imaginal discs through FLP/FRT clone formation in haltere and leg discs has been added to Figure 1 Figure supplement 1J, and described in the text.

      Reviewer #3 (Public Review):

      The manuscript "Regeneration following tissue necrosis is mediated by non- apoptotic caspase activity" by Klemm et al. is an exploration of what happens to a group of cells that experience caspase activation after necrosis occurs some distance away from the cells of interest. These experiments have been conducted in the Drosophila wing imaginal disc, which has been used extensively to study the response of a developing epithelium to damage and stress. The authors revise and refine their earlier discovery of apoptosis initiated by necrosis, here showing that many of those presumed apoptotic cells do not complete apoptosis. Thus, the most interesting aspect of the paper is the characterization of a group of cells that experience mild caspase activation in response to an unknown signal, followed by some effector caspase activation and DNA damage, but that then recover from the DNA damage, avoid apoptosis, and proliferate instead. Many questions remain unanswered, including the signal that stimulates the mild caspase activation, and the mechanism through which this activation stimulates enhanced proliferation.

      The authors should consider answering additional questions, clarifying some points, and making some minor corrections:

      Major concerns affecting the interpretation of experimental results:

      Expression of STAT92E RNAi had no apparent effect on the ability of hinge cells to undergo NiA, leading the authors to conclude that other protective signals must exist. However, the authors have not shown that this STAT92E RNAi is capable of eliminating JAK/STAT signaling in the hinge under these experimental conditions. Using a reporter for JAK/STAT signaling, such as the STAT-GFP, as a readout would confirm the reduction or elimination of signaling. This confirmation would be necessary to support the negative result as presented.

      We have included data demonstrating our ability to knock down JAK/STAT activity in the hinge with UAS-Stat92E<sup>RNAi</sup> (Figure 2 – Figure supplement 1E and F). Additionally, we have included a quantification of posterior NiA/NiCP with the Stat92E<sup>RNAi</sup> (as well as wg<sup>RNAi</sup> and Zfh-2<sup>RNAi</sup>, Figure 2L) to strengthen our conclusion that JAK/STAT and WNT signaling acts to regulate NiA formation within the pouch.

      Similarly, the authors should confirm that the Zfh2 RNAi is reducing or eliminating Zfh2 levels in the hinge under these experimental conditions, before concluding that Zfh2 does not play a role in stopping hinge cells from undergoing NiA.

      We have repeated this experiment with a longer knockdown using a GAL4 driver that expresses from early larval stages until our evaluation at L3, but were unable to demonstrate a loss of Zfh-2 with IF labeling. Additionally, we have quantified posterior NiA/NiCP with a Zfh-2RNAi (Figure 2L) and do find a slight increase in NiA/NiCP number, however this change is not significant. We have altered our conclusions to reflect these new data.

      EdU incorporation was quantified by measuring the fluorescence intensity of the pouch and normalizing it to the fluorescence intensity of the whole disc. However, the images show that EdU fluorescence intensity of other regions of the disc, especially the notum, varied substantially when comparing the different genetic backgrounds (for example, note the substantially reduced EdU in the notum of Figure 3 B' and B'). Indeed, it has been shown that tissue damage can lead to suppression of proliferation in the notum and elsewhere in the disc, unless the signaling that induces the suppression is altered. Therefore, the normalization may be skewing the results because the notum EdU is not consistent across samples, possibly because the damage-induced suppression of proliferation in the notum is different across the different genetic backgrounds.

      To more accurately reflect the observations that we have made with the EdU assay, we have changed our terminology to indicate that the EdU signal is more localized to the damaged tissue in ablated discs, thus taking into account the relative changes across the disc, rather than referring to it as an increase in the pouch. To further strengthen our observation that damage results in a localized proliferation, we have included a quantification of the E2F time course presented in Figure 3A (Figure 3 – Figure supplement 1C), which underscores the trend observed in our EdU experiments.

      The authors expressed p35 to attempt to generate "undead cells". They take an absence of mitogen secretion or increased proliferation as evidence that undead cells were not generated. However, there could be undead cells that do not stimulate proliferation non-autonomously, which could be detected by the persistence of caspase activity in cells that do not complete apoptosis. Indeed, expressing p35 and observing sustained effector caspase activation could help answer the later question of what percentage of this cell population would otherwise complete apoptosis (NiA, rescued by p35) vs reverse course and proliferate (NiCP, unaffected by p35).

      In our previous work, we showed that P35 expression impairs our ability to detect effector caspases with IF-based tools. This can also be seen in Figure 4 of this work (Figure 4C and F). Given that P35 expression precludes our ability to label and assay effector caspase activity visually, and thus address the concerns outlined above, we relied on other tools such as reporters of AiP mitogens (wg-lacZ & dpp-lacZ) to assay whether NiA participate in AiP. As a functional readout, we also paired P35 expression with the EdU assay to test whether proliferation was altered by the presence of undead cells. The results discussed in Figure 4 lead us to conclude that NiA likely do not participate in the canonical AiP feedforward loop, although it is possible that these experiments generate another type of undead cell – one that utilizes a different mechanism to promote proliferation.

      It is unclear if the authors' model is that the NiCP cells lead to autonomous or non-autonomous cell proliferation, or both. Could the lineage-tracing experiments and/or the experiments marking mitosis relative to caspase activity answer this question?

      We have added further details to the discussion on the potential for NiA/NiCP to induce cell autonomous/non-autonomous proliferation.

      Many of the conclusions rely on single images. Quantification of many samples should be included wherever possible.

      We have added quantification to strengthen the results of Figures 2, 3 and 5.

      Why does the reduction of Dronc appear to affect regenerative growth in females but not males?

      We have repeated this regeneration scoring experiments and have increased the N for control versus droncI29 mutant males, however the results of the analysis for male wing size remain not significant, although the general trend that droncI29 wings are slightly smaller. While there could be sex-specific differences in the capacity to regenerate that contribute to this observation, it is unclear what the underlying mechanism could be.

      Reviewer #1 (Recommendations for the authors):

      The work in this paper is already very complete and very well worked out. The conclusions are well supported by the data in this manuscript. I do not have any experimental requests, only a few minor and formal requests/questions.

      (1) Why does Diap1 overexpression not affect regenerative proliferation, whereas mir(RHG) and dronc[I29] do, given that Diap1 acts between RHG and Dronc?

      We speculate on this point in the discussion section but have adjusted some of the phrasing for clarity.

      (2) I assume that the authors used the cleaved Dcp-1 antibody from Cell Signaling Technologies. I recommend that the authors refer to this antibody as cDcp-1 in text and figures as this antibody specifically detects the cleaved, and thus activated form of Dcp-1, and not the uncleaved, inactive form of Dcp-1 which has a uniform expression in the discs.

      Changed to cDcp-1.

      (3) Line 299: Hay et al. 1994 did not show that p35 inhibits Drice and Dcp-1 (in fact, both genes were not even cloned yet). This was shown by Meier et al. 2000 and Hawkins et al. 2000. Please correct references.

      Corrected.

      (4) Line 574/575. Meier et al. 2000 did not show that Dronc is mono-ubiquitylated. This was shown by Kamber-Kaya et al., 2017. Please correct.

      Corrected.

      Reviewer #2 (Recommendations for the authors):

      (1) Does domeless knockdown cause apoptosis without tissue ablation (Figures 2C-E)? Currently, the non-ablation control is not shown.

      Domeless knockdown does not cause apoptosis in the absence of ablation (Added Figure 2 – Figure supplement 1A).

      (2) The supplemental experiment with zfh2-RNAi is hard to interpret because there is no evidence of RNAi knockdown based on the staining with the anti-Zfh2 antibody.

      As noted above, a longer zfh-2 knockdown does not appear to alter Zfh-2 protein levels. A quantification of posterior NiA/NiCP following knockdown shows a slight (non-significant) increase in posterior NiA/NiCP. Considering these new results, we have altered our interpretation within the appropriate results and discussion sections.

      (3) The authors should consider adding a diagram showing where mir(RHG) and DIAP1 are in the apoptotic/caspase activation pathway (Figure 7N).

      Completed, Figure 7N and 7O.

      Reviewer #3 (Recommendations for the authors):

      (1) Figure 2 I -The purported increase in NiA should be quantitated relative to the NiA in G across many discs.

      Completed (Figure 2L)

      (2) Figure 2 M - contrary to the conclusion drawn, the posterior Dcp1 does not appear different from that in the control (K). This conclusion that the NiA does not occur in the margin could be better supported with more images/quantification.

      We have exchanged the image for a representative one that more clearly shows the lack of margin NiA and highlighted with an arrowhead (Figure 2K)

      (3) Figure 2 supp 1 E - the "slight increase" in NiA in the pouch is relative to which control? Can this conclusion be supported by quantification?

      Figure 2L now quantifies this change.

      (4) Figure 2 Supp 1 D, E - these discs supposedly have Zfh2 RNAi expressed, but there appears to be no reduction in Zfh2.

      We were unable to demonstrate a reduction of Zfh2, even with a longer knockdown. Considering these new data, we have altered our conclusions from the Zfh2 experiments.

      (5) Figure 2 Supp 1 I - please quantitate the Dcp-1 across many discs to support the conclusion.

      This is the UAS-wg experiment, which we decided to remove from the quantification given the non-specific increase in cDcp-1 throughout the disc (likely as a result from ectopic Wg expression).

      (6) Figure 4 legend M - The authors conclude that the experiment indicates that "NiA promote proliferation independent of AiP". It would be more precise to say that NiA cells do not secrete AiP mitogens and do not increase the proliferation of surrounding cells when prevented from completing apoptosis. To say that the NiA-induced proliferation does not require AiP would require eliminating AiP, perhaps through reaper hid grim knockdown or mitogen knockdown.

      Corrected.

      Minor concerns and clarification needed:

      (7) Line 61 - consider the distinction between a feed-forward loop and a positive feedback loop.

      Corrected.

      (8) Line 338 - it would be helpful to have a brief explanation of what the GC3Ai consists of and how it reports caspase activity.

      Corrected.

      (9) Line 343 - the authors should clarify by what they mean when they state GC3Ai-positive cells are "associated with" mitotic cells. Are the GC3Ai cells undergoing mitosis? Or is the increase in mitosis non-autonomous?

      Adjusted. “associated with adjacent proliferative cells”.

      (10) Lines 392-394 - the authors should add brief descriptions of how the Drice-Based sensor and the CasExpress function, so the readers can better understand the distinctions between these sensors and the previously mentioned sensors (anti-Dcp1 and GC3Ai). In addition, please clarify how the Gal80ts modulates the sensitivity of the CasExpress.

      Descriptions of DBS and CasExpress and additional clarification provided.

      (11) Line 413: How does Gal80ts suppress the background developmental caspase signal, and how does this suppression lead to NiCP cells expressing GFP?

      This section has been reworded to clarify.

      (12) Line 417 - which GFP label is referred to here?

      This section has been reworded to clarify.

      (13) Line 445 is the first mention of the CARD domain - it could be introduced more fully and explained why the DroncDN's lack of effect on proliferation excludes the CARD domain as being important.

      Clarified. See also the discussion for the significance of the CARD domain as dispensable for regenerative proliferation following necrosis.

      (14) Line 452 - "As mentioned" - the manuscript has not previously mentioned DIAP1 modification of the CARD domain and what that modification does. Perhaps the previous explanatory text was inadvertently removed?

      Corrected.

      (15) The Discussion is a lengthy list of experiments that the authors did not do or observations they were unable to make. This section could benefit from a more in-depth discussion of necrosis and the possibility that NiCP cells contribute to repair after injury across contexts and species.

      We have made several changes to the discussion that elaborate on some of the points listed in the public reviews.

      (16) All figures: Consider making single-channel panels grayscale to aid visualization. Also consider using color combinations that can be distinguished by color-blind readers.

      We appreciate these suggestions and will consider them for future manuscripts.

      (17) All figure legends - are error bars SD or SEM?

      Standard deviation. Added to appropriate legends.

      (18) Figure 1A,C - it would be helpful in the diagrams to note when the necrosis occurs/completes.

      The endpoint of necrosis is not well defined, given the simultaneous changes that occur with regeneration. Thus, we opted to not include an indicator of when necrotic ablation ends.

      (19) Figure 1B - it would be helpful to name the GAL4 drivers whose expression domain is depicted to correlate with the terms used in the text.

      Completed.

      (20) Figure 1 legend- what do the different colors of the arrowheads denote? The dotted lines are in R' and S', not N' and O'.

      Completed.

      (21) Figure 2G - the yellow dashed line is not in the same place in the two images.

      Corrected.

      (22) Figure 2I - what is the open arrowhead?

      Completed (Figure 2I legend).

      (23) Figure 3 legend - please describe what the time course is observing (EdU).

      Completed.

      (24) Figure 4 - please include the yellow boxes in the Dcp-1 channels.

      Completed.

      (25) Figure 5 F' - add the arrowheads to all the panels. The yellow arrowhead appears to be pointing to nothing.

      Completed.

      (27) Figure 5 legend - what is a "cytoplasmic undisturbed cell"? What is the arrowhead in G? J and J' should show the same view at different time points or different views at the same time point.

      Figure legend has been corrected.

      (28) Figure 5 Supp 1 would be especially helped by having more single-channel panels in grayscale.

      For clarity and consistency, we chose to maintain the different color channels.

      (29) Figure 5 Supp 1 D and E - It would be helpful to have higher magnification and arrows pointing to the cells of interest. Why are there TUNEL+ cells that do not have caspase activation (green)?

      We have added arrowheads as suggested. We believe the disparity in TUNEL and GC3Ai signals are a result of the different sensitivities of the IF staining and the TUNEL assay.

      (30) Figure 5 Supp 1 F - perhaps the arrowheads should be in all panels - they point to empty spaces with no H2Av staining in the final panel. Perhaps a higher magnification image would make the "strong overlap" of the two signals more apparent?

      We have added arrowheads where appropriate.

      (31) Figure 6 D-E - does the widespread GFP lineage tracing signal suggest that most cells in the repaired tissue originated from cells that once had caspases activity?

      Possibly, however given that CasExpress leads to significant developmental labeling, we were unable to determine to what extent the signal in this experiment comes from NiA/NiCP activity versus developmental labeling. Note that tubGAL80ts is not present in this experiment.

      (32) Writing corrections:

      Line 343 "positive" is misspelled.

      Completed

      Line 429 - a word may be missing.

      Completed

      Line 639 - the word "day" may be missing.

      Completed

      Line 658 - what temperature was the recovery?

      Completed

      Lines 706-708 - were the discs incubated in 55 mL and 65 mL of liquid, or a smaller volume?

      Completed

    1. Author response:

      Reviewer #1:

      Overall I find the evidence very well presented and the study compelling. It offers an important new perspective on the key properties of neoblasts. I do have some comments to clarify the presentation and significance of the work.

      We thank the reviewer for the positive feedback and plan to improve the presentation of the work.

      Reviewer #2:

      However, the absence of a cell-cell feedback mechanism during colony growth and the likelihood of the difference needs to be clarified. Is there any difference in interpreting the results if this mechanism is considered?

      We will improve the description of the model assumptions and the interpretation of the data on the basis of these assumptions.

      Although hnf-4 and foxF have been silenced together to validate the model, a deeper understanding of the tgs-1+ cell type and the non-significant reduction of tgs-1+ neoblasts in zfp-1 RNAi colonies is necessary, considering a high neural lineage frequency.

      We will improve the analysis of this result in light of the experimentally determined frequency of the tgs-1+ neoblast population.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Cheng et al explore the utility of analyte ratios instead of relative abundance alone for biological interpretation of tissue in a MALDI MSI workflow. Utilizing the ratio of metabolites and lipids that have complimentary value in metabolic pathways, they show the ratio as a heat map which enhances the understanding of how multiple analytes relate to each other spatially. Normally, this is done by projecting each analyte as a unique color but using a ratio can help clarify visualization and add to biological interpretability. However, existing tools to perform this task are available in open-source repositories, and fundamental limitations inherent to MALDI MSI need to be made clear to the reader. The study lacks rigor and controls, i.e. without quantitative data from a variety of standards (internal isotopic or tissue mimetic models for example), the potential delta in ionization efficiencies of different species subtracts from the utility of pathway analysis using metabolite ratios.

      We thank the reviewer for comments on the availability of four other commercial and open-source tools for performing ratio imaging: ENVI® Geospatial Analysis Software, MATLAB image processing toolbox, Spectral Python (SPy) and QGIS. We now highlight these in the introduction (page 3 line 80-86). However, in contrast to these target ratio imaging methods, our approach uniquely enables the untargeted discovery of correlated (or anti-correlated) ratios of molecular features, whether the species are structurally known or unknown.

      ENVI® Geospatial Analysis Software and MATLAB image processing toolbox for hyperspectral imaging are both paid programs, limiting free access and software evaluation for the potential application of untargeted ratio-metric imaging. We are able to evaluate the application of MATLAB RatioImage since Weill Cornell Medicine has an institutional subscription for Mathwork-MATLAB. Notably, MATLAB RatioImage computes and displays an individual intensity modulated ratiometric image by choosing a numerator and denominator image. This software tool only images the ratios of selected metabolites from an input list of multiple species and does not allow for the possibility of untargeted ratiometric images of all metabolite pairs.

      While Spectral Python (SPy) and QGIS are both freely-available software packages, and both can perform individual metabolite ratio images, neither allows for untargeted ratiometric imaging of all pairs from a multiple metabolite input list. Table S1 (below) provides a comparison of the ratio imaging tool that we offer in comparison with other previously available tools.

      We appreciate the reviewer’s insightful comments on differential ionization efficiency among metabolites and the importance of using stable isotope internal standard to gain absolute quantification.

      A fundamental advantage of our ratiometric imaging tool is to provide better image contrast for tissue regions with differential ionization efficiency, with the potential to discover new “metabolic” regions that can be revealed by metabolite ratio. Note that comparison for ratio image abundance is limited to tissue groups in the equivalent region which is expected to have similar ionization efficiency for given metabolites. Furthermore, the power of our strategy is to provide untargeted (and targeted) ratio imaging as a hypothesis generation tool and this use does not require absolute quantification. If cost was not an issue, an extensive group of stable isotope standards could theoretically be used for absolute metabolite quantification of target metabolites with known identity.

      Using the tissue mimetic model, we generate calibration curve for stable isotope standards spiked in carboxymethylcellulose (CMC)-embedded brain homogenate cryosections and quantify the concentration of brain glucose, lactate and ascorbate concentrations. Similar ratio images among these metabolites are obtained from abundance data compared to quantified concentration data (Fig S3). While stable isotope standards are often used to obtain quantitative concentration of metabolite/lipid of interest, it is not applicable for untargeted metabolite ratios that include an assessment of structurally undefined species. Nevertheless, our data indicates that absolute quantification is not necessary for the targeted and untargeted ratio imaging described here (Page 6, line 196-205).

      Reviewer #2 (Public Review):

      Summary:

      In the article, "Untargeted Pixel-by-Pixel Imaging of Metabolite Ratio Pairs as a Novel Tool for Biomedical Discovery in Mass Spectrometry Imaging" the authors describe their software package in R for visualizing metabolite ratio pairs. I think the novelty of this manuscript is overstated and there are several notable issues with the figures that prevent detailed assessment but the work would be of interest to the mass spectrometry community.

      Strengths:

      The authors describe a software that would be of use to those performing MALDI MSI. This software would certainly add to the understanding of metabolomics data and enhance the identification of critical metabolites.

      Weaknesses:

      The authors are missing several references and discussion points, particularly about SIMS MSI, where ratio imaging has been previously performed.

      There are several misleading sentences about the novelty of the approach and the limitations of metabolite imaging.

      Several sentences lack rigor and are not quantitative enough.

      The figures are difficult to interpret/ analyze in their current state and lack some critical components, including labels and scale bars.

      We thank reviewer for very helpful comments. The tone of the manuscript has been adjusted to highlight the real novelty of this method in the ease of computing and application to MS specific projects (abstract line 26-30 ). All figures have been updated to include labels and scale bars with improved resolution. References for ratio imaging use of SIMS MSI has been added in the introduction (Page 3, line 80-89).

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Major Comments:

      In the Abstract it is stated that: "the research community lacks a discovery tool that images all metabolite abundance ratio pairs." However, the following tools exist that perform this fundamental task.

      A "pixel by pixel" data frame in .csv form has a very similar data structure to many instruments like satellite imaging or other hyperspectral tools. It is true this does not exist in the MALDI-specific context, but it would not be difficult to perform this task on the following programs. Highlight the novelty here is not ratios but the ease of computing them and the application in the specific project. Also, describe the available tools and what shortcomings others lack that this package provides. A supplemental table of MSI data analysis tools and the function of each would be a good addition.

      List of tools to perform band ratio computation with minimal modification:

      (1) ENVI IDL: geospatial imaging tool that allows ratio computation between spectral bands.

      (2) MATLAB image processing toolbox for hyperspectral imaging.

      (3) Spectral Python package (SPy).

      (4) QGIS with plugins can be used for hyperspectral image analysis with a ratio between bands.

      We revised the abstract and introduction to include novelty and comparison to other existing methods listed in Table S1.

      "untargeted R package workflow" - If there are functions used outside the SCiLS Lab API client then write it up and include a GitHub link for open access to fit the mission of eLife.

      As shown in Scheme I. We develop two types of codes for untargeted ratio imaging. The first type uses Scils lab API client to extend the function of targeted and targeted ratio imaging and all related spatial image analysis. This is suitable for Scils lab users. The second type does not require Scils lab API, it allows extracting pixel data from imzml file then proceed targeted and untargeted imaging and analysis. Both codes are now deposit in Github via public access (https://github.com/qic2005/Untargeted-massspectrometry-ratio-imaging.git).

      "across cells and tissue subregions" The value in reporting cell type and tissue type-specific differences in any metric is powerful, but not done in this paper. Only whole samples are compared such as "KO vs WT" and the annotations in Figure 3 are not leveraged for increased biological relevance. This paper treats each image as a homogenization experiment in a practical sense beyond just visually inspecting each image. Remove this claim or do the calculations on region/tissue/cell-type specific differences with the appropriate tools to show the data beyond simple heat map images.

      We have deleted the sentence containing across cells and tissue subregions from the abstract.

      "enhances spatial image resolution" Clarify. The resolution in MALDI is set by the raster size of the pixels which is an instrument parameter and cannot be changed post-acquisition. Image-specific methods to increase resolution exist, but dividing the value in one peak column by another does not change functional resolution in the context of the instruments here.

      We thank reviewer for pointing out this typo. We have changed it to enhance spatial image contrast in the abstract (line 34).

      "pixel-by-pixel imaging of the ratio of an enzyme's substrate to its derived product offers an opportunity to view the distribution of functional activity for a given metabolic pathway across tissue" - Appropriately calibrate the impact of this work and correct this statement to better reflect the capabilities of this approach. Do not oversell the exploration of pathway activity since the raw quantity reported as relative abundance does not provide biologically interpretable pathway information. This is due to unaccounted differences in ionization efficiencies between analytes in a pathway and lack of determination of rate. Without a calibration curve and more techniques on the analytical chemistry side of the project, it is possible a relative abundance of one analyte (like the product of a pathway) could be higher than the relative abundance of another analyte (a precursor), but due to structural differences, the actual quantity of the higher relative abundance species could be significantly different or even lower than its counterpart. Secondly, "functional activity" cannot be assessed in this manner without isotopic labeling or additional techniques. This does not subtract from the overall validity and impact of the work, but highlighting these shortcomings and slight alterations to the claim are important for a multidisciplinary audience.

      Although we show that abundance ratio results in similar image to concentration ratio for brain metabolites such as lactate, glucose and ascorbate, we agree with the reviewer that abundance ratio is different from the absolute concentration ratio in numerical value due to difference in ionization efficiency. We delete the sentence “pixel-by-pixel imaging of the ratio of an enzyme's substrate to its derived product offers an opportunity to view the distribution of functional activity for a given metabolic pathway across tissue" from the abstract. We apologize for not clarifying this application more clearly. We meant to compare pathway activity among the equivalent and similar pixel/regions of tissues from different biological groups, given the assumption that ionization efficiency is identical for equivalent pixel from different tissue sections ( i.e. same cell type and microenvironment), especially for metabolites with similar functional structure in the same pathway. For example, fatty acids with different chain length and phospholipid with same head groups are expected to have similar ionization efficiency in the same tissue pixel/region. We have thereby rewritten this section (Page 7, line 239-247).

      "We further show that ratio imaging minimizes systematic variations in MSI data by sample handling and instrument drift, improves image resolution, enables anatomical mapping of metabotype heterogeneity, facilitates biomarker discovery, and reveals new spatially resolved tissue regions of interest (ROIs) that are metabolically distinct but otherwise unrecognized."

      Instrument drift is not accounted for by ratios as it impacts the process before ratio computation. "metabotype" - spelling?

      Instrument drift here refers to individual ion abundance changes during long data acquisition. Ratio may offer a better read-out than individual metabolite abundance alone. However, for acquired data after total ion normalization, ratio data would not have difference from non-ratio data. Therefore, we delete instrument drift from the sentence (Page 2, line 33, and Page 3, line 99)

      Metabotype is a term widely used for metabolomics field. It is categorized by similar metabolic profiles, which are based on combinations of specific metabolites. https://nutritionandmetabolism.biomedcentral.com/articles/10.1186/s12986-020-00499-z

      Results 3: Justify the claim that the ratio reduces artifacts. A ratio is the value from one m/z area over another and would seem that the quality of the ratio would be always lower than the individually higher quality pixel signal of the two analytes that compose a ratio.

      Ratio images are indeed the heatmaps of pixel-by-pixel ratio data, set by the scale of all ratio values. For very abundant ion pairs, their individual image may not be better than the ratio image, depending on the abundance changes among pixels within tissue sections. Similarly, the quality of ratio image may not be higher than the individual image if distribution of ratios does not change much among pixels in tissue sections. For example, metabolite or lipids in Figures 2 and 5 are abundant, but non-ratio images do not have better quality than ratio images. Furthermore, ratio image provides additional information on how the ratio of the two metabolite pair changes pixel-by pixel in all tissue sections, such additional information could be useful for data interpretation.

      Results 4: The metabolite pairs are biologically sensible but should be clearly stated that they do not account for differences in ionization efficiency between metabolites and cannot provide quantitative pathway analysis with a high degree of biological confidence.

      We apologize for not clarifying this application more clearly. We meant to compare pathway activity among the equivalent and similar pixel/regions of tissues from different biological groups, given the assumption that ionization efficiency is identical for equivalent pixel from different tissue sections ( i.e. same cell type and microenvironment), especially for metabolites with similar functional structure in the same pathway. For example, fatty acids with different chain length and phospholipid with same head groups are expected to have similar ionization efficiency in the same tissue pixel/region. We have thereby rewritten this section (Page 7, 239-247, 254-255).

      Results 4: "cell-type specific metabolic activity at cellular (10 µm) spatial resolution" Prove the cell type differences with IHC coregistration or MALDI IHC if you want to make claims about them. Just visually determining a tissue type of a scan of a slide is inadequate to support this claim.

      We agree with reviewer’s comments. We meant to provide additional information on cellular level metabolic activity such as adenosine nucleotide phosphorylation status (ATP/AMP) ratio at 10µm resolution. Hippocampus neurons provide a good example for depicting this utility. We have rewritten the claim to highlight the role of ratio imaging in providing additional metabolic information (Page 8, line 288-290).

      Minor Comments:

      Table 2 "Aspartiate" spelling

      We have corrected it.

      Describe the process and mathematical background for ratio computation in the Methods section. As this paper introduces a package, describing its underlying functions has value.

      We have added R-script comments to illustrate the untargeted ratio calculation using the R-mathematical function of combination and division between any two metabolite pairs in a data matrix (Page 4, line 139-141)

      "we annotate missing values with 1/5 the minimum value quantified in all pixels in which it was detected" This is explicit (ie only values with exactly 1/5 the value are annotated" - make it clear this is a threshold.

      We apologize for misunderstanding. Missing values are either have no value or have solid zero in their abundance. We first calculate the minimum abundance of a particular m/z among all pixels with detectable abundance ( i.e. excluding non-missing values), then use 1/5 this minimum value as a threshold to annotate missing value (Page 4, 133-139).

      Figure 1: legend scils is branded SCiLS and EXCEL does not need caps lock (Excel).

      Figure 1 legend has been corrected.

      Conflicts of interest "None" - there are Bruker employees on a paper about MALDI method development in a field they dominate.

      We added Joshua Fischer as a Bruker employee.

      Figure 3: The legend does not describe the purple arrow in J.

      Purple arrow description is added to figure legend.

      Figure 5: Fix orientation inconsistencies in G, H, I, and J. Especially in J - they are opposite directions. This is arbitrary and determined in SCiLS lab with simple rotation.

      Orientation has been made consistent in G,H, I and J.

      Figure S8: Provide exact number of biological and technical replicates used to generate this figure.

      Figure S8, now Figure S9, was generated from 4 biological replicates of KO and 4 biological replicates of WT brain section in the ROI7 region. This information has been added to the figure legend.

      Figure S9: Make consistent orientation of all brains

      We have made brain orientations consistent.

      In addition to ionization efficiencies impacting the value of the numeric relative abundance where ratio computation originates from, it should be mentioned how different classes of metabolites are differentially impacted by the euthanasia and collection methods used for various tissue types. For example, it is well established the ATP/AMP ratio can change drastically from tissue collection.

      We have added this to page 8, line 315-319.

      Perform standards to adjust for ionization efficiency between different m/z features.

      Untargeted ratio imaging serves as an add-on MSI data analysis tool with primary use in comparing ratio among equivalent regions/pixels with similar ionization efficiencies. It is a hypothesis generation tool. Standards adjust for ionization efficiency would be a great idea for a more accurate assessment of ratio values. Due to the cost and availability of stable isotope standards for different m/z, we chose glucose, lactate and ascorbate to showcase that abundance ratio and concentration ratio result in similar images among example brain metabolite lactate, glucose and ascorbate (page 6, 196-205).

      Add more controls to support the claims.

      We have 4 biological replicates for each genotype of brain. We have added the number of controls in all figure legends.

      Significantly tone down the claims, it is unclear how knowledgeable the authors are about the current literature of SW regarding MALDI.

      The tone has been significantly tuned down throughout the revised manuscript.

      Reviewer #2 (Recommendations For The Authors):

      Abstract:

      "relative abundance of structurally identified and yet-undefined metabolites across tissue cryosections" is misleading, since tandem MS can be performed in an imaging context and is often also compatible with the same instrument.

      We have deleted this sentence in the abstract.

      Intro:

      Paragraph 1: The authors mention MALDI and DESI, but I would argue that SIMS is more abundantly used than DESI within single-cell applications.

      We have added SIMS to the introduction Page 3, line 67.

      Paragraph 2: While it may not be all detected pairs, there are many examples of ratio imaging in the MALDI MSI and SIMS communities, particularly for bacterial signaling. These would be important examples to reference.

      We have added the application of SIMS ratio imaging to the introduction, page 3, line 74-75.

      Materials :

      Paragraph 1: More specificity on sample size is required. 3 or 4 per group is not specific. Which has four and which has three? Why are they different?

      We have corrected sample numbers for specific genotype in the text and figure legends. The number of sections per group is different due to the availability of fresh-frozen tissues (Page 4, line 115-117).

      Results:

      Paragraph 1: Am I correct in reading that an .imzml can't be used directly? Why not?

      Imaging Mass Spectrometry Markup Language (imzml) is a common data format for mass spectrometry imaging. It was developed to allow the flexible and efficient exchange of large MS imaging data between different instruments and data analysis software (Schramm et al, 2012). It contains two sets of data: the mass spectral data which is stored in a binary file (.ibd file) to ensure efficient storage and the XML metadata (.imzml file) which stores instrumental parameters, sample details. Therefore, it can’t be used directly. We have added this to result 1(Page 5, line 160-169).

      Paragraph 4: "Additionally, nonlipid small molecule metabolites suffer from smearing and/or diffusion during cryosection processing, including over the course of matrix deposition for MALDI-MSI." This is misleading. There are several examples of MALDI MSI of small metabolites that are nonlipids, where smearing or diffusion have not occurred. It would be beneficial to have a more accurate discussion of this instead. The authors should also provide some evidence of this, since they continue to focus on it for the full paragraph and don't provide references.

      We initially meant the poor image quality of small molecule metabolites is due to its interaction with aqueous phase of spraying solution, rapid degradation rate and matrix interference. We have deleted this sentence in the revised version.

      Section 5 Paragraph 2; "However, ratio imaging revealed a much greater aspartate to glutamate ratio in an unusual "moon arc" region across the amygdala and hypothalamus relative to the rest of the coronal brain." Much greater isn't scientifically accurate or descript. Use real numbers and be quantitative.

      We used pixel data from all 8 sections to obtain quantitative changes in the ratio-generated “moon arc” region compared to the rest of coronal brain (page 8, line 331-337). Ratio imaging revealed a average of 1.59-fold increase in aspartate to glutamate ratio in an unusual “moon arc” region across the amygdala and hypothalamus (mean abundance 0.563 in 6345 pixels) relative to the rest of the coronal brain (mean abundance 0.353 in 45742 pixels, Figure 5D). Similar but different arc-like structures are encompassed within the ventral thalamus and hypothalamus, wherein glutamate to glutamine ratio show a 1.63-fold increase in intensity compared to the rest of the brain (mean abundance of 0.695 in 7108 pixels vs 0.428 in 44979 pixels, Figure 5E).

      Section 8 Paragraph 2: "UMAPing" is not scientifically written.

      We have replaced UMAPing with UMAP.

      Figure 2 is difficult to interpret, given the small sizes of the images. Align the images, reduce the white space, clearly label the different tissues, add scale bars, increase size, etc. This applies to all figures, except for 3. This will make it possible to review.

      All figures have been resized by removing extra space between sections.

      Figure 3. There seems to be a change in tissue after section I, so a different diagram would be helpful. SCD has a high abundance in an area that seems to be off of the tissue. Can the authors explain this? Some of the images also appear to be low signal-to-noise. Example spectra in the SI would be helpful, so I can more accurately judge the quality of the data.

      We apologize for the discrepancy. All images are from the same sample. We initially cropped the individual image from multiple page PDF plot, then inserted it in Figure 3. Resizing and cropping inconsistency may lead to the small difference in image size. In the revised version, we plot all images in one page, which eliminates the inconsistency.

      Figure 3 example pixel data, ratio pixel data, mass spectra and ratio images can be downloaded below:

      https://wcm.box.com/s/2d5jch45ar8upjzytljnylt6doewcsqc

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Reviewer #1 (Public review):

      In this revised manuscript, the authors aim to elucidate the cytological mechanisms by which conjugated linoleic acids (CLAs) influence intramuscular fat deposition and muscle fiber transformation in pig models. They have utilized single-nucleus RNA sequencing (snRNA-seq) to explore the effects of CLA supplementation on cell populations, muscle fiber types, and adipocyte differentiation pathways in pig skeletal muscles. Notably, the authors have made significant efforts in addressing the previous concerns raised by the reviewers, clarifying key aspects of their methodology and data analysis.

      Strengths:

      (1) Thorough validation of key findings: The authors have addressed the need for further validation by including qPCR, immunofluorescence staining, and western blotting to verify changes in muscle fiber types and adipocyte populations, which strengthens their conclusions.

      (2) Improved figure presentation: The authors have enhanced figure quality, particularly for the Oil Red O and Nile Red staining images, which now better depict the organization of lipid droplets (Figure 7A). Statistical significance markers have also been clarified (Figure 7I and 7K).

      Thanks!

      Weaknesses:

      (1) Cross-species analysis and generalizability of the results: Although the authors could not perform a comparative analysis across species due to data limitations, they acknowledged this gap and focused on analyzing regulatory mechanisms specific to pigs. Their explanation is reasonable given the current availability of snRNA-seq datasets on muscle fat deposition in other human and mouse.

      Thanks for your suggestion!

      (2) Mechanistic depth in JNK signaling pathway: While the inclusion of additional experiments is a positive step, the exploration of the JNK signaling pathway could still benefit from deeper analysis of downstream transcriptional regulators. The current discussion acknowledges this limitation, but future studies should aim to address this gap fully.

      Thanks! As we discussed in discussion part, further studies should focus on the downstream transcriptional regulators of JNK signaling pathway on IMF deposition.

      (3) Limited exploration of other muscle groups: The authors did not expand their analysis to additional muscle groups, leaving some uncertainty regarding whether other muscle groups might respond differently to CLA supplementation. Further studies in this direction could enhance the understanding of muscle fiber dynamics across the organism.

      Thanks for your suggestion! In this study, we mainly focused on the adipocytes, muscles and FAPs subpopulations, which play important roles in lipid deposition. As you suggested, our further study will focus on other subpopulations such as endothelial cells and immune cells.

      Reviewer #2 (Public review):

      Summary:

      This study comprehensively presents data from single nuclei sequencing of Heigai pig skeletal muscle in response to conjugated linoleic acid supplementation. The authors identify changes in myofiber type and adipocyte subpopulations induced by linoleic acid at depth previously unobserved. The authors show that linoleic acid supplementation decreased the total myofiber count, specifically reducing type II muscle fiber types (IIB), myotendinous junctions, and neuromuscular junctions, whereas type I muscle fibers are increased. Moreover, the authors identify changes in adipocyte pools, specifically in a population marked by SCD1/DGAT2. To validate the skeletal muscle remodeling in response to linoleic acid supplementation, the authors compare transcriptomics data from Laiwu pigs, a model of high intramuscular fat, to Heigai pigs. The results verify changes in adipocyte subpopulations when pigs have higher intramuscular fat, either genetically or diet-induced. Targeted examination using cell-cell communication network analysis revealed associations with high intramuscular fat with fibro-adipogenic progenitors (FAPs). The authors then conclude that conjugated linoleic acid induces FAPs towards adipogenic commitment. Specifically, they show that linoleic acid stimulates FAPs to become SCD1/DGAT2+ adipocytes via JNK signaling. The authors conclude that their findings demonstrate the effects of conjugated linoleic acid on skeletal muscle fat formation in pigs, which could serve as a model for studying human skeletal muscle diseases.

      Strengths:

      The comprehensive data analysis provides information on conjugated linoleic acid effects on pig skeletal muscle and organ function. The notion that linoleic acid induces skeletal muscle composition and fat accumulation is considered a strength and demonstrates the effect of dietary interactions on organ remodeling. This could have implications for the pig farming industry to promote muscle marbling. Additionally, these data may inform the remodeling of human skeletal muscle under dietary behaviors, such as elimination and supplementation diets and chronic overnutrition of nutrient-poor diets. However, the biggest strength resides in thorough data collection at the single nuclei level, which was extrapolated to other types of Chinese pigs.

      Weaknesses:

      Although the authors compiled a substantial and comprehensive dataset, the scope of cellular and molecular-level validation still needs to be expanded. For instance, the single nuclei data suggest changes in myofiber type after linoleic acid supplementation, but these findings need more thorough validation. Further histological and physiological assessments are necessary to address fiber types and oxidative potential. Similarly, the authors propose that linoleic acid alters adipocyte populations, FAPs, and preadipocytes; however, there are limited cellular and molecular analyses to confirm these findings. The identified JNK signaling pathways require additional follow-ups on the molecular mechanism or transcriptional regulation. However, these issues are discussed as potential areas for future exploration. While various individual studies have been conducted on mouse/human skeletal muscle and adipose tissues, these have only been briefly discussed, and further investigation is warranted. Additionally, the authors incorporate two pig models into their results, but they only examine one muscle group. Exploring whether other muscle groups respond similarly or differently to linoleic acid supplementation would be valuable. Furthermore, the authors should discuss how their results translate to human and pig nutrition, such as the desirability and cost-effectiveness for pig farmers and human diets high in linoleic acid. Notably, while the single nuclei data is comprehensive, there needs to be a statement on data deposition and code availability, allowing others access to these datasets.

      Thanks for your suggestion!

      Recommendations for the authors:

      Reviewer #2 (Recommendations for the authors):

      The authors have discussed and provided some experimental evidence to address the related issues to help justify their conclusions. The reviewer believes that authors should deposit their single-cell sequencing data and code for the broader research community.

      Thank you! We have uploaded our raw dataset in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences and data availability part has been updated (line 575-579).

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This study investigates the role of macrophage lipid metabolism in the intracellular growth of Mycobacterium tuberculosis. By using a CRISPR-Cas9 gene-editing approach, the authors knocked out key genes involved in fatty acid import, lipid droplet formation, and fatty acid oxidation in macrophages. Their results show that disrupting various stages of fatty acid metabolism significantly impairs the ability of Mtb to replicate inside macrophages. The mechanisms of growth restriction included increased glycolysis, oxidative stress, pro-inflammatory cytokine production, enhanced autophagy, and nutrient limitation. The study demonstrates that targeting fatty acid homeostasis at different stages of the lipid metabolic process could offer new strategies for host-directed therapies against tuberculosis.

      The work is convincing and methodologically strong, combining genetic, metabolic, and transcriptomic analyses to provide deep insights into how host lipid metabolism affects bacterial survival.

      Strengths:

      The study uses a multifaceted approach, including CRISPR-Cas9 gene knockouts, metabolic assays, and dual RNA sequencing, to assess how various stages of macrophage lipid metabolism affect Mtb growth. The use of CRISPR-Cas9 to selectively knock out key genes involved in fatty acid metabolism enables precise investigation of how each step-lipid import, lipid droplet formation, and fatty acid oxidation affect Mtb survival. The study offers mechanistic insights into how different impairments in lipid metabolism lead to diverse antimicrobial responses, including glycolysis, oxidative stress, and autophagy. This deepens the understanding of macrophage function in immune defense.

      The use of functional assays to validate findings (e.g., metabolic flux analyses, lipid droplet formation assays, and rescue experiments with fatty acid supplementation) strengthens the reliability and applicability of the results.

      By highlighting potential targets for HDT that exploit macrophage lipid metabolism to restrict Mtb growth, the work has significant implications for developing new tuberculosis treatments.

      Weaknesses:

      The experiments were primarily conducted in vitro using CRISPR-modified macrophages. While these provide valuable insights, they may not fully replicate the complexity of the in vivo environment where multiple cell types and factors influence Mtb infection and immune responses.

      We thank the reviewer for pointing this out. We acknowledge that our in vitro system may indeed not fully replicate the complex in vivo environment given of what is becoming to light of macrophage heterogenous responses to Mtb infection in whole animal models. We do believe, however, that the Hoxb8 in vitro model provides a powerful genetic tool to interrogate host-Mtb interactions using primary macrophages that represent the bone marrow-derived macrophage lineage.

      Reviewer #2 (Public review):

      Summary:

      Host-derived lipids are an important factor during Mtb infection. In this study, using CRISPR knockouts of genes involved in fatty acid uptake and metabolism, the authors claim that a compromised uptake, storage, or metabolism of fatty acid restricts Mtb growth upon infection. Further, the authors claim that the mechanism involves increased glycolysis, autophagy, oxidative stress, pro-inflammatory cytokines, and nutrient limitation. The authors also claim that impaired lipid droplet formation restricts Mtb growth. However, promoting lipid droplet biogenesis does not reverse/promote Mtb growth.

      Strengths:

      The strength of the study is the use of clean HOXB8-derived primary mouse macrophage lines for generating CRISPR knockouts.

      Weaknesses:

      There are many weaknesses of this study, they are clubbed into four categories below

      (1) Evidence and interpretations: The results shown in this study at several places do not support the interpretations made or are internally contradictory or inconsistent. There are several important observations, but none were taken forward for in-depth analysis.

      a) The phenotypes of PLIN2<sup>-/-</sup>, FATP1<sup>-/-</sup>, and CPT-/- are comparable in terms of bacterial growth restriction; however, their phenotype in terms of lipid body formation, IL1B expression, etc., are not consistent. These are interesting observations and suggest additional mechanisms specific to specific target genes; however, clubbing them all as altered fatty acid uptake or catabolism-dependent phenotypes takes away this important point.

      We thank the reviewer for highlighting this. Our focus was on assessing the impact of manipulating lipid homeostasis in macrophages at several stages and the consequences this has on the intracellular growth of Mtb. Throughout the manuscript (abstract, results and discussion), we have continuously emphasized that interfering with lipid handling at several stages in macrophages results in both conserved and divergent antimicrobial responses against intracellular Mtb.

      b) Finding the FATP1 transcript in the HOXB8-derived FATP1<sup>-/-</sup> CRISPR KO line is a bit confusing. There is less than a two-fold decrease in relative transcript abundance in the KO line compared to the WT line, leaving concerns regarding the robustness of other experiments as well using FATP1<sup>-/-</sup> cells.

      CRISPR-Cas9 targeting of genes with single sgRNAs as is the case with our mutants generates insertions and deletions (INDELs) at the CRISPR cut site. These INDELs do not block mRNA transcription totally, and this is widely reported in the field.  Because of this, quantitative RT-PCR or RNA-seq methods are not routinely used to verify CRISPR knockouts as they are not sensitive enough to identify INDELs. We provide INDEL quantification and knockout efficiencies by ICE analysis in supplemental file 1 for all the mutants used in the study. We also demonstrate protein depletion by western blot and flow cytometry for all the mutants (Figure 1 - figure supplement 1). Only mutants with greater than >90% protein depletion were used for subsequent characterization.

      c) No gene showing differential regulation in FATP<sup>-/-</sup> macrophages, which is very surprising.

      We assume the reviewer is referring to the Mtb transcriptome response in FATP1<sup>-/-</sup> macrophages, which we agree was unexpected.  However, we saw a significant compensatory response in the host cell (at transcriptional level) in FATP1<sup>-/-</sup> macrophages as evidenced by an upregulation of other fatty acid transporters (Figure 5 - figure supplement 1, now Figure 6 - figure supplement 1). We believe that these compensatory responses could, in part, alleviate the stresses the bacteria experience within the cell. We discuss this point in the manuscript.

      d) ROS measurements should be done using flow cytometry and not by microscopy to nail the actual pattern.

      We thank the reviewer for the suggestion. However, confocal imaging is also widely used to measure ROS with similar quantitative power and individual cell resolution (PMID: 32636249, 35737799).

      (2) Experimental design: For a few assays, the experimental design is inappropriate

      a) For autophagy flux assay, immunoblot of LC3II alone is not sufficient to make any interpretation regarding the state of autophagy. This assay must be done with BafA1 or CQ controls to assess the true state of autophagy.

      We would like to point out that monitoring LC3I to LC3II conversion by western blot, confocal imaging of LC3 puncta and qPCR analysis of autophagy related genes are all validated assays for monitoring autophagic flux in a wide variety of cells. We refer the reviewer to the latest extensive guidelines on the subject (PMID: 33634751). Furthermore, Bafilomycin A and chloroquine are not specific inhibitors of autophagy and therefore are of limited value as controls. BafA is an inhibitor of the proton-ATPase apparatus and can indirectly impact autophagy through activity on the Ca-P60A/SERCA pathway. Chloroquine impacts vacuole acidification, autophagosome/lysosome fusion and slows phagosome maturation. So, while BafA and chloroquine will reduce autophagy; their effects are pleotropic and their impact on Mtb is unknown.

      b) Similarly, qPCR analyses of autophagy-related gene expression do not reflect anything on the state of autophagy flux.

      See our response above.

      (3) Using correlative observations as evidence:

      a) Observations based on RNAseq analyses are presented as functional readouts, which is incorrect.

      We are not entirely sure where we used our RNA-seq data sets as functional readouts. We used our transcriptome data to provide a preliminary identification of anti-microbial responses in the mutant macrophages infected with Mtb and we mention this at the beginning of the RNA-seq results sections. Where applicable, we followed up and confirmed the more compelling RNA-seq data either by metabolic flux analyzes, qPCR, ROS measurements, and quantitative imaging.

      b) Claiming that the inability to generate lipid droplets in PLIN2<sup>-/-</sup> cells led to the upregulation of several pathways in the cells is purely correlative, and the causal relationship does not exist in the data presented.

      It was not our intention to infer causality. We have re-written the beginning of the sentence, and it now starts with “Meanwhile, Mtb infection of PLIN2<sup>-/-</sup> macrophages led to upregulation” which hopefully eliminates any association to causality.

      (4) Novelty: A few main observations described in this study were previously reported. That includes Mtb growth restriction in PLIN2 and FATP1 deficient cells. Similarly, the impact of Metformin and TMZ on intracellular Mtb growth is well-reported. While that validates these observations in this study, it takes away any novelty from the study.

      To the best of our knowledge, Mtb growth restrictions in PLIN2 and FATP1 deficient macrophages have not been reported elsewhere. To the contrary, PLIN2 knockout macrophages obtained from PLIN2 deficient mice have been reported to robustly support Mtb replication (PMID: 29370315). We extensively discuss these discrepancies in the manuscript. We also discuss and cite appropriate references where Mtb growth restriction for similar macrophage mutants have been reported (CD36<sup>-/-</sup> and CPT2<sup>-/-</sup>). Our aim was to carry out a systematic myeloid specific genetic interference of fatty acid import, storage and catabolism to assess the effect on Mtb growth at all stages of lipid handling instead of focusing on one target. In the chemical approach, we used TMZ and Metformin deliberately because they had already been reported as being active against intracellular Mtb and we wished to place our data in the context of existing literature.  These studies have been referenced extensively in the text.

      (5) Manuscript organisation: It will be very helpful to rearrange figures and supplementary figures.

      New figures have been added, and existing ones have been re-arranged where necessary. See our responses to recommendations for authors.

      Reviewer #3 (Public review):

      Summary:

      This study provides significant insights into how host metabolism, specifically lipids, influences the pathogenesis of Mycobacterium tuberculosis (Mtb). It builds on existing knowledge about Mtb's reliance on host lipids and emphasizes the potential of targeting fatty acid metabolism for therapeutic intervention.

      Strengths:

      To generate the data, the authors use CRISPR technology to precisely disrupt the genes involved in lipid import (CD36, FATP1), lipid droplet formation (PLIN2), and fatty acid oxidation (CPT1A, CPT2) in mouse primary macrophages. The Mtb Erdman strain is used to infect the macrophage mutants. The study, reveals specific roles of different lipid-related genes. Importantly, results challenge previous assumptions about lipid droplet formation and show that macrophage responses to lipid metabolism impairments are complex and multifaceted. The experiments are well-controlled and the data is convincing.

      Overall, this well-written paper makes a meaningful contribution to the field of tuberculosis research, particularly in the context of host-directed therapies (HDTs). It suggests that manipulating macrophage metabolism could be an effective strategy to limit Mtb growth.

      Weaknesses:

      None noted. The manuscript provides important new knowledge that will lead mpvel to host-directed therapies to control Mtb infections.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      The study presents compelling and well-supported conclusions based on a solid body of evidence. However, the clarity of several figures could be improved for better understanding.

      (1) In Figure 1, panels B and C are referenced incorrectly in the text.

      We thank the reviewer for identifying the error. This has now been corrected

      (2) Figures 2 and S2 would benefit from being combined or reorganized to display the data related to infected and uninfected cells together, making it easier for the reader to interpret.

      We thank the reviewer for the suggestion. However, we believe that combining the two figures would further complicate the merged figure making it even more difficult to interpret. We decided to highlight the mutant macrophage’s responses upon Mtb infection in Figure 2 and put the uninfected data sets in supplementary information given that the OCR and ECAR trends were similar and as expected in both infected and uninfected states.

      (3) Figure 3 is mislabeled, with four panels shown in the figure, but only panels A and B are mentioned in both the text and the figure legend.

      We thank the reviewer for the observation. Figure 3 has been extensively revised. We have included new blots, statistical comparisons and a corresponding new supplementary figure (Figure 3 - figure supplement 1). We have verified that the figure panels are labelled correctly and appropriately referenced in the manuscript text.

      (4) Figure 5 is overly complex and difficult to interpret. Simplifying the figure, possibly by reducing the amount of data or breaking it into more digestible parts, would enhance its readability.

      We thank the reviewer for the suggestion. We have separated the figure into two parts which are now Figure 5 for the PCA and Venn diagrams and Figure 6 for the pathway enrichment figure panels. We have increased the resolution of both figures in the revised manuscript to improve readability.

      (5) Panel 6A is not particularly informative and could either be omitted with a more detailed explanation provided in the text, or replaced with a clearer visual representation, such as Venn diagrams, to improve data visualization.

      We thank the reviewer for the suggestion. We have removed Figure 6A given that detailed explanation of the panel is already available in the manuscript text.

      (6) Additionally, on line 309, the word "to" is missing before "generate".

      We thank the reviewer for identifying this. This sentence has now been re-written to address some unintended inferences of causation in line with recommendations from reviewer 2.

      Reviewer #2 (Recommendations for the authors):

      (1) Manuscript Organisations: The manuscript is very poorly organised. Supplemental figures are labelled very unconventionally, and that creates much confusion in following the manuscript. Some of the results in the supplementary figures could be easily kept in the main figures, as it is difficult to compare plots between the main figures and the supple figures. The results of RNAseq experiments are impossible to follow with very small fonts. Overall, the figures are very casually organised and can certainly be improved.

      We would like to clarify that supplemental figures are labelled and organized as is in line with the eLife formatting of supplemental figures. We deliberately put some redundant figures like Figure 2 - figure supplement 1 in supplementary information (see our response to reviewer 1 recommendations on the same). We have split the RNA-seq Figure 5 into two separate figures (now Figure 5 and 6) and increased their resolution to improve readability.

      (2) Figure 3: Among the KO lines, only PLIN2<sup>-/-</sup> had a higher HIF1a level before infection. Infection surely leads to higher levels across the three cases.

      We have generated replicate western blots and provide statistical quantitation for both HIF1a, AMPK and pAMPK. Figure 3 has now been revised extensively, replicate blots are in Figure 3 - figure supplement 1. We have updated the text to reflect the reviewer observation which was also consistent with our statistical quantification.

      (3) pAMPK blots are of very poor quality. Without quantification, the trend mentioned in the text is not clearly visible.

      We have provided two more replicate blots for AMPK/pAMPK and provide statistical quantification as described above.

      (4) Line 230: Regarding autophagy flux, neither the data suggest what is interpreted nor is this experiment correctly done. LC3 WB and autophagy gene qPCR: Unfortunately, LC3 WB, the way it was done, does not tell anything about the state of autophagy in these cells. A very mild LC3II increase is noted in CPT2<sup>-/-</sup> cells upon infection; the rest of the others do not show any change. This assay is not done correctly. To interpret LC3II WB, one needs to include the Bafilomycin A1 control, usually +Baf and -Baf run in the adjacent wells in the gel. Similarly, qPCR results are not indicative of any increase in autophagy. Regulation of ATG7, MAP1LC3B, and ULK1 is more at the post-translational level than the transcriptional level.

      We have provided an additional replicate blot together with statistical quantification of LC3II/LC3I ratios in the revised Figure 3 - figure supplement 2. Our quantifications remain consistent with our prior assertations in the manuscript text. See our response in the public review section concerning autophagy assays and the use of Baf or chloroquine as controls.

      (5) Exogenous oleate fails to rescue the Mtb icl1-deficient mutant in FATP1<sup>-/-</sup>, PLIN2<sup>-/-</sup> and CPT2<sup>-/-</sup> macrophages: this result is confusing. Lipid uptake and metabolism have been the central players so far; however, here, the phenotypes of FATP1 and CPT2 in terms of lipid body accumulation are very distinct. Therefore, the assessment that Mtb growth inhibition is due to factors other than limited access to fatty acid is not consistent with the theme of the study.

      Nutrient limitation is a distinct transcriptional signature of Mtb, at least in PLIN2<sup>-/-</sup> macrophages (Figure 7). We used the oleate supplementation assay with the Mtb Dicl1 mutant to assess whether nutrient restriction was the sole anti-microbial pathway against Mtb in the knockout macrophages. This would have been the case (to a certain extent) if the growth of the Mtb Dicl1 mutant was rescuable upon addition of exogenous oleate in the knockout macrophages. Our data clearly shows that this is not the case and that in addition to nutrient limitation, interference with lipid processing results in several other macrophage anti-microbial responses against the bacteria. We extensively discuss these points in the abstract, results and discussion sections of the manuscript.

      (6) Line 309: "Meanwhile, inability generate lipid droplets in Mtb infected PLIN2<sup>-/-</sup> macrophages led to upregulation in pathways involved in ribosomal biology, MHC class 1 antigen presentation, canonical glycolysis, ATP metabolic processes and type 1 interferon responses (Figure 5C, Supplementary file 3)." This is just a correlative observation. However, it is mentioned here as a causal mechanism.

      We have revised this sentence to remove any unintended inference of causation.

      (7) IL-1b is upregulated in FATP-/- macrophages, no effect in CPT2<sup>-/-</sup> macrophages, but downregulated in PLIN2<sup>-/-</sup> macrophages. Moreover, this effect is very transient, and by 24 hours, all these differences are lost. This suggests the mechanism of action, as their pro-bacterial function shown in Figure 1, is very distinct for different proteins, and FA metabolism is probably not the common denominator across these phenotypes.

      We agree with the reviewer, and we extensively discuss this in the manuscript text (results and discussion). Clearly, they are shared anti-microbial responses across the mutants, but they are also points of divergence. We would like to further clarify that pro-inflammatory responses (IL-1b or IFN-B) in Mtb infected macrophages show a biphasic early upregulation (up to 8 hours of infection) followed by a rapid resolution phase (24-48 hours post infection). This is well reported in the literature (PMID: 30914513). It is common for pro-inflammatory gene expression differences to be temporary lost during the resolution phase (PMID: 30914513, 39472457). IL-1b expression profiles return to the 4-hour equivalent profile in Mtb infected FATP1<sup>-/-</sup> and PLIN2<sup>-/-</sup> macrophages 4 days post infection (Figure 6A, Figure 6 - figure supplement 2B, Supplementary file 2)

      (8) It is very surprising that FATP-/- macrophages do not show any change in Mtb gene expression. The robustness of this experiment and analysis appears doubtful, given that the phenotype in terms of bacterial growth was clean.

      See our response to this comment in the public reviews section

      (9) Figure 5, Supplementary Figure 1: Among the FA transporters, authors also show data for FATP1. I am surprised to see FATP1 expression levels in the FATP1<sup>-/-</sup> cells. This puts into doubt every dataset using FATP-/- cells in this study.

      See our response to this comment in the public reviews section

      (10) Unfortunately, with the kind of evidence presented, it is far-fetched to claim that PLIN2<sup>-/-</sup> macrophages restrict Mtb growth by increasing ROS production. There is no evidence for this statement. The MFI units in Figure 6, Supplementary 1 are too small to extract meaningful interpretations. Moreover, the data appears to be arrived at by combining multiple technical replicates. Usually, flow cytometry data are more reliable for CellROX assays. Microscopy is not the technique of choice for this assay.

      We would like to point out that MFIs are arbitrary units set to predetermined reference points. In our case, the reference was background fluorescence in CellROX unstained cells and cells stained with CellROX equivalent fluorophore conjugated isotype antibodies. We are not entirely sure what the reviewer means by “small” in these contexts. And the data is not entirely from technical replicates. Reported MFIs are from three independent repeats with MFI reads of at least 30 cells per replicate. We have added this clarification in Figure 6 - figure supplement 1 legend, now Figure 7 - figure supplement 1. See our response in the public reviews section on the use of confocal microcopy to image and quantify ROS. Furthermore, the Mtb transcriptional response in PLIN2<sup>-/-</sup> and CPT2<sup>-/-</sup> macrophages is clearly indicative of increased oxidative stresses (Figure 7).

      (11) The CFU results with Metformin and TMZ are on the expected lines, as published earlier by others. FATP1 In data is good and aligned with the knockout phenotype.

      We thank the reviewer for the note.

      (12) Western blots, when interpreted for quantitative differences, must be quantified, and data should be represented as plots with statistical analysis.

      Replicate blots have been provided and statistical quantifications performed.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public reviews

      Reviewer #1 (Public review):

      Overall I find the evidence very well presented and the study compelling. It offers an important new perspective on the key properties of neoblasts. I do have some comments to clarify the presentation and significance of the work.

      We thank the reviewer for the positive feedback and plan to improve the presentation of the work.

      Reviewer #2 (Public review):

      However, the absence of a cell-cell feedback mechanism during colony growth and the likelihood of the difference needs to be clarified. Is there any difference in interpreting the results if this mechanism is considered?

      We will improve the description of the model assumptions and the interpretation of the data on the basis of these assumptions.

      Although hnf-4 and foxF have been silenced together to validate the model, a deeper understanding of the tgs-1+ cell type and the non-significant reduction of tgs-1+ neoblasts in zfp-1 RNAi colonies is necessary, considering a high neural lineage frequency.

      We will improve the analysis of this result in light of the experimentally determined frequency of the tgs-1+ neoblast population.

      Recommendations for the authors

      Reviewing Editor Comments:

      After consultation, we have compiled a list of the key changes to be made to the manuscript, along with reviewer-specific recommendations to follow.

      (1) Include a section that explicitly describes the assumptions and limitations of the study, particularly with respect to the following assumptions:

      We thank the reviewers for the comment. We added a description of the model assumptions in the methods section “Assumptions underlying neoblast colony growth model”.

      a) All known types of specialized neoblasts cycle at the same rate (see points from Reviewer 1).

      We thank the reviewers for the comment. The current data used to estimate τ (Lei et al., Dev Cell, 2016) does not allow the direct estimation of individual cycling behaviors. Consequently, we assume that all specialized neoblasts cycle at the same average rate, a simplification supported by the model's accurate prediction of colony growth.

      b) The assumption that any FSTF-like gene would behave like zfp1 or foxF and hnfA genes. The manuscript does not mention that there may be fundamental differences among these different FSTFs that could be uncovered by future work. A strong addition to the paper would be to test other epithelial genes (e.g. p53, chd4, egr5) to show reproducible behavior within a single lineage.

      We thank the reviewers for the comment. Colony size reduction following inhibition of Smed-p53 and failure to produce epidermal progenitors is strongly supported by previous analysis (Wagner et al., Cell Stem Cell, 2012). We refer to this observation in the paper in the section titled: “Inhibition of zfp-1 does not induce overexpression of other lineages in homeostasis”. We added the following sentence to the discussion (Line 460-462): Interestingly, suppression of Smed-p53, a TF expressed in neoblasts and required for epidermal cell production, has resulted in a similar reduction in colony size (Wagner et al., Cell Stem Cell, 2012).

      Of note, Chd4 expression is not limited to specialized neoblasts or to a specific lineage (Scinome et al., Development, 2010), and therefore its inhibition likely has a more complex outcome than an effect on a single lineage. Furthermore, egr-5 is not expressed in neoblasts (Tu et al, eLife, 2015), making this experimental condition more challenging to examine in the context of neoblast colonies at the time points assessed in this study.

      c) The fact that the data used to feed the model relies on radiated animals which are likely to have altered cell cycle rates compared to unirradiated animals (see comment by Reviewer 1). Of note, the model predicts a steady increase in colony size, but colony size does not change between 9dpi and 12dpi.

      We thank the reviewers for the comment. The colony size in control animals increased between 9 and 12 dpi (Fig 3B), as predicted by the model. In zfp-1 (RNAi) animals, the median colony size has also increased over this period, at a slower rate, which we attribute to the increase in q. We attribute the unchanged average colony size to an increase in the frequency of cells failing to proliferate, because of selection of a fate they cannot fully differentiate into.

      d) In light of both reviewers' comments about colony expansion vs. feedback, the authors should discuss how predicted changes to division frequencies might change as homeostasis is reached, or explain how their model accounts for the predicted rate differences under homeostatic conditions in which overall neoblast numbers do not change. Can the model estimate when this transition might occur?

      We thank the reviewers for the comment. Our colony assays are constrained by the animals survival following sub-total irradiation (16 to 20 days). In this timeframe, the neoblast population is overwhelmingly smaller in comparison to non-irradiated animals. Therefore, the animals do not reach homeostasis during the experiment, and the model does not allow to estimate the time the system would need to return to homeostasis.

      (2) In Figure 2D, the assumption is that these adjacent smedwi-1+ cells are sisters. Previous data analyzing this relied on EdU or H3P staining to show a shared division history. When these images were collected is therefore extremely critical to include (the methods suggest 7, 9, or 12 days). The authors should justify why they believe that these adjacent cells are derived from a single neoblast that has divided only once.

      We thank the reviewers for the comment. The images were collected at 7 dpi. We modified the figure legend and the associated methods to include this information. At this early time point, smedwi-1+ cell dyads are spatially separated from other neighboring cells, suggesting that they are the product of a single cell division. Importantly, our data is in complete agreement with previous estimates of symmetric renewal division rate (Raz et al., Cell Stem Cell, 2021; Lei et al, Developmental Cell, 2016).

      (3) Clarify the wording 'pre-selected' in the abstract as described by Reviewer 1.

      We thank the reviewers for the comment, and for clarity we replaced the wording “pre-select” with “select”. 

      (4) Experimental details that are important to the interpretation should be added. For example, how is belonging to a colony defined? This is important because some of the data (e.g. Figure S1A: similar numbers of smedwi-1+ cells are observed at 2dpi and 4dpi, but 4dpi is considered a colony whereas 2dpi is not). The timing of quantification should be included in each figure (it is missing in Figure S2, and Figure 3C and 3D). How the authors distinguish biological vs technical replicates is not mentioned.

      We thank the reviewers for the comment. Subtotal irradiation may result in formation of a spatially-isolated cluster of neoblasts that is not distributed throughout the animal (Wagner et al., Science, 2011). This localized cluster of neoblasts is defined as a neoblast colony (Wagner et al., Science, 2011; Wagner et al., Cell Stem Cell, 2012). The small number of high smedwi-1+ cells observed at 4 dpi in our experiments aligns with this definition (Fig S1A). By contrast, the low smedwi-1 expression detected across the animal 2 dpi does not fit this definition and likely reflects remnants of dying neoblasts resulting from irradiation. The following text was added to the figure legend: “isolated cells expressing low levels of smedwi-1+ were scattered in the planarian parenchyma, likely reflecting remnants of dying neoblasts”.

      (5) Figure 5F appears to use SMEDWI-1 antibody (based on capital letters and increased signal in the brain). Is this the case? The methods do not mention the use of a SMEDWI-1 antibody, and the text indicates that these are progenitors, but SMEDWI-1 protein is well known to not mark neoblasts. If the antibody was used, the authors should not claim that these are neoblasts.

      We thank the reviewers for the comment. The SMEDWI-1 antibody used in the experiments described in Figure 5F indeed labels neoblasts and their progeny (Guo et al., Developmental cell, 2006). The methods section “Immunofluorescence combined with FISH” details the labeling procedure, which combines FISH and IF using this antibody.

      All microscopy images are difficult to see. Perhaps this is because they are formatted as CMYK images. They should be converted to RGB format to make them appear less dull.

      We thank the reviewer for the comment. Improved version of the figures has now been uploaded.

      The terminology used in Figure 5 to describe upregulation should not be "overexpression".  We thank the reviewers for the comment.

      We changed the terminology to “upregulated”.

      Reviewer #1 (Recommendations for the authors):

      I think the authors should include a section that explicitly lays out the assumptions and limitations of the study. For example, I believe that determining tau requires assuming that all different types of specialized neoblasts cycle at the same rates. Also there is the assumption that any FSTF-like gene would behave like zfp1 or foxF and hnfA genes. It seems to remain possible that a future study could find that a subset of FSTFs might indeed exert "either/or" decisions in fating, just not the particular genes under investigation here.

      We thank the reviewer for the comment. We added a description of the model assumptions in the methods section.

      In the abstract, the wording "pre-selected" is somewhat puzzling to me. I would interpret a preselection as a process that defines the next specified state prior to its manifestation. Instead, and as I understand the authors argue this as well, the study provides good evidence that the determination mechanism is random in that subsequent neoblast choices do not likely depend on prior states. So I would suggest changing that wording.

      We thank the reviewer for the comment. We replaced “pre-select” with “select”

      Is it possible to determine the uncertainty in measuring tau the cell cycle time and would this have an impact on subsequent modeling?

      We thank the reviewers for the comment. The current data that was used to estimate tau (Lei et al., Dev Cell, 2016) does not allow us to directly estimate the uncertainty in measuring τ.

      For lines 154-164 I would suggest doing a little more to explicitly write out the logic of determining the growth constants within the main text and not just in methods, for ease of reading.

      We thank the reviewer for the comment, and added explanations for how we determined the growth constant in the text. The text now reads (lines 160-166): “Considering an average cell cycle length of 29.7 hours, we calculated the value of q using the following approach: the probabilities of all cell division outcomes must sum to 1. Our experimental data showed that symmetric renewal (p) and asymmetric division (a) occur at equal rates (i.e., p = a). By fitting these parameters to the experimental data, we determined that the difference between the probabilities of symmetric renewal and symmetric differentiation (i.e., p - q) was = 0.345 (Fig 2E, S1D-E). Therefore, with these criteria, we estimated the probabilities of cell division outcomes in the colony as p = 0.45, a = 0.45, and q = 0.1 (Fig 2G; Methods).”

      Line 192 why does post-mitotic progeny number linearly relate to neoblast number? In clones, a change in q has an exponential effect. I feel like I am missing something.

      We thank the reviewer for the comment. In colonies, 50% of cell divisions result in the production of post-mitotic progeny (asymmetric division). Therefore, the number of produced progenitors in a given cell cycle is linearly correlated with the number of neoblasts. This statement is in line with previous analysis of planarian colony size (Wagner et al., Cell Stem Cell, 2012).

      Line103 it also seems possible, although less likely, that the specified state is not fixed within a given cell cycle and could be that cells that try to switch into zeta-neoblasts mid-cell cycle arrest in proliferation etc just for that time.

      We thank the reviewer for the comment and agree that this is a possibility. However, our observations suggest that incorporating this factor into the model is unnecessary for accurately predicting colony size.

      In terms of the feedback mechanism proposed to operate in homeostasis, I think in the case of zfp-1 it is quite likely that loss of epidermal differentiation results in wound responses (this phenomenon has been documented in egr-5 RNAi in Tu et al 2015 I believe). This could play out differently in the clone assay because the effects of sublethal irradiation on this process would predominate in both control versus zfp1(RNAi) conditions.

      We thank the reviewer for the comment. Our RNA-seq analysis following zfp-1 inhibition did not show overexpression of injury-induced genes at an early time point (6 days; Fig. 5B-C). However, an increase in cycling cells was detected much earlier via EdU labeling (3 days; Fig. 5D). In the case of egr-5 suppression, Tu et al. analyzed injury-induced gene expression at a later stage (21 days of RNAi), where they found significant epidermal defects (see Fig. 5C in Tu et al.). We agree that sublethal irradiation effects likely predominate in colony analysis for both control and zfp-1 (RNAi) animals. In homeostasis, additional factors likely influence cell proliferation and differentiation.

      It seems likely that some of the differences noted between homeostasis versus clone growth could ultimately arise from the different growth parameters under each setting. Could the rate parameters be estimated from prior data in homeostasis as well? It seems to me that with the framework the authors use, homeostasis must involve a net zero change to neoblast abundance (also shown by Wagner 2011 by the sigmoidal curve of neoblast abundance at the endpoint of clone expansion). Therefore, in these conditions p=q by definition. Experimental evidence from Lei 2016 (Figure S7M) suggests asymmetric divisions and symmetric renewing divisions are about equally abundant (5/12 41% sym renewing vs 7/12 69% asymmetric renewing). Therefore, under homeostasis, there would be an estimated p=q=0.3 and a=0.4. Compared to clone growth conditions then, in homeostasis, it seems that roughly the rate of symmetric renewal decreases and the rate of symmetric differentiation also increases. I wonder, could this kind of difference potentially account for the differences between homeostasis versus clone expansion settings? It is also worth noting that the clone expansion context has been used as a sensitized genetic background for identifying effects of gene inhibition on neoblast self-renewal, so perhaps the reason this works is that the rates of selfrenewal are relatively less in homeostasis so that clone expansion represents a case where there is greater demand for self-renewal.

      We thank the reviewer for the comment. We agree that under homeostatic conditions, where the population size remains stable, the average probability of symmetric renewal matches the average probability of symmetric differentiation or elimination. By contrast, during colony expansion, the probability of symmetric renewal exceeds that of symmetric differentiation or elimination. The differences in response to a lineage block between homeostasis and colony expansion can have multiple interpretations. However, data from homeostatic animals does not permit the analysis of individual neoblasts or their specific responses to a lineage block. Consequently, we cannot determine whether the proliferative response following the lineage block during homeostasis is a direct response to the lineage block or an indirect effect resulting from changes in other neoblasts. We discuss these possibilities further in lines 472 - 484.

      In terms of the memory effect, I recall some arguments presented in the Raz 2021 study that were consistent with a slight memory for neoblast specification being retained. I believe this was a minor point from detecting a slightly higher likelihood of identifying 2-cell clones that both took on prog1+ identity compared to the population average. If this is the case, it may be worth the authors commenting on reconciling those observations with their model.

      We thank the reviewer for their comment. Raz et al. (Cell Stem Cell, 2021) reported that in the asymmetric division of a zeta-neoblast, which generates a prog-2+ cell and a neoblast, there was a slightly higher observed frequency of zfp-1 expression in the neoblast compared to the expected rate (Expected: 32%, Observed: 44%). This small increase may reflect a mild memory effect, experimental variability, or both. However, statistical analysis using Fisher's exact test yielded a non-significant p-value (p = 0.1), suggesting that this difference could be attributed to experimental variability. Other data from Raz et al., such as lineage representation in early colonies, also did not show significant memory effects, indicating that any such effects, if present, are minimal and difficult to detect. Therefore, while we do not, and cannot, rule out the presence of minor memory effects, we expect that effects of this magnitude will have minimal impact on our model.

      Reviewer #2 (Recommendations for the authors):

      Figure 2C and 2D:

      Please provide the specific time points for the data presented.

      We thank the reviewer for the comment. The information was added to the figure legend.

      Colony growth and homeostasis:

      It would be beneficial to estimate a time point at which colony growth transitions to a model with a cell-cell feedback mechanism, similar to that observed in homeostasis. This would help in understanding the dynamics and timing of these processes.

      We thank the reviewers for the comment. Our colony assays were constrained by the animals survival following sub-total irradiation (16 to 20 days). Neoblast numbers are substantially reduced compared to unirradiated animals, preventing us from determining the time point at which homeostasis is achieved.

      Methods:

      μl should be μL  

      The text was changed accordingly.

      Line 526: H2O should be H2O

      The text was changed accordingly.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This well-written report uses functional neuroimaging in human observers to provide convincing evidence that activity in the early visual cortex is suppressed at locations that are frequently occupied by a task-irrelevant but salient item. This suppression appears to be general to any kind of stimulus, and also occurs in advance of any item actually appearing. The work in its present form will be valuable to those examining attention, perception, learning and prediction, but with a few additional analyses could more informatively rule out potential alternative hypotheses. Further discussion of the mechanistic implications could clarify further the broad extent of its significance. 

      We thank the editor and the reviewers for the positive evaluation of our manuscript and the thoughtful comments. Below we provide a detailed point-by-point reply to the reviewers’ comments.

      In addition to addressing the reviewers' comments, we have improved the figure legends by explicitly describing the type of error bars depicted in the figures, information which was previously only listed in the Materials and Methods section. Specifically, the statement: “Error bars denote within-subject SEM” was added to several figures, as applicable. We believe that briefly reiterating this information in the figure legends enhances clarity and enables readers to interpret the results more accurately and efficiently. We also updated our code and data sharing statement, as well as opened the repository for the public: “Analysis and experiment code, as well as data required to replicate the results reported in this manuscript are available here: https://doi.org/10.17605/OSF.IO/G4RXV. Raw MRI data is available upon request.”

      Public Reviews

      Reviewer #1 (Public review): 

      Summary: 

      The authors investigated if/how distractor suppression derived from statistical learning may be implemented in early visual cortex. While in a scanner, participants conducted a standard additional singleton task in which one location more frequently contained a salient distractor. The results showed that activity in EVC was suppressed for the location of the salient distractor as well as for neighbouring neutral locations. This suppression was not stimulus specific - meaning it occurred equally for distractors, targets and neutral items - and it was even present in trials in which the search display was omitted. Generally, the paper was clear, the experiment was well-designed, and the data are interesting. Nevertheless, I do have several concerns mostly regarding the interpretation of the results. 

      (1) My biggest concern with the study is regarding the interpretation of some of the results. Specifically, regarding the dynamics of the suppression. I appreciate that there are some limitations with what you might be able to say here given the method but I do feel as if you have committed to a single interpretation where others might still be at play. Below I've listed a few alternatives to consider. 

      We agree with the reviewer that there are important alternatives to consider. Adequately addressing these alternatives will substantially increase the inferences we can draw from our data. Therefore, we address each alternative interpretation in detail below.

      (a) Sustained Suppression. I was wondering if there is anything in your results that would speak for or against the suppression being task specific. That is, is it possible that people are just suppressing the HPDL throughout the entire experiment (i.e., also through ITI, breaks, etc., rather than just before and during the search). Since the suppression does not seem volitional, I wonder if participants might apply a blanket suppression to HPDL un l they learn otherwise. Since your localiser comes a er the task you might be able to see hints of sustained suppression in the HPDL during these trials.  

      It is indeed possible that participants suppressed the HPDL throughout the entire experiment, instead of proactively instantiating suppression on each trial. While possible, we believe that this account is less likely to explain the present results, given the utilized analysis approach, a voxel-wise GLM fit to the BOLD data per run (see Materials and Methods for details). Specifically, we derived parameter estimates from this GLM per location to estimate the relative suppression. Sustained suppression would modulate BOLD responses throughout the run, i.e. presumably also during the implicit baseline period used to estimate the contrast parameter estimates per location. Hence, sustained suppression should not result in a differential modulation between locations, as the BOLD response at the HPDL during the baseline period would be equally suppressed as during the trial. Inspired by the reviewer’s comment, we now clarify this critical point in the manuscript’s Discussion section:

      “Third, participants might have suppressed the HPDL consistently throughout the experiment. This sustained suppression account differs from the proactive suppression proposed here. While this alternative is plausible, we believe that it is less likely to account for the present results, given the analysis conducted. Specifically, we computed voxel-wise parameter estimates and contrasted the obtained betas between locations. Under a sustained suppression account, the HPDL would show suppression even during the implicit baseline period, which would obscure the observed BOLD suppression at and near the HPDL.” 

      (b) Enhancement followed by suppression. Another alternative that wasn't discussed would be an initial transient enhancement of the HPDL which might be brought on by the placeholders followed by more sustained suppression through the search task. Of course, on the whole this would look like suppression, but this still seems like it would hold different implications compared to simply "proactive suppression". This would be something like search and destroy however could be on the location level before the actual onset of the search display.  

      R1 correctly points out that BOLD data, given the poor temporal resolution, do not allow for the detection of potential transient enhancements at the HPDL followed by a later and more pronounced suppression (akin to “search and destroy”). We fully agree with this assessment. However, we also argue that a transient enhancement followed by sustained suppression before search display onset constitutes proactive suppression in line with our interpretation, because suppression would still arise proactively (i.e., before search, and hence distractor, onset). Whether transient enhancement precedes suppression cannot be elucidated by our data, but we believe that it constitutes an interesting avenue for future studies using me-resolved and spatially specific recording methods. We now clarify this important implementational variation in the updated manuscript.

      “Finally, due to the limited temporal resolution of BOLD data, the present data do not elucidate whether the present suppression is preceded by a brief attentional enhancement of the HPDL, as implied by some prior work (Huang et al., 2024). On this account the HPDL would see transient enhancement, followed by sustained suppression, akin to a ‘search and destroy’ mechanism. Critically, we believe that this variation would nonetheless constitute proactive distractor suppression as the suppression would still arise before search onset. Using temporally and spatially resolved methods to explore potential transient enhancements preceding suppression is a promising avenue for future research charting the neural mechanisms underlying distractor suppression.”

      (2) I was also considering whether your effects might be at least partially attributable to priming type effects. This would be on the spatial (not feature) level as it is clear that the distractors are switching colours. Basically, is it possible that on trial n participants see the HPDL with the distractor in it and then on trial n+1 they suppress that location. This would be something distinct from the statistical learning framework and from the repetition suppression discussion you have already included. To test for this, you could look at the trials that follow omission or trials. If there is no suppression or less suppression on these trials it would seem fair to conclude that the suppression is at least in part due to the previous trial. 

      We agree with the reviewer that it is plausible that participants particularly suppress locations which on previous trials contained a distractor. To address this possibility, we conducted a new analysis and adjusted the manuscript accordingly:

      “Second, participants may have suppressed locations that contained the distractor on the previous trial, reflecting a spatial priming effect. This account constitutes a complementary but different perspective than statistical learning, which integrates implicit prior knowledge across many trials. We ruled out that spatial priming explains the present results by contrasting BOLD suppression magnitudes on trials with the distractor at the HPDL and trials where the distractor was not at the HPDL on the previous trial. Results, depicted in Supplementary Figure 4 showed that distractor suppression was statistically significant across both trial types, including trials without a distractor at the HPDL on the preceding trial. This indicates that the observed BOLD suppression is unlikely to be driven by priming and is instead more consistent with statistical learning. Moreover, results did not yield a statistically significant difference between trial types based on the distractor location in the preceding trial. However, these results should not be taken to suggest that spatial priming cannot contribute to distractor suppression; for details see: Supplementary Figure 4.” (p. 13).

      We note that this analysis approach slightly differs from the reviewer’s suggestion, which considered omission trials. However, we decided to exclude trials immediately following an omission to ensure that both conditions were matched as closely as possible. In particular, omission trials represent extended rest periods, which could alter participants’ state and especially modulate the visually evoked BOLD responses (e.g., potentially increasing the dynamic range) compared to trials that did not follow omissions. Our analysis approach avoids this difference while still addressing the hypothesis put forward by the reviewer. We now provide the full explanation and results figure of this priming analysis in the figure text of Supplementary Figure 4: 

      Reviewer #2 (Public review): 

      The authors of this work set out to test ideas about how observers learn to ignore irrelevant visual information. Specifically, they used fMRI to scan participants who performed a visual search task. The task was designed in such a way that highly salient but irrelevant search items were more likely to appear at a given spatial location. With a region-of-interest approach, the authors found that activity in visual cortex that selectively responds to that location was generally suppressed, in response to all stimuli (search targets, salient distractors, or neutral items), as well as in the absence of an anticipated stimulus. 

      Strengths of the study include: A well-written and well-argued manuscript; clever application of a region of interest approach to fMRI design, which allows articulating clear tests of different hypotheses; careful application of follow-up analyses to rule out alternative, strategy-based accounts of the findings; tests of the robustness of the findings to detailed analysis parameters such as ROI size; and exclusion of the role of regional baseline differences in BOLD responses. 

      We thank the reviewer for the positive evaluation of our manuscript.

      The report might be enhanced by analyses (perhaps in a surface space) that distinguish amongst the multiple "early" retinotopic visual areas that are analysed in the aggregate here. 

      We agree with the reviewer that an exploratory analysis separating early visual cortex (EVC) into its retinotopic areas could be an interesting addition. Our reasoning to combine early visual areas into one mask in the original analyses was two-fold: First, we did not have an a priori reason to expected distinct neural suppression between these early ROIs. Therefore, we did not acquire retinotopy data to reliably separate early visual areas (e.g. V1, V2 and V3), instead opting to increase the number of search task trials. The lack of retinotopy data inherently limits the reliability of the resulting cortical segmentation. However, we now performed an analysis separating early visual cortex into V1 and V2 and report the details as Supplementary Text 1:

      “In an exploratory analysis we investigated whether subdivisions of EVC exhibit different representations of priority signals. In brief, we used FreeSurfer to reconstruct brain surfaces (recon-all) from each subject’s anatomical scan. From these reconstructions we derived V1_exvivo and V2_exvivo labels, which were transformed into volume space using ‘mri_label2vol’ and merged into a bilateral mask for each ROI. We then selected the voxels within each ROI that were most responsive to the four stimulus locations, based on independent localizer data. This voxel selection followed the procedure outlined in the Materials and Methods: Region of Interest (ROI) Definition. To accommodate the subdivision into two ROIs (V1 and V2) compared to the single EVC ROI in the main analysis, we halved the number of voxels selected per location. Finally, we applied the same ROI analysis to investigate distractor suppression during search and omission trials, following the procedure described in Materials and Methods: Statistical Analysis. 

      Results of this more fine-grained ROI analyses are depicted in Supplementary Figure 1. First, the results from V2 qualitatively mirrored our primary ROI analysis. BOLD responses in V2 differed significantly between stimulus types (main effect of stimulus type: F<sub>(2,54)</sub> = 31.11, p < 0.001, 𝜂 = 0.54). Targets elicited larger BOLD responses compared to distractors (t<sub>(27)</sub> = 3.05, p<sub>holm</sub> = 0.004, d = 0.06) and neutral stimuli (t<sub>(27)</sub> = 7.82, p<sub>holm</sub> < 0.001, d = 0.14). Distractors also evoked larger responses than neutral stimuli (t<sub>(27)</sub> = 4.78, p<sub>holm</sub> < 0.001, d = 0.09). These results likely reflect top-down modulation due to target relevance and bo om-up effects of distractor salience. Consistent with the primary ROI analysis, the manipula on of distractor predictability showed a distinct pattern of location specific BOLD suppression in V2 (main effect of location: F<sub>(1.1,52.8)</sub> = 5.01, p = 0.030, 𝜂 = 0.16). Neural populations with receptive fields at the HPDL showed significantly reduced BOLD responses compared to the diagonally opposite neutral location (NL-far; post hoc test HPDL vs NL-far: t<sub>(27)</sub> = 2.69, p<sub>holm</sub> = 0.022, d = 0.62). Again, this suppression was not confined to the HPDL but also extended to close by neutral locations (NL-near vs NL-far: t<sub>(27)</sub> = 2.79, p<sub>holm</sub> = 0.022, d = 0.65). BOLD responses did not differ between HPDL and NL-near locations (HPDL vs NL-near: t<sub>(27)</sub> = 0.11, p<sub>holm</sub> = 0.915, d = 0.03; BF<sub>10</sub> = 0.13). As in the EVC ROI analysis, this suppression pattern was consistent across distractor, target, and neutral stimuli presented at the HPDL and NL-near locations compared to NL-far. In sum, neural responses in V2 were significantly modulated by the distractor contingencies, evident as reduced BOLD responses in neural populations with receptive fields at the HPDL and neutral locations near the location of the frequent distractor (NL-near), relative to the neutral location diagonally across the HPDL (NL-far). 

      In V1, BOLD responses also differed significantly between stimulus types (main effect of stimulus type: F<sub>(1.3,35.6)</sub> = 6.69, p = 0.009, 𝜂 = 0.20). Targets elicited larger BOLD responses compared neutral stimuli (t<sub>(27)</sub> = 3.52, p<sub>holm</sub> = 0.003, d = 0.12) and distractors evoked larger responses than neutral stimuli (t<sub>(27)</sub> = 2.62, p<sub>holm</sub> = 0.023, d = 0.09). However, no difference between targets and distractors was observed (t<sub>(27)</sub> = 0.90, p<sub>holm</sub> = 0.375, d = 0.03; BF<sub>10</sub> = 0.17), suggesting reduced sensitivity to task-related effects in V1. Indeed, analyzing the effect of distractor predictability for BOLD responses in V1 showed a different result than in V2 and the combined EVC ROI. There was no significant main effect of location (F<sub>(2,54)</sub> = 2.20, p = 0.120, 𝜂 = 0.08; BF<sub>10</sub> = 0.77). BOLD responses at NL-near and NL-far were similar (BF<sub>10</sub> = 0.171), with the only reliable difference found between target stimuli at the HPDL and NL-far locations (W = 94, p<sub>holm</sub> = 0.012, r = 0.54).”  

      We include the new result figure as Supplementary Figure 5

      We now include reference to these results in the manuscript’s Discussion section:

      “Are representations of priority signals uniform across EVC? A priori we did not have any hypotheses regarding distinct neural suppression profiles across different early visual areas, hence our primary analyses focused stimulus responses neural populations in EVC, irrespective of subdivision. However, an exploratory analysis suggests that distractor suppression may show different patterns in V1 compared to V2 (Supplementary Figure 5 and Supplementary Text 1). In brief, results in V2 mirrored those reported for the combined EVC ROI (Figure 4). In contrast, results in V1 appeared to be only partially modulated by distractor contingencies, and if so, the modulation was less robust and not as spatially broad as in V2. This suggests the possibility of different effects of distractor predictability across subdivisions of early visual areas. However, these results should be interpreted with caution. First, our design did not optimize the delineation of early visual areas (e.g., no functional retinotopy), limiting the accuracy of V1 and V2 segmentation. Additionally, analyses were conducted in volumetric space, which further reduces spatial precision. Future studies could improve this by including retinotopy runs to accurately delineate V1, V2, and V3, and by performing analyses in surface space. Higher-resolution functional and anatomical MRI sequences would also help elucidate how distractor suppression is implemented across EVC with greater precision.”

      Furthermore, the study could benefit from an analysis that tests the correlation over observers between the magnitude of their behavioural effects and their neural responses. 

      R2 highlights that behavioral facilitation and neural suppression could be correlated across participants. The rationale is that if neural suppression in EVC is related to the facilitation of behavioral responses, we should expect a positive relationship between neural suppression at the HPDL and RTs across participants. In this analysis we focused on the contrast between HPDL and NL-far, as this contrast was statistically significant in both the RT (Figure 2) and the neural suppression analysis (Figure 4). First, we computed for each participant the behavioural benefit of distractor suppression as: RT<sub>facilitation</sub> = RT<sub>NL-far</sub> – RT<sub>HPDL</sub>. Thereby RT facilitation reflects the response speeding due to a distractor appearing at the high probability distractor location compared to the far neutral location. Next, we computed neural suppression as: BOLD<sub>suppression</sub> = BOLD<sub>NL-far</sub> – BOLD<sub>HPDL</sub> Thus, positive values reflect the suppression of BOLD responses at the HPDL comparted to the NL-far location. The BOLD suppression index was computed for each stimulus type separately, as in the main ROI analysis (i.e. for Targets, Neutrals and Distractors). Finally, we correlated RT<sub>facilitation</sub> with BOLD<sub>suppression</sub> across participants using Pearson correlation. Results showed a small, but not statistically significant correlation between RT facilitation and BOLD suppression for distractor (r<sub>(26)</sub> = 0.22, p = 0.257), target (r<sub>(26)</sub> = 0.10, p = 0.598) and neutral (r<sub>(26)</sub> = 0.13, p = 0.519) stimuli. Thus, while the direc on of the correlation was in line with the specula on by the reviewer in the “ Recommendations for the authors”, results were not statistically reliable and therefore inconclusive. As also noted in our preliminary reply to the reviewer comments, it was a priori unlikely that this analysis would yield a statistically significant correlation. An a priori power analysis suggested that, to reach a power of 0.8 at a standard alpha of 0.05, given the present sample size of n=28, the effect size would need to exceed r > 0.75, which seemed unlikely for the correlation of behavioural and neural difference scores. Given the inconclusive nature of the results, we prefer to not include this additional analysis in the manuscript, as we believe that it does not add to the main message of the paper but have it accessible to the interested reader in the public “peer review process”.

      The study provides an advance over previous studies, which iden fied enhancement or suppression in visual cortex as a function of search target/distractor predictability, but in less spatially-specific way. It also speaks to open questions about whether such suppression/enhancement is observed only in response to the arrival of visual information, or instead is preparatory, favouring the la er view. The theoretical advance is moderate, in that it is largely congruent with previous frameworks, rather than strongly excluding an opposing view or providing a major step change in our understanding of how distractor suppression unfolds. 

      We agree with the reviewer that our results are an advancement of prior work, particularly with respect to narrowing down the role of sensory areas and the proactive nature of distractor suppression. However, we argue that this represents a significant step forward for several reasons. First, to our knowledge, the literature on distractor suppression, and visual search in general, is by no means unanimous with respect to the conclusion that distractor suppression is instantiated proactively (Huang et al., 2021, 2022). Indeed, there are several studies suggesting the opposite account; reactive suppression (Chang et al., 2023) or contributions by both proactive and reactive mechanisms (Sauter et al., 2021; Wang et al., 2019). Moreover, studies in support of proactive distractor suppression did not investigate the involvement of (early) sensory areas during suppression. Conversely, to our knowledge most studies investigating the involvement of sensory cortex during distractor suppression did not address the question whether suppression arises proactive or reactively.

      Recommendations for the authors: 

      Reviewer #1 ( Recommendations for the authors): 

      Minor Points: 

      (1) There are several disconnects between the behaviour and the MR results - i.e. not stimulus specific yet there are no deficits for targets appearing the HPDL, also no behavioural suppression for the NLNear but neural suppression found. Nevertheless, the behaviour is used as a way to rule out potential attentional strategies when considering whether there is enhancement in the NL-Far condition. I realise you have a few other points here, but I think it's worth addressing what could be seen as a double standard.

      The reviewer points out an important concern, which we feel could have better been addressed in the manuscript. From our point of view a partial dissociation between neural modulations in EVC and eventual behavioural facilitation is not surprising, given the extensive neural processing beyond EVC required for behaviour. However, this assessment may differ, if one stresses an explicit volitional attentional strategy over an implicit statistical learning account. That said, we clearly do not want to create the impression of using a double standard. The lack of behavioural facilitation for targets at NLfar is not a critical part of our argument against explicit attentional strategies. Therefore, we rephrased the relevant paragraph in the Discussion section to now emphasize the importance of the control analysis excluding participants who reported the correct HPDL in the questionnaire (Figure 5), but nonetheless yielded qualitatively identical results to the main ROI analysis (Figure 4). In our opinion, this control analysis provides more compelling evidence against a volitional attentional strategy account without the risk of crea ng the impression of applying a double standard in the interpretation of behavioural data. Additionally, we now acknowledge the limitation of relying on behavioral data in ruling out volitional attentional strategies in the updated manuscript:

      “It is well established that attention enhances BOLD responses in visual cortex (Maunsell, 2015; Reynolds & Chelazzi, 2004; Williford & Maunsell, 2006). If participants learned the underlying distractor contingencies, they could deploy an explicit strategy by directing their attention away from the HPDL, for example by focusing attention on the diagonally opposite neutral location. This account provides an alternative explanation for the observed EVC modulations. However, while credible, the current findings are not consistent with such an interpretation. First, there was no behavioral facilitation for target stimuli presented at the far neutral location, contrary to what one might expect if participants employed an explicit strategy. However, given the partial dissociation between neural suppression in EVC and behavioral facilitation, additional neural data analyses are required to rule out volitional attention strategies. Thus, we performed a control analysis that excluded all participants that indicated the correct HPDL location in the questionnaire, thereby possibly expressing explicit awareness of the contingencies. This control analysis yielded qualitatively identical results to the full sample, showing significant distractor suppression in EVC. Therefore, it is unlikely that explicit attentional strategies, and the enhancement of locations far from the HPDL, drive the results observed here. Instead the current finding are consistent with an account emphasizing the automa c deployment of spatial priors (He et al., 2022) based on implicitly learned statistical regularities.”

      (2) Does the level of suppression change in any way through the experiment? I.e., does it get stronger in the second vs. first half of the experiment? 

      The reviewer askes an interesting question, whether BOLD suppression may change across the experiment. To address this question, we performed an additional analysis testing BOLD suppression in EVC during the first compared to second half of the MRI experiment. Here we defined BOLD suppression as: BOLD<sub>suppression</sub> = ((BOLD<sub>NL-far</sub> – BOLD<sub>HPDL</sub>) + (BOLD<sub>NL-far</sub> – BOLD<sub>NL-near</sub>)) / 2. Thus, in this formula on of BOLD suppression we summarize the two primary BOLD suppression effects observed in our main results (Figure 4). Additionally, as we previously did not observe any significant differences in BOLD suppression magnitudes between different stimulus types (i.e. suppression was similar for target, distractor and neutral stimuli), we collapsed across stimulus types in this analysis.

      Results, depicted below, showed that during both the initial (Run 1+2) and later part (Run 4+5) of the MRI experiment BOLD suppression was statistically significant (BOLD suppression Run 1+2: W = 331, p = 0.003, r = 0.63; BOLD suppression Run 4+5: W = 320, p = 0.007, r= 0.58) , confirming our main results of reliable distractor suppression even in this subset of trials. However, we did not observe any statistically significant differences between early and late runs of the experiment (t<sub>(27)</sub> = -0.21, p = 0.835, d = -0.04). In fact, a Bayesian paired t-test provided evidence for the absence of a difference in BOLD suppression between early compared to later runs (BF<sub>10</sub> = 0.205), suggesting that distractor suppression in EVC was stable throughout the experiment. A qualitatively similar, pattern was evident during omission trials, with significant distractor suppression during early runs (t<sub>(27)</sub> = 2.70, p = 0.012, d = 0.51), but not quite a statistically significant modulation for later runs (t<sub>(27)</sub> = 1.97, p = 0.059, d = 0.37). Again, there was no evidence for a difference in suppression magnitudes across the experiment (W = 198, p = 0.920, d = -0.025) and support for the absence of a difference in BOLD suppression between early and late runs (BF<sub>10</sub> = 0.278).

      Author response image 1.

      Analysis of BOLD suppression magnitudes in EVC across the MRI experiment phases. BOLD suppression was comparable between early (Run 1+2) and late (Run 4+5) phases of the MRI experiment, suggesting consistent suppression in EVC following statistical learning. Error-bars denote within-subject SEM. * p < 0.05, ** p < 0.01, = BF<sub>10</sub> < 1/3.

      In sum, results suggest that distractor suppression in EVC was stable across runs and did not change significantly throughout the experiment. This result was a priori likely, given that participants already underwent behavioral training before entering the MRI. This enabled them to establish modified spatial priority maps, containing the high probability distractor location contingencies, already before the first MRI run. While specula ve, it is possible that participants may still have consolidated the spatial priority maps during the initial runs, but that this additional consolation is not evident in the data, as later runs may see less engagement by participants due to increasing fa gue towards the end of the MRI experiment. Indeed, rapid learning and stable suppression throughout the remainder of the experiment is also reported by prior work (Lin et al., 2021). We believe that it is highly interesting for future studies to investigate the development of distractor suppression across learning, with initial exposure to the contingencies inside the MRI. However, as the present results are inconclusive, we prefer to not include this analysis in the main manuscript, as it may not provide significant additional insight into the neural mechanisms underlying distractor suppression. 

      (3) In the methods vs. results you have reported the probabili es slightly differently. In the methods you say the HPDL was 6x more likely to contain a distractor whereas in the results you say 4x. Based on the reported trial numbers I think it should be 4, but probably you want to double check that this is consistent and correct throughout. 

      We thank the reviewer for bringing this inconsistency to our attention. We have corrected this oversight in the adjusted manuscript: 

      “One of the four locations of interest was designated the high probability distractor location (HPDL), which contained distractor stimuli (unique color) four mes more o en than any of the remaining three locations of interest. In other words, if a distractor was present on a given trial (42 trials per run), the distractor appeared 57% (24 trials per run) at the HPDL and at one of the other three locations with equal probability (i.e., 14% or 6 trials per run per location).” 

      Reviewer #2 ( Recommendations for the authors): 

      The authors have performed their analyses in the volume rather than the surface, and have grouped together V1, V2, and V3 as "early visual cortex". As the authors' claims lean heavily on the idea that they are measuring "early" visual responses, the study would be improved by delinea ng the ROIS within these different retinotopic regions. Such an approach might be facilitated by analysing data on the reconstructed surface. 

      Please refer to our reply to this analysis suggested in the Public review.

      The authors rightly tread carefully on the causal link between their neural findings and the behavioural outcomes. The picture might be clarified somewhat further by testing for a positive relationship between behavioural effect sizes and neural effect sizes across participants. e.g. to what extent is the search advantage when distractors are presented at the "HPDL" linked to greater suppression of BOLD at the HDPL region of early visual cortex? 

      Please refer to our reply to this analysis suggested in the Public review.

      Some of the claims based on null hypotheses would be better supported by Bayesian tests e.g. page 6 "This pattern of results was the same regardless whether the distractor, target, or a neutral stimulus presented at the HPDL and NL-near locations compared to NL-far ..." and "BOLD responses between HPDL and NL-near locations did not reliably differ ..." This is similar to the approach that the authors adopted later in the section "Ruling out attentional modulation".

      We agree with the reviewer that our ROI analyses would benefit from providing evidence for the absence of a modulation. Accordingly, we updated our results by adding equivalent Bayesian tests. Bayes Factors were computed using JASP 0.18.2 (JASP Team, 2024; RRID:SCR_015823) with default settings; i.e. for Bayesian paired t-tests with a Cauchy prior width of 0.707. Qualitative interpretations of BFs were based on Lee and Wagenmakers (2014). We now report the obtained BF in the Results section. 

      “BOLD responses between HPDL and NL-near locations did not reliably differ (HPDL vs NL-near: t<sub>(27)</sub> = 0.47, p<sub>holm</sub> = 0.643, d = 0.08; BF<sub>10</sub> = 0.19).”

      And:

      “Neural responses at HPDL and NL-near did not reliably differ (t<sub>(27)</sub> = 0.21, p<sub>holm</sub> = 0.835 d = 0.04; BF<sub>10</sub> = 0.21).”

      Moreover, we now denote any equivalent results (defined as BF<sub>10</sub><1/3) in Fig. 4 and Fig. 5, and included the descrip on of the associated symbol in the figure text (“ = BF<sub>10</sub> < 1/3”).

      Additionally, we now also report the BF for all paired t-tests reported in Supplementary Table 1.

      Finally, we addressed the statement: “This pattern of results was the same regardless whether the distractor, target, or a neutral stimulus presented at the HPDL and NL-near locations compared to NLfar”. Our inten on was to emphasize that the pattern of results reported in the sentence preceding it was evident for distractor, target, or neutral stimulus, and not to suggest that the magnitude of the effect is the same. Hence, to more accurate reflect the results, we changed this sentence to:  “This pattern of results was present regardless whether the distractor, target, or a neutral stimulus presented at the HPDL and NL-near locations compared to NL-far”

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This manuscript describes the role of PRDM16 in modulating BMP response during choroid plexus (ChP) development. The authors combine PRDM16 knockout mice and cultured PRDM16 KO primary neural stem cells (NSCs) to determine the interactions between BMP signaling and PRDM16 in ChP differentiation.

      They show PRDM16 KO affects ChP development in vivo and BMP4 response in vitro. They determine genes regulated by BMP and PRDM16 by ChIP-seq or CUT&TAG for PRDM16, pSMAD1/5/8, and SMAD4. They then measure gene activity in primary NSCs through H3K4me3 and find more genes are co-repressed than co-activated by BMP signaling and PRDM16. They focus on the 31 genes found to be co-repressed by BMP and PRDM16. Wnt7b is in this set and the authors then provide evidence that PRDM16 and BMP signaling together repress Wnt activity in the developing choroid plexus.

      Strengths:

      Understanding context-dependent responses to cell signals during development is an important problem. The authors use a powerful combination of in vivo and in vitro systems to dissect how PRDM16 may modulate BMP response in early brain development.

      Main weaknesses of the experimental setup:

      (1) Because the authors state that primary NSCs cultured in vitro lose endogenous Prdm16 expression, they drive expression by a constitutive promoter. However, this means the expression levels are very different from endogenous levels (as explicitly shown in Supplementary Figure 2B) and the effect of many transcription factors is strongly dose-dependent, likely creating differences between the PRDM16-dependent transcriptional response in the in vitro system and in vivo.<br />

      We acknowledge that our in vitro experiments may not ideally replicate the in vivo situation, a common limitation of such experiments, our primary aim was to explore the molecular relationship between PRDM16 and BMP signaling in gene regulation. Such molecular investigations are challenging to conduct using in vivo tissues. In vitro NSCs treated with BMP4 has been used a model to investigate NSC proliferation and quiescence, drawing on previous studies (e.g., Helena Mira, 2010; Marlen Knobloch, 2017). Crucially, to ensure the relevance of our in vitro findings to the in vivo context, we confirmed that cultured cells could indeed be induced into quiescence by BMP4, and this induction necessitated the presence of PRDM16. Furthermore, upon identifying target genes co-regulated by PRDM16 and SMADs, we validated PRDM16's regulatory role on a subset of these genes in the developing Choroid Plexus (ChP) (Fig. 7 and Suppl.Fig7-8). Only by combining evidence from both in vitro and in vivo experiments could we confidently conclude that PRDM16 serves as an essential co-factor for BMP signaling in restricting NSC proliferation.

      (2) It seems that the authors compare Prdm16_KO cells to Prdm16 WT cells overexpressing flag_Prdm16. Aside from the possible expression of endogenous Prdm16, other cell differences may have arisen between these cell lines. A properly controlled experiment would compare Prdm16_KO ctrl (possibly infected with a control vector without Prdm16) to Prdm16_KO_E (i.e. the Prdm16_KO cells with and without Prdm16 overexpression.)

      We agree that Prdm16 KO cells carrying the Prdm16-expressing vector would be a good comparison with those with KO_vector. However, despite more than 10 attempts with various optimization conditions, we were unable to establish a viable cell line after infecting Prdm16 KO cells with the Prdm16-expressing vector. The overall survival rate for primary NSCs after viral infection is low, and we observed that KO cells were particularly sensitive to infection treatment when the viral vector was large (the Prdm16 ORF is more than 3kb).

      As an alternative oo assess vector effects, we instead included two other control cell lines, wt and KO cells infected with the 3xNLS_Flag-tag viral vector, and presented the results in supplementary Fig 2.  When we compared the responses of the four lines — wt, KO, wt infected with the Flag vector, KO infected with the Flag vector — to the addition and removal of BMP4, we confirmed that the viral infection itself has no significant impacts on the responses of these cells to these treatments regarding changes in cell proliferation and Ttr induction.

      Given that wt cells and the KO cells, with or without viral backbone infection behave quite similarly in terms of cell proliferation, we speculate that even if we were successful in obtaining a cell line with Prdm16-expressing vector in the KO cells, it may not exhibit substantial differences compared to wt cells infected with Prdm16-expressing vector.

      Other experimental weaknesses that make the evidence less convincing:

      (1) The authors show in Figure 2E that Ttr is not upregulated by BMP4 in PRDM16_KO NSCs. Does this appear inconsistent with the presence of Ttr expression in the PRDM16_KO brain in Figure1C?<br />

      The reviwer’s point is that there was no significant increase in Ttr expression in Prdm16_KO cells after BMP4 treatment (Fig. 2E), but there remained residule Ttr mRNA signals in the Prdm16 mutant ChP (Fig. 1C). We think the difference lies in the measuable level of Ttr expression between that induced by BMP4 in NSC culture and that in the ChP. This is based on our immunostaining expreriment in which we tried to detect Ttr using a Ttr antibody. This antibody could not detect the Ttr protein in BMP4-treated Prdm16_expressing NSCs but clearly showed Ttr signal in the wt ChP. This means that although Ttr expression can be significantly increased by BMP4 in vitro to a level measurable by RT-qPCR, its absolute quantity even in the Prdm16_expressing condition is much lower compared to that in vivo. Our results in Fig 1C and Fig 2E, as well as Fig 7B, all consistently showed that Prdm16 depletion significantly reduced Ttr expression in in vitro and in vivo.

      (2) Figure 3: The authors use H3K4me3 to measure gene activity. This is however, very indirect, with bulk RNA-seq providing the most direct readout and polymerase binding (ChIP-seq) another more direct readout. Transcription can be regulated without expected changes in histone methylation, see e.g. papers from Josh Brickman. They verify their H3K4me3 predictions with qPCR for a select number of genes, all related to the kinetochore, but it is not clear why these genes were picked, and one could worry whether these are representative.

      H3K4me3 has widely been used as an indicator of active transcription and is a mark for cell identity genes. And it has been demonstrated that H3K4me3 has a direct function in regulating transciption at the step of RNApolII pausing release. As stated in the text, there are advantages and disadvantages of using H3K4me3 compared to using RNA-seq. RNA-seq profiles all gene products, which are affected by transcription and RNA stability and turnover. In contrast, H3K4me3 levels at gene promoter reflects transcriptional activity. In our case, we aimed to identify differential gene expression between proliferation and quiescence states. The transition between these two states is fast and dynamic. RNA-seq may not be able to identify functionally relevant genes but more likely produces false positive and negative results. Therefore, we chose H3K4me3 profiling.

      We agree that transcription may change without histone methylation changes. This may cause an under-estimation of the number of changed genes between the conditions. 

      We validated 7 out of 31 genes (Wnt7b, Id3, Mybl2, Spc24, Spc25, Ndc80 and Nuf2). We chose these genes based on two critira: 1) their function is implicated in cell proliferation and cell-cycle regulation based on gene ontology analysis; 2) their gene products are detectable in the developing ChP based on the scRNA-seq data. Three of these genes (Wnt7b, Id3, Mybl2) are not related to the kinetochore. We now clarify this description in the revised text.

      (3) Line 256: The overlap of 31 genes between 184 BMP-repressed genes and 240 PRDM16-repressed genes seems quite small.

      This indicates that in addition to co-repressing cell-cycle genes, BMP and PRDM16 have independent fucntions. For example, it was reported that BMP regulates neuronal and astrocyte differentiation (Katada, S. 2021), while our previous work demonstrated that Prdm16 controls temporal identity of NSCs (He, L. 2021).

      (4) The Wnt7b H3K4me3 track in Fig. 3G is not discussed in the text but it shows H3K4me3 high in _KO and low in _E regardless of BMP4. This seems to contradict the heatmap of H3K4me3 in Figure 3E which shows H3K4me3 high in _E no BMP4 and low in _E BMP4 while omitting _KO no BMP4. Meanwhile CDKN1A, the other gene shown in 3G, is missing from 3E.

      The track in Fig 3G shows the absolute signal of H3K4me3 after mapping the sequencing reads to the genome and normaliz them to library size. Compare the signal in Prdm16_E with BMP4 and that in Prdm16_E without BMP4, the one with BMP4 has a lower peak. The same trend can be seen for the pair of Prdm16_KO cells with or without BMP4.  The heatmap in Fig. 3E shows the relative level of H3K4me3 in three conditions. The Prdm16_E cells with BMP4 has the lowest level, while the other two conditions (Prdm16_KO with BMP4 and Prdm16_E without BMP4) display a higher level. These two graphs show a consistent trend of H3K4me3 changes at the Wnt7b promoter across these conditions.

      (5) The authors use PRDM16 CUT&TAG on dissected dorsal midline tissues to determine if their 31 identified PRDM16-BMP4 co-repressed genes are regulated directly by PRDM16 in vivo. By manual inspection, they find that "most" of these show a PRDM16 peak. How many is most? If using the same parameters for determining peaks, how many genes in an appropriately chosen negative control set of genes would show peaks? Can the authors rigorously establish the statistical significance of this observation? And why wasn't the same experiment performed on the NSCs in which the other experiments are done so one can directly compare the results? Instead, as far as I could tell, there is only ChIP-qPCR for two genes in NSCs in Supplementary Figure 4D.

      In our text, we indicated the genes containing PRDM16 binding peaks in the figures and described them as “Text in black in Fig. 6A and Supplementary Fig. 5A”. We will add the precise number “25 of these genes” in the main text to clarify it. To define a negative control set of genes, we will use BMP-only repressed 184-31 =153 genes (excluding PRDM16-BMP4 co-repressed), and of these 153 genes, we will determine how many have PRDM16 peaks in the E12.5 ChP data, say X. Then we will use binomial test to calculate p-value binom_test(25, 31, X/153, alternative=“greater).

      We are confused with the second part of the comment “And why wasn't the same experiment performed on the NSCs in which the other experiments are done so one can directly compare the results? Instead, as far as I could tell, there is only ChIP-qPCR for two genes in NSCs in Supplementary Figure 4D.” If the reviewer meant why we didn’t sequence the material from sequential-ChIP or validate more taget genes, the reason is the limitation of the material. Sequential ChIP requires a large quantity of the antibodies, and yields little material barely sufficient for a few qPCR after the second round of IP. This yielded amount was far below the minimum required for library construction. The PRDM16 antibody was a gift, and the quantity we have was very limited. We made a lot of efforts to optimize all available commercial antibodies in ChIP and Cut&Tag, but none of them worked.

      (6) In comparing RNA in situ between WT and PRDM16 KO in Figure 7, the authors state they use the Wnt2b signal to identify the border between CH and neocortex. However, the Wnt2b signal is shown in grey and it is impossible for this reviewer to see clear Wnt2b expression or where the boundaries are in Figure 7A. The authors also do not show where they placed the boundaries in their analysis. Furthermore, Figure 7B only shows insets for one of the regions being compared making it difficult to see differences from the other region. Finally, the authors do not show an example of their spot segmentation to judge whether their spot counting is reliable. Overall, this makes it difficult to judge whether the quantification in Figure 7C can be trusted.

      To address these questions, in the revised manuscript we will include an individal channel of Wnt2b and mark the boundaries. We will also provide full-view images and examples of spot segmentation in supplementary figures as space limitation in the main figures.

      (7) The correlation between mKi67 and Axin2 in Figure 7 is interesting but does not convincingly show that Wnt downstream of PRDM16 and BMP is responsible for the increased proliferation in PRDM16 mutants.

      We agree that this result (the correlation between mKi67 and Axin2) alone only suggests that Wnt signaling is related to the proliferation defect in the Prdm16 mutant, and does not necessarily mean that Wnt is downstream of PRDM16 and BMP. Our concolusion is backed up by two additional lines of evidences:  the Cut&Tag data in which PRDM16 binds to regulatory regions of Wnt7b and Wnt3a; BMP and PRDM16 co-repress Wnt7b in vitro.

      An ideal result is that down-regulating Wnt signaling in Prdm16 mutant can rescue Prdm16 mutant phenotype. Such an experiment is technically challenging. Wnt plays diverse and essential roles in NSC regulation, and one would need to use a celltype-and stage-specific tool to down-regulate Wnt in the background of Prdm16 mutation. Moreover, Wnt genes are not the only targets regulated by PRDM16 in these cells, and downregulating Wnt may not be sufficient to rescue the phenotype. 

      Weaknesses of the presentation:

      Overall, the manuscript is not easy to read. This can cause confusion.

      We will revise the text to improve the clarity.

      Reviewer #2 (Public review):

      Summary:

      This article investigates the role of PRDM16 in regulating cell proliferation and differentiation during choroid plexus (ChP) development in mice. The study finds that PRDM16 acts as a corepressor in the BMP signaling pathway, which is crucial for ChP formation.

      The key findings of the study are:

      (1) PRDM16 promotes cell cycle exit in neural epithelial cells at the ChP primordium.

      (2) PRDM16 and BMP signaling work together to induce neural stem cell (NSC) quiescence in vitro.

      (3) BMP signaling and PRDM16 cooperatively repress proliferation genes.

      (4) PRDM16 assists genomic binding of SMAD4 and pSMAD1/5/8.

      (5) Genes co-regulated by SMADs and PRDM16 in NSCs are repressed in the developing ChP.

      (6) PRDM16 represses Wnt7b and Wnt activity in the developing ChP.

      (7) Levels of Wnt activity correlate with cell proliferation in the developing ChP and CH.

      In summary, this study identifies PRDM16 as a key regulator of the balance between BMP and Wnt signaling during ChP development. PRDM16 facilitates the repressive function of BMP signaling on cell proliferation while simultaneously suppressing Wnt signaling. This interplay between signaling pathways and PRDM16 is essential for the proper specification and differentiation of ChP epithelial cells. This study provides new insights into the molecular mechanisms governing ChP development and may have implications for understanding the pathogenesis of ChP tumors and other related diseases.

      Strengths:

      (1) Combining in vitro and in vivo experiments to provide a comprehensive understanding of PRDM16 function in ChP development.

      (2) Uses of a variety of techniques, including immunostaining, RNA in situ hybridization, RT-qPCR, CUT&Tag, ChIP-seq, and SCRINSHOT.

      (3) Identifying a novel role for PRDM16 in regulating the balance between BMP and Wnt signaling.

      (4) Providing a mechanistic explanation for how PRDM16 enhances the repressive function of BMP signaling. The identification of SMAD palindromic motifs as preferred binding sites for the SMAD/PRDM16 complex suggests a specific mechanism for PRDM16-mediated gene repression.

      (5) Highlighting the potential clinical relevance of PRDM16 in the context of ChP tumors and other related diseases. By demonstrating the crucial role of PRDM16 in controlling ChP development, the study suggests that dysregulation of PRDM16 may contribute to the pathogenesis of these conditions.

      Weaknesses:

      (1) Limited investigation of the mechanism controlling PRDM16 protein stability and nuclear localization in vivo. The study observed that PRDM16 protein became nearly undetectable in NSCs cultured in vitro, despite high mRNA levels. While the authors speculate that post-translational modifications might regulate PRDM16 in NSCs similar to brown adipocytes, further investigation is needed to confirm this and understand the precise mechanism controlling PRDM16 protein levels in vivo.

      While mechansims controlling PRDM16 protein stability and nuclear localization in the developing brain are interesting, the scope of this paper is revealing the function of PRDM16 in the choroid plexus and its interaction with BMP signaling. We will be happy to pursuit this direction in our next study.

      (2) Reliance on overexpression of PRDM16 in NSC cultures. To study PRDM16 function in vitro, the authors used a lentiviral construct to constitutively express PRDM16 in NSCs. While this approach allowed them to overcome the issue of low PRDM16 protein levels in vitro, it is important to consider that overexpressing PRDM16 may not fully recapitulate its physiological role in regulating gene expression and cell behavior.

      As stated above, we acknowledge that findings from cultured NSCs may not directly apply to ChP cells in vivo. We are cautious with our statements. The cell culture work was aimed to identify potential mechanisms by which PRDM16 and SMADs interact to regulate gene expression and target genes co-regulated by these factors. We expect that not all targets from cell culture are regulated by PRDM16 and SMADs in the ChP, so we validated expression changes of several target genes in the developing ChP and now included the new data in Fig. 7 and Supplementary Fig. 7. Out of the 31 genes identified from cultured cells, four cell cycle regulators including Wnt7b, Id3, Spc24/25/nuf2 and Mybl2, showed de-repression in Prdm16 mutant ChP. These genes can be relevant downstream genes in the ChP, and other target genes may be cortical NSC-specific or less dependent on Prdm16 in vivo.

      (3) Lack of direct evidence for AP1 as the co-factor responsible for SMAD relocation in the absence of PRDM16. While the study identified the AP1 motif as enriched in SMAD binding sites in Prdm16 knockout cells, they only provided ChIP-qPCR validation for c-FOS binding at two specific loci (Wnt7b and Id3). Further investigation is needed to confirm the direct interaction between AP1 and SMAD proteins in the absence of PRDM16 and to rule out other potential co-factors.

      We agree that the finding of the AP1 motif enriched at the PRDM16 and SMAD co-binding regions in Prdm16 KO cells can only indirectly suggest AP1 as a co-factor for SMAD relocation. That’s why we used ChIP-qPCR to examine the presence of C-fos at these sites. Although we only validated two targets, the result confirms that C-fos binds to the sites only in the Prdm16 KO cells but not Prdm16_expressing cells, suggesting AP1 is a co-factor.  We results cannot rule out the presence of other co-factors.

      Reviewer #3 (Public review):

      Summary:

      Bone morphogenetic protein (BMP) signaling instructs multiple processes during development including cell proliferation and differentiation. The authors set out to understand the role of PRDM16 in these various functions of BMP signaling. They find that PRDM16 and BMP co-operate to repress stem cell proliferation by regulating the genomic distribution of BMP pathway transcription factors. They additionally show that PRDM16 impacts choroid plexus epithelial cell specification. The authors provide evidence for a regulatory circuit (constituting of BMP, PRDM16, and Wnt) that influences stem cell proliferation/differentiation.

      Strengths:

      I find the topics studied by the authors in this study of general interest to the field, the experiments well-controlled and the analysis in the paper sound.

      Weaknesses:

      I have no major scientific concerns. I have some minor recommendations that will help improve the paper (regarding the discussion).

      We will revise the discussion according the suggestions.

    1. Author response:

      eLife Assessment 

      The authors utilize a valuable computational approach to exploring the mechanisms of memorydependent klinotaxis, with a hypothesis that is both plausible and testable. Although they provide a solid hypothesis of circuit function based on an established model, the model's lack of integration of newer experimental findings, its reliance on predefined synaptic states, and oversimplified sensory dynamics, make the investigation incomplete for both memory and internal-state modulation of taxis.  

      We would like to express our gratitude to the editor for the assessment of our work. However, we respectfully disagree with the assessment that our investigation is incomplete, if the negative assessment is primarily due to the impact of AIY interneuron ablation on the chemotaxis index (CI) which was reported in Reference [1]. It is crucial to acknowledge that the CI determined through experimental means incorporates contributions from both klinokinesis and klinotaxis [1]. It is plausible that the impact of AIY ablation was not adequately reflected in the CI value. Consequently, the experimental observation does not necessarily diminish the role of AIY in klinotaxis. Anatomical evidence provided by the database (http://ims.dse.ibaraki.ac.jp/ccep-tool/) substantiates that ASE sensory neurons and AIZ interneurons, which have been demonstrated to play a crucial role in klinotaxis [Matsumoto et al., PNAS 121 (5) e2310735121], have the highest number of synaptic connections with AIY interneurons. These findings provide substantial evidence supporting the validity of the presented minimal neural network responsible for salt klinotaxis.

      Public Reviews: 

      Reviewer #1 (Public review): 

      Summary: 

      This research focuses on C. elegans klinotaxis, a chemotactic behavior characterized by gradual turning, aiming to uncover the neural circuit mechanism responsible for the context-dependent reversal of salt concentration preference. The phenomenon observed is that the preferred salt concentration depends on the difference between the pre-assay cultivation conditions and the current environmental salt levels. 

      We would like to express our gratitude for the time and consideration you have dedicated to reviewing our manuscript.

      The authors propose that a synaptic-reversal plasticity mechanism at the primary sensory neuron, ASER, is critical for this memory- and context-dependent switching of preference. They build on prior findings regarding synaptic reversal between ASER and AIB, as well as the receptor composition of AIY neurons, to hypothesize that similar "plasticity" between ASER and AIY underpins salt preference behavior in klinotaxis. This plasticity differs conceptually from the classical one as it does not rely on any structural changes but rather synaptic transmission is modulated by the basal level of glutamate, and can switch from inhibitory to excitatory. 

      To test this hypothesis, the study employs a previously established neuroanatomically grounded model [4] and demonstrates that reversing the ASER-AIY synapse sign in the model agent reproduces the observed reversal in salt preference. The model is parameterized using a computational search technique (evolutionary algorithm) to optimize unknown electrophysiological parameters for chemotaxis performance. Experimental validity is ensured by incorporating constraints derived from published findings, confirming the plausibility of the proposed mechanism. 

      Finally. the circuit mechanism allowing C. elegans to switch behaviour to an exploration run when starved is also investigated. This extension highlights how internal states, such as hunger, can dynamically reshape sensory-motor programs to drive context-appropriate behaviors.  

      We would like to thank the reviewer for the appropriate summary of our work. 

      Strengths and weaknesses: 

      The authors' approach of integrating prior knowledge of receptor composition and synaptic reversal with the repurposing of a published neuroanatomical model [4] is a significant strength.

      This methodology not only ensures biological plausibility but also leverages a solid, reproducible modeling foundation to explore and test novel hypotheses effectively.

      The evidence produced that the original model has been successfully reproduced is convincing.

      The writing of the manuscript needs revision as it makes comprehension difficult.  

      We would like to thank the reviewer for recognizing the usefulness of our approach. In the revised version, we will improve the explanation.  

      One major weakness is that the model does not incorporate key findings that have emerged since the original model's publication in 2013, limiting the support for the proposed mechanism. In particular, ablation studies indicate that AIY is not critical for chemotaxis, and other interneurons may play partially overlapping roles in positive versus negative chemotaxis. These findings challenge the centrality of AIY and suggest the model oversimplifies the circuit involved in klinotaxis.

      We would like to express our gratitude for the constructive feedback we have received. We concur with some of your assertions. In fact, our model is the minimal network for salt klinotaxis, which includes solely the interneurons that are connected to each other via the highest number of synaptic connections. It is important to note that our model does not consider redundant interneurons that exhibit overlapping roles. Consequently, the model is not applicable to the study of the impact of interneuron ablation. In the reference [1], the influence of interneuron ablations on the chemotaxis index (CI) has been investigated. The experimentally determined CI value incorporates the contributions from both klinokinesis and klinotaxis. Consequently, it is plausible that the impact of AIY ablation was not significantly reflected in the CI value. The experimental observation does not necessarily diminish the role of AIY in klinotaxis. 

      Reference [1] also shows that ASER neurons exhibit complex, memory- and context-dependent responses, which are not accounted for in the model and may have a significant impact on chemotactic model behaviour. 

      As pointed out by the reviewer, our model does not incorporate the context-dependent response of the ASER. Instead, the salt concentration-dependent glutamate release from the ASRE [S. Hiroki et al. Nat Commun 13, 2928 (2022)] as the result of the ASER responses is considered in the present study.

      The hypothesis of synaptic reversal between ASER and AIY is not explicitly modeled in terms of receptor-specific dynamics or glutamate basal levels. Instead, the ASER-to-AIY connection is predefined as inhibitory or excitatory in separate models. This approach limits the model's ability to test the full range of mechanisms hypothesized to drive behavioral switching.  

      We would like to thank the reviewer for the helpful comments. In the revised version, we will mention the limitation.

      While the main results - such as response dependence on step inputs at different phases of the oscillator - are consistent with those observed in chemotaxis models with explicit neural dynamics (e.g., Reference [2]), the lack of richer neural dynamics could overlook critical effects. For example, the authors highlight the influence of gap junctions on turning sensitivity but do not sufficiently analyze the underlying mechanisms driving these effects. The role of gap junctions in the model may be oversimplified because, as in the original model [4], the oscillator dynamics are not intrinsically generated by an oscillator circuit but are instead externally imposed via $z_¥text{osc}$. This simplification should be carefully considered when interpreting the contributions of specific connections to network dynamics. Lastly, the complex and contextdependent responses of ASER [1] might interact with circuit dynamics in ways that are not captured by the current simplified implementation. These simplifications could limit the model's ability to account for the interplay between sensory encoding and motor responses in C. elegans chemotaxis. 

      We might not understand the substance of your assertions. However, we understand that the oscillator dynamics were not generated by an oscillator neural circuit in our modeling. On the other hand, the present study focuses on how the sensory input and resulting interneuron dynamics regulate the oscillatory activity of SMB motor neurons to generate klinotaxis. 

      Appraisal: 

      The authors show that their model can reproduce memory-dependent reversal of preference in klinotaxis, demonstrating that the ASER-to-AIY synapse plays a key role in switching chemotactic preferences. By switching the ASER-AIY connection from excitatory to inhibitory they indeed show that salt preference reverses. They also show that the curving/turn rate underlying the preference change is gradual and depends on the weight between ASER-AIY. They further support their claim by showing that curving rates also depend on cultivated (set-point).  

      We would like to thank the reviewer for assessing our work.

      Thus within the constraints of the hypothesis and the framework, the model operates as expected and aligns with some experimental findings. However, significant omissions of key experimental evidence raise questions on whether the proposed neural mechanisms are sufficient for reversal in salt-preference chemotaxis.  

      We agree with your opinion. The present hypothesis should be verified by experiments.

      Previous work [1] has shown that individually ablating the AIZ or AIY interneurons has essentially no effect on the Chemotactic Index (CI) toward the set point ([1] Figure 6). Furthermore, in [1] the authors report that different postsynaptic neurons are required for movement above or below the set point. The manuscript should address how this evidence fits with their model by attempting similar ablations. It is possible that the CI is rescued by klinokinesis but this needs to be tested on an extension of this model to provide a more compelling argument.  

      We would like to express our gratitude for the constructive feedback we have received. In the reference [1], the influence of interneuron ablations on the chemotaxis index (CI) has been investigated. It is important to acknowledge that the experimentally determined CI value encompasses the contributions of both klinokinesis and klinotaxis. It is plausible that the impact of AIY ablation was not reflected in the CI value. Consequently, these experimental observations do not necessarily diminish the role of AIY in klinotaxis. The neural circuit model employed in the present study constitutes a minimal network for salt klinotaxis, encompassing solely interneurons that are connected to each other via the highest number of synaptic connections. Anatomical evidence provided by the database (http://ims.dse.ibaraki.ac.jp/cceptool/) substantiates that ASE sensory neurons and AIZ interneurons, which have been demonstrated to play a crucial role in klinotaxis [Matsumoto et al., PNAS 121 (5) e2310735121], have the highest number of synaptic connections with AIY interneurons. Our model does not take into account redundant interneurons with overlapping roles, thus rendering it not applicable to the study of the effects of interneuron ablation.

      The investigation of dispersal behaviour in starved individuals is rather limited to testing by imposing inhibition of the SMB neurons. Although a circuit is proposed for how hunger states modulate taxis in the absence of food, this circuit hypothesis is not explicitly modelled to test the theory or provide novel insights.  

      As pointed out by the reviewer, the neural circuit that inhibits the SMB motor neurons was not explicitly incorporated in our model. We then examined whether our minimal network model could reproduce dispersal behavior under starvation conditions solely due to the experimentally identified inhibitory effect of SMB motor neurons.

      Impact : 

      This research underscores the value of an embodied approach to understanding chemotaxis, addressing an important memory mechanism that enables adaptive behavior in the sensorimotor circuits supporting C. elegans chemotaxis. The principle of operation - the dependence of motor responses to sensory inputs on the phase of oscillation - appears to be a convergent solution to taxis. Similar mechanisms have been proposed in Drosophila larvae chemotaxis [2], zebrafish phototaxis [3], and other systems. Consequently, the proposed mechanism has broader implications for understanding how adaptive behaviors are embedded within sensorimotor systems and how experience shapes these circuits across species.

      We would like to express our gratitude for useful suggestion. We will add the argument that the reviewer mentioned in the revised version.  

      Although the reported reversal of synaptic connection from excitatory to inhibitory is an exciting phenomenon of broad interest, it is not entirely new, as the authors acknowledge similar reversals have been reported in ASER-to-AIB signaling for klinokinesis ( Hiroki et al., 2022). The proposed reversal of the ASER-to-AIY synaptic connection from inhibitory to excitatory is a novel contribution in the specific context of klinotaxis. While the ASER's role in gradient sensing and memory encoding has been previously identified, the current paper mechanistically models these processes, introducing a hypothesis for synaptic plasticity as the basis for bidirectional salt preference in klinotaxis.  

      The research also highlights how internal states, such as hunger, can dynamically reshape sensory-motor programs to drive context-appropriate behaviors.  

      The methodology of parameter search on a neural model of a connectome used here yielded the valuable insight that connectome information alone does not provide enough constraints to reproduce the neural circuits for behaviour. It demonstrates that additional neurophysiological constraints are required.  

      We would like to acknowledge the appropriate recognition of our work.

      Additional Context 

      Oscillators with stimulus-driven perturbations appear to be a convergent solution for taxis and navigation across species. Similar mechanisms have been studied in zebrafish phototaxis [3],

      Drosophila larvae chemotaxis [2], and have even been proposed to underlie search runs in ants.

      The modulation of taxis by context and memory is a ubiquitous requirement, with parallels across species. For example, Drosophila larvae modulate taxis based on current food availability and predicted rewards associated with odors, though the underlying mechanism remains elusive. The synaptic reversal mechanism highlighted in this study offers a compelling framework for understanding how taxis circuits integrate context-related memory retrieval more broadly.  

      We would like to express our gratitude for the insightful commentary. In the revised version, we will incorporate the discussion that the similar oscillator mechanism with stimulus-driven perturbations has been observed for zebrafish phototaxis [3] and Drosophila larvae chemotaxis [2].

      As a side note, an interesting difference emerges when comparing C. elegans and Drosophila larvae chemotaxis. In Drosophila larvae, oscillatory mechanisms are hypothesized to underlie all chemotactic reorientations, ranging from large turns to smaller directional biases (weathervaning). By contrast, in C. elegans, weathervaning and pirouettes are treated as distinct strategies, often attributed to separate neural mechanisms. This raises the possibility that their motor execution could share a common oscillator-based framework. Re-examining their overlap might reveal deeper insights into the neural principles underlying these maneuvers. 

      We would like to acknowledge your thoughtfully articulated comment. As pointed out by the reviewer, from the anatomical database (http://ims.dse.ibaraki.ac.jp/ccep-tool/), we found that the neural circuits underlying weathervaning and pirouettes in C. elegans are predominantly distinct but exhibit partial overlap. When we restrict our search to the neurons that are connected to each other with the highest number of synaptic connections, we identify the projections from the neural circuit of weathervaning to the circuit of pirouettes; however we observed no reversal projections. This finding suggests that the neural circuit of weathervaning, namely, our minimal neural network, is not likely to be affected by that of pirouettes, which consists of AIB interneurons and interneurons and motor neurons the downstream. 

      (1) Luo, L., Wen, Q., Ren, J., Hendricks, M., Gershow, M., Qin, Y., Greenwood, J., Soucy, E.R., Klein, M., Smith-Parker, H.K., & Calvo, A.C. (2014). Dynamic encoding of perception, memory, and movement in a C. elegans chemotaxis circuit. Neuron, 82(5), 1115-1128. 

      (2) Antoine Wystrach, Konstantinos Lagogiannis, Barbara Webb (2016) Continuous lateral oscillations as a core mechanism for taxis in Drosophila larvae eLife 5:e15504. 

      (3) Wolf, S., Dubreuil, A.M., Bertoni, T. et al. Sensorimotor computation underlying phototaxis in zebrafish. Nat Commun 8, 651 (2017). 

      (4) Izquierdo, E.J. and Beer, R.D., 2013. Connecting a connectome to behavior: an ensemble of neuroanatomical models of C. elegans klinotaxis. PLoS computational biology, 9(2), p.e1002890. 

      Reviewer #2 (Public review): 

      Summary: 

      This study explores how a simple sensorimotor circuit in the nematode C. elegans enables it to navigate salt gradients based on past experiences. Using computational simulations and previously described neural connections, the study demonstrates how a single neuron, ASER, can change its signaling behavior in response to different salt conditions, with which the worm is able to "remember" prior environments and adjust its navigation toward "preferred" salinity accordingly.  

      We would like to express our gratitude for the time and consideration the reviewer has dedicated to reviewing our manuscript.

      Strengths: 

      The key novelty and strength of this paper is the explicit demonstration of computational neurobehavioral modeling and evolutionary algorithms to elucidate the synaptic plasticity in a minimal neural circuit that is sufficient to replicate memory-based chemotaxis. In particular, with changes in ASER's glutamate release and sensitivity of downstream neurons, the ASER neuron adjusts its output to be either excitatory or inhibitory depending on ambient salt concentration, enabling the worm to navigate toward or away from salt gradients based on prior exposure to salt concentration.

      We would like to thank the reviewer for appreciating our research. 

      Weaknesses: 

      While the model successfully replicates some behaviors observed in previous experiments, many key assumptions lack direct biological validation. As to the model output readouts, the model considers only endpoint behaviors (chemotaxis index) rather than the full dynamics of navigation, which limits its predictive power. Moreover, some results presented in the paper lack interpretation, and many descriptions in the main text are overly technical and require clearer definitions.  

      We would like to thank the reviewer for the constructive feedback. As the reviewer noted, the fundamental assumptions posited in the study have yet to be substantiated by biological validation. Consequently, these assumptions must be directly assessed by biological experimentation. The model performance for salt klinotaxis is evaluated by multiple factors, including not only a chemotaxis index but also the curving rate vs. bearing (Fig. 4a, the bearing is defined in Fig. A3) and the curving rate vs. normal gradient (Fig. 4c). The subsequent two parameters work to characterize the trajectory during salt klinotaxis. In the revised version, we will meticulously revise the manuscript according to the suggestions by the reviewer. We would like to express our sincere gratitude for your insightful review of our work.

    1. Author response:

      We thank all the reviewers for their detailed comments. In response, we will address the comments with further analysis, experiments and an expanded discussion.

      In terms of each specific reviewer's comments:

      Reviewer 1 was positive overall but had several suggestions and requested further rigorously controls. These are highly constructive technical concerns and will be addressed through additional experimentation and methods for quantification.

      Reviewer 2 summarised the strengths of the study as being largely confirmatory. They have perhaps not fully appreciated that this is the first published functional assessment of cerebral vascular permeability in a pericyte deficient zebrafish model.

      The reviewer has made a number of very helpful suggestions to improve technical aspects of the analysis. Many align with the suggestions of Reviewer 1. Additional experiments that include more rigorous controls and further methods to quantify vessel permeability will address these concerns in revision.

      We also note that the reviewer calls for a more nuanced and careful discussion section. We take the reviewers point and do appreciate their concerns. We were limited by wordcount in the initial submission in short report format, but in response will expand and provide a more thorough discussion.

      Reviewer 3 was positive overall but has suggested additional controls and experiments to further strengthen the findings and support our conclusions. Some align with the suggestions of Reviewers 1 and 2. We agree and aim to address them through additional work in revision.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews: 

      Reviewer #1 (Public review):

      Summary:

      The investigators in this study analyzed the dataset assembly from 540 Salmonella isolates, and those from 45 recent isolates from Zhejiang University of China. The analysis and comparison of the resistome and mobilome of these isolates identified a significantly higher rate of cross-region dissemination compared to localized propagation. This study highlights the key role of the resistome in driving the transition and evolutionary history of S. Gallinarum.

      Strengths:

      The isolates included in this study were from 16 countries in the past century (1920 to 2023). While the study uses S. Gallinarun as the prototype, the conclusion from this work will likely apply to other Salmonella serotypes and other pathogens.

      Thank you very much for your positive feedback. We recognize, as you noted, that emphasizing Salmonella enterica Serovar Gallinarum in the title may lead readers to perceive our methods and conclusions as overly restrictive. In light of your evaluation of our work, we have revised the title to: “Avian-specific Salmonella transition to endemicity is accompanied by localized resistome and mobilome interaction” We believe this final version not only reflects the applicability of our conclusions, as you appreciated, but also addresses your previous suggestion to highlight the resistome and mobilome.

      Revisions in the manuscript Lines: 1-3

      Weaknesses:

      While the isolates came from 16 countries, most strains in this study were originally from China.

      We believe that this issue was discussed in detail in our previous response. Although potential bias exists, we have minimized its impact by constructing the largest global S. Gallinarum genome dataset to date. In addition, we have further emphasized these limitations in the manuscript.

      Comments on revisions:

      This reviewer is happy with the detailed responses from the authors regarding revising this manuscript. I do not have further comments.

      We greatly appreciate your positive feedback and are pleased that our responses have addressed your concerns.

      Reviewer #2 (Public review):

      Summary:

      The authors sequence 45 new samples of S. Gallinarum, a commensal Salmonella found in chickens, which can sometimes cause disease. They combine these sequences with around 500 from public databases, determine the population structure of the pathogen, and coarse relationships of lineages with geography. The authors further investigate known anti-microbial genes found in these genomes, how they associate with each other, whether they have been horizontally transferred, and date the emergence of clades.

      Strengths:

      - It doesn't seem that much is known about this serovar, so publicly available new sequences from a high burden region are a valuable addition to the literature.

      - Combining these sequences with publicly available sequences is a good way to better contextualise any findings.

      - The genomic analyses have been greatly improved since the first version of the manuscript, and appropriately analyse the population and date emergence of clades.

      - The SNP thresholds are contextualised in terms of evolutionary time.

      - The importance and context of the findings are fairly well described.

      Thank you so much for your thorough review and constructive comments on the manuscript.

      Weaknesses:

      -  There are still a few issues with the genomic analyses, although they no longer undermine the main conclusions:

      We are grateful for the valuable time and effort you have dedicated to improving our manuscript. In this revision, we have provided a point-by-point response to each of your concerns. Moreover, with the addition of new supplementary materials and modifications to the figures, we have re-examined and adjusted the numbering of figures and supplementary materials in the text to ensure they appear correctly in the manuscript.

      (1) Although the SNP distance is now considered in terms of time, the 5 SNP distance presented still represents ~7yrs evolution, so it is unlikely to be a transmission event, as described. It would be better to use a much lower threshold or describe the interpretation of these clusters more clearly. Bringing in epidemiological evidence or external references on the likely time interval between transmissions would be helpful.

      We sincerely thank you for highlighting this issue. We appreciate your concern regarding the use of a 5-SNP threshold to define a transmission event, especially given the approximate 7-year evolutionary timeframe. Considering our updated estimate for the evolutionary rate of S. Gallinarum (approximately 0.74 SNPs per year, with a 95% HPD range of 0.42 to 1.06), we have revised the manuscript to use a 2-SNP threshold (approximately representing less than two years of evolution) to better control the temporal span of transmission events. In addition, we have updated the manuscript to reflect this new threshold and demonstrated that the use of a more stringent SNP threshold does not affect the overall conclusions of the study.

      Specifically, we adopted the newly established 2-SNP threshold to update Figure 3a and corresponding Supplementary Figure 8. The heatmap on the far right of New Figure 3a illustrates the SNP distances among 45 newly isolated S. Gallinarum strains from two locations in Zhejiang Province (Taishun and Yueqing). New Supplementary Figure 8 simulates potential transmission events between the bvSP strains isolated from Zhejiang Province (n=95) and those from other regions of China with available provincial information (n=435). These analyses collectively demonstrate the localized transmission patterns of bvSP within China.

      For New Figure 3a, we found that even with the 2-SNP threshold, the number of potential transmission events among the 45 newly isolated S. Gallinarum strains from the two Zhejiang locations (Taishun and Yueqing) remains unchanged. In fact, we observed that the results from SNP tracing using an SNP threshold of less than 5 are consistent (see Author response image 1). 

      Author response image 1.

      Clustering results of 45 newly isolated S. Gallinarum strains using different SNP thresholds of 1, 2, 3, 4, and 5 SNPs. The five subplots represent the clustering results under each threshold. Each point corresponds to an individual strain, and lines connect strains with potential transmission relationships.

      For New Supplementary Figure 8, we employed the 2-SNP threshold and found that the number of transmission events between the bvSP strains isolated from Zhejiang Province (n=95) and those from other Chinese provinces (n=435) decreased from 91 to 53. The names of the strains involved in these potential transmission events are listed in Supplementary Table 5.

      Revisions in the manuscript

      Lines: 352-357

      Figures: Figure 3; Supplementary Figure 8

      Table: Supplementary Table 5

      (2) The HGT definition has not fundamentally been changed and therefore still has some issues, mainly that vertical evolution is still not systematically controlled for. 

      We sincerely thank you for highlighting this issue. We hope the following explanation will help clarify and improve our manuscript, as well as address your concerns.

      In bacteria, mobile genetic elements (MGEs) such as plasmids, transposons, integrons, and prophages, as mentioned in our manuscript, are segments of DNA that encode enzymes and proteins responsible for mediating the movement of genetic material between bacterial genomes (commonly referred to as “jumping genes”). These MGEs contribute to the mechanisms of horizontal gene transfer (HGT) in Salmonella, including transduction (via prophages), conjugation (via plasmids), and transposition (via integrons and transposons) (Nat Rev Microbiol. 2005 Sep;3(9):722-32). These “jumping genes” can enable Salmonella to acquire additional antimicrobial resistance genes (ARGs), which may not only originate from other Salmonella strains but also from distantly related species.

      To further address your concern regarding the systematic control of vertical evolution, we employed the HGTphyloDetect pipeline developed by Le Yuan et al. (Brief Bioinform. 2023 Mar 19;24(2):bbad035) to control for vertical evolution in the ARG sequences mentioned in our manuscript. We chose HGTphyloDetect because, as noted, "jumping genes" often occur among evolutionarily distant species, rendering the use of Gubbins potentially unsuitable for these distant HGT events.

      Using the HGTphyloDetect pipeline, we extracted base sequences for the eight ARGs shown in Figure 6b with an HGT frequency greater than zero (bla<sup>TEM-1B</sup>, sul1, dfrA17, aadA5, sul2, aph(3’’)-Ib, tet(A), aph(6)-Id). For bla<sup>TEM-1B</sup>, sul1, dfrA17, aadA5, and sul2, the HGT frequency reached 100% across different isolates, indicating that these ARG sequences have a unique sequence type. In contrast, due to the ResFinder settings requiring both similarity and coverage to meet a minimum value of 90%, the base sequences for aph(3’’)-Ib, tet(A), and aph(6)-Id are not unique. Consequently, we applied the HGTphyloDetect pipeline individually to each sequence type of ARGs to verify their association with HGT events. Specifically, among 436 bvSP isolates collected in China, we identified two sequence types of aph(3’’)-Ib, four sequence types of tet(A), and three sequence types of aph(6)-Id.

      Subsequently, to identify potential ARGs horizontally acquired from evolutionarily distant organisms, we queried the translated amino acid sequences of each ARG against the National Center for Biotechnology Information (NCBI) non-redundant protein database. We then evaluated whether these sequences were products of HGT by calculating Alien Index (AI) scores and out_perc values.

      The calculation of AI score is as follows:

      In this study, bbhG and bbhO represent the E-values of the best blast hit in ingroup and outgroup lineages, respectively. The outgroup lineage is defined as all species outside of the kingdom, while the ingroup lineage encompasses species within the kingdom but outside of the subphylum. An AI score ≥ 45 is considered a strong indicator that the gene in question is likely derived from an HGT event.

      Regarding the calculation method for out_perc:

      Finally, according to the definition provided by the HGTphyloDetect pipeline, ARGs with AI score ≥ 45 and out_perc ≥ 90% are presumed to be potential candidates for HGT from evolutionarily distant species. We have compiled the calculation results for the aforementioned genes in New Supplementary Table 9. The results indicate that all ARGs presented in Figure 6b, which exhibited a HGT frequency greater than zero, were acquired horizontally by S. Gallinarum. Based on these findings, we have revised the manuscript accordingly.

      Revisions in the manuscript

      Lines: 302-307; 616-650; 955-957

      Table: Supplementary Table 9

      Using a 5kb window is not sufficient, as LD may extend across the entire genome.

      We agree with your point that linkage disequilibrium (LD) could influence the transmission of genes within chromosomal regions. LD can lead to the non-random cooccurrence of alleles at different loci within a population. Considering that horizontal gene transfer (HGT) events involving more distantly related ARGs may be accompanied by vertical propagation on chromosomes, and to simultaneously assess the impact of LD, we conducted two evaluations.

      It is important to note that the following assessments are based on the assumption that plasmid replicons detected by PlasmidsFinder are part of self-replicating, extrachromosomal DNA.

      (1) In the revised pipeline used to calculate ARG HGT frequencies, we categorized a total of 621 ARGs carried by 436 bvSP isolates collected in China and found that 415 of these ARGs were located on MGEs. We further investigated the distribution of these 415 ARGs across different MGEs, taking into account the complex nesting relationships among them. We observed that 90% of the ARGs (372/415) were located on plasmid contigs. It is important to clarify that this finding does not contradict our statement in the manuscript regarding plasmids and transposons as the primary reservoirs for resistome geo-temporal dissemination. This is because transposons, integrons, and prophages carrying ARGs can also be found on plasmids. Additionally, only 25 bvSG isolates from China contained ARGs, which were likely acquired via transposons or integrons located on the chromosome.  

      (2) In our manuscript, we searched for ARGs within a 5kb upstream and downstream region (a total of 10kb) of transposons and integrons (The BLASTn parameters used in the Bacant pipeline to identify transposons and integrons were set to a coverage threshold of 60%, rather than 100%). However, in light of the potential impact of LD on vertical transmission, we expanded our search to include a 10kb upstream and downstream range (a total of 20kb)  for these 25 isolates. The decision to expand the search range to 10kb upstream and downstream range is based on the following two considerations: 1) Based on literature, we determined the overall lengths of the integrons and transposons carried by the 25 isolates (Tn801, Tn6205, Tn1721, In498, In1440, In473, and In282), and found that the maximum length of these elements is ~13.5 kb. Using a 10kb upstream and downstream threshold effectively covers these integrons/transposons. 2) The limitation posed by genomic fragmentation due to next-generation sequencing, which restrict the search range. We present the results of this expanded search for colocalization of ARGs with transposons and integrons at: Figshare:  https://doi.org/10.6084/m9.figshare.28129130.v1

      We found that these results were consistent with those obtained using the previous search range.

      Taken together, these results suggest that although linkage disequilibrium may influence genetic processes within chromosomal regions—particularly for the few chromosomeassociated antibiotic resistance genes linked to integrons and transposons—the overall impact in our study is likely minimal. This conclusion is supported by the observation that 90% of the ARGs in our dataset are located on plasmids, and even an expanded search range does not alter this outcome. Additionally, by incorporating Alien Index scores and calculating out_perc, we can further confirm the occurrence of horizontal gene transfer events.

      However, it is undeniable that other studies using our current pipeline may be affected. As a temporary remedial measure, we have included a note in the "README" file  as below (https://github.com/tjiaa/Cal_HGT_Frequency):

      “Note: Considering that ARGs located on the chromosome and carried by mobile genetic elements—such as integrons and transposons—may introduce potential computational errors, we recommend evaluating the number of ARGs associated with these elements on the chromosome during your analysis. If a majority of ARGs in your dataset fall into this category, we suggest using additional methods to evaluate the potential impact of linkage disequilibrium. Additionally, by modifying the “MGE_start” and “MGE_end” parameters in the “eLife_MGE_ARG_Co_location.ipynb” script, you can assess the distance between different ARGs and integrons or transposons on the chromosome. This approach will further aid in evaluating the impact of linkage disequilibrium on the genetic process.”

      We believe this approach will assist researchers in further assessing the potential impact of vertical evolution and help other users determine whether additional methods are necessary to account for such effects.

      As the authors have now run gubbins correctly, they could use the results from this existing analysis to find recent HGT.

      We sincerely thank you for your valuable suggestion. Utilizing additional methods to predict potential horizontal gene transfer (HGT) events could indeed enhance the robustness of the results. However, "jumping genes" often occur among evolutionarily distant species, rendering the use of Gubbins potentially unsuitable for these distant HGT events.

      Furthermore, the primary focus of our study is to identify HGT of antimicrobial resistance genes (ARGs) in the Salmonella genome driven by mobile genetic elements. Therefore, we employed the HGTphyloDetect pipeline developed by Le Yuan et al. (Brief Bioinform. 2023 Mar 19;24(2):bbad035) to control for vertical evolution in the ARG sequences. The specific computational methods and conclusions have been detailed above.

      To definite mobilisation, perhaps a standard pipeline such (e.g. https://github.com/EBIMetagenomics/mobilome-annotation-pipeline) would be more convincing.

      Thank you for your valuable suggestion. We agree that defining mobilization using a standardized pipeline can add rigor and clarity to our analysis. The pipeline you referenced (https://github.com/EBI-Metagenomics/mobilome-annotation-pipeline) is an excellent resource and provides a robust approach to the identification and annotation of mobile genetic elements.

      We have examined and run this pipeline, which uses “IntegronFinder” and “ICEfinder” to detect integrons, “geNomad” to identify plasmids, and “geNomad” and “VIRify” to detect prophages. Our initial checks revealed that the numbers of integrons, plasmids, and prophages identified using this pipeline were consistent with those detected in our study. However, due to the significantly different output formats, the results from this pipeline could not be integrated with the pipeline we used for calculating HGT frequency.

      We will incorporate the standardized pipeline you suggested in future studies to further improve the reliability of our findings.

      (3) The invasiveness index is better described, but the authors still did not provide convincing evidence that the small difference is actually biologically meaningful (there was no statistical difference between the two strains provided in response Figure 6). What do other Salmonella papers using this approach find, and can their links be brought in? If there is still no good evidence, a better description of this difference would help make the conclusions better supported.

      We sincerely appreciate your thoughtful feedback. The initial introduction of the invasiveness index in our manuscript aimed to quantitatively assess the differences in invasiveness between two geographically distinct strains of S. Gallinarum (isolated from Taishun and Yueqing) by comparing the degradation of 196 top predicted genes associated with invasiveness in their genomes. We found a highly significant statistical difference (P < 0.0001) in the invasiveness index between them.

      Several studies have also employed the invasiveness index to predict biological relevance in Salmonella strains, and we believe these examples provide further context for our approach:

      (1) Caisey V. Pulford et al, Nat Microbiol, 2021, used the same method to calculate the invasiveness index for Salmonella Typhimurium and employed it to characterize the invasiveness of different lineage strains. They found that Salmonella in Lineage-3 exhibited the highest invasiveness index, suggesting an adaptation from an intestinal to a systemic lifestyle. The authors noted, "Although the invasiveness index cannot yet be experimentally validated, Salmonella isolates with different invasiveness indices produce distinct clinical symptoms in a human population (BMC Med. 2020 Jul 17; 18(1):212)". They emphasized the necessity of developing more robust methods to measure Salmonella invasiveness.

      (2) Sandra Van Puyvelde et al, Nat Commun, 2019, reported that Salmonella Typhimurium sequence type 313 (ST313) lineage II.1 exhibited a higher invasiveness index compared to lineage II, suggesting that the two lineages might have distinct adaptations to an invasive lifestyle. Further experiments demonstrated significant differences between these lineages in terms of biofilm formation (A red dry and rough (RDAR) assay) and metabolic capacity for carbon compounds.

      (3) Wim L. Cuypers et al, Nat Commun, 2023, calculated the invasiveness index for 284 global Salmonella Concord strains across different lineages and found that Lineage-4 potentially exhibited the highest invasiveness.

      Given these evidences, we acknowledge that no significant difference in mortality was observed between the L2b and L3b S. Gallinarum strains in 16-day-old SPF chicken embryos. Existing literature suggests that strains with higher invasiveness indices may still exhibit differences in biofilm formation and metabolic capacities, reflecting their adaptation to different host environments. As such, we maintain that the invasiveness index remains a valuable metric for evaluating the genomic differences between S. Gallinarum strains from Taishun and Yueqing. We plan to further investigate these differences through phenotypic experiments in our next research.

      In the revised manuscript, we have added the following discussion along with additional references:

      Lines 358-365: “Moreover, the invasiveness index of bvSP from Taishun and Yueqing suggests that different lineages of S. Gallinarum recovered from distinct regions may exhibit biological differences. Previous studies have shown that strains with higher invasiveness indexes tend to be more virulent in hosts (30, 31), potentially causing neurological or arthritic symptoms in S. Gallinarum infections. Furthermore, strains with varying invasiveness indexes have been confirmed to differ in their biofilm formation abilities and metabolic capacities for carbon compounds (32).”

      Revisions in the manuscript:

      Lines: 358-365, 806-827.

      In summary, the analysis is broadly well described and feels appropriate. Some of the conclusions are still not fully supported, although the main points and context of the paper now appear sound.

      Thank you so much for your positive evaluation of our work. We hope that the revised manuscript meets your expectations and offers a more accurate interpretation of our findings.

      Recommendations for the authors:

      Reviewer #2 (Recommendations for the authors):

      This is a great improvement over the first version and I thank the authors for a thorough response, as well as changing their conclusions in response to their improvements.

      Other small remaining issues:

      Figure 3: Heatmap of SNPs is hard to read in grayscale. It also just represents the between clade distances already shown by the tree. It would be more useful to present intraclade distances only to see the SNP resolution _within_ each lineage. Using a better colour scheme would also help.

      Thank you for your insightful comments and suggestions regarding Figure 3. We agree that the grayscale heatmap may present challenges in terms of visual clarity. To address this, we have updated the heatmap with a more distinct color gradient, ensuring better contrast and easier interpretation (New Figure 3). 

      Regarding your second suggestion: "It would be more useful to present intraclade distances only to see the SNP resolution within each lineage," we believe it is already addressed in the current version of New Figure 3. Specifically, the heatmap on the right side of New Figure 3 illustrates the SNP distances between S. Gallinarum isolates from Taishun and Yueqing, with the goal of demonstrating that genomic variation within isolates from a single region is generally smaller compared to those from different regions. In this figure, 45 newly isolated S. Gallinarum strains are categorized into two lineages: L2b and L3b. The heatmap on the right side of Figure 3 displays the SNP distances between all pairwise combinations of these 45 strains, where the intraclade distances are represented by the red regions (highlighting the pairwise distances within each lineage, specifically L3b and L2b, which are indicated by two triangles). The between-clade distances are shown by the blue regions.

      We also believe in further exploring the intraclade distances across the entire dataset of 580 S. Gallinarum strains, as it could provide additional insights. However, this analysis would extend beyond the scope of the current section.

      Revisions in the manuscript Line: 998

      Figure: Figure 3

      Please remove Figure 6c, it does not add anything to the paper and raises questions about performing this regression.

      Thank you for pointing out this issue. We have removed Figure 6c and the corresponding description in the "Results" section from the manuscript (New Figure 6).

      Revisions in the manuscript Lines: 316, 319, 1035-1041.

      Figure: Figure 6

      Again, thank you all for your time and efforts in reviewing our work. We believe the improved manuscript meets the high standards of the journal.

    1. Author response:

      The following is the authors’ response to the current reviews.

      We thank Reviewers for highlighting the strengths of our work along with suggestions for future directions.

      We agree with the Reviewers that RPS26 depletion may impact not only RAN translation initiation and codon selection (as showed in the experiments in Figure 4G), but also other mechanisms, such as speed of PIC scanning, as we stated in the discussion. Although, we did provide the data showing that mRNA of exogenous FMR1-GFP does not change upon RPS26 depletion (Figure 3B&C), hence observed effect most likely stems from translation regulation. In addition, an experiment with ASO-ACG treatment (Figure 4G) suggests that near cognate start codon selection or speed of PIC scanning may be a part of the regulation of RAN translation sensitive to RPS26 depletion. In addition, our latest unpublished results (Niewiadomska D. et al., in revision), indicate that FMRpolyG in fusion with GFP is fairly stable, in particular, while derived from long repeats (>90xCGG), suggesting that the protein stability is not at play in RPS26-dependent regulation.

      We would like to stress that in order to avoid bias in result interpretation and to mimic the natural situation, the majority of experiments concerning levels of FMRpolyG were performed in cell models with stable expression of ACG-initiated FMRpolyG. Currently, we do not possess a cell model with stable expression of AUG-initiated FMRpolyG, and the experiments based on transient transfection system would not necessarily be comparable to the results obtained in stable expression system. However, we believe that the experiment presented in Figure 2B serves as a good control for overall translation level upon RPS26 depletion indicating that RPS26 insufficiency does not affect global translation and the observed regulation is specific to some mRNAs including the one encoding FMRpolyG frame. We also show that the level of ca. 80% of identified canonical proteins, including FMRP, did not change upon RPS26 silencing (SILAC-MS, Figure 4A). Indeed, we did not explore the ribosome composition upon RPS26 and TSR2 depletion, although, most likely the pool of functional ribosomes in the cell is sufficient enough to support the basal translation level (SUnSET assays, Figure 2B & 5C). However, we cannot exclude possibility that for some mRNAs, including one encoding for FMRpolyG, the observed effect can be partially caused by lowering the number of fully active ribosomes, especially in experiments with transient transfection experiments where transgene expression is hundreds times higher than for average native mRNA.

      Finally, we agree with the Reviewer that in vitro translation assay would provide the evidence of direct effect of RPS26 on FMRpolyG level, however, we did not manage to overcome technical difficulties in obtaining cellular lysate devoid of RPS26 from vendor companies.


      The following is the authors’ response to the original reviews.

      General Comments

      We thank Reviewers for the critical comments and experimental suggestions. We considered most of the advices in the revised version of the manuscript, which allowed for a more balanced interpretation of the results presented, and further supported major statement of the manuscript that insufficiency of the RPS26 and RPS25 plays a role in modulating the efficiency of noncanonical RAN translation from FMR1 mRNA, which results in the production of toxic polyglycine protein (FMRpolyG). Firstly, performing new experiments, we showed that silencing of the RPS26 and its chaperone protein TSR2, which regulates loading/exchange of RPS26 in maturing small ribosome subunit, did not elicit global translation inhibition. Secondly, we demonstrated that in contrary to RPS26 and RPS25 depletion, silencing the RPS6 protein, a core component of 40S subunit, did not affect FMRpolyG production, further supporting the specific effect of RPS26 and RPS25 on RAN translation regulation of mutant FMR1 mRNA. We also observed that depletion of RPS26, RPS25 and RPS6 had significant negative effect on cells proliferation which is in line with previously published results indicating that insufficiencies of ribosomal proteins negatively affect cell growth. Moreover, we showed that FMRpolyG production is significantly affected by RPS26 depletion while initiated at ACG, but not other near cognate start codons. Importantly, translation of FMRP initiated at canonical AUG codon of the same mRNA upstream the CGGexp was not affected by RPS26 silencing, similarly to vast majority of the human proteome. This implies that RAN translation of FMR1 mRNA mediated by RPS26 insufficiency is likely to be dependent on start codon selection/fidelity. In essence, we provide a series of evidences indicating that cellular amount of 40S ribosomal proteins RPS26 and RPS25 is important factor of CGGrelated RAN translation regulation. Finally, we also decided to tone down our claims. Now, we state that the RPS26/25/TSR2 insufficiency or depletion, affects RAN translation, rather than composition of 40S ribosomal subunit per se influences RAN translation. We have addressed all specific concerns below and made changes to the new version of manuscript.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this manuscript, Tutak et al use a combination of pulldowns, analyzed by mass spectrometry, reporter assays, and fluorescence experiments to decipher the mechanism of protein translation in fragile X-related diseases. The topic is interesting and important.

      Although a role for Rps26-deficient ribosomes in toxic protein translation is plausible based on already available data, the authors' data are not carefully controlled and thus do not support the conclusions of the paper.

      We sincerely appreciate your rigorous, insightful, and constructive feedback throughout the revision process. We believe your guidance has been instrumental in significantly enhancing the quality of our research. Below, we have addressed your comments pointby-point.

      Strengths:

      The topic is interesting and important.

      Weaknesses:

      In particular, there is very little data to support the notion that Rps26-deficient ribosomes are even produced under the circumstances. And no data that indicate that they are involved in the RAN translation. Essential controls (for ribosome numbers) are lacking, no information is presented on the viability of the cells (Rps26 is an essential protein), and the differences in protein levels could well arise from block in protein synthesis, and cell division coupled to differential stability of the proteins.

      We agree that data presented in the first version of the manuscript did not directly address the following processes: ribosome content, global translation rate and cell viability upon RPS26 depletion. Therefore we addressed some of the issues in the revised version of the manuscript. In particular, we showed that RPS26 and TSR2 knock down did not inhibit global translation (new Figure 2B & 4C), hence we concluded that the changes of FMRpolyG level did not arise from general translational shut down. On the other hand, RPS26, RPS25 and RPS6 depletion negatively affected cells proliferation (new Figure 2A,5D,6C), which is in line with a number of previously published researches (e.g. Cheng et al, 2019; Havkin-Solomon et al, 2023). However, the rate of proliferation abnormalities is limited. We agree that observed effects on RAN translation from mutant FMR1 mRNA may stem from the combination of altered protein synthesis, conditions of the cells but also cis-acting factors of mRNA sequence/structure. In new experiments we showed that single nucleotide substitution of ACG by other near cognate start codons change sensitivity of RAN translation to insufficiency of RPS26 (new Figure 4F). Also the inhibitory effect of antisense oligonucleotide binding to the region of 5’UTR containing ACG initiation codon (ASO_ACG) is different in cells differing in amount of RPS26 (new Figure 4G).

      We also agree that our data only partially supports the role of RPS26-defficient ribosomes in RAN translation. Therefore, we have toned down our claims. Now, we state that the RPS26/25/TSR2 insufficiency or depletion affects RAN translation. We also changed the title of the manuscript to: “Insufficiency of 40S ribosomal proteins, RPS26 and RPS25, negatively affects biosynthesis of polyglycine-containing proteins in fragile-X associated conditions” (Previously it was: “Ribosomal composition affects the noncanonical translation and toxicity of polyglycine-containing proteins in fragile X-associated conditions”.

      Specific points:

      (1) Analysis of the mass spec data in Supplemental Table S3 indicates that for many of the proteins that are differentially enriched in one sample, a single peptide is identified. So the difference is between 1 peptide and 0. I don't understand how one can do a statistical analysis on that, or how it would give out anything of significance. I certainly do not think it is significant. This is exacerbated by the fact that the contaminants in the assay (keratins) are many, many-fold more abundant, and so are proteins that are known to be mitochondrial or nuclear, and therefore likely not actual targets (e.g. MCCC1, PC, NPM1; this includes many proteins "of significance" in Table S1, including Rrp1B, NAF1, Top1, TCEPB, DHX16, etc...).

      The data in Table S6/Figure 3A suffer from the same problem.

      I am not convinced that the mass spec data is reliable.

      We thank Reviewer for the comment concerning MS data; however, we believe that it may stem from misunderstanding of the data presented in Table S3 and S6. Both tables represent the output from MaxQuant analysis (so-called ProteinGroup) of MS .raw files, without any filtering. As stated in the Material&Methods, we applied default parameters suggested by MaxQuant developers to analyze MS data, these include identification of proteins based on at least 1 unique peptide, and thus some of the proteins with only 1 unique peptide are shown in Tables S1 and S3. Reviewer is also right that in this output table common contaminants, such as keratins are included. However, these identifications are denoted as “CON_”, and are further filtered out during statistical analysis in Perseus software. During the statistical analysis we first filtered out irrelevant protein groups identifications, such as contaminants, or only identified by site modifications.

      We have changed the names of Supplementary Table files, giving more detailed description. We hope this will help to avoid misunderstanding for broader public. Secondly, when comparing the data presented in Table S3 and volcano plot presented in Figure 1B, one can notice that indeed the majority of identified proteins are not statistically significant (grey points), thus not selected for further stratification. Lack of significance of these proteins may be partially due to poor MS identification, however, they are not included in the following parts of the manuscript. Further, we selected only eight proteins (out of over 150) for stratification by orthogonal techniques, thus we argue that this step validates the biological relevance of chosen candidate RAN-translation modifiers. One should also keep in mind that pull down samples analyzed by MS often yield lower intensity and identification rates, when comparing to whole cell analysis, as a result of lower protein input or stringent washes used during sample preparation.

      Regarding the data presented in Table S6 (SILAC data), we argue that these data are of very good quality. More than 2,000 proteins were identified in a 125min gradient, with over 80% of proteins that were identified with at least 2 unique peptides. Each of three biological replicates was analyzed three times (technical replicates), giving total of 9 high resolution MS runs. Together, we strongly believe that this data is of high confidence.

      (2) The mass-spec data however claims to identify Rps26 as a factor binding the toxic RNA specifically. The rest of the paper seeks to develop a story of how Rps26-deficient ribosomes play a role in the translation of this RNA. I do not consider that this makes sense.

      Indeed, we identified RPS26 as a protein that co-precipitated with FMR1 containing expanded CGG repeats (Supplementary Figure 1G) and found that depletion of RPS26 hindered RAN translation of FMRpolyG, suggesting that RPS26 positively affects RAN translation. However, we did not state that RPS26 directly interacts with toxic RNA. In order to confirm the specificity of RAN translation regulation by RPS26 insufficiency, we tested whether depletion of other 40S ribosomal protein, RPS6, affects FMRpolyG synthesis. Our experiments showed that there was no any significant effect on RAN translation efficiency post RPS6 silencing (new Figure 5C). Importantly, we showed that RPS26 depletion did not inhibit global translation (new Figure 2B). In addition, mutagenesis of near-cognate start codon (new Figure 4F) and ASO_ACG treatment (new Figure 4G) provided the evidences that modulation of FMRpolyG biosynthesis by RPS26 level may depend on start codon selection. In essence, our data suggest that RPS26 depletion specifically affects synthesis of FMRpolyG, but not FMRP derived from the same FMR1 mRNA with CGGexp. However, we do not claim that the observed effect is the consequence of a direct interaction between RPS26 and 5’UTR of FMR1 mRNA. Downregulation of FMRpolyG biosynthesis could be an outcome of the alteration of ribosomal assembly, decrease of efficiency and fidelity of PIC scanning/initiation or impeded elongation or a combination of all these processes. In the manuscript we presented the results of experiments which tested many of these possibilities.

      (3) Rps26 is an essential gene, I am sure the same is true for DHX15. What happens to cell viability? Protein synthesis? The yeast experiments were carefully carried out under experiments where Rps26 was reduced, not fully depleted to give small growth defects.

      We agree with the Reviewer that RPS26 and DHX15 are essential proteins, similarly to all RNA binding proteins, and caution should be taken during experimental design. To address this, we titrated different concentrations of siRPS26, and found that administration of 5 nM siRPS26, which just partially silenced RPS26, decreased FMRpolyG by around 50% (new Figure 1D). This impact was even greater with 15 nM siRPS26, as we observed around 80% decrease of FMRpolyG.

      Havkin-Solomon et al. (2023), showed that proliferation rate is decreased in cells with mutated C-terminus of RPS26, which is required for contacting mRNA. In accordance with this study, we showed that cells with knocked down RPS26 proliferate less efficiently (new Figure 2A), but depletion of RPS26 did not impact the global translation (new Figure 2B). In addition, our SILAC-MS data indicates that ~80% of proteins with determined expression level were not affected by RPS26 insufficiency, and ~20% of the proteins turned out to be sensitive to RPS26 decrease. Although, these data do not take into account the protein stability.

      (4) Knockdown efficiency for all tested genes must be shown to evaluate knockdown efficiency.

      The current version of the manuscript contains representative western blots with validation of knock-down efficiency (for example in Figure 3B, C, E, Figure 6A) and we included knock-down validations where applicable (Figures 1D, 2B, 4G and 5C).

      (5) The data in Figure 1E have just one mock control, but two cell types (control si and Rps26 depletion).

      Mock control corresponds to the cells treated with lipofectamine reagent and was included in the study to determine the “background” signal from cells treated with delivery agent and reagents used to measure the apoptosis process. These cells were neither expressing FMRpolyG, nor siRNAs. Luminescence signals were normalized to the values obtained from mock control. We added more details describing this assay in the Figure 1 legend.

      (6) The authors' data indicate that the effects are not specific to Rps26 but indeed also observed upon Rps25 knockdown. This suggests strongly that the effects are from reduced ribosome content or blocked protein synthesis. Additional controls should deplete a core RP to ascertain this conclusion.

      We agree that observed effects may stem from reduced ribosome content, however, we argue that this is the only possibility and explanation. Previously, it was shown that RPS25 regulates G4C2-related RAN translation, but knock out of RPS25 does not affect global translation (Yamada S, 2019, Nat. Neuroscience). Similarly, we showed that KD of RPS26 or TSR2 did not reduce significantly global translation rate (SUnSET assay; new Figure 2B and 5C, respectively).

      Moreover, in a new version of manuscript we included a control experiment, where we silenced core ribosomal protein (RPS6) and found that RPS6 depletion did not affect RAN translation from mutant FMR1 mRNA (new Figure 5C), thus strengthening our conclusion about specific RAN translation regulation by the level of RPS26 and RPS25.

      Finally, our observation aligns well with current knowledge about how deficiency of different ribosomal proteins alters translation of some classes of mRNAs (Luan Y, 2022, Nucleic Acids Res; Cheng Z, 2019, Mol Cell). It was shown that depletion of RPS26 affects translation rate of different mRNAs compared to depletion of other proteins of small ribosomal subunit.

      (7) Supplemental Figure S3 demonstrates that the depletion of S26 does not affect the selection of the start codon context. Any other claim must be deleted. All the 5'-UTR logos are essentially identical, indicating that "picking" happens by abundance (background).

      Supplementary Figure 3D represents results indicating that the mutation in -4 position (from G to A) did not affect the RAN translation regardless of RPS26 presence or depletion. However, this result does not imply that RPS26 does not affect the selection of start codon of sequence- or RNA structure-context. We verified this particular -4 position, as it was suggested previously as important RPS26-sensitive site in yeasts (Ferretti M, 2017, Nat Struct Mol Biol). We agree with Reviewer that all 5’UTR logos presented in our paper did not show statistical significance for neither tested position for human mRNAs. On the contrary, we observed that regulation sensitive to RPS26 level depends on the selection of start codon of RAN translation, in particular ACG initiation (new Figure 4F&G). RPS26 depletion affected ACG-initiated but not GTG- or CTG-initiated RAN translation.

      In the previous version of the manuscript, we wrote that we did not identify any specific motifs or enrichment within analyzed transcripts in comparison to the background. On the other hand, we found that the GC-content among analyzed transcripts is higher within 5’UTRs and in close proximity to ATG in coding sequences (Figure 4D), what suggests the importance of RNA stable structures in this region. In addition, we showed that mRNAs encoding proteins responding to RPS26 depletion have shorter than average 5’UTRs (new Figure 4E).

      (8) Mechanism is lacking entirely. There are many ways in which ribosomes could have mRNA-specific effects. The authors tried to find an effect from the Kozak sequence, unsuccessfully (however, they also did not do the experiment correctly, as they failed to recognize that the Kozak sequence differs between yeast, where it is A-rich, and mammalian cells, where it is GGCGCC). Collisions could be another mechanism.

      Indeed, collisions as well as other mechanisms such as skewed start codon fidelity may have an effect on efficiency of FMRpolyG biosynthesis. In the current version of the manuscript, we show that RPS26 amount-sensitive regulation seems to be start codonselection dependent (new Figure 4F&G).

      Reviewer #2 (Public Review):

      Summary:

      Translation of CGG repeats leads to the accumulation of poly G, which is associated with neurological disorders. This is a valuable paper in which the authors sought out proteins that modulate RAN translation. They determined which proteins in Hela cells bound to CGG repeats and affected levels of polyG encoded in the 5'UTR of the FMR1 mRNA. They then showed that siRNA depletion of ribosomal protein RPS26 results in less production of FMR1polyG than in control. There are data supporting the claim that RPS26 depletion modulates RAN translation in this RNA, although for some results, the Western results are not strong. The data to support increased aggregation by polyG expression upon S26 KD are incomplete.

      We thank the Reviewer for critical comments and suggestions. We sincerely appreciate your rigorous, insightful, and constructive feedback throughout the revision process.

      Below each specific point, we addressed the mentioned issues.

      Strengths:

      The authors have proteomics data that show the enrichment of a set of proteins on FMR1 RNA but not a related RNA.

      We thank Reviewer for appreciation of provided MS-screening results, which identified proteins enriched on FMR1 RNA with expanded CGG repeats.

      Weaknesses:

      - It is insinuated that RPS26 binds the RNA to enhance CGG-containing protein expression. However, RPS26 reduction was also shown previously to affect ribosome levels, and reduced ribosome levels can result in ribosomes translating very different RNA pools.

      In previous version of the manuscript we did not state that RPS26 binds directly to RNA with expanded CGG repeats and we did not show the experiment indicating direct interaction between studied RNA and RPS26. What we showed is that RPS26 was enriched on FMR1 RNA MS samples, however, we did not verify whether it is direct or indirect interaction. We also tried to test hypothesis that lack of RPS26 in PIC complex may affect efficiency of RAN translation initiation via specific, previously described in yeast Kozak context (Ferretti M, 2017, Nat Struct Mol Biol). As we described this hypothesis was negatively validated. However, we showed that other features of 5’UTR sequences (e.g. higher GC-content or shorter leader sequence) are potentially important for translation efficiency in cells with depleted RPS26.

      Indeed, RPS26 is involved in 40S maturation steps (Plassart L, 2021, eLife) and its insufficiency or mutations or blocking its inclusion to 40S ribosome may result in incomplete 40S maturation, which subsequently might negatively affect translation per se. However, we did not observe global translation inhibition after RPS26 depletion or depletion of TSR2, the chaperon involved in incorporation/exchange RPS26 to small ribosomal subunit (new Figure 2B and 5C). In addition, our SILAC-MS data indicates that majority of studied proteins (including FMRP, the main product of FMR1 gene) were not affected by RPS26 depletion which can be carefully extrapolated to global translation. In revised manuscript we also showed that relatively low silencing of RPS26 also decreased FMRpolyG production in model cells (new Figure 1D).

      We agree that reduced ribosome levels can result in different efficiency of translation of different RNA pools. We enhance this statement in revised manuscript. However, we also showed that the same mRNA containing different near cognate start codons (single/two nucleotide substitution) specific to RAN translation, or targeting this codon with antisense oligonucleotides resulted in altered sensitivity of FMR1 mRNA translation to RPS26 depletion (new Figure 4F).

      - A significant claim is that RPS26 KD alleviates the effects of FMRpolyG expression, but those data aren't presented well.

      We thank the Reviewer for this comment. In the new version of the manuscript, we have added new microscopic images and improved the explanation of Figure 1E. We have also completed the interpretation of Figure 1F in the main text, figure image as well as figure legend, and we hope that these changes will ameliorate understanding of our data.

      Recommendations For The Authors:

      - A significant claim is that RPS26 KD alleviates the effects of FMR polyG expression, but those data aren't presented well:

      Figure 1D (supporting data in S2) and 2D - the authors need to show representative images of a control that has aggregation and indicate aggregates being counted on an image. The legend states that there are no aggregates, but the quantification of aggregates/nucleus is ~1, suggesting there are at least 1 per cell. It is preferred to show at least a representative of what is quantified in the main figure instead of a bar graph.

      The representative images of control and siRPS26-treated cells are now shown in revised version of Figure 1E. Additionally, we completed the Figure legend concerning this part, as well as extended description of the experiment in Materials&Methods section.

      Figure 1E - it is unclear what luminescence signal is being measured. Is this a dye for an apoptotic marker? More information is needed in the legend.

      This information was added to the legend of modified Figure 1F (previously 1E) as suggested.

      - Some of the Western blots are not very convincing. Better evidence for the changes in bar graphs would improve how convincing the data are:

      Fig 2B. The western for FMR95G in the first model is not very convincing. The difference by eye for the second siRNA seems to give a larger effect than the first for 95G construct but they appear almost the same on the graph. More supporting information for the quantification is needed.

      We provided better explanation for WB quantification in M&M section in the manuscript. Alos, we provided additional blot demonstrating independent biological replicate of the mentioned experiment in supplementary materials (Supplementary Figure S2E).

      Figure 4A, the blots for RPS26 and FMR95G are not convincing. They are quite smeary compared to all of the others shown for these proteins in other figures. Could a different replicate be shown?

      We provided additional blot demonstrating the effect on transiently expressed FMRpolyG affected by depletion of TSR2 in COS7 cell line (Supplementary Figure S4A).

      Figure 5A and 5B blots are not ideal. Could a different replicate be shown? Or show multiple replicates in the supplemental figure?

      We provided additional blots from the same experiment, although data is not statistically significant, most likely due to low quality of normalization factor, which is Vinculin (Supplementary Figure S5A). Nevertheless, the level of FMRpolyG is decreased by ~70% after RPS25 silencing in SH-SY5Y cells.

      Figure 2C. Please use the same y axes for all four Westerns in B and C. One would like to compare 95 and 15 repeats, but it is difficult when the y axes are different.

      Thank you for this comment. The y axis was adjusted as suggested by the Reviewer.

      Figure 3D-The text suggests a significant difference between positive and negative responders that is not clear in the figure.

      In the main body of the manuscript we state that: “We did not observe any significant differences in the frequency of individual nucleotide positions in the 20-nucleotide vicinity of the start codon relative to the expected distribution in the BG”, which is in line with the graph showed in Figure 4D (previously 3D).

      Reviewer #3 (Public Review):

      Tutak et al provide interesting data showing that RPS26 and relevant proteins such as TSR2 and RPS25 affect RAN translation from CGG repeat RNA in fragile X-associated conditions. They identified RPS26 as a potential regulator of RAN translation by RNAtagging system and mass spectrometry-based screening for proteins binding to CGG repeat RNA and confirmed its regulatory effects on RAN translation by siRNA-based knockdown experiments in multiple cellular disease models and patient-derived fibroblasts. Quantitative mass spectrometry analysis found that the expressions of some ribosomal proteins are sensitive to RPS26 depletion while approximately 80% of proteins including FMRP were not influenced. Since the roles of ribosomal proteins in RAN translation regulation have not been fully examined, this study provides novel insights into this research field. However, some data presented in this manuscript are limited and preliminary, and their conclusions are not fully supported.

      (1) While the authors emphasized the importance of ribosomal composition for RAN translation regulation in the title and the article body, the association between RAN translation and ribosomal composition is apparently not evaluated in this work. They found that specific ribosomal proteins (RPS26 and RPS25) can have regulatory effects on RAN translation (Figures 1C, 2B, 2C, 2E, 4A, 5A, and 5B), and that the expression levels of some ribosomal proteins can be changed by RPS26 knockdown (Figure 3B, however, the change of the ribosome compositions involved in the actual translation has not been elucidated). Therefore, their conclusive statement, that is, "ribosome composition affects RAN translation" is not fully supported by the presented data and is misleading.

      We thank the Reviewer for critical comments and suggestions. We agree that the initial title and some statements in the text were misleading and the presented data did not fully support the aforementioned statement regarding ribosomal composition affecting FMRpolyG synthesis. Therefore, in the revised version of the manuscript we included a control experiment indicating that depletion of another core 40S ribosomal protein (RPS6) did not impact the FMRpolyG synthesis (new Figure 5C), which supports our hypothesis that RPS26 and RPS25 are specific CGG-related RAN translation modifiers. To precisely deliver a main message of our work, we changed the title that will indicate the specific effect of RPS26 and RPS25 insufficiency on RAN translation of FMRpolyG. Proposed title: “Insufficiency of 40S ribosomal proteins, RPS26 and RPS25 negatively affects biosynthesis of polyglycine-containing proteins in fragile-X associated conditions”. We also changed all statements regarding “ribosomal composition” in main text of the new version of manuscript.

      (2) The study provides insufficient data on the mechanisms of how RPS26 regulates RAN translation. Although authors speculate that RPS26 may affect initiation codon fidelity and regulate RAN translation in a CGG repeat sequence-independent manner (Page 9 and Page 11), what they really have shown is just identification of this protein by the screening for proteins binding to CGG repeat RNA (Figure 1A, 1B), and effects of this protein on CGG repeat-RAN translation. It is essential to clarify whether the regulatory effect of RPS26 on RAN translation is dependent on CGG repeat sequence or near-cognate initiation codons like ACG and GUG in the 5' upstream sequence of the repeat. It would be better to validate the effects of RPS26 on translation from control constructs, such as one composed of the 5' upstream sequence of FMR1 with no CGG repeat, and one with an ATG substitution in the 5' upstream sequence of FMR1 instead of near-cognate initiation codons.

      We agree that the data presented in the manuscript implies that insufficiency of RPS26 plays a pivotal role in the regulation of CGG-related RAN translation and in the revised version of the manuscript we included a series of experiments indicating that ACG codon selection seems to be an important part of RPS26 level-dependent regulation of polyglycine production (new Figure 4F&G; see point 3 below for more details). Importantly, in the luciferase assay showed on Figure 4F we used the AUG-initiated firefly luciferase reporter as normalization control.

      Moreover, to verify if FMRpolyG response to RPS26 deficiency depends on the type of reporter used, we repeated many experiments using FMRpolyG fused with different tags. The luciferase-based assays were in line with experiments conducted on constructs with GFP tag (new Figure 1D), thus strengthening our previous data. Moreover, in the series of experiments, we show that FMRP synthesis which is initiated from ATG codon located in FMR1 exon 1, was not affected by RPS26 depletion (Figure 3E & 4C), even though its translation occurs on the same mRNA as FMRpolyG. This indicates a specific RPS26 regulation of polyglycine frame initiated from ACG near cognate codon.

      (3) The regulatory effects of RPS26 and other molecules on RAN translation have all been investigated as effects on the expression levels of FMRpolyG-GFP proteins in cellular models expressing CGG repeat sequences Figures 1C, 2B, 2C, 2E, 4A, 5A, and 5B). In these cellular experiments, there are multiple confounding factors affecting the expression levels of FMRpolyG-GFP proteins other than RAN translation, including template RNA expression, template RNA distribution, and FMRpolyG-GFP protein degradation. Although authors evaluated the effect on the expression levels of template CGG repeat RNA, it would be better to confirm the effect of these regulators on RAN translation by other experiments such as in vitro translation assay that can directly evaluate RAN translation.

      We agree that there are multiple factors affecting final levels of FMRpolyG-GFP proteins including aforementioned processes. We evaluated the level of FMR1 mRNA, which turned out not to be decreased upon RPS26 depletion (Figure 3B&C), therefore, we assumed that what we observed, was the regulation on translation level, especially that RPS26 is a ribosomal protein contacting mRNA in E-site. We believe that direct assays such as in vitro translation may be beneficial, however, depletion of RPS26 from cellular lysate provided by the vendor seems technically challenging, if not completely impossible. Instead, we focused on sequence/structure specific regulation of RAN translation with the emphasis on start-codon initiation selection. It resulted in generating the valuable results pointing out the RPS26 role in start codon fidelity (Figure 4F&G). These new results showed that translation from mRNAs differing just in single or two nucleotide substitution in near cognate start codon (ACG to GUG or ACG to CUG), although results in exactly the same protein, is differently sensitive to RPS26 silencing (new Figure 4F). Similar differences were observed for translation efficiency from the same mRNA targeted or not with antisense oligonucleotide complementary to the region of RAN translation initiation codon (new Figure 4G). These results also suggest that stability of FMRpolyG is not affected in cells with decreased level of RPS26.

      (4) While the authors state that RPS26 modulated the FMRpolyG-mediated toxicity, they presented limited data on apoptotic markers, not cellular viability (Figure 1E), not fully supporting this conclusion. Since previous work showed that FMRpolyG protein reduces cellular viability (Hoem G, 2019,Front Genet), additional evaluations for cellular viability would strengthen this conclusion.

      We thank the Reviewer for this suggestion. We addressed the apoptotic process in order to determine the effect of RPS26 depletion on RAN translation related toxicity (Figure 1F). In revised version of the manuscript, we also added the evaluation on how cells proliferation was affected by RPS26, RPS25, RPS6 and TSR2 depletion. Our data indicate that TSR2 silencing slightly impacted the cellular fitness (new Figure 5D), whereas insufficiencies of RPS26, RPS25 and RPS6 had a much stronger negative effect on proliferation (new Figure 2A, 5D, 6C), which is in line with previous data (Cheng Z 2019, Mol Cell; Luan Y, 2022, Nucleic Acids Res). The difference in proliferation rate after treatment with siRPS26 makes proper interpretation of cellular viability assessment very difficult.

      Recommendations For The Authors:

      (1) It would be nice to validate the effects of overexpression of RPS26 and other regulators on RAN translation, not limited to knockdown experiments, to support the conclusion.

      We did not performed such experiments because we believed that RPS26 overexpression may have no or marginal effect on translation or RAN translation. It is likely impossible to efficiently incorporate overexpressed RPS26 into 40S subunits, because the concentration of all ribosomal proteins in the cells is very high.

      (2) It would be better to explain how authors selected 8 proteins for siRNA-based validation (Figure 1C, 1D, S1D) from 32 proteins enriched in CGG repeat RNA in the first screening.

      We selected those candidates based on their functions connected to translation, structured RNA unwinding or mRNA processing. For example, we tested few RNA helicases because of their known function in RAN translation regulation described by other researchers. This explanation was added to the revised version of the manuscript.

      (3) Original image data showing nuclear FMRpolyG-GFP aggregates should be presented in Figure 1D.

      The representative images of control and siRPS26-treated cells are now shown in modified version of Figure 1E and described with more details in the legend.

      (4) Image data in Figure 2A and 2D have poor signal/noise ratio and the resolution should be improved. In addition, aggregates should be clearly indicated in Figure 2D in an appropriate manner.

      The stable S-FMR95xG cellular model is characterized by very low expression of RANtranslated FMR95xG, therefore, it is challenging to obtain microscopic images of better quality with higher GFP signal. In the L-99xCGG model expression of transgene is higher. Therefore, we provided new image in the new version of Figure 3D (former 2D). Moreover, we showed aggregates on the image obtained using confocal microscopy (new Supplementary Figure 2D).

      (5) The detailed information on patient-derived fibroblast (age and sex of the patient, the number of CGG repeats, etc.) in Figure 2F needed to be presented.

      This information was added to the figure legend (Figure 3F; previously 2F) and in the Material and Methods section as suggested.

      (6) It would be better to normalize RNA expression levels of FMR1 and FMR1-GFP by the housekeeping gene in Figure S2C, like other RT-qPCR experimental data such as Figure 2B.

      Normalization of FMR1-GFP to GAPDH is now shown in modified version of Figure S2C (right graph) as requested by the Reviewer.

      (7) It would be better to add information on molecular weight on all Western blotting data.

      (8) Marks corresponding to molecular weight ladder were added to all images.

      Full blots, including protein ladders were deposited in Zenodo repository, under doi: 10.5281/zenodo.13860370

      References

      Cheng Z, Mugler CF, Keskin A, Hodapp S, Chan LYL, Weis K, Mertins P, Regev A, Jovanovic M & Brar GA (2019) Small and Large Ribosomal Subunit Deficiencies Lead to Distinct Gene Expression Signatures that Reflect Cellular Growth Rate. Mol Cell 73: 36-47.e10

      Havkin-Solomon T, Fraticelli D, Bahat A, Hayat D, Reuven N, Shaul Y & Dikstein R (2023) Translation regulation of specific mRNAs by RPS26 C-terminal RNA-binding tail integrates energy metabolism and AMPK-mTOR signaling. Nucleic Acids Res 51: 4415–4428

      Hoem,G., Larsen,K.B., Øvervatn,A., Brech,A., Lamark,T., Sjøttem,E. and Johansen,T. (2019) The FMRpolyGlycine protein mediates aggregate formation and toxicity independent of the CGG mRNA hairpin in a cellular model for FXTAS. Front. Genet., 10, 1–18.

      Luan Y, Tang N, Yang J, Liu S, Cheng C, Wang Y, Chen C, Guo YN, Wang H, Zhao W, et al (2022) Deficiency of ribosomal proteins reshapes the transcriptional and translational landscape in human cells. Nucleic Acids Res 50: 6601–6617

      Plassart L, Shayan R, Montellese C, Rinaldi D, Larburu N, Pichereaux C, Froment C, Lebaron S, O’donohue MF, Kutay U, et al (2021) The final step of 40s ribosomal subunit maturation is controlled by a dual key lock. Elife 10

    1. Author response:

      The following is the authors’ response to the original reviews.

      It would be great if the authors could add clarification about the NMDS analyses and the associated results (Fig. 1, Table 1 and Tables S2-4). The overall aim of these analyses was to see how plot characteristics (e.g. canopy cover) and composition of one taxonomic group were related to the composition of another taxonomic group. The authors quantified species composition by two axes from NMDS. (1) This analysis may yield an interpretation problem: if we only find one of the axes, but not the other, was significantly related to one variable, it would be difficult to determine whether that specific variable is important to the species composition because the composition is co-determined by two axes. (2) It is unclear how the authors did the correlation analyses for Tables S2-4. If correlation coefficients were presented in these tables, then these coefficients should be the same or very similar if we switch the positions of y vs. x. That is, the correlation between host vs. parasite phylogenetic composition would be very close to the correlation between parasite vs. phylogenetic composition, but not as the author found that these two relationships were quite different, leading to the interpretation of bottom-up or top-down processes. It is also unclear which correlation coefficient was significant or not because only one P value was provided per row in these tables. (3) In addition to the issues of multiple axes (point 1), NMDS axes simply define the relative positions of the objects in multi-dimensional space, but not the actual dissimilarities. Other methods, such as generalized dissimilarity modeling, redundancy analysis and MANOVA, can be better alternatives.

      Thank you for the thorough and constructive review. We have taken the concerns and questions raised by the editors and reviewers into account and provided clarification about the NMDS analyses as well as additional analyses to confirm our results. First, we have now added a brief explanation in the manuscript regarding the interpretation of the two NMDS axes and how they relate to species composition. Specifically, we clarified that while NMDS defines the relative positions of objects in multi-dimensional space, the two axes together provide a more comprehensive representation of the community composition, which is not solely determined by either axis independently. Second, we acknowledge that alternative approaches could help further strengthen our conclusions. To address this, we incorporated Mantel tests and PERMANOVA (with ‘adonis2’) as additional validation methods. These analyses allowed us to summarize compositional patterns while testing our hypotheses within the framework of the plot characteristics and taxonomic relationships. We have added these analyses and their results in the manuscript to reinforce our findings.

      In methods: L478-481 “To strengthen the robustness of our findings based on NMDS, we further validated the results using Mantel test and PERMANOVA (with ‘adonis2’) for correlation between communities and relationships between communities and environmental variables.”

      L469-475 “NMDS was used to summarize the variation in species composition across plots. The two axes extracted from the NMDS represent gradients in community composition, where each axis reflects a subset of the compositional variation. These axes should not be interpreted in isolation, as the overall species composition is co-determined by their combined variation. For clarity, results were interpreted based on the relationships of variables with the compositional gradients captured by both axes together."

      In results: L172-177 “The PERMANOVA analysis also highlighted the important role of canopy cover for host and parasitoid community (Table S6-9). The Mantel test revealed a consistent pattern with the NMDS analysis, highlighting a pronounced relationship between the species composition of hosts and parasitoids (Table S10). However, the correlation between the phylogenetic composition of hosts and parasitoids was not significant.”

      In discussion: L257-261 “However, this significant pattern was observed only in the NMDS analysis and not in the Mantel test, suggesting that the non-random interactions between hosts and parasitoids could not be simply predicted by their community similarity and associations between the phylogenetic composition of hosts and parasitoids are more complex and require further investigation in the future.”

      -- One additional minor point: "site" would be better set as a fixed rather than random term in the linear mixed-effects models, because the site number (2) is too small to make a proper estimate of random component.

      Now we treated “site” as a fixed factor in our models, interacting with tree species richness/tree MPD and tree functional diversity to reflect the variation of spatial and tree composition between the two sites. We found the main results did not change, as both sites showed consistent patterns for effects of tree richness/MPD on network metrics, which is more pronounced in one site.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The authors analyzed how biotic and abiotic factors impact antagonistic host-parasitoid interaction systems in a large BEF experiment. They found the linkage between the tree community and host-parasitoid community from the perspective of the multi-dimensionality of biodiversity. Their results revealed that the structure of the tree community (habitat) and canopy cover influence host-parasitoid compositions and their interaction pattern. This interaction pattern is also determined by phylogenetic associations among species. This paper provides a nice framework for detecting the determinants of network topological structures.

      Strengths:

      This study was conducted using a five-year sampling in a well-designed BEF experiment. The effects of the multi-dimensional diversity of tree communities have been well explained in a forest ecosystem with an antagonistic host-parasitoid interaction.

      The network analysis has been well conducted. The combination of phylogenetic analysis and network analysis is uncommon among similar studies, especially for studies of trophic cascades. Still, this study has discussed the effect of phylogenetic features on interacting networks in depth.

      Weaknesses:

      (1) The authors should examine species and interaction completeness in this study to confirm that their sampling efforts are sufficient.

      (2) The authors only used Rao's Q to assess the functional diversity of tree communities. However, multiple metrics of functional diversity exist (e.g., functional evenness, functional dispersion, and functional divergence). It is better to check the results from other metrics and confirm whether these results further support the authors' results.

      (3) The authors did not elaborate on which extinction sequence was used in robustness analysis. The authors should consider interaction abundance in calculating robustness. In this case, the author may use another null model for binary networks to get random distributions.

      (4) The causal relationship between host and parasitoid communities is unclear. Normally, it is easy to understand that host community composition (low trophic level) could influence parasitoid community composition (high trophic level). I suggest using the 'correlation' between host and parasitoid communities unless there is strong evidence of causation.

      Thank you very much for your thoughtful and constructive review of our manuscript. We have carefully addressed your comments and made several revisions to improve the clarity and robustness of our work.1) We appreciate your suggestion regarding species and interaction completeness. To confirm that our sampling efforts were sufficient, we have now included a figure (Fig. S1) showing the species accumulation curve and the coverage of interactions in our study. This ensures that the data collected provide a comprehensive representation of the system. 2) Regarding the use of only Rao’s Q to assess functional diversity, we acknowledge that multiple metrics of functional diversity exist. However, due to the large number of predictors in our analysis, we decided to streamline our approach and focus on Rao’s Q as it provides a robust measure for our research objectives. We have discussed this decision in the revised manuscript and clarified that, while additional metrics could be informative, we believe Rao’s Q sufficiently captures the key aspects of functional diversity in our study. 3) We have elaborated on the robustness analysis and the null model used in our study. Specifically, we now clarified which extinction sequence (random extinction) was used in our manuscript, and explained interaction abundance was incorporated into the robustness calculations (networklevel function, weighted=TURE; see L506). 4) We have revised the text to clarify the relationship between host and parasitoid communities. As you correctly pointed out, while it is intuitive that host community composition influences parasitoid community composition, we have reframed our analysis to emphasize the correlation between the two communities rather than implying causation without strong evidence. We have revised the manuscript to reflect this distinction.

      Reviewer #2 (Public Review):

      Summary:

      In their manuscript, Multi-dimensionality of tree communities structure host-parasitoid networks and their phylogenetic composition, Wang et al. examine the effects of tree diversity and environmental variables on communities of reed-nesting insects and their parasitoids. Additionally, they look for the correlations in community composition and network properties of the two interacting insect guilds. They use a data set collected in a subtropical tree biodiversity experiment over five years of sampling. The authors find that the tree species, functional, and phylogenetic diversity as well as some of the environmental factors have varying impacts on both host and parasitoid communities. Additionally, the communities of the host and parasitoid showed correlations in their structures. Also, the network metrices of the host-parasitoid network showed patterns against environmental variables.

      Strengths:

      The main strength of the manuscript lies in the massive long-term data set collected on host-parasitoid interactions. The data provides interesting opportunities to advance our knowledge on the effects of environmental diversity (tree diversity) on the network and community structure of insect hosts and their parasitoids in a relatively poorly known system.

      Weaknesses:

      To me, there are no major issues regarding the manuscript, though sometimes I disagree with the interpretation of the results and some of the conclusions might be too far-fetched given the analyses and the results (namely the top-down control in the system). Additionally, the methods section (especially statistics) was lacking some details, but I would not consider it too concerning. Sometimes, the logic of the text could be improved to better support the studied hypotheses throughout the text. Also, the results section cannot be understood as a stand-alone without reading the methods first. The study design and the rationale of the analyses should be described somewhere in the intro or presented with the results.

      Thank you very much for your valuable comments and suggestions on our manuscript! We appreciate your feedback and have made revisions accordingly. Specifically, we have rephrased the interpretation of the results and conclusions to better align with the analyses and avoid overstatements, particularly concerning the top-down control in the system. In addition, we have expanded the methods section by providing more details, especially regarding the statistical approaches, to address the points you raised. To enhance the clarity of the manuscript, we have also ensured that the logic of the text better supports the hypotheses throughout. Please see our point-by-point responses below for additional clarifications.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Line 120: "... and large ecosystems susceptible to global change (add citation here)": Citation(s)?

      Now we provided the missed citations.

      Line 141: Add sampling completeness information.

      Now we provide a new figure about sampling completeness (Fig. S1) in the supplementary materials, showing the adequate sampling effort for our study.

      Line 151: use more metrics in the evaluation of functional diversity

      We used tree functional diversity Rao’s Q, which is an integrated and wildly used metric to represent functional dissimilarity of trees. As our study focus on multiple diversity indices of trees, it would be better to do not pay more attention to one type of diversity. Thank you for your suggestion!

      Line 164: host vulnerability. Although generality and vulnerability are commonly used in network analysis, it is better to link these metrics with the trophic level, like the 'host vulnerability' you used. Thus, you can use 'parasitoid generality' instead of 'generality'.

      Thanks for your suggestion. Now the metrics were labeled with the trophic levels in the full text.

      Line 169: two'.'

      Corrected.

      Line 173: 'parasitoid robustness' Or 'robustness of parasitoids'?

      Now changed it to ‘robustness of parasitoid’.

      Lines 173, 468: For the robustness estimations, maybe use null model for binary networks to get random distributions?

      Thanks for the suggestion. Actually, we have used Patefield null models to compare the randomized robustness and observed, helping to assess whether the robustness of the observed network is significantly different compared to expected by chance. All robustness indices across plots were significantly different from a random distribution, See results section L197-201.

      Line 184: modulating interacting communities of hosts and parasitoids.

      Changed accordingly.

      Line 186: determined host-parasitoid interaction patterns

      Changed accordingly.

      Line 191: Biodiversity loss in this study refers to low trophic levels.

      Now we clarified this point.

      Line 190: understand

      Changed accordingly.

      Lines 215-216: Reorganize these sentences

      Line 227: indirectly influenced by...

      Changed accordingly.

      Line 238: Be more specific. Which type of further study?

      Rephased it more specific.

      Lines 297-299: rewrite this sentence to make it more transparent.

      Now we rewrote the sentence accordingly.

      Line 302: Certain

      Changed accordingly.

      Line 453: effective

      Changed accordingly.

      Finally, the authors should check the text carefully to avoid grammatical errors.

      Thanks, now we have checked the full text to avoid grammatical errors.

      Reviewer #2 (Recommendations For The Authors):

      I feel that the authors have very interesting data and have a solid set of analyses. I do not have major issues regarding the manuscript, though sometimes I disagree with the interpretation of the results and some of the conclusions might be too far-fetched given the analyses and the results. Additionally, the methods section (especially statistics) was lacking some details, but I would not consider it too concerning at this point.

      I feel that the largest caveat of the manuscript remains in the representation of the rationale of the study. I felt the introduction could be more concise and be better focused to back up the study questions and hypotheses. Many times, the sentences were too vague and unspecific, and thus, it was difficult to understand what was meant to be said. The authors could mention something more about how community composition of hosts and parasitoids are expected to change with the studied experimental design regarding the metrices you mention in the introduction (stronger hypotheses). The results section cannot be understood as a stand-alone without reading the methods first. The study design and the rationale of the analyses must be described somewhere in the intro or results, if the journal/authors want to keep the methods last structure. Also, the results and discussion could be more focused around the hypotheses. Naturally, these things can be easily fixed.

      I also disagree with the interpretation of results finding top-down control in the system (it might well be there, but I do not think that the current methods and tests are suitable in finding it). First, the used methodology cannot distinguish parasitoids if the hosts are not there and the probability to detect parasitoid likely depends on the abundance of the host. Thus, the top-down regulation is difficult to prove (is it the parasitoids that have driven the host population down). Secondly, I would be hesitant to say anything about the top-down and bottom-up control in the systems as the data in the manuscript is pooled across five years while the top-down/bottom-up regulation in insect systems usually spans only one season/generation in time (much shorter than five years). Consequently, the analyses are comparing the communities of species that some of most likely do not co-exist (they were found in the same space but not during the same time). Luckily, the top-down/bottom-up effects could potentially be explored by using separately the time steps of the now pooled community data: e.g., does the population of the host decrease in t if the parasitoids are abundant in t-1? There are also other statistical tests to explore these patterns.

      In the manuscript "Phylogenetic composition" refers to Mean Pairwise Distance. I would use "phylogenetic diversity" instead throughout the text. Also, to my understanding, in trees both "phylogenetic composition" and "phylogenetic diversity" are used even though based on their descriptions, they are the same.

      Detailed comments:

      Punctuation needs to be checked and edited at some point (I think copy-pasting had left things in the wrong places). Please check that "-" instead of "-" is used in host-parasitoid.

      1-2 The title is not very matching with the content. "Multi-dimensionality" is not mentioned in the text. "phylogenetic composition" -> "phylogenetic diversity"

      We didn’t find the role of functional diversity of trees in host-parasitoid interactions, but we still have tree richness and phylogenetic diversity. I also disagree with that using phylogenetic diversity to replace phylogenetic composition, because diversity highlights higher or lower phylogenetic distance among communities, while the later highlights the phylogenetic dissimilarity across communities.

      53-57 This sentence is quite vague and because of it, difficult to follow. Consider rephrasing and avoiding unspecified terms such as "tree identity", "genetic diversity", and "overall community composition of higher trophic levels" (at least, I was not sure what taxa/level you meant with them).

      Rephased.

      L58-61 “Especially, we lack a comprehensive understanding of the ways that biotic factors, including plant richness, overall community phylogenetic and functional composition of consumers, and abiotic factors such as microclimate, determining host–parasitoid network structure and host–parasitoid community dynamics.”

      56 I would remove "interact" as no interactions were tested.

      Removed accordingly.

      59-60 This needs rephrasing. I feel "taxonomic and phylogenetic composition should be just "species composition". To better match, what was done: "taxonomic, phylogenetic, and network composition of both host and parasitoid communities" -> "species and phylogenetic diversity of both host and parasitoid communities and the composition their interaction networks"

      Changed accordingly.

      62 Remove "tree composition".

      Done.

      62 Replace "taxonomic" with "species". Throughout the text.

      Done.

      63-64 "Generally, top-down control was stronger than bottom-up control via phylogenetic association between hosts and parasitoids" I disagree, see my comments elsewhere.

      Now we rephased the sentence.

      L68-70 “Generally, phylogenetic associations between hosts and parasitoids reflect non-randomly structured interactions between phylogenetic trees of hosts and parasitoids.”

      68 "habitat structure and heterogeneity" This is too strong and general of a statement based on the results. You did not really measure habitat structure or heterogeneity.

      Now we rephased the statement to avoid strong and general description.

      L71-73 “Our study indicates that the composition of higher trophic levels and corresponding interaction networks are determined by plant diversity and canopy cover especially via trophic phylogenetic links in species-rich ecosystems.”

      69 Specify "phylogenetic links". Trophic links?

      Specified to “trophic phylogenetic links”.

      75-77 The sentence is a bit difficult to follow. Consider rephrasing.

      Now we rephased it.

      L79-82 “Changes in network structure of higher trophic levels usually coincide with variations in their diversity and community, which could be in turn affected by the changes in producers via trophic cascades”

      76 Be more specific about what you mean by "community of trophic levels".

      Specified to “community composition”.

      79 Remove "basal changes of", it only makes the sentence heavier.

      Done.

      81 What is "species codependence"?

      We sim to describe the species co-occurrence depending on their closely relationships. For clarity, now we changed to “species coexistence”

      82 What do you mean by "complex dynamics"?

      Rephased to “mechanisms on dynamics of networks”.

      83 onward: I would not focus so much on top-down/bottom-up as I feel that your current analyses cannot really say anything too strong about these causalities but are rather correlative.

      Thanks, we now removed the relevant contents from the discussion. However, we kept one sentence in the Introduction, because it should be highlighted to make reviewers aware of this (the other text on about this were removed).

      89 Remove "environmental".

      Done.

      90 Specify what you mean by "these forces".

      Done.

      98-99 I have difficulties following the logic here "potential specialization of their hosts may cascade up to impact the parasitoids' presence or absence". Consider rephrasing.

      Now we rephased it.

      L101-102 “…and their host fluctuations may cascade up to impact the parasitoids’ presence or absence.”

      100 Be more specific with "habitat-level changes".

      Specified to “community-level changes”

      100 I do not see why host-parasitoid systems would be ideal to study "species interactions". There are much simpler and easier systems available.

      Changed to “… one of ideal…”

      101-103 "influence of" on what?

      Now we rephased the sentence.

      L104-105 “Previous studies mainly focused on the influence of abiotic factors on host-parasitoid interactions”

      104 Be more specific in "the role of multiple components of plant diversity".

      Now we specified "the role of multiple components of plant diversity".

      L107-108 “…the role of multiple components of plant diversity (i.e. taxonomic, functional and phylogenetic diversity)…”

      106 "diversity associations" of what?

      “diversity associations between host and parasitoids”.

      108 Specify the "direct and indirect effects".

      Now we specified it to “…direct and indirect effects (i.e. one pathway and more pathways via other variables)…”

      110-113 A bit heavy sentence to follow. Consider rephrasing.

      Now we rephased the sentence to make it more readable.

      114 Give an example of "phylogenetic dependences".

      Done. Phylogenetic dependences (e.g. phylogenetic diversity)

      117 Move the "e.g. taxonomic, phylogenetic, functional" within brackets in 117 after "dimensions of biodiversity".

      Done.

      120 "(add citation here)" Yes please!

      Done.

      120-121 Specify "such relationships".

      Done. Specified to “multiple dimensions of biodiversity”

      128-130 This is difficult to follow. Please rephrase.

      Now we rephased the sentence.

      L135-137 “We aimed to discern the primary components of the diversity and composition of tree communities that affect higher trophic level interactions via quantifying the strength and complexity of associations between hosts and parasitoid.”

      131-132 Remove "phylogenetic and". It is redundant to phylogenetic diversity.

      Done.

      128 Tested robustness does not really capture "stability of associations".

      Yes, we agree. Now we rephased the sentence and exclude the “stability” description.

      133 Specify "phylogenetic processes".

      Now we specified “phylogenetic processes”.

      L140-141 “…especially via phylogenetic processes (e.g. lineages of trophic levels diverge and evolve over time)…”

      141 I would like to have more details on the data set somewhere in the results. How many individuals and species were found in each plot (on average)? Was there a lot of temporal variation (e.g. between the seasons)? On how many sites were the insect species found?

      Thanks for your suggestion. Now we provide more details on the data set in the results (L153-156), including mean values of individuals and species in each plot. However, the temporal variation should be studied for another relative independent topic, as our study focus on the general patter of interactions between hosts and parasitoids. Therefore, we would not put more information on temporal changes to make readers get lost in the text.

      153-156 “Among them, we found 56 host species (12 bees and 44 wasps, mean abundance and richness are 400.05 and 45.14, respectively, for each plot) and 50 parasitoid species (38 Hymenoptera and 12 Diptera, mean abundance and richness are 14.07 and 9.05, respectively, for each plot).”

      149 tree -> trees

      Done.

      149 Should there read also some else than "NMDS scores"?

      Thanks! Now we provided more details about “NMDS scores”.

      L161-162 “(NMDS axis scores; i.e. preserving the rank order of pairwise dissimilarities between samples)”

      149 You could mention the amount of variation explained by the first two axes of the NMDSs. Now it is difficult to estimate how much the models actually explain.

      Thanks for your comments! However, we could not directly provide the explanatory power of the two axes, because NMDS is based on rank-order distances rather than linear relationships like in PCA. However, the goodness of fit for the NMDS solution is typically evaluated using the stress value. We provide the stress value in the figure caption.

      150 "tree MPD" is mentioned for the first time. Spell it out.

      Done.

      150 Explain "eastness".

      Done.

      L163-164 “…eastness (sine-transformed radian values of aspect) )”

      151 How was "tree functional diversity" quantified?

      Please see methods. L437-L438.

      160 Specify that you talk about phylogenetic compositions of the host and parasitoid communities here.

      We would keep it refined here, keeping consistent with species composition here. Phylogenetic composition just represents the dissimilarities of phylogenetic linages within a community.

      161 Describe "parafit" test here when first mentioned.

      Done, see methods L485-487.

      182 Keep on referring to tables and figures in the discussion! Also, more clearly discuss your hypotheses. There are lots of discussions on top-down/bottom-up control. It could be good to form a hypothesis on them and predict what kind of patterns would suggest either one and what would you expect to find regarding them.

      Now we referred figures and tables in the discussion. As the contents on top-down and bottom-up control were not fit very well with our study (as also suggested by reviewers), so we rephased the discussion and also clearly discuss our hypotheses in the discussion. See L218, L226, and L237 etc.

      186 "partly determined host-parasitoid networks" Be more specific.

      Done.

      L206-207 “…partly determined host-parasitoid network indices, including vulnerability, linkage density, and interaction evenness.”

      195 Tell what you mean by "other biotic factors".

      Specified it: “…other biotic factors such as elevation and slope…”

      197-198 "It seems likely that these results are based on bee linkages to pollen resources" I would be hesitant to conclude this as the bees most likely forage way beyond the borders of the 30m by 30m study plots.

      Thanks for your concern about this problem. While it is true that bees can forage beyond 30 x 30m, the study focuses on their nesting behavior and activity within this defined area, rather than their entire foraging range. Existing literature shows bees often forage locally when resources are available (e.g. Ebeling et al., 2012 Oecologia; Guo et al., year, Basic and Applied Ecology). Therefore, we are confident that this pattern could be associated with the resources around the trap nests.

      223 "This could be further tested by collecting the food directly used by the wasps (caterpillars)" A bit unnecessary addition.

      Thanks for your suggestion. Yes, this definitely is a good point, but currently we don’t have enough data of caterpillars, but we will follow this in the future.

      232-238 I disagree with the authors on the interpretation of the causality of the results here. I think that the community of parasitoids simply indicates which host species are available, while the host community does not have an as strong effect on parasitoid community as parasitoids do not utilise the whole species pool of the hosts. (Presence of parasitoid tells that the host is around while the presence of the host does not necessarily tell about the presence of the parasitoid.) To me, this would rather indicate a bottom-up than top-down regulation. Similar patterns are also visible in species communities of hosts and parasites.

      Thank you for your suggestion. We agree with you that parasitoids are more depended on hosts, as host could not be always attacked by parasitoids. Now we rephased our explanation to follow this argument.

      L254-256 “Such pattern could be further confirmed by the significant association between host phylogenetic composition and parasitoid phylogenetic composition (Fig. 1c), which suggested that their interactions are phylogenetically structured to some extent.”

      247-266 The logic in this section is difficult to follow. Try rephrasing.

      Now we rephased the section for a clearer logic.

      L270-287 “Tree community species richness did not significantly influence the diversity of hosts targeted by parasitoids (parasitoid generality), but caused a significant increase in the diversity of parasitoids per host species (host vulnerability) (Fig. 3a; Table 2). This is likely because niche differentiation often influences network specialization via potential higher resource diversity in plots with higher tree diversity (Lopez-Carretero et al. 2014). Such positive relationship between host vulnerability and tree species richness suggested that host-parasitoid interactions could be driven through bottom-up effects via benefit from tree diversity. For example, parasitoid species increases more than host diversity with increasing tree species richness (Guo et al. 2021), resulting increasing of host vulnerability at community level. According to the enemies hypothesis (Root 1973), which posits a positive effects of plant richness on natural enemies, the higher trophic levels in our study (e.g. predators and parasitoids) would benefit from tree diversity and regulate herbivores thereby (Staab and Schuldt 2020). Indeed, previous studies at the same site found that bee parasitoid richness and abundance were positively related to tree species richness, but not their bee hosts (Fornoff et al. 2021, Guo et al. 2021). Because our dataset considered all hosts and reflects an overall pattern of host-parasitoid interactions, the effects of tree species richness on parasitoid generality might be more complex and difficult to predict, as we found that neither tree species richness nor tree MPD were related to parasitoid generality.”

      249 "This is likely because niche differentiation often influences network specialization via potential higher resource diversity in plots with higher tree diversity" This is a bit contradicting your vulnerability results as niche differentiation should increase specialization and diversity and specialization should decrease vulnerability (less host per parasitoid).

      Thanks! We understand that the concepts of “generality” and “vulnerability” can be a bit confusing. To clarify, “fewer hosts per parasitoid” actually corresponds to lower generality at the community level.

      332-337 How did you select the species growing on your plots? Or was only species number considered? What was the pool of tree species growing on the selected plots? Was the selection similar at both sites?

      Now we provided more information on the experiment design.

      L354-356 “The species pools of the two plots are nonoverlapping (16 species for each site). The composition of tree species within the study plots is based on a “broken-stick” design (see Bruelheide et al. 2014).”

      342 Remove "centrally per plot"?

      Done.

      346-347 Was the selection of different reed diameters similar in all the plots?

      Diameters and the relative distribution of diameters was similar in all trap nests.

      399 & 432 Are "phylogenetic diversity of the tree communities" and "phylogenetic composition of trees" the same? They are both described as mean pairwise distance.

      These two are actually different, as we use this to distinguish the phylogenetic diversity with communities and rank order of dissimilarities between tree communities. Here, the phylogenetic diversity of the tree communities is mean pairwise phylogenetic distance of species for tree communities. Tree phylogenetic composition is the rank order of pairwise dissimilarities between tree communities based on NMDS.

      400 Do you think that MPD makes any sense with the monocultures (value is always 0)? Does this have a potential to bias your analyses and result?

      We agree your point. However, we do not think that this is a major problem in the analyses. We followed the experimental design and considered low phylogenetic relatedness of tree species in a plot (Likewise in monocultures, the tree species richness is always 1).

      402-405 MNTD is not mentioned before or after this. Consider removing this section.

      We tested the potential effects of MNTD in our models. Now we mentioned it in our results.

      L194-195 “Tree mean nearest taxon distance (MNTD) was unrelated to any network indices.”

      405 "Phylogenetic metrics of trees" Which ones?

      Both tree MPD and MNTD. Now we have noted it in the manuscript. (L432)

      410 Further details on "Rao's Q" and how the functional diversity of the communities was calculated are needed.

      Now more details were provided.

      L435-438 “Specifically, seven leaf traits were used for calculation of tree functional diversity, which was calculated as the mean pairwise distance in trait values among tree species, weighted by tree wood volume, and expressed as Rao's Q”

      413 Specify "higher trophic levels".

      Now we specified the trophic levels.

      L440-441 “…higher trophic levels in our study area, such as herbivores and predators”

      417-424 What about the position of the plots within study sites? Is there potential for edge effects (e.g. bees finding easier the trap nest close to the edge of the experimental forest)? Were there any differences between the two sites? What is the elevation range of the plots?

      Thanks for concerning the details of our study. First, all the plots were randomly distributed within the study sites (see Fig. S2). Admittedly, there are several plots are located in the edges of the site. However, we did not consider the potential edge effects in our analysis. Of course, this will be a good point in our future studies. Moreover, the biggest difference between the two is the non-overlapping tree species pool, and the two study sites are apart from 5 km in the same town. Finally, there is not too distinct elevation gradient across the plots (112 m - 260 m).

      432-434 "The species and phylogenetic composition of trees, hosts, and parasitoids were quantified at each plot with nonmetric multidimensional scaling (NMDS) analysis based on Morisita-Horn distances" This section needs to be more specific and detailed. Did you do the NMDS separately for each plot as suggested in the text?

      We provided more details of the section.

      L462-465 “The minimum number of required dimensions in the NMDS based on the reduction in stress value was determined in the analysis (k = 2 in our case). We centred the results to acquire maximum variance on the first dimension, and used the principal components rotation in the analysis.”

      435 Specify how picante was used (function and arguments)!

      Now we specified the function.

      L465-467 “The phylogenetic composition was calculated by mean pairwise distance among the host or parasitoid communities per plot with the R package “picante” with ‘mpd’ function.”

      436 "standardized values" Of what? How was the standardisation done?

      Now we citied a supplementary table (Table S2) to specify it (see L469). For the standardization, we used ‘scale’ function in R, which standardizes data by centering and scaling data. Specifically, it subtracts the mean and divides by the standard deviation for each variable.

      443 Provide more details on parafit.

      Actually, we have provided the reason why we use the parafit test and the usage.

      L483-486 “We used a parafit test (9,999 permutations) with the R package “ape” to test whether the associations were non-random between hosts and parasitoids. This is widely used to assess host-parasite co-phylogeny by analyzing the congruence between host and parasite phylogenies using a distance-based matrix approach.”

      449-451 Rephrase the sentence.

      Rephased.

      L490-491 “We constructed quantitative host-parasitoid networks at community level with the R package “bipartite” for each plot of the two sites.”

      451 "six" Should this be five?

      Yes, should be five, thanks.

      470-481 What package and function were used for the LMMs?

      As we now used linear models, we do no longer use a R package for LMMs.

      470 "mix" -> mixed

      Changed to linear models.

      472 "six" Should this be five?

      Again, we changed it to five.

      479-481 How did you treat the variables from the two different sites when testing for the correlations to avoid two geographic clusters of data points?

      Now we considered the two study sites as fixed factor in our linear models. Moreover, tree-based variables were additionally included as interaction terms with the study sites.

      501 "mix" -> mixed

      Changed to linear models.

      The panel selection for figures 3 and 4 seems random. Justify it!

      Thank you. To avoid including too many figures in the main text, which could potentially confuse readers, we have selected the key results that are of primary interest. The remaining figures are provided in the appendix for reference.

      533 "Note that axes are on a log scale for tree species richness." Why the log-scale if the analyses were performed with linear fit? Also, the drawn regression lines do not match the model description (non-linear, while a linear model is described in the text). The models should probably be described in more detail.

      We used log-transformed to promote the normality of the data. The drawn regression lines are linear lines, which fit our models.

      539 "Values were adjusted for covariates of the final regression model." How?

      We used residual plot to directly visualizes the relationship between the predictor and the response variable with the fitted regression line, making it easier to assess the model's fit.

      Fig. S4 text does not match the figure.

      Thanks! We now deleted the unmatched text in the figure.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review): 

      Summary: 

      In this work, Noorman and colleagues test the predictions of the "four-stage model" of consciousness by combining psychophysics and scalp EEG in humans. The study relies on an elegant experimental design to investigate the respective impact of attentional and perceptual blindness on visual processing. 

      The study is very well summarised, the text is clear and the methods seem sound. Overall, a very solid piece of work. I haven't identified any major weaknesses. Below I raise a few questions of interpretation that may possibly be the subject of a revision of the text. 

      We thank the reviewer for their positive assessment of our work and for their extremely helpful and constructive comments that helped to significantly improve the quality of our manuscript.

      (1) The perceptual performance on Fig1D appears to show huge variation across participants, with some participants at chance levels and others with performance > 90% in the attentional blink and/or masked conditions. This seems to reveal that the procedure to match performance across participants was not very successful. Could this impact the results? The authors highlight the fact that they did not resort to postselection or exclusion of participants, but at the same time do not discuss this equally important point. 

      Performance was indeed highly variable between observers, as is commonly found in attentional-blink (AB) and masking studies. For some observers, the AB pushes performance almost to chance level, whereas for others it has almost no effect. A similar effect can be seen in masking. We did our best to match accuracy over participants, while also matching accuracy within participants as well as possible, adjusting mask contrast manually during the experimental session. Naturally, those that are strongly affected by masking need not be the same participants as those that are strongly affected by the AB, given the fact that they rely on different mechanisms (which is also one of the main points of the manuscript). To answer the research question, what mattered most was that at the group-level, performance was well matched between the two key conditions. As all our statistical inferences, both for behavior and EEG decoding, rest on this group level. We do not think that variability at the individualsubject level detracts from this general approach.  

      In the Results, we added that our goal was to match performance across participants:

      “Importantly, mask contrast in the masked condition was adjusted using a staircasing procedure to match performance in the AB condition, ensuring comparable perceptual performance in the masked and the AB condition across participants (see Methods for more details).”

      In the Methods, we added:

      “Second, during the experimental session, after every 32 masked trials, mask contrast could be manually updated in accordance with our goal to match accuracy over participants, while also matching accuracy within participants as well as possible.”

      (2) In the analysis on collinearity and illusion-specific processing, the authors conclude that the absence of a significant effect of training set demonstrates collinearity-only processing. I don't think that this conclusion is warranted: as the illusory and nonillusory share the same shape, so more elaborate object processing could also be occurring. Please discuss. 

      We agree with this qualification of our interpretation, and included the reviewer’s account as an alternative explanation in the Discussion section:  

      “It should be noted that not all neurophysiological evidence unequivocally links processing of collinearity and of the Kanizsa illusion to lateral and feedback processing, respectively (Angelucci et al., 2002; Bair et al., 2003; Chen et al., 2014), so that overlap in decoding the illusory and non-illusory triangle may reflect other mechanisms, for example feedback processes representing the triangular shapes as well.”

      (3) Discussion, lines 426-429: It is stated that the results align with the notion that processes of perceptual segmentation and organization represent the mechanism of conscious experience. My interpretation of the results is that they show the contrary: for the same visibility level in the attentional blind or masking conditions, these processes can be implicated or not, which suggests a role during unconscious processing instead. 

      We agree with the reviewer that the interpretation of this result depends on the definition of consciousness that one adheres to. If one takes report as the leading metric for consciousness (=conscious access), one can indeed conclude that perceptual segmentation/organization can also occur unconsciously. However, if the processing that results in the qualitative nature of an image (rather than whether it is reported) is taken as leading – such as the processing that results in the formation of an illusory percept – (=phenomenal) the conclusion can be quite different. This speaks to the still ongoing debate regarding the existence of phenomenal vs access consciousness, and the literature on no-report paradigms amongst others (see last paragraph of the discussion). Because the current data do not speak directly to this debate, we decided to remove  the sentence about “conscious experience”, and edited this part of the manuscript (also addressing a comment about preserved unconscious processing during masking by Reviewer 2) by limiting the interpretation of unconscious processing to those aspects that are uncontroversial:

      “Such deep feedforward processing can be sufficient for unconscious high-level processing, as indicated by a rich literature demonstrating high-level (e.g., semantic) processing during masking (Kouider & Dehaene, 2007; Van den Bussche et al., 2009; van Gaal & Lamme, 2012). Thus, rather than enabling deep unconscious processing, preserved local recurrency during inattention may afford other processing advantages linked to its proposed role in perceptual integration (Lamme, 2020), such as integration of stimulus elements over space or time.”

      (4) The two paradigms developed here could be used jointly to highlight nonidiosyncratic NCCs, i.e. EEG markers of visibility or confidence that generalise regardless of the method used. Have the authors attempted to train the classifier on one method and apply it to another (e.g. AB to masking and vice versa)? What perceptual level is assumed to transfer? 

      To avoid issues with post-hoc selection of (visible vs. invisible) trials (discussed in the Introduction), we did not divide our trials into conscious and unconscious trials, and thus did not attempt to reveal NCCs, or NCCs generalizing across the two paradigms. Note also that this approach alone would not resolve the debate regarding the ‘true’ NCC as it hinges on the operational definition of consciousness one adheres to; also see our response to the previous point the reviewer raised. Our main analysis revealed that the illusory triangle could be decoded with above-chance accuracy during both masking and the AB over extended periods of time with similar topographies (Fig. 2B), so that significant cross-decoding would be expected over roughly the same extended period of time (except for the heightened 200-250 ms peak). However, as our focus was on differences between the two manipulations and because we did not use post-hoc sorting of trials, we did not add these analyses.

      (5) How can the results be integrated with the attentional literature showing that attentional filters can be applied early in the processing hierarchy? 

      Compared to certain manipulations of spatial attention, the AB phenomenon is generally considered to represent an instance of  “late” attentional filtering. In the Discussion section we included a paragraph on classic load theory, where early and late filtering depend on perceptual and attentional load. Just preceding this paragraph, we added this:  

      “Clearly, these findings do not imply that unconscious high-level (e.g., semantic) processing can only occur during inattention, nor do they necessarily generalize to other forms of inattention. Indeed, while the AB represents a prime example of late attentional filtering, other ways of inducing inattention or distraction (e.g., by manipulating spatial attention) may filter information earlier in the processing hierarchy (e.g., Luck & Hillyard, 1994 vs. Vogel et al., 1998).”

      Reviewer #2 (Public Review): 

      Summary: 

      This is a very elegant and important EEG study that unifies within a single set of behaviorally equated experimental conditions conscious access (and therefore also conscious access failures) during visual masking and attentional blink (AB) paradigms in humans. By a systematic and clever use of multivariate pattern classifiers across conditions, they could dissect, confirm, and extend a key distinction (initially framed within the GNWT framework) between 'subliminal' and 'pre-conscious' unconscious levels of processing. In particular, the authors could provide strong evidence to distinguish here within the same paradigm these two levels of unconscious processing that precede conscious access : (i) an early (< 80ms) bottom-up and local (in brain) stage of perceptual processing ('local contrast processing') that was preserved in both unconscious conditions, (ii) a later stage and more integrated processing (200-250ms) that was impaired by masking but preserved during AB. On the basis of preexisting studies and theoretical arguments, they suggest that this later stage could correspond to lateral and local recurrent feedback processes. Then, the late conscious access stage appeared as a P3b-like event. 

      Strengths: 

      The methodology and analyses are strong and valid. This work adds an important piece in the current scientific debate about levels of unconscious processing and specificities of conscious access in relation to feed-forward, lateral, and late brain-scale top-down recurrent processing. 

      Weaknesses: 

      - The authors could improve clarity of the rich set of decoding analyses across conditions. 

      - They could also enrich their Introduction and Discussion sections by taking into account the importance of conscious influences on some unconscious cognitive processes (revision of traditional concept of 'automaticity'), that may introduce some complexity in Results interpretation 

      - They should discuss the rich literature reporting high-level unconscious processing in masking paradigms (culminating in semantic processing of digits, words or even small group of words, and pictures) in the light of their proposal (deeper unconscious processing during AB than during masking). 

      We thank the reviewer for their positive assessment of our study and for their insightful comments and helpful suggestions that helped to significantly strengthen our paper. We provide a more detailed point-by-point response in the “recommendations for the authors” section below. In brief, we followed the reviewer’s suggestions and revised the Results/Discussion to include references to influences on unconscious processes and expanded our discussion of unconscious effects during masking vs. AB.  

      Reviewer #3 (Public Review): 

      Summary: 

      This work aims to investigate how perceptual and attentional processes affect conscious access in humans. By using multivariate decoding analysis of electroencephalography (EEG) data, the authors explored the neural temporal dynamics of visual processing across different levels of complexity (local contrast, collinearity, and illusory perception). This is achieved by comparing the decidability of an illusory percept in matched conditions of perceptual (i.e., degrading the strength of sensory input using visual masking) and attentional impairment (i.e., impairing topdown attention using attentional blink, AB). The decoding results reveal three distinct temporal responses associated with the three levels of visual processing. Interestingly, the early stage of local contrast processing remains unaffected by both masking and AB. However, the later stage of collinearity and illusory percept processing are impaired by the perceptual manipulation but remain unaffected by the attentional manipulation. These findings contribute to the understanding of the unique neural dynamics of perceptual and attentional functions and how they interact with the different stages of conscious access. 

      Strengths: 

      The study investigates perceptual and attentional impairments across multiple levels of visual processing in a single experiment. Local contrast, collinearity, and illusory perception were manipulated using different configurations of the same visual stimuli. This clever design allows for the investigation of different levels of visual processing under similar low-level conditions. 

      Moreover, behavioural performance was matched between perceptual and attentional manipulations. One of the main problems when comparing perceptual and attentional manipulations on conscious access is that they tend to impact performance at different levels, with perceptual manipulations like masking producing larger effects. The study utilizes a staircasing procedure to find the optimal contrast of the mask stimuli to produce a performance impairment to the illusory perception comparable to the attentional condition, both in terms of perceptual performance (i.e., indicating whether the target contained the Kanizsa illusion) and metacognition (i.e., confidence in the response). 

      The results show a clear dissociation between the three levels of visual processing in terms of temporal dynamics. Local contrast was represented at an early stage (~80 ms), while collinearity and illusory perception were associated with later stages (~200-250 ms). Furthermore, the results provide clear evidence in support of a dissociation between the effects of perceptual and attentional processes on conscious access: while the former affected both neuronal correlates of collinearity and illusory perception, the latter did not have any effect on the processing of the more complex visual features involved in the illusion perception. 

      Weaknesses: 

      The design of the study and the results presented are very similar to those in Fahrenfort et al. (2017), reducing its novelty. Similar to the current study, Fahrenfort et al. (2017) tested the idea that if both masking and AB impact perceptual integration, they should affect the neural markers of perceptual integration in a similar way. They found that behavioural performance (hit/false alarm rate) was affected by both masking and AB, even though only the latter was significant in the unmasked condition. An early classification peak was instead only affected by masking. However, a late classification peak showed a pattern similar to the behavioural results, with classification affected by both masking and AB. 

      The interpretation of the results mainly centres on the theoretical framework of the recurrent processing theory of consciousness (Lamme, 2020), which lead to the assumption that local contrast, collinearity, and the illusory perception reflect feedforward, local recurrent, and global recurrent connections, respectively. It should be mentioned, however, that this theoretical prediction is not directly tested in the study. Moreover, the evidence for the dissociation between illusion and collinearity in terms of lateral and feedback connections seems at least limited. For instance, Kok et al. (2016) found that, whereas bottom-up stimulation activated all cortical layers, feedback activity induced by illusory figures led to a selective activation of the deep layers. Lee & Nguyen (2001), instead, found that V1 neurons respond to illusory contours of the Kanizsa figures, particularly in the superficial layers. They all mention feedback connections, but none seem to point to lateral connections. 

      Moreover, the evidence in favour of primarily lateral connections driving collinearity seems mixed as well. On one hand, Liang et al. (2017) showed that feedback and lateral connections closely interact to mediate image grouping and segmentation. On the other hand, Stettler et al. (2002) showed that, whereas the intrinsic connections link similarly oriented domains in V1, V2 to V1 feedback displays no such specificity. Furthermore, the other studies mentioned in the manuscript did not investigate feedback connections but only lateral ones, making it difficult to draw any clear conclusions. 

      We thank the reviewer for their careful review and positive assessment of our study, as well as for their constructive criticism and helpful suggestions. We provide a more detailed point-by-point response in the “recommendations for the authors” section below. In brief, we addressed the reviewer’s comments and suggestions by better relating our study to Fahrenfort et al.’s (2017) paper and by highlighting the limitations inherent in linking our findings to distinct neural mechanisms (in particular, to lateral vs. feedback connections).

      Recommendations for the authors:  

      Reviewer #1 (Recommendations For The Authors): 

      -  Methods: it states that "The distance between the three Pac-Man stimuli as well as between the three aligned two-legged white circles was 2.8 degrees of visual angle". It is unclear what this distance refers to. Is it the shortest distance between the edges of the objects? 

      It is indeed the shortest distance between the edges of the objects. This is now included in the Methods.

      -  Methods: It's unclear to me if the mask updating procedure during the experimental session was based on detection rate or on the perceptual performance index reported on Fig1D. Please clarify. 

      It was based on accuracy calculated over 32 trials. We have included this information in the Methods.

      -  Methods and Results: I did not understand why the described procedure used to ensure that confidence ratings are not contaminated by differences in perceptual performance was necessary. To me, it just seems to make the "no manipulations" and "both manipulations" less comparable to the other 2 conditions. 

      To calculate accurate estimates of metacognitive sensitivity for the two matched conditions, we wanted participants to make use of the full confidence scale (asking them to distribute their responses evenly over all ratings within a block). By mixing all conditions in the same block, we would have run the risk of participants anchoring their confidence ratings to the unmatched very easy and very difficult conditions (no and both manipulations condition). We made this point explicit in the Results section and in the Methods section:

      “To ensure that the distribution of confidence ratings in the performancematched masked and AB condition was not influenced by participants anchoring their confidence ratings to the unmatched very easy and very difficult conditions (no and both manipulations condition, respectively), the masked and AB condition were presented in the same experimental block, while the other block type included the no and both manipulations condition.”

      “To ensure that confidence ratings for these matched conditions (masked, long lag and unmasked, short lag) were not influenced by participants anchoring their confidence ratings to the very easy and very difficult unmatched conditions (no and both manipulations, respectively), one type of block only contained the matched conditions, while the other block type contained the two remaining, unmatched conditions (masked, short lag and unmasked, long lag).”

      - Methods: what priors were used for Bayesian analyses? 

      Bayesian statistics were calculated in JASP (JASP Team, 2024) with default prior scales (Cauchy distribution, scale 0.707). This is now added to the Methods.

      - Results, line 162: It states that classifiers were applied on "raw EEG activity" but the Methods specify preprocessing steps. "Preprocessed EEG activity" seems more appropriate. 

      We changed the term to “preprocessed EEG activity” in the Methods and to “(minimally) preprocessed EEG activity (see Methods)” in the  Results, respectively.

      - Results, line 173: The effect of masking on local contrast decoding is reported as "marginal". If the alpha is set at 0.05, it seems that this effect is significant and should not be reported as marginal. 

      We changed the wording from “marginal” to “small but significant.”  

      - Fig1: The fixation cross is not displayed. 

      Because adding the fixation cross would have made the figure of the trial design look crowded and less clear, we decided to exclude it from this schematic trial representation. We are now stating this also in the legend of figure 1.  

      - Fig 3A: In the upper left panel, isn't there a missing significant effect of the "local contrast training and testing" condition in the first window? If not, this condition seems oddly underpowered compared to the other two conditions. 

      Thanks for the catch! The highlighting in bold and the significance bar were indeed lacking for this condition in the upper left panel (blue line). We corrected the figure in our revision.

      - Supplementary text and Fig S6: It is unclear to me why the two control analyses (the black lines vs. the green and purple lines) are pooled together in the same figure. They seem to test for different, non-comparable contrasts (they share neither training nor testing sets), and I find it confusing to find them on the same figure. 

      We agree that this may be confusing, and deleted the results from one control analysis from the figure (black line, i.e., training on contrast, testing on illusion), as the reviewer correctly pointed out that it displayed a non-comparable analysis. Given that this control analysis did not reveal any significant decoding, we now report its results only in the Supplementary text.  

      - Fig S6: I think the title of the legend should say testing on the non-illusory triangle instead of testing on the illusory triangle to match the supplementary text. 

      This was a typo – thank you! Corrected.  

      Reviewer #2 (Recommendations For The Authors): 

      Issue #1: One key asymmetry between the three levels of T2 attributes (i.e.: local contrast; non-illusory triangle; illusory Kanisza triangle) is related to the top-down conscious posture driven by the task that was exclusively focusing on the last attribute (illusory Kanisza triangle). Therefore, any difference in EEG decoding performance across these three levels could also depend to this asymmetry. For instance, if participants were engaged to report local contrast or non-illusory triangle, one could wonder if decoding performance could differ from the one used here. This potential confound was addressed by the authors by using decoders trained in different datasets in which the main task was to report one the two other attributes. They could then test how classifiers trained on the task-related attribute behave on the main dataset. However, this part of the study is crucial but not 100% clear, and the links with the results of these control experiments are not fully explicit. Could the author better clarity this important point (see also Issue #1 and #3). 

      The reviewer raises an important point, alluding to potential differences between decoded features regarding task relevance. There are two separate sets of analyses where task relevance may have been a factor, our main analyses comparing illusion to contrast decoding, and our comparison of collinearity vs. illusion-specific processing.  

      In our main analysis, we are indeed reporting decoding of a task-relevant feature (illusion) and of a task-irrelevant feature (local contrast, i.e., rotation of the Pac-Man inducers). Note, however, that the Pac-Man inducers were always task-relevant, as they needed to be processed to perceive illusory triangles, so that local contrast decoding was based on task-relevant stimulus elements, even though participants did not respond to local contrast differences in the main experiment. However, we also ran control analyses testing the effect of task-relevance on local contrast decoding in our independent training data set and in another (independent) study, where local contrast was, in separate experimental blocks, task-relevant or task-irrelevant. The results are reported in the Supplementary Text and in Figure S5. In brief, task-relevance did not improve early (70–95 ms) decoding of local contrast. We are thus confident that the comparison of local contrast to illusion decoding in our main analysis was not substantially affected by differences in task relevance. In our previous manuscript version, we referred to these control analyses only in the collinearity-vs-illusion section of the Results. In our revision, we added the following in the Results section comparing illusion to contrast decoding:

      “In the light of evidence showing that unconscious processing is susceptible to conscious top-down influences (Kentridge et al., 2004; Kiefer & Brendel, 2006; Naccache et al., 2002), we ran control analyses showing that early local contrast decoding was not improved by rendering contrast task-relevant (see Supplementary Information and Fig. S5), indicating that these differences between illusion and contrast decoding did not reflect differences in task-relevance.”

      In addition to our main analysis, there is the concern that our comparison of collinearity vs. illusion-specific processing may have been affected by differences in task-relevance between the stimuli inducing the non-illusory triangle (the “two-legged white circles”, collinearity-only) and the stimuli inducing the Kanizsa illusion (the PacMan inducers, collinearity-plus-illusion). We would like to emphasize that in our main analysis classifiers were always used to decode T2 illusion presence vs. absence (collinearity-plus-illusion), and never to decode T2 collinearity-only. To distinguish collinearity-only from collinearity-plus-illusion processing, we only varied the training data (training classifiers on collinearity-only or collinearity-plus-illusion), using the independent training data set, where collinearity-only and collinearity-plus-illusion (and rotation) were task-relevant (in separate blocks). As discussed in the Supplementary Information, for this analysis approach to be valid, collinearity-only processing should be similar for the illusory and the non-illusory triangle, and this is what control analyses demonstrated (Fig. S7). In any case, general task-relevance was equated for the collinearity-only and the collinearity-plus-illusion classifiers.  

      Finally, in supplementary Figure 6 we also show that our main results reported in Figure 2 (discussed at the top of this response) were very similar when the classifiers were trained on the independent localizer dataset in which each stimulus feature could be task-relevant.  

      Together, for the reasons described above, we believe that differences in EEG decoding performance across these three stimulus levels did  are unlikely to depend also depend on a “task-relevance” asymmetry.

      Issue #2: Following on my previous point the authors should better mention the concept of conscious influences on unconscious processing that led to a full revision of the notion of automaticity in cognitive science [1 , 2 , 3 , 4]. For instance, the discovery that conscious endogenous temporal and spatial attention modulate unconscious subliminal processing paved the way to this revision. This concept raises the importance of Issue#1: equating performance on the main task across AB and masking is not enough to guarantee that differences of neural processing of the unattended attributes of T2 (i.e.: task-unrelated attributes) are not, in part, due to this asymmetry rather than to a systematic difference of unconscious processing strengtsh [5 , 6-8]. Obviously, the reported differences for real-triangle decoding between AB and masking cannot be totally explained by such a factor (because this is a task-unrelated attribute for both AB and masking conditions), but still this issue should be better introduced, addressed, clarified (Issue #1 and #3) and discussed. 

      We would like to refer to our response to the previous point: Control analyses for local contrast decoding showed that task relevance had no influence on our marker for feedforward processing. Most importantly, as outlined above, we did not perform real-triangle decoding – all our decoding analyses focused on comparing collinearity-only vs. collinearity-plus-illusion were run on the task-relevant T2 illusion (decoding its presence vs. absence). The key difference was solely the training set, where the collinearity-only classifier was trained on the (task-relevant) real triangle and the collinearity-plus-illusion classifier was trained on the (task-relevant) Kanizsa triangle. Thus, overall task relevance was controlled in these analyses.  

      In our revision, we are now also citing the studies proposed by the reviewer, when discussing the control analyses testing for an effect of task-relevance on local contrast decoding:

      “In the light of evidence showing that unconscious processing is susceptible to conscious top-down influences (Kentridge et al., 2004; Kiefer & Brendel, 2006; Naccache et al., 2002), we ran control analyses showing that early local contrast decoding was not improved by rendering contrast task-relevant (see Supplementary Information and Fig. S5), indicating that these differences between illusion and contrast decoding did not reflect differences in task-relevance.”

      Issue #3: In terms of clarity, I would suggest the authors to add a synthetic figure providing an overall view of all pairs of intra and cross-conditions decoding analyses and mentioning main task for training and testing sets for each analysis (see my previous and related points). Indeed, at one point, the reader can get lost and this would not only strengthen accessibility to the detailed picture of results, but also pinpoint the limits of the work (see previous point). 

      We understand the point the reviewer is raising and acknowledge that some of our analyses, in particular those using different training and testing sets, may be difficult to grasp. But given the variety of different analyses using different training and testing sets, different temporal windows, as well as different stimulus features, it was not possible to design an intuitive synthetic figure summarizing the key results. We hope that the added text in the Results and Discussion section will be sufficient to guide the reader through our set of analyses.  

      In our revision, we are now more clearly highlighting that, in addition to presenting the key results in our main text that were based on training classifiers on the T1 data, “we replicated all key findings when training the classifiers on an independent training set where individual stimuli were presented in isolation (Fig. 3A, results in the Supplementary Information and Fig. S6).” For this, we added a schematic showing the procedure of the independent training set to Figure 3, more clearly pointing the reader to the use of a separate training data set.  

      Issue #4: In the light of these findings the authors should discuss more thoroughly the question of unconscious high-level representations in masking versus AB: in particular, a longstanding issue relates to unconscious semantic processing of words, numbers or pictures. According to their findings, they tend to suggest that semantic processing should be more enabled in AB than in masking. However, a rich literature provided a substantial number of results (including results from the last authors Simon Van Gaal) that tend to support the notion of unconscious semantic processing in subliminal processing (see in particular: [9 , 10 , 11 , 12 , 13]). So, and as mentioned by the authors, while there is evidence for semantic processing during AB they should better discuss how they would explain unconscious semantic subliminal processing. While a possibility could be to question the unconscious attribute of several subliminal results, the same argument also holds for AB studies. Another possible track of discussion would be to differentiate AB and subliminal perception in terms of strength and durability of the corresponding unconscious representations, but not necessarily in terms of cognitive richness. Indeed, one may discuss that semantic processing of stimuli that do not need complex spatial integration (e.g.: words or digits as compared to illusory Kanisza tested here) can still be observed under subliminal conditions. 

      We thank the reviewer for pointing us to this shortcoming of our previous Discussion. Note that our data does not directly speak to the question of high-level unconscious representations in masking vs AB, because such conclusions would hinge on the operational definition of consciousness one adheres to (also see response to Reviewer 1). Nevertheless, we do follow the reviewer’s suggestions and added the following in the Discussion (also addressing a point about other forms of attention raised by Reviewer 1):

      “Clearly, these findings do not imply that unconscious high-level (e.g., semantic) processing can only occur during inattention, nor do they necessarily generalize to other forms of inattention. Indeed, while the AB represents a prime example of late attentional filtering, other ways of inducing inattention or distraction (e.g., by manipulating spatial attention) may filter information earlier in the processing hierarchy (e.g., Luck & Hillyard, 1994 vs. Vogel et al., 1998).”

      And, in a following paragraph in the Discussion:

      “Such deep feedforward processing can be sufficient for unconscious high-level processing, as indicated by a rich literature demonstrating high-level (e.g., semantic) processing during masking (Kouider & Dehaene, 2007; Van den Bussche et al., 2009; van Gaal & Lamme, 2012). Thus, rather than enabling high-level unconscious processing, preserved local recurrency during inattention may afford other processing advantages linked to its proposed role in perceptual integration (Lamme, 2020), such as integration of stimulus elements over space or time.  

      Reviewer #3 (Recommendations For The Authors): 

      (1) The objective of Fahrenfort et al., 2017 seems very similar to that of the current study. What are the main differences between the two studies? Moreover, Fahrenfort et al., 2017 conducted similar decoding analyses to those performed in the current study.

      Which results were replicated in the current study, and which ones are novel? Highlighting these differences in the manuscript would be beneficial. 

      We now provide a more comprehensive coverage of the study by Fahrenfort et al., 2017. In the Introduction, we added a brief summary of the key findings, highlighting that this study’s findings could have reflected differences in task performance rather than differences between masking and AB:

      “For example, Fahrenfort and colleagues (2017) found that illusory surfaces could be decoded from electroencephalogram (EEG) data during the AB but not during masking. This was taken as evidence that local recurrent interactions, supporting perceptual integration, were preserved during inattention but fully abolished by masking. However, masking had a much stronger behavioral effect than the AB, effectively reducing task performance to chance level. Indeed, a control experiment using weaker masking, which resulted in behavioral performance well above chance similar to the main experiment’s AB condition, revealed some evidence for preserved local recurrent interactions also during masking. However, these conditions were tested in separate experiments with small samples, precluding a direct comparison of perceptual vs. attentional blindness at matched levels of behavioral performance. To test …”

      In the Results , we are now also highlighting this key advancement by directly referencing the previous study:

      “Thus, whereas in previous studies task performance was considerably higher during the AB than during masking (e.g., Fahrenfort et al., 2017), in the present study the masked and the AB condition were matched in both measures of conscious access.” When reporting the EEG decoding results in the Results section, we continuously cite the Fahrenfort et al. (2017) study to highlight similarities in the study’s findings. We also added a few sentences explicitly relating the key findings of the two studies:

      “This suggests that the AB allowed for greater local recurrent processing than masking, replicating the key finding by Fahrenfort and colleagues (2017). Importantly, the present result demonstrates that this effect reflects the difference between the perceptual vs. attentional manipulation rather than differences in behavior, as the masked and the AB condition were matched for perceptual performance and metacognition.”

      “This similarity between behavior and EEG decoding replicates the findings of Fahrenfort and colleagues  (2017) who also found a striking similarity between late Kanizsa decoding (at 406 ms) and behavioral Kanizsa detection. These results indicate that global recurrent processing at these later points in time reflected conscious access to the Kanizsa illusion.”  

      We also more clearly highlighted where our study goes beyond Fahrenfort et al.’s (2017), e.g., in the Results:

      “The addition of this element of collinearity to our stimuli was a key difference to the study by Fahrenfort and colleagues (2017), allowing us to compare non-illusory triangle decoding to illusory triangle decoding in order to distinguish between collinearity and illusion-specific processing.”

      And in the Discussion:

      “Furthermore, the addition of line segments forming a non-illusory triangle to the stimulus employed in the present study allowed us to distinguish between collinearity and illusion-specific processing.”

      Also, in the Discussion, we added a paragraph “summarizing which results were replicated in the current study, and which ones are novel”, as suggested by the reviewer:

      “This pattern of results is consistent with a previous study that used EEG to decode Kanizsa-like illusory surfaces during masking and the AB (Fahrenfort et al., 2017). However, the present study also revealed some effects where Fahrenfort and colleagues (2017) failed to obtain statistical significance, likely reflecting the present study’s considerably larger sample size and greater statistical power. For example, in the present study the marker for feedforward processing was weakly but significantly impaired by masking, and the marker for local recurrency was significantly impaired not only by masking but also by the AB, although to a lesser extent. Most importantly, however, we replicated the key findings that local recurrent processing was more strongly impaired by masking than by the AB, and that global recurrent processing was similarly impaired by masking and the AB and closely linked to task performance, reflecting conscious access. Crucially, having matched the key conditions behaviorally, the present finding of greater local recurrency during the AB can now unequivocally be attributed to the attentional vs. perceptual manipulation of consciousness.”

      Finally, we changed the title to “Distinct neural mechanisms underlying perceptual and attentional impairments of conscious access despite equal task performance” to highlight one of the crucial differences between the Fahrenfort et al., study and this study, namely the fact that we equalized task performance between the two critical conditions (AB and masking).

      (2) It is not clear from the text the link between the current study and the literature on the role of lateral and feedback connections in consciousness (Lamme, 2020). A better explanation is needed. 

      To our knowledge, consciousness theories such as recurrent processing theory by Lamme make currently no distinction between the role of lateral and feedback connections for consciousness. The principled distinction lies between unconscious feedforward processing and phenomenally conscious or “preconscious” local recurrent processing, where local recurrency refers to both lateral (or horizontal) and feedback connections. We added a sentence in the Discussion:

      “As current theories do not distinguish between the roles of lateral vs. feedback connections for consciousness, the present findings may enrich empirical and theoretical work on perceptual vs. attentional mechanisms of consciousness …”

      (3) When training on T1 and testing on T2, EEG data showed an early peak in local contrast classification at 75-95 ms over posterior electrodes. The authors stated that this modulation was only marginally affected by masking (and not at all by AB); however, the main effect of masking is significant. Why was this effect interpreted as nonrelevant? 

      Following this and Reviewer 1’s comment, we changed the wording from “marginal” to “weak but significant.” We considered this effect “weak” and of lesser relevance, because its Bayes factor indicated that the alternative hypothesis was only 1.31 times more likely than the null hypothesis of no effect, representing only “anecdotal” evidence, which is in sharp contrast to the robust effects of the consciousness manipulations on illusion decoding reported later. Furthermore, later ANOVAs comparing the effect of masking on contrast vs. illusion decoding revealed much stronger effects on illusion decoding than on contrast decoding (BFs>3.59×10<sup>4</sup>).

      (4) The decoding analysis on the illusory percept yielded two separate peaks of decoding, one from 200 to 250 ms and another from 275 to 475 ms. The early component was localized occipitally and interpreted as local sensory processing, while the late peak was described as a marker for global recurrent processing. This latter peak was localized in the parietal cortex and associated with the P300. Can the authors show the topography of the P300 evoked response obtained from the current study as a comparison? Moreover, source reconstruction analysis would probably provide a better understanding of the cortical localization of the two peaks. 

      Figure S4 now shows the P300 from electrode Pz, demonstrating a stronger positivity between 375 and 475 ms when the illusory triangle was present than when it was absent. We did not run a source reconstruction analysis.  

      (5) The authors mention that the behavioural results closely resembled the pattern of the second decoding peak results. However, they did not show any evidence for this relationship. For instance, is there a correlation between the two measures across or within participants? Does this relationship differ between the illusion report and the confidence rating? 

      This relationship became evident from simply eyeballing the results figures: Both in behavior and EEG decoding performance dropped from the both-manipulations condition to the AB and masked conditions, while these conditions did not differ significantly. Following a similar observation of a close similarity between behavior and the second/late illusion decoding peak in the study by Fahrenfort et al. (2017), we adopted their analysis approach and ran two additional ANOVAs, adding “measure” (behavior vs. EEG) as a factor. For this analysis, we dropped the both-manipulations condition due to scale restrictions (as noted in footnote 1: “We excluded the bothmanipulations condition from this analysis due to scale restrictions: in this condition, EEG decoding at the second peak was at chance, while behavioral performance was above chance, leaving more room for behavior to drop from the masked and AB condition.”). The analysis revealed that there were no interactions with condition:

      “The pattern of behavioral results, both for perceptual performance and metacognitive sensitivity, closely resembled the second decoding peak: sensitivity in all three metrics dropped from the no-manipulations condition to the masked and AB conditions, while sensitivity did not differ significantly between these performancematched conditions (Fig. 2C). Two additional rm ANOVAs with the factors measure (behavior, second EEG decoding peak) and condition (no-manipulations, masked, AB)<sup>1</sup> for perceptual performance and metacognitive sensitivity revealed no significant interaction (performance: F</iv><sub>2,58</sub>=0.27, P\=0.762, BF<sub>01</sub>=8.47; metacognition: F</iv><sub>2,58</sub=0.54, P\=0.586, BF<sub>2,58</sub>=6.04). This similarity between behavior and EEG decoding replicates the findings of Fahrenfort and colleagues  (2017) who also found a striking similarity between late Kanizsa decoding (at 406 ms) and behavioral Kanizsa detection. These results indicate that global recurrent processing at these later points in time reflected conscious access to the Kanizsa illusion.”

      (6) The marker for illusion-specific processing emerged later (200-250 ms), with the nomanipulation decoding performing better after training on the illusion than the nonillusory triangle. This difference emerged only in the AB condition, and it was fully abolished by masking. The authors confirmed that the illusion-specific processing was not affected by the AB manipulations by running a rm ANOVA which did not result in a significant interaction between condition and training set. However, unlike the other non-significant results, a Bayes Factor is missing here. 

      We added Bayes factors to all (significant and non-significant) rm ANOVAs.

      (7) The same analysis yielded a second illusion decoding peak at 375-475 ms. This effect was impaired by both masking and AB, with no significant differences between the two conditions. The authors stated that this result was directly linked to behavioural performance. However, it is not clear to me what they mean (see point 5). 

      We added analyses comparing behavior and EEG decoding directly (see our response to point 5).

      (8) The introduction starts by stating that perceptual and attentional processes differently affect consciousness access. This differentiation has been studied thoroughly in the consciousness literature, with a focus on how attention differs from consciousness (e.g., Koch & Tsuchiya, TiCS, 2007; Pitts, Lutsyshyna & Hillyard, Phil. Trans. Roy. Soc. B Biol. Sci., 2018). The authors stated that "these findings confirm and enrich empirical and theoretical work on perceptual vs. attentional mechanisms of consciousness clearly distinguishing and specifying the neural profiles of each processing stage of the influential four-stage model of conscious experience". I found it surprising that this aspect was not discussed further. What was the state of the art before this study was conducted? What are the mentioned neural profiles? How did the current results enrich the literature on this topic? 

      We would like to point out that our study is not primarily concerned with the conceptual distinction between consciousness and attention, which has been the central focus of e.g., Koch and Tsuchiuya (2007). While this literature was concerned with ways to dissociate consciousness and attention, we tacitly assumed that attention and consciousness are now generally considered as different constructs. Our study is thus not dealing with dissociations between attention and consciousness, nor with the distinction between phenomenal consciousness and conscious access, but is concerned with different ways of impairing conscious access (defined as the ability to report about a stimulus), either via perceptual or via attentional manipulations. For the state of the art before the study was conducted, we would like to refer to the motivation of our study in the Introduction, e.g., previous studies’ difficulties in unequivocally linking greater local recurrency during attentional than perceptual blindness to the consciousness manipulation, given performance confounds (we expanded this Introduction section). We also expanded a paragraph in the discussion to remind the reader of the neural profiles of the 4-stage model and to highlight the novelty of our findings related to the distinction between lateral and feedback processes:

      “As current theories do not distinguish between the roles of lateral vs. feedback connections for consciousness, the present findings may enrich empirical and theoretical work on perceptual vs. attentional mechanisms of consciousness (Block, 2005; Dehaene et al., 2006; Hatamimajoumerd et al., 2022; Lamme, 2010; Pitts et al., 2018; Sergent & Dehaene, 2004), clearly distinguishing the neural profiles of each processing stage of the influential four-stage model of conscious experience (Fig. 1A). Along with the distinct temporal and spatial EEG decoding patterns associated with lateral and feedback processing, our findings suggest a processing sequence from feedforward processing to local recurrent interactions encompassing lateral-tofeedback connections, ultimately leading to global recurrency and conscious report.”  

      (9) When stating that this is the first study in which behavioural measures of conscious perception were matched between the attentional blink and masking, it would be beneficial to highlight the main differences between the current study and the one from Fahrenfort et al., 2017, with which the current study shares many similarities in the experimental design (see point 1). 

      We would like to refer the reviewer to our response to point 1), where we detail how we expanded the discussion of similarities and differences between our present study and Fahrenfort et al. (2017).

      (10) The discussion emphasizes how the current study "suggests a processing sequence from feedforward processing to local recurrent interactions encompassing lateral-to-feedback connections, ultimately leading to global recurrency and conscious report". For transparency, it is though important to highlight that one limit of the current study is that it does not provide direct evidence for the specified types of connections (see point 6). 

      We added a qualification in the Discussion section:

      “Although the present EEG decoding measures cannot provide direct evidence for feedback vs. lateral processes, based on neurophysiological evidence, …”

      Furthermore, we added this qualification in the Discussion section:

      “It should be noted that the not all neurophysiological evidence unequivocally links processing of collinearity and of the Kanizsa illusion to lateral and feedback processing, respectively (Angelucci et al., 2002; Bair et al., 2003; Chen et al., 2014), so that overlap in decoding the illusory and non-illusory triangle may reflect other mechanisms, for example feedback processing as well.”

      References

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      Bair, W., Cavanaugh, J. R., & Movshon, J. A. (2003). Time course and time-distance relationships for surround suppression in macaque V1 neurons. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 23(20), 7690–7701.

      Block, N. (2005). Two neural correlates of consciousness. Trends in Cognitive Sciences, 9(2), 46–52.

      Chen, M., Yan, Y., Gong, X., Gilbert, C. D., Liang, H., & Li, W. (2014). Incremental integration of global contours through interplay between visual cortical areas. Neuron, 82(3), 682–694.

      Dehaene, S., Changeux, J.-P., Naccache, L., Sackur, J., & Sergent, C. (2006). Conscious, preconscious, and subliminal processing: a testable taxonomy. Trends in Cognitive Sciences, 10(5), 204–211.

      Hatamimajoumerd, E., Ratan Murty, N. A., Pitts, M., & Cohen, M. A. (2022). Decoding perceptual awareness across the brain with a no-report fMRI masking paradigm. Current Biology: CB. https://doi.org/10.1016/j.cub.2022.07.068

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      Kiefer, M., & Brendel, D. (2006). Attentional Modulation of Unconscious “Automatic” Processes: Evidence from Event-related Potentials in a Masked Priming Paradigm. Journal of Cognitive Neuroscience, 18(2), 184–198.

      Kouider, S., & Dehaene, S. (2007). Levels of processing during non-conscious perception: a critical review of visual masking. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1481), 857–875.

      Lamme, V. A. F. (2010). How neuroscience will change our view on consciousness. Cognitive Neuroscience, 1(3), 204–220.

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      Naccache, L., Blandin, E., & Dehaene, S. (2002). Unconscious masked priming depends on temporal attention. Psychological Science, 13(5), 416–424.

      Pitts, M. A., Lutsyshyna, L. A., & Hillyard, S. A. (2018). The relationship between attention and consciousness: an expanded taxonomy and implications for ‘noreport’ paradigms. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 373(1755), 20170348.

      Sergent, C., & Dehaene, S. (2004). Is consciousness a gradual phenomenon? Evidence for an all-or-none bifurcation during the attentional blink. Psychological Science, 15(11), 720–728.

      Van den Bussche, E., Van den Noortgate, W., & Reynvoet, B. (2009). Mechanisms of masked priming: a meta-analysis. Psychological Bulletin, 135(3), 452–477. van Gaal, S., & Lamme, V. A. F. (2012). Unconscious high-level information processing: implication for neurobiological theories of consciousness: Implication for neurobiological theories of consciousness. The Neuroscientist: A Review Journal Bringing Neurobiology, Neurology and Psychiatry, 18(3), 287–301.

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    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this study, Kroll et al. conduct an in-depth behavioral analysis of F0 knockouts of 4 genes associated with late-onset Alzheimer's Disease (AD), together with 3 genes associated with early-onset AD. Kroll and colleagues developed a web application (ZOLTAR) to compare sleep-associated traits between genetic mutants with those obtained from a panel of small molecules to promote the identification of affected pathways and potential therapeutic interventions. The authors make a set of potentially important findings vis-à-vis the relationship between AD-associated genes and sleep. First, they find that loss-of-function in late-onset AD genes universally results in night-time sleep loss, consistent with the well supported hypothesis that sleep disruption contributes to Alzheimer's-related pathologies. psen-1, an early-onset associated AD gene, which the authors find is principally responsible for the generation of AB40 and AB42 in zebrafish, also shows a slight increase in activity at night and slight decreases in night-time sleep. Conversely, psen-2 mutations increase daytime sleep, while appa/appb mutations have no impact on sleep. Finally, using ZOLTAR, the authors identify serotonin receptor activity as potentially disrupted in sorl1 mutants, while betamethasone is identified as a potential therapeutic to promote reversal of psen2 knockout-associated phenotypes.

      This is a highly innovative and thorough study, yet a handful of key questions remain. First, are night-time sleep loss phenotypes observed in all knockouts for late-onset AD genes in the larval zebrafish a valid proxy for AD risk?

      We cannot say, but it is an interesting question. We selected the four late-onset Alzheimer’s risk genes (APOE, CD2AP, CLU, SORL1) based on human genetics data and brain expression in zebrafish larvae, not based on their likelihood to modify sleep behaviour, which we could have tried by searching for overlaps with GWAS of sleep phenotypes, for example. Consequently, we find it remarkable that all four of these genes caused a night-time sleep phenotype when mutated. We also find it reassuring that knockout of appa/appb and psen2 did not cause a night-time sleep phenotype, which largely excludes the possibility that the phenotype is a technical artefact (e.g. caused by the F0 knockout method) or a property of every gene expressed in the larval brain.

      Having said that, it could still be a coincidence, rather than a special property of genes associated with late-onset AD. In addition to testing additional late-onset Alzheimer’s risk genes, the ideal way to answer this question would be to test in parallel a random set of genes expressed in the brain at this stage of development. From this random set, one could estimate the proportion of genes that cause a night-time sleep phenotype when mutated. One could then use that information to test whether late-onset Alzheimer’s risk genes are indeed enriched for genes that cause a night-time sleep phenotype when mutated.

      For those mutants that cause night-time sleep disturbances, do these phenotypes share a common underlying pathway? e.g. Do 5-HT reuptake inhibitors promote sleep across all 4 late-onset genes in addition to psen1? Can 5-HT reuptake inhibitors reverse other AD-related pathologies in zebrafish? Can compounds be identified that have a common behavioral fingerprint across all or multiple AD risk genes? Do these modify sleep phenotypes?

      To attempt to answer these questions, we used ZOLTAR to generate predictions for all the knockout behavioural fingerprints presented in the study, in the same way as for sorl1 in Fig. 5 and Fig. 5–supplement 1. Here are the indications, targets, and KEGG pathways which are shared by the largest number of knockouts (Author response image 1):

      – One indication is shared by 4/7 knockouts: “opioid dependence” (significant for appa/appb, psen1, apoea/apoeb, cd2ap).

      – Four targets are shared by 4/7 knockouts: “strychnine-binding glycine receptor” (psen1, apoea/apoeb, clu, sorl1); “neuronal acetylcholine receptor beta-2” (psen1, apoea/apoeb, cd2ap, clu); thyroid peroxidase (psen1, apoea/apoeb, cd2ap, clu); carbonic anhydrase IV (appa/appb, psen1, psen2, cd2ap).

      – Three KEGG pathways are shared by 5/7 knockouts: “cholinergic synapse” (psen1, apoea/apoeb, cd2ap, clu, sorl1); tyrosine metabolism (psen2, apoea/apoeb, cd2ap, clu, sorl1); and “nitrogen metabolism” (appa/appb, psen1, psen2, apoea/apoeb, cd2ap).

      As reminder, we hypothesised that loss of Sorl1 affected serotonin signalling based on the following annotations being significant: indication “depression”, target “serotonin transporter”, and KEGG pathway “serotonergic synapse”. Indication “depression” is only significant for sorl1 knockouts; target “serotonin transporter” is also significant for appa/appb and psen2 knockouts; and KEGG pathway “serotonergic synapse” is also significant for psen2 knockouts. ZOLTAR therefore does not predict serotonin signalling to be a major theme common to all mutants with a night-time sleep loss phenotype.

      Particularly interesting is cholinergic signalling appearing in the most common targets and KEGG pathways. Acetylcholine signalling is a major theme in research on AD. For example, the first four drugs ever approved by the FDA to treat AD were acetylcholinesterase inhibitors, which increase acetylcholine signalling by preventing its breakdown by acetylcholinesterase. These drugs are generally considered only to treat symptoms and not modify disease course, but this view has been called into question (Munoz-Torrero, 2008; Relkin, 2007). If, as ZOLTAR suggests, mutations in several Alzheimer’s risk genes affect cholinergic signalling early in development, this would point to a potential causal role of cholinergic disruption in AD.

      Author response image 1.

      Common predictions from ZOLTAR for the seven Alzheimer’s risk genes tested. Predictions from ZOLTAR which are shared by multiple knockout behavioural fingerprints presented in the study. Only indications, targets, and KEGG pathways which are significant for at least three of the seven knockouts tested are shown, ranked from the annotations which are significant for the largest number of knockouts.

      Finally, the web- based platform presented could be expanded to facilitate comparison of other behavioral phenotypes, including stimulus-evoked behaviors.

      Yes, absolutely. The behavioural dataset we used (Rihel et al., 2010) did not measure other stimuli than day/night light transitions, but the “SauronX” platform and dataset (MyersTurnbull et al., 2022) seems particularly well suited for this. To provide some context, we and collaborators have occasionally used the dataset by Rihel et al. (2010) to generate hypotheses or find candidate drugs that reverse a behavioural phenotype measured in the sleep/wake assay (Ashlin et al., 2018; Hoffman et al., 2016). The present work was the occasion to enable a wider and more intuitive use of this dataset through the ZOLTAR app, which has already proven successful. Future versions of ZOLTAR may seek to incorporate larger drug datasets using more types of measurements.

      Finally, the authors propose but do not test the hypothesis that sorl1 might regulate localization/surface expression of 5-HT2 receptors. This could provide exciting / more convincing mechanistic support for the assertion that serotonin signaling is disrupted upon loss of AD-associated genes.

      While working on the Author Response, we made some changes to the analysis ran by ZOLTAR to calculate enrichments (see Methods and github.com/francoiskroll/ZOLTAR, notes on v2). With the new version, 5-HT receptor type 2 is not a significantly enriched target for the sorl1 knockout fingerprint but type 4 is. 5-HT receptor type 4 was also shown to interact with sorting nexin 27, a subunit of retromer, so is a promising candidate (Joubert et al., 2004). Antibodies against human 5-HT receptor type 2 and 4a exist; whether they would work in zebrafish remains to be tested. In our experience, the availability of antibodies suitable for immunohistochemistry in the zebrafish is a serious experimental roadblock.

      Note, all the results presented in the “Version of Records” are from ZOLTAR v2.

      Despite these important considerations, this study provides a valuable platform for highthroughput analysis of sleep phenotypes and correlation with small-molecule-induced sleep phenotypes.

      Strengths:

      - Provides a useful platform for comparison of sleep phenotypes across genotypes/drug manipulations.

      - Presents convincing evidence that night-time sleep is disrupted in mutants for multiple late onset AD-related genes.

      - Provides potential mechanistic insights for how AD-related genes might impact sleep and identifies a few drugs that modify their identified phenotypes

      Weaknesses:

      - Exploration of potential mechanisms for serotonin disruption in sorl1 mutants is limited.

      - The pipeline developed can only be used to examine sleep-related / spontaneous movement phenotypes and stimulus-evoked behaviors are not examined.

      - Comparisons between mutants/exploration of commonly affected pathways are limited.

      Thank you for these excellent suggestions, please see our answers above.

      Reviewer #2 (Public Review):

      Summary:

      This work delineates the larval zebrafish behavioral phenotypes caused by the F0 knockout of several important genes that increase the risk for Alzheimer's disease. Using behavioral pharmacology, comparing the behavioral fingerprint of previously assayed molecules to the newly generated knockout data, compounds were discovered that impacted larval movement in ways that suggest interaction with or recovery of disrupted mechanisms.

      Strengths:

      This is a well-written manuscript that uses newly developed analysis methods to present the findings in a clear, high-quality way. The addition of an extensive behavioral analysis pipeline is of value to the field of zebrafish neuroscience and will be particularly helpful for researchers who prefer the R programming language. Even the behavioral profiling of these AD risk genes, regardless of the pharmacology aspect, is an important contribution. The recovery of most behavioral parameters in the psen2 knockout with betamethasone, predicted by comparing fingerprints, is an exciting demonstration of the approach. The hypotheses generated by this work are important stepping stones to future studies uncovering the molecular basis of the proposed gene-drug interactions and discovering novel therapeutics to treat AD or co-occurring conditions such as sleep disturbance.

      Weaknesses:

      - The overarching concept of the work is that comparing behavioral fingerprints can align genes and molecules with similarly disrupted molecular pathways. While the recovery of the psen2 phenotypes by one molecule with the opposite phenotype is interesting, as are previous studies that show similar behaviorally-based recoveries, the underlying assumption that normalizing the larval movement normalizes the mechanism still lacks substantial support. There are many ways that a reduction in movement bouts could be returned to baseline that are unrelated to the root cause of the genetically driven phenotype. An ideal experiment would be to thoroughly characterize a mutant, such as by identifying a missing population of neurons, and use this approach to find a small molecule that rescues both behavior and the cellular phenotype. If the connection to serotonin in the sorl1 was more complete, for example, the overarching idea would be more compelling.

      Thank you for this cogent criticism.

      On the first point, we were careful not to claim that betamethasone normalises the molecular/cellular mechanism that causes the psen2 behavioural phenotype. Having said that, yes, to a certain extent that would be the hope of the approach. As you say, every compound which normalises the behavioural fingerprint will not normalise the underlying mechanism, but the opposite seems true: every compound that normalises the underlying mechanism should also normalise the behavioural fingerprint. We think this logic makes the “behaviour-first” approach innovative and interesting. The logic is to discover compounds that normalise the behavioural phenotype first, only subsequently test whether they also normalise the molecular mechanism, akin to testing first whether a drug resolves the symptoms before testing whether it actually modifies disease course. While in practice testing thousands of drugs in sufficient sample sizes and replicates on a mutant line is challenging, the dataset queried through ZOLTAR provides a potential shortcut by shortlisting in silico compounds that have the opposite effect on behaviour.

      You mention a “reduction in movement bouts” but note here that the number of behavioural parameters tested is key to our argument. To take the two extremes, say the only behavioural parameter we measured in psen2 knockout larvae was time active during the day, then, yes, any stimulant used at the right concentration could probably normalise the phenotype. In this situation, claiming that the stimulant is likely to also normalise the underlying mechanism, or even that it is a genuine “phenotypic rescue”, would not be convincing. Conversely, say we were measuring thousands of behavioural parameters under various stimuli, such as swimming speed, position in the well, bout usage, tail movements, and eye angles, it seems almost impossible for a compound to rescue most parameters without also normalising the underlying mechanism. The present approach is somewhere inbetween: ZOLTAR uses six behavioural parameters for prediction (e.g. Fig 6a), but all 17 parameters calculated by FramebyFrame can be used to assess rescue during a subsequent experiment (Fig. 6c). For both, splitting each parameter in day and night increases the resolution of the approach, which partly answers your criticism. For example, betamethasone rescued the day-time hypoactivity without causing night-time hyperactivity, so we are not making the “straw man argument” explained above of using any broad stimulant to rescue the hypoactivity phenotype.

      Furthermore, for diseases where the behavioural defect is the primary concern, such as autism or bipolar disorder, perhaps this behaviour-first approach is all that is needed, and whether or not the compound precisely rescues the underlying mechanism is somewhat secondary. The use of lithium to prevent manic episodes in bipolar disorder is a good example. It was initially tested because mania was thought to be caused by excess uric acid and lithium can dissolve uric acid (Mitchell and Hadzi-Pavlovic, 2000). The theory is now discredited, but lithium continues to be used without a precise understanding of its mode of action. In this example, behavioural rescue alone, assuming the secondary effects are tolerable, is sufficient to be beneficial to patients, and whether it modulates the correct causal pathway is secondary.

      On the second point, we agree that testing first ZOLTAR on a mutant for which we have a fairly good understanding of the mechanism causing the behavioural phenotype could have been a productive approach. Note, however, that examples already exist in the literature (Ashlin et al., 2018; Hoffman et al., 2016). The example from Hoffman et al. (2016) is especially convincing. Drugs generating behavioural fingerprints that positively correlate with the cntnap2a/cntnap2b double knockout fingerprint were enriched with NMDA and GABA receptor antagonists. In experiments analogous to our citalopram and fluvoxamine treatments (Fig. 5c,d and Fig. 5–supplement 1c,d), cntnap2a/cntnap2b knockout larvae were overly sensitive to the NMDA receptor antagonist MK-801 and the GABAA receptor antagonist pentylenetetrazol (PTZ). Among other drugs tested, zolpidem, a GABAA receptor agonist, caused opposite effects on wild-type and cntnap2a/cntnap2b knockout larvae. Knockout larvae were found to have fewer GABAergic neurons in the forebrain. While these studies did not use precisely the same analysis that ZOLTAR runs, they used the same rationale and behavioural dataset to make these predictions (Rihel et al., 2010), which shows that approaches like ZOLTAR can point to causal processes.

      On your last point, we hope our experiment testing fluvoxamine, another selective serotonin reuptake inhibitor (SSRI), makes the connection between Sorl1 and serotonin signalling more convincing.

      - The behavioral difference between the sorl1 KO and scrambled at the higher dose of the citalopram is based on a small number of animals. The KO Euclidean distance measure is also more spread out than for the other datasets, and it looks like only five or so fish are driving the group difference. It also appears as though the numbers were also from two injection series. While there is nothing obviously wrong with the data, I would feel more comfortable if such a strong statement of a result from a relatively subtle phenotype were backed up by a higher N or a stable line. It is not impossible that the observed difference is an experimental fluke. If something obvious had emerged through the HCR, that would have also supported the conclusions. As it stands, if no more experiments are done to bolster the claim, the confidence in the strength of the link to serotonin should be reduced (possibly putting the entire section in the supplement and modifying the discussion). The discussion section about serotonin and AD is interesting, but I think that it is excessive without additional evidence.

      We mostly agree with this criticism. One could interpret the larger spread of the data for sorl1 KO larvae treated with 10 µM citalopram as evidence that the knockout larvae do indeed react differently to the drug at this dose, regardless of being driven by a subset of the animals. The result indeed does not survive removing the top 5 (p = 0.87) or top 3 (p = 0.18) sorl1 KO + 10 µM larvae, but this amounts to excluding 20 (3/14) or 35 (5/14) % of the datapoints as potential outliers, which is unreasonable. In fact, excluding the top 5 sorl1 KO + 10 µM is equivalent to calling any datapoint with z-score > 0.2 an outlier (z-scores of the top 5 datapoints are 0.2–1.8). Applying consistently the same criterion to the scrambled + 10 µM group would remove the top 6 datapoints (z-scores = 0.5–3.9). Comparing the resulting two distributions again gives the sorl1 KO + 10 µM distribution as significantly higher (p = 0.0015). We would also mention that Euclidean distance, as a summary metric for distance between behavioural fingerprints, has limitations. For example, the measure will be more sensitive to changes in some parameters but not others, depending on how much room there is for a given parameter to change. We included this metric to lend support to the observation one can draw from the fingerprint plot (Fig. 5c) that sorl1 mutants respond in an exaggerated way to citalopram across many parameters, while being agnostic to which parameter might matter most.

      Given that the HCR did not reveal anything striking, we agree with you that too much of our argument relied on this result being robust. As you and Reviewer #3 suggested, we repeated this experiment with a different SSRI, fluvoxamine (Fig. 5–supplement 1). We cannot readily explain why the result was opposite to what we found with citalopram, but in both cases sorl1 knockout larvae reacted differently than their control siblings, which adds an argument to our claim that ZOLTAR correctly predicted serotonin signalling as a disrupted pathway from the behavioural fingerprint. Accordingly, we mostly kept the Discussion on Sorl1 the same, although we concede that we may not have identified the molecular mechanism.

      - The authors suggest two hypotheses for the behavioral difference between the sorl1 KO and scrambled at the higher dose of the citalopram. While the first is tested, and found to not be supported, the second is not tested at all ("Ruling out the first hypothesis, sorl1 knockouts may react excessively to a given spike in serotonin." and "Second, sorl1 knockouts may be overly sensitive to serotonin itself because post-synaptic neurons have higher levels of serotonin receptors."). Assuming that the finding is robust, there are probably other reasons why the mutants could have a different sensitivity to this molecule. However, if this particular one is going to be mentioned, it is surprising that it was not tested alongside the first hypothesis. This work could proceed without a complete explanation, but additional discussion of the possibilities would be helpful or why the second hypothesis was not tested.

      There are no strong scientific reasons why this hypothesis was not tested. The lead author (F Kroll) moved to a different lab and country so the project was finalised at that time. We do not plan on testing this hypothesis at this stage. However, we adapted the wording to make it clear this is one possible alternative hypothesis which could be tested in the future. The small differences found by HCR are actually more in line with the new results from the fluvoxamine experiment, so it may also be that both hypotheses (pre-synaptic neurons releasing less serotonin when reuptake is blocked; or post-synaptic neurons being less sensitive) contribute. The fluvoxamine experiment was performed in a different lab (ICM, Paris; all other experiments were done in UCL, London) in a different wild-type strain (TL in ICM, AB x Tup LF in UCL), which complicates how one interprets this discrepancy.

      - The authors claim that "all four genes produced a fairly consistent phenotype at night". While it is interesting that this result arose in the different lines, the second clutch for some genes did not replicate as well as others. I think the findings are compelling, regardless, but the sometimes missing replicability should be discussed. I wonder if the F0 strategy adds noise to the results and if clean null lines would yield stronger phenotypes. Please discuss this possibility, or others, in regard to the variability in some phenotypes.

      For the first part of this point, please see below our answer to Reviewer #3, point (2) c.

      Regarding the F0 strategy potentially adding variability, it is an interesting question which we tested in a larger dataset of behavioural recordings from F0 and stable knockouts for the same genes (unpublished). In summary, the F0 knockout method does not increase clutchto-clutch or larva-to-larva variability in the assay. F0 knockout experiments found many more significant parameters and larger effect sizes than stable knockout experiments, but this difference could largely be explained by the larger sample sizes of F0 knockout experiments. In fact, larger sample sizes within individual clutches appears to be a major advantage of the F0 knockout approach over in-cross of heterozygous knockout animals as it increases sensitivity of the assay without causing substantial variability. We plan to report in more detail on this analysis in a separate paper as we think it would dilute the focus of the present work.

      - In this work, the knockout of appa/appb is included. While APP is a well-known risk gene, there is no clear justification for making a knockout model. It is well known that the upregulation of app is the driver of Alzheimer's, not downregulation. The authors even indicate an expectation that it could be similar to the other knockouts ("Moreover, the behavioural phenotypes of appa/appb and psen1 knockout larvae had little overlap while they presumably both resulted in the loss of Aβ." and "Comparing with early-onset genes, psen1 knockouts had similar night-time phenotypes, but loss of psen2 or appa/appb had no effect on night-time sleep."). There is no reason to expect similarity between appa/appb and psen1/2. I understand that the app knockouts could unveil interesting early neurodevelopmental roles, but the manuscript needs to be clarified that any findings could be the opposite of expectation in AD.

      On “there is no reason to expect similarity […]”, we disagree. Knockout of appa/appb and knockout of psen1 will both result in loss of Aβ (appa/appb encode Aβ and psen1 cleaves Appa/Appb to release Aβ, cf. Fig. 3e). Consequently, a phenotype caused by the loss of Aβ, or possibly other Appa/Appb cleavage products, should logically be found in both appa/appb and psen1 knockouts.

      On “it is well known that the upregulation of APP is the driver of Alzheimer’s, not downregulation”; we of course agree. Among others, the examples of Down syndrome, APP duplication (Sleegers et al., 2006), or mouse models overexpressing human APP show definitely that overexpression of APP is sufficient to cause AD. Having said that, we would not be so quick in dismissing APP knockout as potentially relevant to understanding of AD.

      Loss of soluble Aβ due to aggregation could contribute to pathology (Espay et al., 2023). Without getting too much into this intricate debate, links between levels of Aβ and risk of disease are often counter-intuitive too. For example, out of 138 PSEN1 mutations screened in vitro, 104 reduced total Aβ production and 11 even seemingly abolished the production of both Aβ40 and Aβ42 (Sun et al., 2017). In short, loss of soluble Aβ occurs in both AD and in our appa/appb knockout larvae.

      We added a sentence in Results (section psen2 knockouts […]) to briefly justify our appa/appb knockout approach. To be clear, we do not want to imply, for example, that the absence of a night-time sleep phenotype for appa/appb is contradictory to the body of literature showing links between Aβ and sleep, including in zebrafish (Özcan et al., 2020). As you say, our experiment tested loss of App, including Aβ, while the literature typically reports on overexpression of APP, as in APP/PSEN1-overexpressing mice (Jagirdar et al., 2021).

      Reviewer #3 (Public Review):

      In this manuscript by Kroll and colleagues, the authors describe combining behavioral pharmacology with sleep profiling to predict disease and potential treatment pathways at play in AD. AD is used here as a case study, but the approaches detailed can be used for other genetic screens related to normal or pathological states for which sleep/arousal is relevant. The data are for the most part convincing, although generally the phenotypes are relatively small and there are no major new mechanistic insights. Nonetheless, the approaches are certainly of broad interest and the data are comprehensive and detailed. A notable weakness is the introduction, which overly generalizes numerous concepts and fails to provide the necessary background to set the stage for the data.

      Major points

      (1) The authors should spend more time explaining what they see as the meaning of the large number of behavioral parameters assayed and specifically what they tell readers about the biology of the animal. Many are hard to understand--e.g. a "slope" parameter.

      We agree that some parameters do not tell something intuitive about the biology of the animal. It would be easy to speculate. For example, the “activity slope” parameter may indicate how quickly the animal becomes tired over the course of the day. On the other hand, fractal dimension describes the “roughness/smoothness” of the larva’s activity trace (Fig. 2–supplement 1a); but it is not obvious how to translate this into information about the physiology of the animal. We do not see this as an issue though. While some parameters do provide intuitive information about the animal’s behaviour (e.g. sleep duration or sunset startle as a measure of startle response), the benefit of having a large number of behavioural parameters is to compare behavioural fingerprints and assess rescue of the behavioural phenotype by small molecules (Fig. 6c). For this purpose, the more parameters the better. The “MoSeq” approach from Wiltschko et al., 2020 is a good example from literature that inspired our own Fig. 6c. While some of the “behavioural syllables” may be intuitive (e.g. running or grooming), it is probably pointless to try to explain the ‘meaning’ of the “small left turn in place with head motion” syllable (Wiltschko et al., 2020). Nonetheless, this syllable was useful to assess whether a drug specifically treats the behavioural phenotype under study without causing too many side effects. Unfortunately, ZOLTAR has to reduce the FramebyFrame fingerprint (17 parameters) to just six parameters to compare it to the behavioural dataset from Rihel et al., 2010, but here, more parameters would almost certainly translate into better predictions too, regardless of their intuitiveness.

      It is true however that we did not give much information on how some of the less intuitive parameters, such as activity slope or fractal dimension, are calculated or what they describe about the dataset (e.g. roughness/smoothness for fractal dimension). We added a few sentences in the legend of Fig. 2–supplement 1.

      (2) Because in the end the authors did not screen that many lines, it would increase confidence in the phenotypes to provide more validation of KO specificity. Some suggestions include:

      a. The authors cite a psen1 and psen2 germline mutant lines. Can these be tested in the FramebyFrame R analysis? Do they phenocopy F0 KO larvae?

      We unfortunately do not have those lines. We investigated the availability of importing a psen2 knockout line from abroad, but the process of shipping live animals is becoming more and more cost and time prohibitive. However, we observed the same pigmentation phenotype for psen2 knockouts as reported by Jiang et al., 2018, which is at least a partial confirmation of phenocopying a loss of function stable mutant.  

      b. psen2_KO is one of the larger centerpieces of the paper. The authors should present more compelling evidence that animals are truly functionally null. Without this, how do we interpret their phenotypes?

      We disagree that there should be significant doubt about these mutants being truly functionally null, given the high mutation rate and presence of the expected pigmentation phenotype (Jiang et al., 2018, Fig. 3f and Fig. 3–supplement 3a). The psen2 F0 knockouts were virtually 100% mutated at three exons across the gene (mutation rates were locus 1: 100 ± 0%; locus 2: 99.99 ± 0.06%; locus 3: 99.85 ± 0.24%). Additionally, two of the three mutated exons had particularly high rates of frameshift mutations (locus 1: 97 ± 5%; locus 2: 88 ± 17% frameshift mutation rate). It is virtually impossible that a functional protein is translated given this burden of frameshift mutations. Phenotypically, in addition to the pigmentation defect, double psen1/psen2 F0 knockout larvae had curved tails, the same phenotype as caused by a high dose of the γ-secretase inhibitor DAPT (Yang et al., 2008). These double F0 knockouts were lethal, while knockout of psen1 or psen2 alone did not cause obvious morphological defects. Evidently, most larvae must have been psen2 null mutants in this experiment, otherwise functional Psen2 would have prevented early lethality.

      Translation of zebrafish psen2 can start at downstream start codons if the first exon has a frameshift mutation, generating a seemingly functional Psen2 missing the N-terminus (Jiang et al., 2020). Zebrafish homozygous for this early frameshift mutation had normal pigmentation, showing it is a reliable marker of Psen2 function even when it is mutated. This mechanism is not a concern here as the alternative start codons are still upstream of two of the three mutated exons (the alternative start codons discovered by Jiang et al., 2020 are in exon 2 and 3, but we targeted exon 3, exon 4, and exon 6).

      We understand that the zebrafish community may be cautious about F0 phenotyping compared to stably generated mutants. As mentioned to Reviewer #2, we are planning to assemble a paper that expressly compares behavioural phenotypes measured in F0 vs. stable mutants to allay some of these concerns. Our current manuscript, which combines CRISPR-Cas9 rapid F0 screening with in silico pharmacological predictions, inevitability represents a first step in characterizing the functions of these genes. 

      c. Related to the above, for cd2AP and sorl1 KO, some of the effect sizes seem to be driven by one clutch and not the other. In other words, great clutch-to-clutch variability. Should the authors increase the number of clutches assayed?

      Correct, there is substantial clutch-to-clutch variability in this behavioural assay. This is not specific to our experiments. Even within the same strain, wild-type larvae from different clutches (i.e. non-siblings) behave differently (Joo et al., 2021). This is why it is essential to compare behavioural phenotypes within individual clutches (i.e. from a single pair of parents, one male and one female), as we explain in Methods (section Behavioural video-tracking) and in the documentation of the FramebyFrame package. We often see two different experimental designs in literature: comparing non-sibling wild-type and mutant larvae, or pooling different clutches which include all genotypes (e.g. pooling multiple clutches from heterozygous in-crosses or pooling wild-type clutches before injecting them). The first experimental design causes false positive findings (Joo et al., 2021), as the clutchto-clutch variability we and others observe gets interpreted as a behavioural phenotype. The second experimental design should not cause false positives but likely decreases the sensitivity of the assay by increasing the spread within genotypes. In both cases, the clutch-to-clutch variability is hidden, either by interpreting it as a phenotype (first case) or by adding it to animal-to-animal variability (second case). Our experimental design is technically more challenging as it requires obtaining large clutches from unique pairs of parents. However, this approach is better as it clearly separates the different sources of variability (clutch-to-clutch or animal-to-animal). As for every experiment, yes, a larger number of replicates would be better, but we do not plan to assay additional clutches at this time. Our work heavily focuses on the sorl1 and psen2 knockout behavioural phenotypes. The key aspects of these phenotypes were effectively tested in four experiments (five to six clutches) as sorl1 knockout larvae were also tracked in the citalopram and fluvoxamine experiments (Fig. 5 and Fig. 5–supplement 1), and psen2 knockout larvae were also tracked in the small molecule rescue experiment (Fig. 6 and Fig. 6–supplement 1).

      The psen2 behavioural phenotype replicated well across the six clutches tested (pairwise cosine similarities: 0.62 ± 0.15; Author response image 2a). 5/6 clutches were less active and initiating more sleep bouts during the day, as we claimed in Fig. 3.

      In the citalopram experiment, the H<sub>2</sub>O-treated sorl1 knockout fingerprint replicated fairly well the baseline recordings in Fig. 4, despite the smaller sample size (cos = 0.30 and 0.78; Author response image 2b, see “KO Fig. 5”). 5/6 of the significant parameters presented in Fig. 4–supplement 4 moved in the same direction, and knockout larvae were also hypoactive during the day but hyperactive at night. Note that two clutches were tracked on the same 96-well plate in this experiment. We calculated each larva’s z-score using the average of its control siblings, then we averaged all the z-scores to generate the fingerprint. The H<sub>2</sub>O treated sorl1 knockout clutch from the fluvoxamine experiment did not replicate well the baseline recordings (cos = 0.08 and 0.11; Author response image 2b, see “KO Fig. 5–suppl. 1”). Knockout larvae were hypoactive during the day as expected, but behaviour at night was not as robustly affected. As mentioned above, knockouts were made in a different genetic background (TL, instead of AB x Tup LF used for all other experiments), which could explain the discrepancy.

      We also took the opportunity to check whether our SSRI treatments replicated well the data from Rihel et al., 2010. For both citalopram (n = 3 fingerprints in the database) and fluvoxamine (n = 4 fingerprints in the database), replication was excellent (cos ≥ 0.67 for all comparisons of a fingerprint from this study vs. a fingerprint from Rihel et al. 2010; Author response image 2c,d). Note that the scrambled + 10 µM citalopram and + 10 µM fluvoxamine fingerprints correlate extremely well (cos = 0.92; can be seen in Author response image 2c,d), which was predicted by the small molecule screen dataset.

      Author response image 2.

      Replication of psen2 and sorl1 F0 knockout fingerprints and SSRI treatments from Rihel et al., 2010. a, (left) Every psen2 F0 knockout behavioural fingerprint generated in this study. Each dot represents the mean deviation from the same-clutch scrambled-injected mean for that parameter (z-score, mean ± SEM). From the experiments in Fig. 6, presented is the psen2 F0 knockout + H<sub>2</sub>O fingerprints. The fingerprints in grey (“not shown”) are from a preliminary drug treatment experiment we did not include in the final study. These fingerprints are from psen2 F0 knockout larvae treated with 0.2% DMSO, normalised to scrambled-injected siblings also treated with 0.2% DMSO. (right) Pairwise cosine similarities (−1.0–1.0) for the fingerprints presented. b, Every sorl1 F0 knockout behavioural fingerprint, as in a). c, The scrambled-injected + citalopram (10 µM) fingerprints (grey) in comparison to the citalopram (10–15 µM) fingerprints from the Rihel et al., 2010 database (green). d, The scrambled-injected + fluvoxamine (10 µM) fingerprint (grey) in comparison to the fluvoxamine fingerprints from the Rihel et al., 2010 database (pink). In c) and d), the scrambled-injected fingerprints are from the experiments in Fig. 5 and Fig. 5–suppl. 1, but were converted here into the behavioural parameters used by Rihel et al., 2010 for comparison. Parameters: 1, average activity (sec active/min); 2, average waking activity (sec active/min, excluding inactive minutes); 3, total sleep (hr); 4, number of sleep bouts; 5, sleep bout length (min); 6, sleep latency (min until first sleep bout).

      (3) The authors make the point that most of the AD risk genes are expressed in fish during development. Is there public data to comment on whether the genes of interest are expressed in mature/old fish as well? Just because the genes are expressed early does not at all mean that early- life dysfunction is related to future AD (though this could be the case, of course). Genes with exclusive developmental expression would be strong candidates for such an early-life role, however. I presume the case is made because sleep studies are mainly done in juvenile fish, but I think it is really a prejy minor point and such a strong claim does not even need to be made.

      This is a fair criticism but we do not make this claim (“early-life dysfunction is related to future AD”) from expression alone. The reviewer is probably referring to the following quote:

      “[…] most of these were expressed in the brain of 5–6-dpf zebrafish larvae, suggesting they play a role in early brain development or function,” which does not mention future risk of AD. We do suggest that these genes have a function in development. After all, every gene that plays a role in brain development must be expressed during development, so this wording seemed reasonable. Nevertheless, we adapted the wording to address this point and Reviewer #2’s complaint below. As noted, the primary goal was to check that the genes we selected were indeed expressed in zebrafish larvae before performing knockout experiments. Our discussion does raise the hypothesis that mutations in Alzheimer’s risk genes impact brain development and sleep early in life, but this argument primarily relies on our observation that knockout of late-onset Alzheimer’s risk genes causes sleep phenotypes in 7-day old zebrafish larvae and from previous work showing brain structural differences in children at high genetic risk of AD (Dean et al., 2014; Quiroz et al., 2015), not solely on gene expression early in life.

      Please also see our answer to a similar point raised by Reviewer #2 below (cf. Author response image 7).

      (4) A common quandary with defining sleep behaviorally is how to rectify sleep and activity changes that influence one another. With psen2 KOs, the authors describe reduced activity and increased sleep during the day. But how do we know if the reduced activity drives increased behavioral quiescence that is incorrectly defined as sleep? In instances where sleep is increased but activity during periods during wake are normal or elevated, this is not an issue. But here, the animals might very well be unhealthy, and less active, so naturally they stop moving more for prolonged periods, but the main conclusion is not sleep per se. This is an area where more experiments should be added if the authors do not wish to change/temper the conclusions they draw. Are psen2 KOs responsive to startling stimuli like controls when awake? Do they respond normally when quiescent? Great care must be taken in all models using inactivity as a proxy for sleep, and it can harm the field when there is no acknowledgment that overall health/activity changes could be a confound. Particularly worrisome is the betamethasone data in Figure 6, where activity and sleep are once again coordinately modified by the drug.

      This is a fair criticism. We agree it is a concern, especially in the case of psen2 as we claim that day-time sleep is increased while zebrafish are diurnal. We do not rely heavily on the day-time inactivity being sleep (the ZOLTAR predictions or the small molecule rescue do not change whether the parameter is called sleep or inactivity), but our choice of labelling can fairly be challenged.

      To address “are psen2 KO responsive to startling stimuli like controls when awake/when quiescent”, we looked at the larvae’s behaviour immediately after lights abruptly switched on in the mornings. Almost every larva, regardless of genotype, responded strongly to every lights-off transition during the experiment. Instead, we chose the lights-on transition for this analysis because it is a weaker startling stimulus for the larvae than the lights-off transition (Fig. 3–supplement 3), potentially exposing differences between genotypes or behavioural states (quiescent or awake). We defined a larva as having reacted to the lights switching on if it made a swimming bout during the second (25 frames) a er the lights-on transition. Across two clutches and two lights-on transitions, an average of 65% (range 52–73%) of all larvae reacted to the stimulus. psen2 knockout larvae were similarly likely, if not more likely, to respond (in average 69% responded, range 60–76%) than controls (60% average, range 44– 75%). When the lights switched on, about half of the larvae (39–51%) would have been classified as asleep according to the one-minute inactivity definition (i.e. the larva did not move in the minute preceding the lights transition). This allowed us to also compare behavioural states, as suggested by the reviewer. For three of the four light transitions, larvae which were awake when lights switched on were more likely to react than asleep larvae, but this difference was not striking (overall, awake larvae were only 1.1× more likely to react; Author response image 3). Awake psen2 knockout larvae were 1.1× (range 1.04–1.11×) more likely to react than awake control larvae, so, yes, psen2 knockout larvae respond normally when awake. Asleep psen2 knockout larvae were 1.4× (range 0.63–2.19×) more likely to react than asleep control larvae, so psen2 knockouts are also more or equally likely to react than control larvae when asleep. In summary, the overall health of psen2 knockouts did not seem to be a significant confound in the experiment. As the reviewer suggested, if psen2 knockout larvae were seriously unhealthy, they would not be as responsive as control larvae to a startling stimulus.

      Author response image 3.

      psen2 F0 knockouts react normally to lights switching on, indicating they are largely healthy. At each lights-on transition (9 AM), each larva was categorised as awake if it had moved in the preceding one minute or asleep if it had been inactive for at least one minute. Darker tiles represent larvae which performed a swimming bout during the second following lights-on; lighter tiles represent larvae which did not move during that second. The total count of each waffle plot was normalised to 25 so plots can be compared to each other. The real count is indicated in the corner of each plot. Data is from the baseline psen2 knockout trackings presented in Fig. 3 and Fig. 3–suppl. 2.

      Next, we compared inactive period durations during the day between psen2 and control larvae. If psen2 knockout larvae indeed sleep more during the day compared to controls, we may predict inactive periods longer than one minute to increase disproportionately compared to the increase in shorter inactive periods. This broadly appeared to be the case, especially for one of the two clutches (Author response image 4). In clutch 1, inactive periods lasting 1–60 sec were equally frequent in both psen2 and control larvae (fold change 1.0× during both days), while inactive periods lasting 1–2 min were 1.5× (day 1) and 2.5× (day 2) more frequent in psen2 larvae compared to control larvae. In clutch 2, 1–60 sec inactive periods were also equally frequent in both psen2 and control larvae, while inactive periods lasting 1–2 min were 3.4× (day 1) and 1.5× (day 2) more frequent in psen2 larvae compared to control larvae. Therefore, psen2 knockouts disproportionately increased the frequency of inactive periods longer than one minute, suggesting they genuinely slept more during the day.

      Author response image 4.

      psen2 F0 knockouts increased preferentially the frequency of longer inactive bouts. For each day and clutch, we calculated the mean distribution of inactive bout lengths across larvae of same genotype (psen2 F0 knockout or scrambled-injected), then compared the frequency of inactive bouts of different lengths between the two genotypes. For example, in clutch 1 during day 2, 0.01% of the average scrambled-injected larva’s inactive bouts lasted 111–120 seconds (X axis 120 sec) while 0.05% of the average psen2 F0 knockout larva lasted this long, so the fold change was 5×. Inactive bouts lasting < 1 sec were excluded from the analysis. In clutch 2, day 1 plot, two datapoints fall outside the Y axis limit: 140 sec, Y = 32×; 170 sec, Y = 16×. Data is from the baseline psen2 knockout trackings presented in Fig. 3 and Fig. 3–suppl. 2.

      Ultimately, this criticism seems challenging to definitely address experimentally. A possible approach could be to use a closed-loop system which, after one minute of inactivity, triggers a stimulus that is sufficient to startle an awake larva but not an asleep larva. If psen2 knockout larvae indeed sleep more during the day, the stimulus should usually not be sufficient to startle them. Nevertheless, we believe the two analyses presented here are consistent with psen2 knockout larvae genuinely sleeping more during the day, so we decided to keep this label. We agree with the reviewer that the one-minute inactivity definition has limitations, especially for day-time inactivity.

      (5) The conclusions for the serotonin section are overstated. Behavioural pharmacology purports to predict a signaling pathway disrupted with sorl1 KO. But is it not just possible that the drug acts in parallel to the true disrupted pathway in these fish? There is no direct evidence for serotonin dysfunction - that conclusion is based on response to the drug. Moreover, it is just one drug - is the same phenotype present with another SSRI? Likewise, language should be toned down in the discussion, as this hypothesis is not "confirmed" by the results (consider "supported"). The lack of measured serotonin differences further raises concern that this is not the true pathway. This is another major point that deserves further experimental evidence, because without it, the entire approach (behavioral pharm screen) seems more shaky as a way to identify mechanisms. There are any number of testable hypotheses to pursue such as a) Using transient transgenesis to visualize 5HT neuron morphology (is development perturbed: cell number, neurite morphology, synapse formation); b) Using transgenic Ca reporters to assay 5HT neuron activity.

      Regarding the comment, “is it not just possible that the drug acts in parallel to the true disrupted pathway”, we think no, assuming we understand correctly the question. Key to our argument is the fact that sorl1 knockout larvae react differently to the drug(s) than control larvae. As an example, take night-time sleep bout length, which was not affected by knockout of sorl1 (Fig. 4–supplement 4). For the sake of the argument, say only dopamine signalling (the “true disrupted pathway”) was affected in sorl1 knockouts and that serotonin signalling was intact. Assuming that citalopram specifically alters serotonin signalling, then treatment should cause the same increase in sleep bout length in both knockouts and controls as serotonin signalling is intact in both. This is not what we see, however. Citalopram caused a greater increase in sleep bout length in sorl1 knockouts than in scrambled-injected larvae. In other words, the effect is non-additive, in the sense that citalopram did not add the same number of z-scores to sorl1 knockouts or controls. We think this shows that serotonin signalling is somehow different in sorl1 knockouts. Nonetheless, we concede that the experiment does not necessarily say much about the importance of the serotonin disruption caused by loss of Sorl1. It could be, for example, that the most salient consequence of loss of Sorl1 is cholinergic disruption (see reply to Reviewer #1 above) and that serotonin signalling is a minor theme.

      Furthermore, we agree with the reviewer and Reviewer #2 that the conclusions were overly confident. As suggested, we decided to repeat this experiment with another SSRI, fluvoxamine. Please find the results of this experiment in Fig. 5–supplement 1. The suggestions to further test the serotonin system in the sorl1 knockouts are excellent as well, however we do not plan to pursue them at this stage.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Major Comments:

      - Data are presented in a variety of different ways, occasionally making comparisons across figures difficult. Perhaps at a minimum, behavioral fingerprints as in Figure 3 - Supplementary Figure 1 should be presented for all mutants in the main figures.

      We like this suggestion! Thank you. We brought the behavioural fingerprints figure (previously Fig. 4–supplement 5) as main Fig. 4, and put the figure focused on the sorl1 knockout behavioural phenotype in supplementary, with the other gene-by-gene figures.

      - It is not clear why some data were selected for supplemental rather than main figures. In many cases, detailed phenotypic data is provided for one example mutant in the main figures, and then additional mutants are described in detail in the supplement. Again, to facilitate comparisons between mutants, fingerprints could be provided for all mutants in a main figure, with detailed analyses moved to the supplements.

      The logic was to dedicate one main figure to psen2 (Fig. 3) as an example of an early-onset Alzheimer’s risk gene, and one to sorl1 (previously Fig. 4) as an example of a late-onset Alzheimer’s risk gene. We focused on them in main figures as they are both tested again later (Fig. 5 and Fig. 6). Having said that, we agree that the fingerprints may be a better use of main figure space than the parameters plots. In addition to the above (fingerprints of lateonset Alzheimer’s risk genes in main figure), we rearranged the figures in the early-onset AD section to have the psen2 F0 knockout fingerprint in main.

      - The explication of the utility of behavioral fingerprinting on page 35 is somewhat confusing. The authors describe drugs used to treat depression as enriched among small molecules anti-correlating with the sorl1 fingerprint. However, in Figure 5 - Supplementary Figure 1, drugs used to treat depression are biased toward positive cosines, which are indicated as having a more similar fingerprint to sorl1. These drugs should be described as more present among compounds positively correlating with the sorl1 fingerprint.

      Sorry, the confusion is about “(anti-)correlating”. Precisely, we meant “correlating and/or anti-correlating”, not just anti-correlating. We changed to that wording. In short, the analysis is by design agnostic to whether compounds with a given annotation are found more on the positive cosines side (le side in Fig. 5–supplement 1a) or the negative cosines side (right side). This is because the dataset often includes both agonists and antagonists to a given pathway but these are difficult to annotate. For example, say 10 compounds in the dataset target the dopamine D4 receptor, but these are an unknown mix of agonists and antagonists. In this case, we want ZOLTAR to generate a low p-value when all 10 compounds are found at extreme ends of the list, regardless of which end(s) that is (e.g. top 8 and bottom 2 should give an extremely low p-value). Initially, we were splitting the list, for each annotation, into positive-cosine fingerprints and negative-cosine fingerprints and testing enrichment on both separately, but we think the current approach is better as it reflects better the cases we want to detect and considers all available examples for a given annotation in one test. In sum, yes, in this case drugs used to treat depression were mostly in the positive-cosine side, but the other drugs on the negative-cosine side also contributed to what the p-value is, so it reflects better the analysis to say “correlating and/or anticorrelating”. You can read more about our logic for the analysis in Methods (section Behavioural pharmacology from sorl1 F0 knockout’s fingerprint).

      - The authors conclude the above-described section by stating: "sorl1 knockout larvae behaved similarly to larvae treated with small molecules targeting serotonin signaling, suggesting that the loss of Sorl1 disrupted serotonin signaling." Directionality here may be important. Are all of the drugs targeting the serotonin transporter SSRIs or similar? If so, then a correct statement would be that loss of Sorl1 causes similar phenotypes to drugs enhancing serotonin signaling. Finally, based on the correlation between serotonin transporter inhibitor trazodone and the sorl1 crispant phenotype, it is potentially surprising that the SSRI citalopram caused the opposite phenotype from sorl1, that is, increased sleep during the day and night. It is potentially interesting that this result was enhanced in mutants, and suggests dysfunction of serotonin signaling, but the statement that "our behavioral pharmacology approach correctly predicted from behaviour alone that serotonin signaling was disrupted" is too strong a conclusion.

      We understand “disrupt” as potentially going either way, but this may not be the common usage. We changed to “altered”.

      The point regarding directionality is excellent, however. We tested the proportion of serotonin transporter agonists and antagonists (SSRIs) on each side of the ranked list of small molecule fingerprints. We used the STITCH database for this analysis as it has more drug–target interactions, but likely less curated, than the Therapeutic Target Database (Szklarczyk et al., 2016). As with the Therapeutic Target Database, most fingerprints of compounds interacting with the serotonin transporter SLC6A4 were found on the side of positive cosines (p ~ 0.005 using the custom permutation test), which replicates Fig. 5a with a different source for the drug–target annotations (Author response image 5). On the side of positive cosines (small molecules which generate behavioural fingerprints correlating with the sorl1 fingerprint), there were 2 agonists and 26 antagonists. On the side of negative cosines (small molecules which generate behavioural fingerprints anti-correlating with the sorl1 fingerprint), there were 3 agonists and 2 antagonists. Using a Chi-squared test, this suggests a significant (p = 0.002) over-representation of antagonists (SSRIs) on the positive side (expected count = 24, vs. 26 observed) and agonists on the negative side (expected count = 1, vs. 3 observed). If SLC6A4 antagonists, i.e. SSRIs, indeed tend to cause a similar behavioural phenotype than knockout of sorl1, this would point in the direction of our original interpretation of the citalopram experiment; which was that excessive serotonin signalling is what causes the sorl1 behavioural phenotype.

      Author response image 5.

      Using the STITCH database as source of annotations also predicts SLC6A4 as an enriched target for the sorl1 behavioural fingerprint. Same figures as Fig. 5a,b but using the STITCH database (Szklarczyk et al., 2016) as source for the drug targets. a, Compounds annotated by STITCH as interacting with the serotonin transporter SLC6A4 tend to generate behavioural phenotypes similar to the sorl1 F0 knockout fingerprint. 40,522 compound–target protein pairs (vertical bars; 1,592 unique compounds) are ranked from the fingerprint with the most positive cosine to the fingerprint with the most negative cosine in comparison with the mean sorl1 F0 knockout fingerprint. Fingerprints of drugs that interact with SLC6A4 are coloured in yellow. Simulated p-value = 0.005 for enrichment of drugs interacting with SLC6A4 at the top (positive cosine) and/or bottom (negative cosine) of the ranked list by a custom permutation test. b, Result of the permutation test for top and/or bottom enrichment of drugs interacting with SLC6A4 in the ranked list. The absolute cosines of the fingerprints of drugs interacting with SLC6A4 (n = 52, one fingerprint per compound) were summed, giving sum of cosines = 15.9. To simulate a null distribution, 52 fingerprints were randomly drawn 100,000 times, generating a distribution of 100,000 random sum of cosines. Here, only 499 random draws gave a larger sum of cosines, so the simulated p-value was p = 499/100,000 = 0.005 **.

      If this were true, we would expect, as the reviewer suggested, SSRI treatment (citalopram or fluvoxamine) on control larvae to give a similar behavioural phenotype as knockout of sorl1. However, this generally did not appear to be the case (sorl1 knockout fingerprint vs. SSRI-treated control fingerprint, cosine = 0.08 ± 0.35; Author response image 6).

      Author response image 6.

      sorl1 F0 knockouts in comparison to controls treated with SSRIs. a, sorl1 F0 knockout fingerprints (baseline recordings and sorl1 + H<sub>2</sub>O fingerprint from the citalopram experiment) in comparison with the scrambled-injected + citalopram (1 or 10 µM) fingerprints. Each dot represents the mean deviation from the same-clutch scrambled-injected H<sub>2</sub>O-treated mean for that parameter (z-score, mean ± SEM). b, As in a), sorl1 F0 knockout fingerprints (baseline recordings and sorl1 + H<sub>2</sub>O fingerprint from the fluvoxamine experiment) in comparison with the scrambled-injected + fluvoxamine (10 µM) fingerprint.

      The comparison with trazodone is an interesting observation, but it is only a weak serotonin reuptake inhibitor (Ki for SLC6A4 = 690 nM, vs. 8.9 nM for citalopram; Owens et al., 1997) and it has many other targets, both as agonist or antagonist, including serotonin, adrenergic, and histamine receptors (Mijur, 2011). In any case, the average trazodone fingerprint does not correlate particularly well to the sorl1 knockout fingerprint (cos = 0.3). Finally, the sorl1 knockout behavioural phenotype could be primarily caused by altered serotonin signalling in the hypothalamus, where we found both the biggest difference in tph1a/1b/2 HCR signal intensity (Fig. 5f) and the highest expression of sorl1 across scRNA-seq clusters (Fig. 1– supplement 2). In this case, it would be correct to expect sorl1 knockouts to react differently to SSRIs than controls, but it would be incorrect to expect SSRI treatment to cause the same behavioural phenotype, as it concurrently affects every other serotonergic neuron in the brain.

      Finally, we agree the quoted conclusion was too strong given the current evidence. We since tested another SSRI, fluvoxamine, on sorl1 knockouts.

      - Also in reference to Figure 5: in panel c, data are presented as deviation from vehicle treated. Because of this data presentation choice, it's no longer possible to determine whether, in this experiment, sorl1 crispants sleep less at night relative to their siblings. Does citalopram rescue / reverse sleep deficits in sorl1 mutants?

      On your first point, please see our response to Reviewer #3 (2)c and Author Response 2b above.

      On “does citalopram rescue/reverse sleep deficits in sorl1 mutants”: citalopram (and fluvoxamine) tends to reverse the key aspects of the sorl1 knockout behavioural phenotype by reducing night-time activity (% time active and total Δ pixels), increasing night-time sleep, and shortening sleep latency (Author response image 7). Extrapolating from the hypothesis presented in Discussion, this may be interpreted as a hint that sorl1 knockouts have reduced levels of 5-HT receptors, as increasing serotonin signalling using an SSRI tends to rescue the phenotype. However, we do not think that focusing on the significant behavioural parameters necessarily make sense here. Rather, one should take all parameters into account to conclude whether knockouts react differently to the drug than wild types (also see answer to Reviewer #3, (7) on this). For example, citalopram increased more the night-time sleep bout length of sorl1 knockouts than the one of controls (Fig. 5), but this parameter was not modified by knockout of sorl1 (Fig. 4). To explain the rationale more informally, citalopram is only used as a tool here to probe serotonin signalling in sorl1 knockouts, whether it worsens or rescues the behavioural phenotype is somewhat secondary, the key question is whether knockouts react differently than controls.

      Author response image 7.

      Comparing untreated sorl1 F0 knockouts vs. treated with SSRIs. a, sorl1 F0 knockout fingerprints (baseline recordings and sorl1 + H<sub>2</sub>O fingerprint from the citalopram experiment) in comparison with the sorl1 knockout + citalopram (1 or 10 µM) fingerprints. Each dot represents the mean deviation from the same-clutch scrambled-injected H<sub>2</sub>O-treated mean for that parameter (z-score, mean ± SEM). b, As in a), sorl1 F0 knockout fingerprints (baseline recordings and sorl1 + H<sub>2</sub>O fingerprint from the fluvoxamine experiment) in comparison with the sorl1 + fluvoxamine (10 µM) fingerprint.

      - Possible molecular pathways targeted by tinidazole, fenoprofen, and betamethasone are not described.

      Tinidazole is an antibiotic, fenoprofen is a non-steroidal anti-inflammatory drug (NSAIDs), betamethasone is a steroidal anti-inflammatory drug. Interestingly, long-term use of NSAIDs reduces the risk of AD (in ’t Veld Bas A. et al., 2001). Several mechanisms are possible (Weggen et al., 2007), including reduction of Aβ42 production by interacting with γ-secretase (Eriksen et al., 2003). However, we did not explore the mechanism of action of these drugs on psen2 knockouts so do not feel comfortable speculating. We do not know, for example, whether these findings apply to betamethasone.

      Minor Comments:

      - On page 25, panel "g" should be labeled as "f".

      Thank you!

      - On page 35, a reference should be provided for the statement "From genomic studies of AD, we know that mutations in genes such as SORL1 modify risk by disrupting some biological processes.".

      Thank you, this is now corrected. There were the same studies as mentioned in Introduction.

      - On page 43, the word "and" should be added - "in wild-type rats and mice, overexpressing mutated human APP and PSEN1, AND restricting sleep for 21 days...".

      Right, this sentence could be misread, we edited it. “overexpressing […]” only applied to the mice, not the rats (as they are wild-type); and both are sleep-deprived.

      - On page 45, a reference should be provided for the statement "SSRIs can generally be used continuously with no adverse effects" and this statement should potentially be softened.

      The reference is at the end of that sentence (Cirrito et al., 2011). You are correct though; we reformulated this statement to: “SSRIs can generally be used safely for many years”. SSRIs indeed have side effects.

      - On page 54, a 60-minute rolling average is described as 45k rows, but this seems to be a 30-minute rolling average.

      Thank you! We corrected. It should have been 90k rows, as in: 25 frames-per-second × 60 seconds × 60 minutes.

      Reviewer #2 (Recommendations For The Authors):

      "As we observed in the scRNA-seq data, most genes tested (appa, appb, psen1, psen2, apoea, cd2ap, sorl1) were broadly expressed throughout the 6-dpf brain (Fig. 1d and Fig. 1supplement 3 and 4)."

      - apoea and appb are actually not expressed highly in the scRNA-seq data, and the apoea in situ looks odd, as if it has no expression. The appb gene mysteriously does not look as though it has high expression in the Raj data, but it is clearly expressed based on the in situ. I had previously noticed the same discrepancy, and I attribute it to the transcriptome used to map the Raj data, as the new DanioCell data uses a new transcriptome and indicates high appb expression in the brain. Please point out the discrepancy and possible explanation, perhaps in the figure legend.

      All excellent points, thank you. We included them directly in Results text.

      "most of these were expressed in the brain of 5-6-dpf zebrafish larvae, suggesting they play a role in early brain development or function."

      - Evidence of expression does not suggest function, particularly not a function in brain development. As one example, almost half of the genome is expressed prior to the maternal-zygotic transition but does not have a function in those earliest stages of development. There are numerous other instances where expression does not equal function. Please change the sentence even as simply as "it is possible that they".

      We mostly agree and edited to “[…], so they could play a role […]”.

      Out of curiosity, we plotted, for each zebrafish developmental stage, the proportion of Alzheimer’s risk gene orthologues expressed in comparison to the proportion of all genes expressed (Author response image 8). We defined “all genes” as every gene that is expressed in at least one of the developmental stages (n = 24,856), not the complete transcriptome, to avoid including genes that are never expressed in the brain or whose expression is always below detection limit. We counted a gene as “expressed” if at least three cells had detectable transcripts. Using these definitions, 82 ± 7% of genes are expressed during development. For every developmental stage except 5 dpf (so 11/12), a larger proportion of Alzheimer’s risk genes than all genes are expressed (+5 ± 4%).

      Author response image 8.

      Proportion of Alzheimer’s risk genes orthologues expressed throughout zebrafish development. Proportion of Alzheimer’s risk genes orthologues (n = 42) and all genes (n = 24,856) expressed in the zebrafish brain at each developmental stage, from 12 hours post-fertilisation (hpf) to 15 days post-fertilisation (dpf). “All genes” corresponds to every gene expressed in the brain at any of the developmental stages, not the complete transcriptome. A gene is considered “expressed” (green) if at least three cells had detectable transcripts. Single-cell RNA-seq dataset from Raj et al., 2020.

      "This frame-by-frame analysis has several advantages over previous methods that analysed activity data at the one-minute resolution."

      - Which methods are these? There are no citations. There are certainly existing methods in the zebrafish field that can produce similar data to the method developed for this project. This new package is useful, as most existing software is not written in R, so it would help scientists who prefer this programming language. However, I would be careful not to oversell its novelty, since many methods do exist that produce similar results.

      We added the references. There were referenced above after “we combined previous sleep/wake analysis methods”, but should have been referenced again here.

      We are not convinced by this criticism. We would obviously not claim that the FramebyFrame package is as sophisticated and versatile as video-tracking tools like SLEAP or DeepLabCut, but we do think it answers a genuine need that was not addressed by other methods. Specifically, we know of many labs recording pixel count data across multiple days using the Zebrabox or DanioVision (we added support for DanioVision data after submission), but there were no packages to extract behavioural parameters from these data. Other methods involved standalone scripts with no documentation or version tracking. We would concede the FramebyFrame package is mostly targeted at these labs, but we already know of six labs routinely using it and were recently contacted by a researcher tracking Daphnia in the Zebrabox.

      "F0 knockouts of both cutches" - "clutches"

      Thank you!

      Reviewer #3 (Recommendations For The Authors):

      I would suggest totally revamping the Introduction section, and being sure to provide readers with the context and background they need for the data that comes thereafter. Key areas to touch on, in no particular order, include:

      • Far more detail on the behavioral pharm screen upon which this paper builds, as a brief overview of that approach and the data generated are needed.

      Thank you for the suggestion, we added a sentence hinting at this work in the last Introduction paragraph.

      • Limitations of current zebrafish sleep/arousal assays that motivated the authors to develop a new, temporally high-resolution system.

      We think this is better explained in Results, as is currently. For example, we need to point to Fig. 2–supplement 2a,b,c to explain that one-minute methods were missing sleep bouts and how FramebyFrame resolves this issue.

      • A paragraph about sleep and AD, that does a better job of citing work in humans, mammalian, and invertebrate models that motivate the interest in the connection pursued here.

      Sorry, we think this would place too much focus on sleep and AD. We want the main topic of the paper to be the behavioural pharmacology approach, not AD or sleep per se. As the Introduction states, we see Alzheimer’s risk genes as a case study for the behavioural pharmacology approach, rather than the reason why the approach was developed. Additionally, presenting sleep and AD in Introduction risks sounding like ZOLTAR is specifically designed for this context, while we conceived of it as much more generalisable and explicitly encourage its use to study genes associated to other diseases. Note that the paragraph you suggest is, we think, mostly present in Discussion (section Disrupted sleep and serotonin signalling […]).

      • I modestly suggest eliminating making such a strong case for a gene-first approach being the best way to understand disease. It is not a zero-sum game, and there is plenty to learn from proteomics, metabolomics, etc. I suspect nobody will argue with the authors saying they leveraged the strength of their system and focused on key AD genes of interest.

      From your point below, we understand the following quote is the source of the issue: “For finding causal processes, studying the genome, rather than the transcriptome or epigenome, is advantageous because the chronology from genomic variant to disease is unambiguous […]”. We did not want to suggest it is a zero-sum game, but we now understand how it can be read this way. We adapted slightly the wording. What we want to do is highlight the causality argument as the advantage of the genomics approach. We feel we do not read this argument often enough, while it remains a ‘magic power’ of genomics. One essentially does not have to worry about causality when studying a pathogenic germline variant, while it is a constant concern when studying the transcriptome or epigenome (i.e. did the change in this transcript’s level cause disease, or vice-versa?). To take an example in the context of AD, arguments based on genomics (e.g. Down syndrome or APP duplication) are often the definite arbiters when debating the amyloid hypothesis, exactly because their causality cannot be doubted.

      Minor comments

      (1) The opening of the introduction is perhaps overly broad, spending an entire paragraph on genome vs transcriptome, etc and making the claim that a gene-first approach is the best path. It isn't zero-sum, and the authors could just get right into AD and study genes of interest. Similar issues occur throughout the manuscript, with sentences/paragraphs that are not necessarily needed.

      Please see our answer to your previous point. On the introduction being overly broad, we perfectly agree it is broad, but related to your point about presenting sleep and AD in the Introduction, we wish to talk about finding causal processes from genomics findings using behavioural pharmacology. We purposefully present research on AD as one instance of this broader goal, not the primary topic of the paper.

      Another example are these sentences, which could be totally removed as the following paragraph starts off making the same point much more succinctly. "From genomic studies of AD, we know that mutations in genes such as SORL1 modify risk by disrupting some biological processes. Presumably, the same processes are disrupted in zebrafish sorl1 knockouts, and some caused the behavioural alterations we observed. Can we now follow the thread backwards and predict some of the biological processes in which Sorl1 is involved based on the behavioural profile of sorl1 knockouts?"

      Thanks for the suggestion, but we think these sentences are useful to place back this Results section in the context of the Introduction. Think of the paper as mainly about the behavioural pharmacology approach, not on Alzheimer’s risk genes. The function of the paragraph here is not simply to explain the method by which we decided to study sorl1; it is to reiterate the rationale behind the behavioural pharmacology approach so that the reader understands where this Results section fits in the overall structure.

      (2) Related to the above, the authors use lecanemab as an example to support their approach, but there has been a great deal of controversy regarding this drug. I don't think such extensive justification is needed. This study uses AD risk genes as a case study in a newly developed behavioral pharm pipeline. A great deal of the rest of the intro seems to just fill space and could be more focused on the study at hand. Interestingly, a er gene selection, the next step in their pipeline is sleep/wake analysis yet nothing is covered about AD and sleep in the intro. Some justification of that approach (why focus on sleep/wake as a starting point for behavioral pharm rather than learning and memory?) would be a better use of intro space.

      There has indeed been controversy about lecanemab, but even the harshest critiques of the amyloid hypothesis concede that it slows down cognitive decline (Espay et al., 2023). That is all that is needed to support our argument, which is that research on AD started primarily from genomics and thereby yielded a disease-modifying drug. The controversy seems mostly focused on whether this effect size is clinically significant, and we think we correctly represent this uncertainty (e.g. “antibodies against Aβ such as lecanemab show promise in slowing down disease progression” and “the beneficial effects from targeting Aβ aggregation currently remain modest”).

      Your next point is entirely fair. We mostly answered it above. To explain further, the primary reason why we measured sleep/wake behaviour is to match the behavioural dataset from Rihel et al., 2010 so we can use it to make predictions, not to study sleep in the context of AD per se. Sure, perhaps learning and memory would have been interesting, but we do not know of any study testing thousands of small molecules on zebrafish larvae during a memory task. We understand it can be slightly confusing though, as we then spend a paragraph of Discussion on sleep as a causal process in AD, but we obviously need to discuss this topic given the findings. However, to reiterate, we purposefully designed FramebyFrame and ZOLTAR to be useful beyond studying sleep/wake behaviour. For example, FramebyFrame would not calculate 17 behavioural parameters if the only goal was to measure sleep. We now mention the Rihel et al., 2010 study in the Introduction as you suggested above (“Far more detail on the behavioral pharm screen […]”), as that is the real reason why sleep/wake behaviour was measured in the first place.

      (3) Also related to the above, another more relevant point that could be talked about in the intro is the need for more refined approaches to analyze sleep in zebrafish, given the effort that went into the new analysis system described here. Again, I think the context for why the authors developed this system would be more meaningful than the current content.

      Thank you, we think we answered this point above (especially below Limitations of current zebrafish sleep/arousal assays […]).

      (4) GWAS can stand for Genome-wide associate studies (plural) so I do not think the extra "s" is needed (GWASs) .

      Indeed, that seems to be the common usage. Thank you.

      (5) AD candidate risk genes were determined from loci using "mainly statistic colocalization". Can the authors add a few more details about what was done and what the "mainly" caveat refers to?

      “Mainly” simply refers to the fact that other methods were used by Schwartzentruber et al. (2021) to annotate the GWAS loci with likely causal genes, but that most calls were ultimately made from statistic colocalisation. Readers can refer to this work to learn more about the methods used.

      (6) The authors write "The loss of psen1 only had mild effects on behaviour" but I think they mean "sleep behaviors" as there could be many other behaviors that are disrupted but were not assessed. The same issue a few sentences later with "Behaviour during the day was not affected" and at the end of the following paragraph.

      Yes, that would be more precise, thank you.

      (7) For the Sorl1 pharmacology data, it is very hard to understand what is being measured behaviorally. Are the authors measuring sleep +/- citalopram, or something else, and why the change to Euclidean distance rather than all the measures we were just introduced to earlier in the manuscript?

      We understand these plots (Fig. 5c,d) are less intuitive, but it is important that we show the difference in behaviour compared to H<sub>2</sub>O-treated larvae of same genotype. The claim is that citalopram has a larger effect on knockouts than on controls, so the reader needs to focus on the effect of the drug on each genotype, not on the effect of sorl1 knockout. We added the standard fingerprints (i.e. setting controls to z-score = 0) here in Author response figures.

      Euclidean distance takes as input all the measures we introduced. The point is precisely not to select a single measure. For example, say we were only plotting active bout number during the day, we would conclude that 10 µM citalopram has the same effect on knockouts and controls. Conversely, if we had taken sleep bout length at night, we would conclude 10 µM has a stronger effect on knockouts. What is the correct parameter to select? Using Euclidean distance resolves this by taking all parameters into account, rather than arbitrarily choosing one.

      And what exactly is a "given spike in serotonin"? and how is this hypothesis the conclusion based on the lack of evidence for the second hypothesis? As the authors say, there could be other ways sorl1 knockouts are more sensitive to citalopram, so the absence of evidence for one hypothesis certainly does not support the other hypothesis.

      We mean a given release of serotonin in the synaptic cleft. We have fixed this wording. 

      We tend to disagree on the second point. We can think of two ways that sorl1 knockouts are more sensitive to citalopram: 1) they produce more serotonin, so blocking reuptake causes a larger spike in knockouts; or 2) blocking reuptake causes the same increase in both knockouts and wild-types but knockouts react more strongly to serotonin. We cannot in fact think of another way to explain the citalopram results. Not finding overwhelming evidence for 1) surely supports 2) somewhat, even if we do not have direct evidence for it. As an analogy, if two diagnoses are possible for a patient, testing negative for the first one supports the other one, even before it is directly tested.

      (8) Again some language is used without enough care. Fish are referred to as "drowsier" under some drug conditions. How do the authors know the animal is drowsy? The phenotype is more specific - more sleep, less activity.

      Thank you, we switched to “Furthermore, fenoprofen worsened the day-time hypoactivity of psen2 knockout larvae […]”.

      (9) This sentence is misleading as it gives the impression that results in this manuscript suggest the conclusion: "Our observation that disruption of genes associated with AD diagnosis after 65 years reduces sleep in 7-day zebrafish larvae suggest that disrupted sleep may be a common mechanism through which these genes exert an effect on risk." That idea is widely held in the field, and numerous other previous manuscripts/reviews should be cited for clarity of where this hypothesis came from.

      This idea is not widely held in the field. You likely read this point as “disrupted sleep is a risk factor for AD”, which, yes, is widely discussed in the field, but is not precisely what we are saying. We hypothesise that mutations in some of the Alzheimer’s risk genes cause disrupted sleep, possibly from a very early age, which then causes AD decades later. Studies and reviews on sleep and AD rarely make this hypothesis, at least not explicitly. The closest we know of are a few recent human genetics studies, typically using Mendelian Randomisation, finding that higher genetic risk of AD correlates with some sleep phenotypes, such as sleep duration (Chen et al., 2022; Leng et al., 2021). The work of Muto et al. (2021) is particularly interesting as it found correlations between higher genetic risk of AD and some sleep phenotypes in men in their early twenties, which seems unlikely to be a consequence of early pathology (Muto et al., 2021). Note, however, that even these studies do not mention sleep possibly being disrupted early in development, which is what our findings in zebrafish larvae support. As we mention, we think a team should test whether sleep is different in infants at higher genetic risk of AD, essentially performing an analogous, but obviously much more difficult, experiment as we did in zebrafish larvae. We do not know of any study testing this or even raising this idea, so evidently it is not widely held. Having said that, the studies we mention here were not referenced in the Discussion paragraph. We have now corrected this.

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    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Weaknesses:<br /> (1) While the overall results are interesting, I am somewhat left confused about how to interpret the difference in the scores derived from different conditions. For example, the authors stated "Comparing the weights for in-group and out-group distractors, the effect of proximity was larger than that of aggression and grooming" in p.8. Does this mean that the proximity is indeed the type of behavior most affected in the out-group condition compared to the in-group condition? The out-group effects are difficult to examine with actual behavioral data, but some in-group effects such as those involving OT can be tested, which possibly provides good insights into interpreting the differences of the weights observed across the experimental conditions.

      Thank you for your thoughtful comments and for highlighting an important aspect of our findings. The statement in page 8 refers to the relative impact of different social behaviors—proximity, aggression, and grooming—on the derived weights for in-group and out-group distractors. Specifically, the data suggest that proximity exerts a stronger influence than aggression or grooming in differentiating the effects of out-group versus in-group distractors. Regarding the out-group condition, we acknowledge that it presents challenges for direct behavioral observation, as interactions involving out-group members are often more difficult to quantify in naturalistic settings. However, we agree with you about the suggestion to test certain in-group effects, particularly those influenced by oxytocin (OT), as they offer a more controlled framework to validate and interpret the observed differences in weights across experimental conditions. In line with this, we examined specific in-group behaviors under OT administration to disentangle their contributions to attentional dynamics (Fig. 4 and Fig. 5 e to h). By integrating controlled experimental manipulations, we think these results could provide deeper insights into how social relationships shape the observed patterns of attention.

      (2) I think it is important to provide how variable spontaneous social interactions were across sessions and how impactful the variability of the interactions is on the SEI and IEI, as it helps to understand how meaningful the differences of weights are across the conditions, but such data are missing. In line with this point, although the conclusions still hold as those data were obtained during the same experimental periods, shouldn't the weights in Fig. 3f and Figs. 4g and 4h (saline) be expected to be similar, if not the same?

      Thank you for your insightful comments. As highlighted, we utilized the entire experimental period as the dataset to evaluate the monkeys' social interactions. The experiments presented in Figures 3 and 4 were designed to examine how social relationships correlate with patterns of social attention under two distinct conditions: without manipulation (Fig. 3) and with nebulized exposure to oxytocin and saline (Fig. 4). Theoretically, the weights observed in the unmanipulated condition and the nebulized saline condition should be similar. However, our results indicate that distractor biases shifted significantly following nebulized saline exposure (Fig. 4) compared to the unmanipulated condition (Fig. 3) (MK: p = 9.3×10<sup>-3</sup>, ML: p = 9.77×10<sup>-4</sup>, MC: p = 9.77×10<sup>-4</sup>, MA: p = 0.09; n<sub>1</sub> = n<sub>2</sub> = 12 experimental days; Two-sided Wilcoxon signed-rank test). This suggests that the nebulization process itself, despite acclimating the monkeys to saline exposure for approximately two weeks prior to the experiments, still influenced their attentional behaviors.

      While the primary goal of nebulization was to assess the effects of oxytocin on social attention, our main conclusions remain robust, even considering the impact of nebulization on distractor biases. We acknowledge that variability in spontaneous social interactions across days or experimental sessions could be an important factor influencing the SEI and IEI. The dynamic nature of social interactions within the colony is likely affected by numerous variables. Future research will aim to integrate these factors into a more comprehensive and dynamic framework to better interpret their influence on social attention metrics.

      Reviewer #2 (Public review):

      Weaknesses:<br /> (1) The study's conclusions are based on observations of only four monkeys, which limits the generalizability of the findings. Larger sample sizes could strengthen the validity of the results.

      Thank you for your valuable comment. We acknowledge that the relatively small sample size could influence the generalizability of the findings.  However, despite this limitation, our work systematically examined multifaceted social relationships among monkeys and their attentional strategies within a well-controlled experimental setup. We reported results across sessions and conditions (e.g., in-group vs. out-group; saline vs. Oxytocin), which strengthens the reliability of the observed effects of social networks within this context. We agree that increasing the sample size would improve the generalizability of the results. Future studies with a larger cohort will be critical for confirming the robustness of our findings and expanding their broader applicability. We have acknowledged this limitation in the revised manuscript and highlighted the potential for further research with larger sample sizes to validate and extend our conclusions.

      (2) The limited set of stimulus images (in-group and out-group faces) may introduce unintended biases. This could be addressed by increasing the diversity of stimuli or incorporating a broader range of out-group members.

      Thank you for your thoughtful comment. We acknowledge that the use of a limited set of six monkey faces as stimuli for in-group and out-group conditions could potentially introduce biases. To address this concern, we conducted an additional analysis to minimize the potential impact of individual images on our findings using the current dataset. Specifically, we randomly excluded one in-group and one out-group image and reanalyzed distractor biases using the remaining two images (Supplementary Fig. 3a). For each subject, this approach generated three sets of two distractors per group, resulting in 81(3<sup>4</sup>) combinations across four monkey subjects, and a total of 81 × 81 subject-distractor pairings. We statistically compared distractor biases between in-group and out-group faces for each combination (Supplementary Fig. 3b). As shown in Supplementary Fig. 3c, 99.30% of the 6,561 combinations demonstrated significantly lower distractor biases towards in-group faces compared to out-group faces (two-sided Wilcoxon signed-rank test, p < 0.05). These results suggest that the observed differences in social attention between in-group and out-group monkeys are unlikely to be driven by specific images within the stimulus set. That said, we agree that increasing the diversity of stimulus images or incorporating a broader range of out-group members would improve the generalizability of the results. We have acknowledged this limitation in the revised manuscript and highlighted the potential for further research to incorporate a more diverse stimulus set to validate and extend our findings.

      “However, these conclusions may be constrained by the relatively small sample size and the homogeneity of stimulus set in the study. Future research focusing on larger, more diverse cohorts and incorporating a broader range of stimuli will enhance the generalizability and applicability of the findings.”

      Reviewer #1 (Recommendations for the authors):

      It is difficult to distinguish "Getting fighted" and "Fighting partner" in Fig. 1b (esp. when printed). I thought Actor showed "Fighting partner" several times in Session 2, but it seems to be "Getting fighted" judging from Figs. 1c and 1d. Is this correct? If so, I would suggest to change the color to improve visibility.

      Thank you for your valuable comment. We apologize for the confusion in the previous version. To improve clarity, we have both terms to “begin fighting” and “being fought”. As shown in Figure 1b, we now explicitly define the identities of the two monkeys as the actor (K) and the partner (L), with all behaviors described from the perspective of the actor. For example, when the actor (K) initiates the fight, it is marked as “begin fighting”, whereas when the partner (L) initiates the fight, the actor (K) is the recipient and labeled as “being fought”. Additionally, we have implemented your suggestion by changing the colors to enhance visibility, especially for the terms “begin fighting” and “being fought”.

      Reviewer #2 (Recommendations for the authors): 

      I have some minor concerns:

      (1) Figure1B, caption for x axis is missing, 4 means 4 days?

      Thank you so much for the comment. We have clarified the x-axis in Figure 1B, where the label "4" corresponds to 4 hours of video typing on each experimental day. The revised figure now includes the appropriate label for better clarity. We appreciate your careful attention to this detail.

      (2) I am slightly concerned about animal safety. How do the experimenters ensure the animals' safety and well-being in cases of aggressive interactions or attacks?

      Thank you for your comment. We share your concern regarding animal safety and take re the well-being of the monkeys in the study. All experimental procedures were reviewed and approved by the Institutional Animal Care and Use Committee at the Institute of Biophysics, Chinese Academy of Sciences (IBP-NHP-002(22)). The monkeys were housed together in the same colony room for over four years, in interconnected cages that allowed for direct physical interaction. Animal behaviors in cages were closely monitored via a live video system to ensure their safety. To prevent potential injuries, a sliding partition system was in place, enabling the isolation of individual animals when necessary, minimizing risks to their well-being.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary:

      his study shows a new mechanism of GS regulation in the archaean Methanosarcina mazei and clarifies the direct activation of GS activity by 2-oxoglutarate, thus featuring another way in which 2-oxoglutarate acts as a central status reporter of C/N sensing.

      Mass photometry and single particle cryoEM structure analysis convincingly show the direct regulation of GS activity by 2-OG promoted formation of the dodecameric structure of GS. The previously recognized small proteins GlnK1 and Sp26 seem to play a subordinate role in GS regulation, which is in good agreement with previous data. Although these data are quite clear now, there remains one major open question: how does 2-OG further increase GS activity once the full dodecameric state is achieved (at 5 mM)? This point needs to be reconsidered.

      Weaknesses:

      It is not entirely clear, how very high 2-OG concentrations activate GS beyond dodecamer formation.

      The data presented in this work are in stark contrast to the previously reported structure of M. mazei GS by the Schumacher lab. This is very confusing for the scientific community and requires clarification. The discussion should consider possible reasons for the contradictory results.

      Importantly, it is puzzling how Schumacher could achieve an apo-structire of dodecameric GS? If 2-OG is necessary for dodecameric formation, this should be discussed. If GlnK1 doesn't form a complex with the dodecameric GS, how could such a complex be resolved there?

      In addition, the text is in principle clear but could be improved by professional editing. Most obviously there is insufficient comma placement.

      We thank Reviewer #1 for the professional evaluation and raising important points. We will address those comments in the updated manuscript and especially improve the discussion in respect to the two points of concern.

      (1) How can GlnA1 activity further be stimulated with further increasing 2-OG after the dodecamer is already fully assembled at 5 mM 2-OG.

      We assume a two-step requirement for 2-OG, the dodecameric assembly and the priming of the active sites. The assembly step is based on cooperative effects of 2-OG and does not require the presence of 2-OG in all 2-OG-binding pockets: 2-OG-binding to one binding pocket also causes a domino effect of conformational changes in the adjacent 2-OG-unbound subunit, as also described for Methanothermococcus thermolithotrophicus GS in Müller et al. 2023. Due to the introduction of these conformational changes, the dodecameric form becomes more favourable even without all 2-OG binding sites being occupied. With higher 2-OG concentrations present (> 5mM), the activity increased further until finally all 2-OG-binding pockets were occupied, resulting in the priming of all active sites (all subunits) and thereby reaching the maximal activity.

      (2) The contradictory results with previously published data on the structure of M. mazei by Schumacher et al. 2023.

      We certainly agree that it is confusing that Schumacher et al. 2023 obtained a dodecameric structure without the addition of 2-OG, which we claim to be essential for the dodecameric form. 2-OG is a cellular metabolite that is naturally present in E. coli, the heterologous expression host both groups used. Since our main question focused on analysing the 2-OG effect on GS, we have performed thorough dialysis of the purified protein to remove all 2-OG before performing MP experiments. In the absence of 2-OG we never observed significant enzyme activity and always detected a fast disassembly after incubation on ice. We thus assume that a dodecamer without 2-OG in Schumacher et al. 2023 is an inactive oligomer of a once 2-OG-bound form, stabilized e.g. by the presence of 5 mM MgCl2.

      The GlnA1-GlnK1-structure (crystallography) by Schumacher et al. 2023 is in stark contrast to our findings that GlnK1 and GlnA1 do not interact as shown by mass photometry with purified proteins. A possible reason for this discrepancy might be that at the high protein concentrations used in the crystallization assay, complexes are formed based on hydrophobic or ionic protein interactions, which would not form under physiological concentrations.

      Reviewer #2 (Public Review):

      Summary:

      Herdering et al. introduced research on an archaeal glutamine synthetase (GS) from Methanosarcina mazei, which exhibits sensitivity to the environmental presence of 2-oxoglutarate (2-OG). While previous studies have indicated 2-OG's ability to enhance GS activity, the precise underlying mechanism remains unclear. Initially, the authors utilized biophysical characterization, primarily employing a nanomolar-scale detection method called mass photometry, to explore the molecular assembly of Methanosarcina mazei GS (M. mazei GS) in the absence or presence of 2-OG. Similar to other GS enzymes, the target M. mazei GS forms a stable dodecamer, with two hexameric rings stacked in tail-to-tail interactions. Despite approximately 40% of M. mazei GS existing as monomeric or dimeric entities in the detectable solution, the majority spontaneously assemble into a dodecameric state. Upon mixing 2-OG with M. mazei GS, the population of the dodecameric form increases proportionally with the concentration of 2-OG, indicating that 2-OG either promotes or stabilizes the assembly process. The cryo-electron microscopy (cryo-EM) structure reveals that 2-OG is positioned near the interface of two hexameric rings. At a resolution of 2.39 Å, the cryo-EM map vividly illustrates 2-OG forming hydrogen bonds with two individual GS subunits as well as with solvent water molecules. Moreover, local side-chain reorientation and conformational changes of loops in response to 2-OG further delineate the 2-OG-stabilized assembly of M. mazei GS.

      Strengths & Weaknesses:

      The investigation studies the impact of 2-oxoglutarate (2-OG) on the assembly of Methanosarcina mazei glutamine synthetase (M mazei GS). Utilizing cutting-edge mass photometry, the authors scrutinized the population dynamics of GS assembly in response to varying concentrations of 2-OG. Notably, the findings demonstrate a promising and straightforward correlation, revealing that dodecamer formation can be stimulated by 2-OG concentrations of up to 10 mM, although GS assembly never reaches 100% dodecamerization in this study. Furthermore, catalytic activities showed a remarkable enhancement, escalating from 0.0 U/mg to 7.8 U/mg with increasing concentrations of 2-OG, peaking at 12.5 mM. However, an intriguing gap arises between the incomplete dodecameric formation observed at 10 mM 2-OG, as revealed by mass photometry, and the continued increase in activity from 5 mM to 10 mM 2-OG for M mazei GS. This prompts questions regarding the inability of M mazei GS to achieve complete dodecamer formation and the underlying factors that further enhance GS activity within this concentration range of 2-OG.

      Moreover, the cryo-electron microscopy (cryo-EM) analysis provides additional support for the biophysical and biochemical characterization, elucidating the precise localization of 2-OG at the interface of two GS subunits within two hexameric rings. The observed correlation between GS assembly facilitated by 2-OG and its catalytic activity is substantiated by structural reorientations at the GS-GS interface, confirming the previously reported phenomenon of "funnel activation" in GS. However, the authors did not present the cryo-EM structure of M. mazei GS in complex with ATP and glutamate in the presence of 2-OG, which could have shed light on the differences in glutamine biosynthesis between previously reported GS enzymes and the 2-OG-bound M. mazei GS.

      Furthermore, besides revealing the cryo-EM structure of 2-OG-bound GS, the study also observed the filamentous form of GS, suggesting that filament formation may be a universal stacking mechanism across archaeal and bacterial species. However, efforts to enhance resolution to investigate whether the stacked polymer is induced by 2-OG or other factors such as ions or metabolites were not undertaken by the authors, leaving room for further exploration into the mechanisms underlying filament formation in GS.

      We thank Reviewer #2 for the detailed assessment and valuable input. We will address those comments in the updated manuscript and clarify the message.

      (1) The discrepancy of the dodecamer formation (max. at 5 mM 2-OG) and the enzyme activity (max. at 12.5 mM 2-OG). We assume that there are two effects caused by 2-OG: 1. cooperativity of binding (less 2-OG needed to facilitate dodecamer formation) and 2. priming of each active site. See also Reviewer #1 R.1). We assume this is the reason why the activity of dodecameric GlnA1 can be further enhanced by increased 2-OG concentration until all catalytic sites are primed.

      (2) The lack of the structure of a 2-OG and ATP-bound GlnA1. Although we strongly agree that this would be a highly interesting structure, it seems out of the scope of a typical revision to request new cryo-EM structures. We evaluate the findings of our present study concerning the 2-OG effects as important insights into the strongly discussed field of glutamine synthetase regulation, even without the requested additional structures.

      (3) The observed GlnA1-filaments are an interesting finding. We certainly agree with the referee on that point, that the stacked polymers are potentially induced by 2-OG or ions. However, it is out of the main focus of this manuscript to further explore those filaments. Nevertheless, this observation could serve as an interesting starting point for future experiments.

      Reviewer #3 (Public Review):

      Summary:

      The current manuscript investigates the effect of 2-oxoglutarate and the Glk1 protein as modulators of the enzymatic reactivity of glutamine synthetase. To do this, the authors rely on mass photometry, specific activity measurements, and single-particle cryo-EM data.

      From the results obtained, the authors convey that glutamine synthetase from Methanosarcina mazei exists in a non-active monomeric/dimeric form under low concentrations of 2-oxoglutarate, and its oligomerization into a dodecameric complex is triggered by higher concentration of 2-oxoglutarate, also resulting in the enhancement of the enzyme activity.

      Strengths:

      Glutamine synthetase is a crucial enzyme in all domains of life. The dodecameric fold of GS is recurrent amongst prokaryotic and archaea organisms, while the enzyme activity can be regulated in distinct ways. This is a very interesting work combining protein biochemistry with structural biology.

      The role of 2-OG is here highlighted as a crucial effector for enzyme oligomerization and full reactivity.

      Weaknesses:

      Various opportunities to enhance the current state-of-the-art were missed. In particular, omissions of the ligand-bound state of GnK1 leave unexplained the lack of its interaction with GS (in contradiction with previous results from the authors). A finer dissection of the effect and role of 2-oxoglurate are missing and important questions remain unanswered (e.g. are dimers relevant during early stages of the interaction or why previous GS dodecameric structures do not show 2-oxoglutarate).

      We thank Reviewer #3 for the expert evaluation and inspiring criticism.

      (1) Encouragement to examine ligand-bound states of GlnK1. We agree and plan to perform the suggested experiments exploring the conditions under which GlnA1 and GlnK1 might interact. We will perform the MP experiments in the presence of ATP. In GlnA1 activity test assays when evaluating the presence/effects of GlnK1 on GlnA1 activity, however, ATP was always present in high concentrations and still we did not observe a significant effect of GlnK1 on the GlnA1 activity.

      (2) The exact role of 2-OG could have been dissected much better. We agree on that point and will improve the clarity of the manuscript. See also Reviewer #1 R.1.

      (3) The lack of studies on dimers. This is actually an interesting point, which we did not consider during writing the manuscript. Now, re-analysing all our MP data in this respect, GlnA1 is likely a dimer as smallest species. Consequently, we will add more supplementary data which supports this observation and change the text accordingly.

      (4) Previous studies and structures did not show the 2-OG. We assume that for other structures, no additional 2-OG was added, and the groups did not specifically analyse for this metabolite either. All methanoarchaea perform methanogenesis and contain the oxidative part of the TCA cycle exclusively for the generation of glutamate (anabolism) but not a closed TCA cycle enabling them to use internal 2-OG concentration as internal signal for nitrogen availability. In the case of bacterial GS from organisms with a closed TCA cycle used for energy metabolism (oxidation of acetyl CoA) like e.g. E. coli, the formation of an active dodecameric GS form underlies another mechanism independent of 2-OG. In case of the recent M. mazei GS structures published by Schumacher et al. 2023, the dodecameric structure is probably a result from the heterologous expression and purification from E. coli. (See also Reviewer #1 R.2). One example of methanoarchaeal glutamine synthetases that do in fact contain the 2-OG in the structure, is Müller et al. 2023.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Specific issues:

      L 141: 2-OG levels increase due to slowing GOGAT reaction (due to Gln limitation as a consequence of N-starvation).... (2-OG also increases in bacteria that lack GDH...)

      As the GS-GOGAT cycle is the major route of ammonium assimilation, consumption of 2-OG by GDH is probably only relevant under high ammonium concentrations.

      In Methanoarchaea, GS is strictly regulated and expression strongly repressed under nitrogen sufficiency - thus glutamate for anabolism is mainly generated by GDH under N sufficiency consuming 2-OG delivered by the oxidative part of the TCA cycle (Methanogenesis is the energy metabolism in methanoarchaea, a closed TCA cycle is not present) thus 2-OG is increasing under nitrogen limitation, when no NH3 is available for GDH.

      L148: it is not clear what is meant by: "and due to the indirect GS activity assay"

      We apologize for not being clear here. The GS activity assay used is the classical assay by Sahpiro & Stadtman 1970 and is a coupled optical test assay (coupling the ATP consumption of the GS activity to the oxidation of NADH by lactate dehydrogenase). Based on the coupled test assay the measurements of low activities show a high deviation. We now added this information in the revised MS respectively.

      L: 177: arguing about 2-OG affinities: more precisely, the 0.75 mM 2-OG is the EC50 concentration of 2-OG for triggering dodecameric formation; it might not directly reflect the total 2-OG affinity, since the affinity may be modulated by (anti)cooperative effects, or by additional sites... as there may be different 2-OG binding sites involved... (same in line 201)

      Thank you for the valuable input. We changed KD to EC50 within the entire manuscript. Concerning possible additional 2-OG binding sites: we did not see any other 2-OG in the cryo-EM structure aside from the described one and we therefore assume that the one described in the manuscript is the main and only one. Considering the high amounts of 2-OG (12.5 mM) used in the structure, it is quite unlikely that additional 2-OG sites exist since they would have unphysiologically low affinities.

      In this respect, instead of the rather poor assay shown in Figure 1D, a more detailed determination of catalytic activation by different 2-OG concentrations should be done (similar to 1A)... This would allow a direct comparison between dodecamerization and enzymatic activation.

      We agree and performed the respective experiments, which are now presented in revised Fig. 1D

      Discussion: the role of 2-OG as a direct activator, comparison with other prokaryotic GS: in other cases, 2-OG affects GS indirectly by being sensed by PII proteins or other 2-OG sensing mechanisms (like 2OG-NtcA-mediated repression of IF factors in cyanobacteria)

      We agree and have added that information in the discussion as suggested.

      290. Unclear: As a second step of activation, the allosteric binding of 2-OG causes a series of conformational.... where is this site located? According to the catalytic effects (compare 1A and 1D) this site should have a lower affinity …

      Thank you very much for pointing this out. Binding of 2-OG only occurs in one specific allosteric binding-site. Binding however, has two effects on the GlnA1: dodecamer assembly and priming of the active site (with two specific EC50, which are now shown in Fig. 1A and D).

      See also public comment #1 (1).

      Reviewer #2 (Recommendations For The Authors):

      The primary concern for me is that mass photometry might lead to incorrect conclusions. The differences in the forms of GS seen in SEC and MP suggest that GS can indeed form a stable dodecamer when the concentration of GS is high enough, as shown in Figure S1B. I strongly suggest using an additional biophysical method to explore the connection between GS and 2-OG in terms of both assembly and activity, to truly understand 2-OG's role in the process of assembly and catalysis.

      We apologize if we did not present this clear enough, however the MP analysis of GlnA1 in the absence of 2-OG showed always (monomers/) dimers, dodecamers were only present in the presence of 2-OG. The SEC analysis in Fig. S1B has been performed in the presence of 12.5 mM 2-OG, we realized this information is missing in the figure legend - we now added this in the revised version. The 2-OG is in addition visible in the Cryo EM structure. Thus, we do not agree to perform additional biophysical methods.

      As for the other experimental findings, they appear satisfactory to me, and I have no reservations regarding the cryoEM data.

      (1) Mass photometry is a fancy technique that uses only a tiny amount of protein to study how they come together. However, the concentration of the protein used in the experiment might be lower than what's needed for them to stick together properly. So, the authors saw a lot of single proteins or pairs instead of bigger groups. They showed in Figure S1B that the M. mazei GS came out earlier than a 440-kDa reference protein, indicating it's actually a dodecamer. But when they looked at the dodecamer fraction using mass photometry, they found smaller bits, suggesting the GS was breaking apart because the concentration used was too low. To fix this, they could try using a technique called analytic ultracentrifuge (AUC) with different amounts of 2-OG to see if they can spot single proteins or pairs when they use a bit more GS. They could also try another technique called SEC-MALS to do similar tests. If they do this, they could replace Figure 1A with new data showing fully formed GS dodecamers when they use the right amount of 2-OG.

      Thank you for this input. In MP we looked at dodecamer formation after removing the 2-OG entirely and re-adding it in the respective concentration. We think that GlnA1 is much more unstable in its monomeric/dimeric fraction and that the complete and harsh removal of 2-OG results in some dysfunctional protein which does not recover the dodecameric conformation after dialysis and re-addition of 2-OG. Looking at the dodecamer-peak right after SEC however, we exclusively see dodecamers, which is now included as an additional supplementary figure (suppl. Fig. 1C). Consequently, we did not perform additional experiments.

      (2) Building on the last point, the estimated binding strength (Kd) between 2-OG and GS might be lower than it really is, because the GS often breaks apart from its dodecameric form in this experiment, even though 2-OG helps keep the pairs together, as seen with cryoEM. What if they used 5-10 times more GS in the mass photometry experiment? Would the estimated bond strength stay the same? Could they use AUC or other techniques like ITC to find out the real, not just estimated, strength of the bond?

      We agree that the term KD is not suitable. We have changed the term KD to EC50 as suggested by reviewer #1, which describes the effective concentration required for 50 % dodecamer assembly. Furthermore, we disagree that the dodecamer breaks apart when the concentrations are as low as in MP experiments. The actual reason for the breaking is rather the harsh dialysis to remove all 2-OG before MP experiments. Right after SEC, the we exclusively see dodecamer in MP (suppl. Fig. S1C). See also #2 (1).

      (3) The fact that the GS hardly works without 2-OG is interesting. I tried to understand the experiment setup, but it wasn't clear as the protocol mentioned in the author's 2021 FEBS paper referred to an old paper from 1970. The "coupled optical test assay" they talked about wasn't explained well. I found other papers that used phosphometry assays to see how much ATP was used up. I suggest the authors give a better, more detailed explanation of their experiments in the methods section. Also, it's unclear why the GS activity keeps going up from 5 to 12.5 mM 2-OG, even though they said it's saturated. They suggested there might be another change happening from 5 to 12.5 mM 2-OG. If that's the case, they should try to get a cryo-EM picture of the GS with lots of 2-OG, both with and without ATP/glutamate (or the Met-Sox-P-ADP inhibitor), to see what's happening at a structural level during this change caused by 2-OG.

      We agree with the reviewer that the GS assay was not explained in detail (since published and known for several years). However, we now added the more detailed description of the assay in the revised MS, which also measures the ATP used up by GS, but couples the generation of ADP to an optical test assay producing pyruvate from PEP with the generated ADP catalysed by pyruvate kinase present in the assay. This generated pyruvate is finally reduced to lactate by the present lactate dehydrogenase consuming NADH, the reduction of which is monitored at 340 nm.

      The still increasing activity of GS after dodecamer formation (max. at 5 mM 2-OG) and the continuously increasing enzyme activity (max. at 12.5 mM 2-OG): See also public reviews, we assume that there are two effects caused by 2-OG: 1. cooperativity of binding (less 2-OG needed to facilitate dodecamer formation) and 2. priming of each active site.

      The suggested additional experiments with and without ATP/Glutamate: Although we strongly agree that this would be a highly interesting structure, it seems out of the scope of a typical revision to request new cryo-EM structures. We evaluate the findings of our present study concerning the 2-OG effects as important insights into the strongly discussed field of glutamine synthetase regulation, even without the requested additional structures.

      (4) Please remake Figure S2, the panels are too small to read the words. At least I have difficulty doing so.

      We assume the reviewer is pointing to Suppl. Fig S3, we now changed this figure accordingly.

      Line 153, the reference Schumacher et al. 23, should be 2023?

      Yes, thank you. We corrected that.

      Line 497. I believe it's UCSF ChimeraX, not Chimera.

      We apologize and corrected accordingly.

      Reviewer #3 (Recommendations For The Authors):

      Recent studies on the Methanothermococcus thermolithotrophicus glutamine synthetase, published by Müller et al., 2024, have identified the binding site for 2-oxoglutarate as well as the conformational changes that were induced in the protein by its presence. In the present study, the authors confirm these observations and additionally establish a link between the presence of 2-oxoglutarate and the dodecameric fold and full activation of GS.

      Curiously, here, the authors could not confirm their own findings that the dodecameric GS can directly interact with the PII-like GlnK1 protein and the small peptide sP26. However, the lack of mention of the GlnK-bound state in these studies is very alarming since it certainly is highly relevant here.

      We agree with the reviewer that we have not observed the interaction with GlnK1 and sP26 in the recent study. Consequently, we speculate that yet unknown cellular factor(s) might be required for an interaction of GlnA1 with GlnK1 and sP26, which were not present in the in vitro experiments using purified proteins, however they were present in the previous pull-down approaches (Ehlers et al. 2005, Gutt et al. 2021). Another reason might be that post-translational modifications occur in M. mazei, which might be important for the interaction, which are also not present in purified proteins expressed in E. coli.

      The manuscript interest could have been substantially increased if the authors had done finer biochemical and enzymatic analyses on the oligomerization process of GS, used GlnK1 bound to known effectors in their assays and would have done some more efforts to extrapolate their findings (even if a small niche) of related glutamine synthetases.

      We thank the reviewer for their valuable encouragement to explore ligand-bound-states of GlnK1. However, in this manuscript we mainly focused on 2-OG as activator of GlnA1 and decided to dedicate future experiments to the exploration of conditions that possibly favor GlnK1-binding.

      In principle, we have explored the ATP bound GlnK1 effects on GlnA1 activity in the activity assays (Fig. 2E) since ATP (3.6 mM) is present. GlnK1 however showed no effects on GlnA1 activity.

      In general, the manuscript is poorly written, with grammatically incorrect sentences that at times, which stands in the way of passing on the message of the manuscript.

      Particular points:

      (1) It is mentioned that 2-OG induces the active oligomeric (dodecamer, 12-mer) state of GlnA1 without detectable intermediates. However, only 62 % of the starting inactive enzyme yields active 12-mers. Note that this is contradicted in line 212.

      Thanks for pointing out this discrepancy. After removing all 2-OG as we did before MP-experiments, GlnA1 doesn’t reach full dodecamers anymore when 2-OG is re-added. This is not because the 2-OG amount is not enough to trigger full assembly, but because the protein is much more unstable in the absence of 2-OG, so we predict that some GlnA1 breaks during dialysis. See also answer reviewer #2 (1) and supplementary figure S1C.

      Is there any protein precipitation upon the addition of 2-OG? Is all protein being detected in the assay, meaning, is monomer/dimer + dodecamer yields close to 100% of the total enzyme in the assay?

      There is no protein precipitation upon the addition of 2-OG, indeed, GlnA1 is much more stable in the presence of 2-OG. In the mass photometry experiments, all particles are measured, precipitated protein would be visible as big entities in the MP.

      Please add to Figure 1 the amount of monomer/dimer during titration. Some debate why there is no full conversion should be tentatively provided.

      We agree with the reviewer and included the amount of monomer/dimer in the figure, as well as some discussion on why it is not fully converted again. GlnA1 is unstable without 2-OG and it was dialysed against buffer without 2-OG before MP measurements. This sample mistreatment resulted in no full re-assembly after re-adding 2-OG (although full dodecamers before dialysis (suppl. Fig. S1C).

      (2) Figure 1B reflects an exemplary result. Here, the addition of 0.1 mM 2-OG seems to promote monomer to dimer transition. Why was this not studied in further detail? It seems highly relevant to know from which species the dodecamer is assembled.

      We thank the reviewer for their comment. However, we would like to point out that, although not shown in the figure, GlnA1 is always mainly present as dimers as the smallest entity. As suggested earlier, we have added the amount of monomers/dimers to Figure 1A, which shows low monomer-counts at all 2-OG concentrations (Fig.1A). Although not depicted in the graph starting at 0.01 mM OG, we also see mainly dimers at 0 mM 2-OG.

      How does the y-axis compare to the number and percentage of counts assigned to the peaks? In line 713, it is written that the percentage of dodecamer considers the total number of counts, and this was plotted against the 2-OG concentration.

      We thank the reviewer for addressing this unclarity. Line 713 corresponds to Figure 1A, where we indeed plotted the percentage of dodecamer against the 2-OG-concentration. Thereby, the percentage of dodecamer corresponds to the percentage calculated from the Gaussian Fit of the MP-dodecamer-peak. In Figure 1 B, however, the y-axis displays the relative amount of counts per mass, multiple similar masses then add up to the percentage of the respective peak (Gaussian Fit above similar masses).

      (3) Lines 714 and 721 (and elsewhere): Why only partial data is used for statistical purposes?

      We in general only show one exemplary biological replicate, since the quality of the respective GlnA1 purification sometimes varied (maximum activity ranging from 5 - 10 U/mg). Therefore, we only compared activities within the same protein purification. For the EC50 calculations of all measurements, we refer to the supplement.

      (4) Lines 192-193: It is claimed that GlnK1 was previously shown to both regulate the activity of GlnA1 and form a complex with GlnA1. Please mention the ratio between GlnK1 and GlnA1 in this complex.

      We now included the requested information (GlnA1:GlnK1 1:1, (Ehlers et al. 2005); His6-GlnA1 (0.95 μM), His6-GlnK1 (0.65 μM); 2:1,4, Gutt et al. 2021).

      It is also known that PII proteins such as GlnK1 can bind ADP, ATP, and 2-OG. Interestingly, however, for various described PII proteins, 2-OG can only bind after the binding of ATP.

      So, the crucial question here is what is the binding state of GlnK1? 

      Were these assays performed in the absence of ATP? This is key to fully understand and connect the results to the previous observations. For example, if the GlnK1 used was bound to ADP but not to ATP, then the added 2-OG might indeed only be able to affect GlnA1 (leading to its activation/oligomerization). If this were true and according to the data reported, ADP would prevent GlnK1 from interacting with any oligomeric form of GlnA1. However, if GlnK1 bound to ATP is the form that interacts with GlnA1 (potentially validating previous results?) then, 2-OG would first bind to GlnK1 (assuming a higher affinity of 2-OG to GlnK1), eventually causing its release from GlnA1 followed by binding and activation of GlnA1.

      These experiments need to be done as they are essential to further understand the process. Given the ability of the authors to produce the protein and run such assays, it is unclear why they were not done here. As written in line 203, in this case, "under the conditions tested" is not a good enough statement, considering what is known in the field and how many more conclusions could easily be taken from such a setup.

      Thanks for the encouragement to investigate the ligand-bound states of GlnK1. We agree and plan to perform the suggested mass photometry experiments exploring the conditions under which GlnA1 and GlnK1 might interact in future work. In GlnA1 activity test assays, when evaluating the presence/effects of GlnK1 on GlnA1 activity, however, ATP was always present in high concentrations and still we did not observe a significant effect of GlnK1 on the GlnA1 activity.

      (5) Figure 2D legend claims that the graphic shows the percentage of dodecameric GlnA1 as a function of the concentration of 2-OG. This is not what the figure shows; Figure 2D shows the dodecamer/dimer (although legend claims monomer was used, in line 732) ratio as a function of 2-OG (stated in line 736!). If this is true, a ratio of 1 means 50 % of dodecamers and dimers co-exist. This appears to be the case when GlnK1 was added, while in the absence of GlnK1 higher ratios are shown for higher 2-OG concentration implying that about 3 times more dodecamers were formed than dimers. However, wouldn´t a 50 % ratio be physiologically significant?

      We apologize for the partially incorrect and also misleading figure legend and corrected it. Indeed, the ratio of dodecamers and dimers is shown. Furthermore, we did not use monomeric GlnA1 (the smallest entity is mainly a dimer, see Fig 1A), however, the molarity was calculated based on the monomer-mass. Concerning the significance of the difference between the maximum ratio of GlnA1 and GlnK1: The ratio does appear higher, but this is mostly because adding large quantities of GlnK1 broadens all peaks at low molecular weight. This happens because the GlnK1 signal starts overlapping with the signal from GlnA1, leading to inflated GlnA1 dimer counts. We therefore do not think that this is biologically significant, especially as the activities do not differ under these conditions.

      (6) Is it possible that the uncleaved GlnA1 tag is preventing interaction with GlnK1? This should be discussed.

      This is of course a very important point. We however realized that Schumacher et al. also used an N-terminal His-tag, so we assume that the N-terminal tag is not hampering the interaction.

      (7) Line 228: Please detail the reported discrepancies in rmsd between the current protein and the gram-negative enzymes.

      The differences in rmsd between our M.mazei GlnA1 structure and the structure of gram-negative enzymes is caused by a) sequence similarity: E.g. M.mazei GlnA1 compared to B.subtilis GlnA have a sequence percent identity of 58.47; b) ligands in the structure: The B.Subtilis structure contains L-Methionine-S-sulfoximine phosphate, a transition state inhibitor, while the M. mazei  structure contains 2OG; c) Methodology: The structural determination methods also contribute to these differences. B. subtilis GlnA was determined using X-ray crystallography, while the M. mazei GlnA1 structure was resolved using Cryo-EM, where the protein behaves differently in ice compared to a crystal.

      (8) Line 747: The figure title claims "dimeric interface" although the manuscript body only refers to "hexameric interface" or "inter-hexamer interface" (line 224). Moreover, the figure 4 legend uses terms such as vertical and horizontal dimers and this too should be uniformized within the manuscript.

      Thank you for your valuable feedback. We have updated both the figure title and the figure legend as well in the main text to ensure consistency in the description.

      (9) Line 752: The description of the color scheme used here is somehow unclear.

      Thanks for pointing this out. We changed the description to make it more comprehensive.

      (10) Please label H14/15 and H14´/H15´in Fig 4C zoom.

      We agree that this has not been very clear. We added helix labels.

      (11) In Figure 4D legend, make sure to note that the binding sites for the substrate are based on homologies with another enzyme poised with these molecules.

      The same should be clear in the text: sites are not known, they are assumed to be, based on homologies (paragraph starting at line 239).

      Concerning this comment we want to point out that we studied the exact same enzyme as the Schumacher group, except that we used 2-OG in our experiments, which they did not.

      (12) Figure 3 appears redundant in light of Figure 4. 

      (13) Line 235: When mentioning F24, please refer to Figure 5.

      Thank you, we changed that accordingly.

      (14) Please provide the distances for the bonds depicted in Figure 4B.

      Thanks for pointing this out, we added distance labels to Figure 4B. For reasons of clarity only to three H-bonds.

      (15) Line 241: D57 is likely serving to abstract a proton from ammonium, what is residue Glu307 potentially doing? The information seems missing in light of how the sentence is built.

      Thanks for pointing this out. According to previous studies both residues are likely involved in proton abstraction - first from ammonium, and then from the formed gamma-ammonium group. Additionally, they contribute in shielding the active site from bulk solvent to prevent hydrolysis of the formed phospho-glutamate.

      (16) Why do the authors assume that increased concentrations of 2-OG are a signal for N starvation only in M. mazei and not in all prokaryotic equivalent systems (line 288)?

      In line 288, we did not claim that this is a unique signal for M. mazei. It is also the central N-starvation signal in Cyanobacteria but not directly perceived by the cyanobacterial GS through binding directly to GS.

      The authors should look into the residues that bind 2-OG and check if they are conserved in other GS. The results of this sequence analysis should be discussed in line with the variable prokaryotic glutamine synthetase types of activity modulation that were exposed in the introduction and Figure 7.

      Please refer to supplementary figure S5, where we already aligned the mentioned glutamine synthetase sequences. Since this was also already discussed in Müller et al. 2024, we did not want to repeat their observations and refer to our supplementary figure in too much detail.

      (17) Figure 5 title: Replace TS by transition state structures of homology enzymes, or alike.

      Thank you for this suggestion. We did not change the title however, since it is not a homologue but the exact same glutamine synthetase from Methanosarcina mazei.

      (18) Line 249: D170 is not shown in Figure 5A or elsewhere in Figure 5.

      Thank you for pointing this out. We added D170 to figure 5A.

      (19) Representative density for the residues binding 2-OG should be provided, maybe in a supplemental figure.

      Thank you for the suggestion. We added the densities of 2-OG-binding residues to figure 4B

      (20) Line 260: Please add a reference when describing the phosphoryl transfer.

      We thank the reviewer for this important point and added that accordingly.

      (21) Line 296: The binding of 2-OG indeed appears to be cooperative, such that at concentrations above its binding affinity to the protein, only dodecamers are seen (under experimental conditions). However, claiming that the oligomerization is fast is not correct when the experimental setup includes 10 minutes of incubation before measurements are done. Please correct this within the entire manuscript.

      A (fast) continuous kinetic assay could have confirmed this point and revealed the oligomerization steps and the intermediaries in the process (maybe monomer/dimers, then dimers/hexamers, and then hexamers/dodecamers). Such assays would have been highly valuable to this study.

      We thank the reviewer for this suggestion, but disagree. It is indeed a rather fast regulation (as activity assays without pre-incubation only takes 1 min longer to reach full activity, see the newly included suppl. Fig S6). Considering other regulation mechanisms like e.g. transcription or translation regulation, an activation that takes only 60 s is actually quite quick.

      (22) Line 305 (and elsewhere in the manuscript): the authors state that 2-OG primes the active site for a transition state. This appears incorrect. The transition state is the highest energy state in an enzymatic reaction progressing from substrate to product. Meaning, the transition state is a state that has a more or less modified form of the original substrate bound to the active site. This is not the case.

      In line 366 an "active open state" appears much more adequate to use. 

      We agree and changed accordingly throughout the manuscript.

      (23) Line 330: Please delete "found". Eventually replace it with "confirmed": As the authors write, others have described this residue as a ligand to glutamine.

      Thanks, we changed that accordingly, although previous descriptions were just based on homologies without the experimental validation.

      (24) The discussion in at various points summarizing again the results. It should be trimmed and improved.

      (25) Line 381: replace "two fast" with "fast"?

      We thank the reviewer for this suggestion, but disagree on this point. We especially wanted to highlight that there are two central nitrogen-metabolites involved in the direct regulation of GlnA1, that means TWO fast direct processes mediated by 2-OG and glutamine.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In their manuscript entitled 'The domesticated transposon protein L1TD1 associates with its ancestor L1 ORF1p to promote LINE-1 retrotransposition', Kavaklıoğlu and colleagues delve into the role of L1TD1, an RNA binding protein (RBP) derived from a LINE1 transposon. L1TD1 proves crucial for maintaining pluripotency in embryonic stem cells and is linked to cancer progression in germ cell tumors, yet its precise molecular function remains elusive. Here, the authors uncover an intriguing interaction between L1TD1 and its ancestral LINE-1 retrotransposon.

      The authors delete the DNA methyltransferase DNMT1 in a haploid human cell line (HAP1), inducing widespread DNA hypo-methylation. This hypomethylation prompts abnormal expression of L1TD1. To scrutinize L1TD1's function in a DNMT1 knock-out setting, the authors create DNMT1/L1TD1 double knock-out cell lines (DKO). Curiously, while the loss of global DNA methylation doesn't impede proliferation, additional depletion of L1TD1 leads to DNA damage and apoptosis.

      To unravel the molecular mechanism underpinning L1TD1's protective role in the absence of DNA methylation, the authors dissect L1TD1 complexes in terms of protein and RNA composition. They unveil an association with the LINE-1 transposon protein L1-ORF1 and LINE-1 transcripts, among others.

      Surprisingly, the authors note fewer LINE-1 retro-transposition events in DKO cells compared to DNMT1 KO alone.

      Strengths:

      The authors present compelling data suggesting the interplay of a transposon-derived human RNA binding protein with its ancestral transposable element. Their findings spur interesting questions for cancer types, where LINE1 and L1TD1 are aberrantly expressed.

      Weaknesses:

      Suggestions for refinement:

      The initial experiment, inducing global hypo-methylation by eliminating DNMT1 in HAP1 cells, is intriguing and warrants more detailed description. How many genes experience misregulation or aberrant expression? What phenotypic changes occur in these cells? Why did the authors focus on L1TD1? Providing some of this data would be helpful to understand the rationale behind the thorough analysis of L1TD1.

      The finding that L1TD1/DNMT1 DKO cells exhibit increased apoptosis and DNA damage but decreased L1 retro-transposition is unexpected. Considering the DNA damage associated with retro-transposition and the DNA damage and apoptosis observed in L1TD1/DNMT1 DKO cells, one would anticipate the opposite outcome. Could it be that the observation of fewer transposition-positive colonies stems from the demise of the most transpositionpositive colonies? Further exploration of this phenomenon would be intriguing.

      Reviewer #2 (Public review):

      In this study, Kavaklıoğlu et al. investigated and presented evidence for a role for domesticated transposon protein L1TD1 in enabling its ancestral relative, L1 ORF1p, to retrotranspose in HAP1 human tumor cells. The authors provided insight into the molecular function of L1TD1 and shed some clarifying light on previous studies that showed somewhat contradictory outcomes surrounding L1TD1 expression. Here, L1TD1 expression was correlated with L1 activation in a hypomethylation dependent manner, due to DNMT1 deletion in HAP1 cell line. The authors then identified L1TD1 associated RNAs using RIPSeq, which display a disconnect between transcript and protein abundance (via Tandem Mass Tag multiplex mass spectrometry analysis). The one exception was for L1TD1 itself, is consistent with a model in which the RNA transcripts associated with L1TD1 are not directly regulated at the translation level. Instead, the authors found L1TD1 protein associated with L1-RNPs and this interaction is associated with increased L1 retrotransposition, at least in the contexts of HAP1 cells. Overall, these results support a model in which L1TD1 is restrained by DNA methylation, but in the absence of this repressive mark, L1TD1 is expression, and collaborates with L1 ORF1p (either directly or through interaction with L1 RNA, which remains unclear based on current results), leads to enhances L1 retrotransposition. These results establish feasibility of this relationship existing in vivo in either development or disease, or both.

      Comments on revised version:

      In general, the authors did an acceptable job addressing the major concerns throughout the manuscript. This revision is much clearer and has improved in terms of logical progression.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      The authors have addressed all my questions in the revised version of the manuscript.

      Reviewer #2 (Recommendations for the authors):

      Revised comments:

      A few points we'd like to see addressed are our comments about the model (Figure S7C), as this is important for the readership to understand this complex finding. Please try to apply some quantification, if possible (question 8). Please do your best to tone down the direct relationship of these findings to embryology (question 11). Based on both reviewer comments, we believe addressing reviewer #1s "Suggestions for refinement" (2 points), would help us change our view of solid to convincing.

      Responses to changes:

      Major

      (1) The study only used one knockout (KO) cell line generated by CRISPR/Cas9.

      Considering the possibility of an off-target effect, I suggest the authors attempt one or both of these suggestions.

      A)  Generate or acquire a similar DMNT1 deletion that uses distinct sgRNAs, so that the likelihood of off-targets is negligible. A few simple experiments such as qRT-PCR would be sufficient to suggest the same phenotype.

      B)  Confirm the DNMT1 depletion also by siRNA/ASO KD to phenocopy the KO effect.

      (2) In addition to the strategies to demonstrate reproducibility, a rescue experiment restoring DNMT1 to the KO or KD cells would be more convincing. (Partial rescue would suffice in this case, as exact endogenous expression levels may be hard to replicate).

      We have undertook several approaches to study the effect of DNMT1 loss or inactivation: As described above, we have generated a conditional KO mouse with ablation of DNMT1 in the epidermis. DNMT1-deficient keratinocytes isolated from these mice show a significant increase in L1TD1 expression. In addition, treatment of primary human keratinocytes and two squamous cell carcinoma cell lines with the DNMT inhibitor aza-deoxycytidine led to upregulation of L1TD1 expression. Thus, the derepression of L1TD1 upon loss of DNMT1 expression or activity is not a clonal effect.

      Also, the spectrum of RNAs identified in RIP experiments as L1TD1-associated transcripts in HAP1 DNMT1 KO cells showed a strong overlap with the RNAs isolated by a related yet different method in human embryonic stem cells. When it comes to the effect of L1TD1 on L1-1 retrotranspostion, a recent study has reported a similar effect of L1TD1 upon overexpression in HeLa cells [4].

      All of these points together help to convince us that our findings with HAP1 DNMT KO are in agreement with results obtained in various other cell systems and are therefore not due to off-target effects. With that in mind, we would pursue the suggestion of Reviewer 1 to analyze the effects of DNA hypomethylation upon DNMT1 ablation.

      Thank you for addressing this concern. The reference to Beck 2021 and the additional cells lines (R2: keratinocytes and R3: squamous cell carcinoma) provides sufficient evidence that this result is unlikely to be a result of clonal expansion or off targets.

      Question: Was the human ES Cell RIP Experiment shown here? What is the overlap?

      We refer to the recently published study by Jin et al. (PMID: 38165001). As stated in the Discussion, the majority of L1TD1-associated transcripts in HAP1 cells (69%) identified in our study were also reported as L1TD1 targets in hESCs suggesting a conserved binding affinity of this domesticated transposon protein across different cell types.  

      (3) As stated in the introduction, L1TD1 and ORF1p share "sequence resemblance" (Martin 2006). Is the L1TD1 antibody specific or do we see L1 ORF1p if Fig 1C were uncropped?

      (6) Is it possible the L1TD1 antibody binds L1 ORF1p? This could make Figure 2D somewhat difficult to interpret. Some validation of the specificity of the L1TD1 antibody would remove this concern (see minor concern below).

      This is a relevant question. We are convinced that the L1TD1 antibody does not crossreact with L1 ORF1p for the following reasons: Firstly, the antibody does not recognize L1 ORF1p (40 kDa) in the uncropped Western blot for Figure 1C (Figure R4A). Secondly, the L1TD1 antibody gives only background signals in DKO cells in the indirect immunofluorescence experiment shown in Figure 1E of the manuscript.

      Thirdly, the immunogene sequence of L1TD1 that determines the specificity of the antibody was checked in the antibody data sheet from Sigma Aldrich. The corresponding epitope is not present in the L1 ORF1p sequence.

      Finally, we have shown that the ORF1p antibody does not cross-react with L1TD1 (Figure R4B).

      Response: Thank you for sharing these images. These full images relieve concerns about specificity. The increase of ORF1P in R4B and Main figure 3C is interesting and pointed out in the manuscript. Not for the purposes of this review, but the observation of reduced transposition despite increased ORF1P could be an interesting follow up to this study (combined with the similar UPF1 result could indicate a complex of some kind).

      (4) In abstract (P2), the authors mentioned that L1TD1 works as an RNA chaperone, but in the result section (P13), they showed that L1TD1 associates with L1 ORF1p in an RNA independent manner. Those conclusions appear contradictory. Clarification or revision is required.

      Our findings that both proteins bind L1 RNA, and that L1TD1 interacts with ORF1p are compatible with a scenario where L1TD1/ORF1p heteromultimers bind to L1 RNA. The additional presence of L1TD1 might thereby enhance the RNA chaperone function of ORF1p. This model is visualized now in Suppl. Figure S7C.

      Response: Thank you for the model. To further clarify, do you mean that L1TD1 can bind L1 RNA, but this is not needed for the effect, however this "bonus" binding (that is enabled by heteromultimerization) appears to enhance the retrotransposition frequency? Do you think L1TD1 is binding L1 RNA in this context or simply "stabilizing" ORF1P (Trimer) RNP?

      Based on our data, L1TD1 associates with L1 RNA and interacts with L1 ORF1p. Both features might contribute to the enhanced retrotransposition frequency. Interestingly, the L1TD1 protein shares with its ancestor L1 ORF1p the non-canonical RNA recognition motif and the coiled-coil motif required for the trimerization but has two copies instead of one of the C-terminal domain (CTD), a structure with RNA binding and chaperone function. We speculate that the presence of an additional CTD within the L1TD1 protein might thereby enhance the RNA binding and chaperone function of L1TD1/ORF1p heteromultimers.

      (5) Figure 2C fold enrichment for L1TD1 and ARMC1 is a bit difficult to fully appreciate. A 100 to 200-fold enrichment does not seem physiological. This appears to be a "divide by zero" type of result, as the CT for these genes was likely near 40 or undetectable. Another qRT-PCR based approach (absolute quantification) would be a more revealing experiment. This is the validation of the RIP experiments and the presentation mode is specifically developed for quantification of RIP assays (Sigma Aldrich RIP-qRT-PCR: Data Analysis Calculation Shell). The unspecific binding of the transcript in the absence of L1TD1 in DNMT1/L1TD1 DKO cells is set to 1 and the value in KO cells represents the specific binding relative the unspecific binding. The calculation also corrects for potential differences in the abundance of the respective transcript in the two cell lines. This is not a physiological value but the quantification of specific binding of transcripts to L1TD1. GAPDH as negative control shows no enrichment, whereas specifically associated transcripts show strong enrichement. We have explained the details of RIPqRT-PCR evaluation in Materials and Methods (page 14) and the legend of Figure 2C in the revised manuscript.

      Response: Thank you for the clarification and additional information in the manuscript.

      (6) Is it possible the L1TD1 antibody binds L1 ORF1p? This could make Figure 2D somewhat difficult to interpret. Some validation of the specificity of the L1TD1 antibody would remove this concern (see minor concern below).

      See response to (3).

      Response: Thanks.

      (7) Figure S4A and S4B: There appear to be a few unusual aspects of these figures that should be pointed out and addressed. First, there doesn't seem to be any ORF1p in the Input (if there is, the exposure is too low). Second, there might be some L1TD1 in the DKO (lane 2) and lane 3. This could be non-specific, but the size is concerning. Overexposure would help see this.

      The ORF1p IP gives rise to strong ORF1p signals in the immunoprecipitated complexes even after short exposure. Under these conditions ORF1p is hardly detectable in the input. Regarding the faint band in DKO HAP1 cells, this might be due to a technical problem during Western blot loading. Therefore, the input samples were loaded again on a Western blot and analyzed for the presence of ORF1p, L1TD1 and beta-actin (as loading control) and shown as separate panel in Suppl. Figure S4A.

      The enhanced image is clearer. Thanks.

      S4A and S4B now appear to the S6A and S6B, is that correct? (This is due to the addition of new S1 and S2, but please verify image orders were not disturbed).

      Yes, the input is shown now as a separate panel in Suppl. Figure S6A.

      (8) Figure S4C: This is related to our previous concerns involving antibody cross-reactivity. Figure 3E partially addresses this, where it looks like the L1TD1 "speckles" outnumber the ORF1p puncta, but overlap with all of them. This might be consistent with the antibody crossreacting. The western blot (Figure 3C) suggests an upregulation of ORF1p by at least 23x in the DKO, but the IF image in 3E is hard to tell if this is the case (slightly more signal, but fewer foci). Can you return to the images and confirm the contrast are comparable? Can you massively overexpose the red channel in 3E to see if there is residual overlap? In Figure 3E the L1TD1 antibody gives no signal in DNMT1/L1TD1 DKO cells confirming that it does not recognize ORF1p. In agreement with the Western blot in Figure 3C the L1 ORF1p signal in Figure 3E is stronger in DKO cells. In DNMT1 KO cells the L1 ORF1p antibody does not recognize all L1TD1 speckles. This result is in agreement with the Western blot shown above in Figure R4B and indicates that the L1 ORF1p antibody does not recognize the L1TD1 protein. The contrast is comparable and after overexposure there are still L1TD1 specific speckles. This might be due to differences in abundance of the two proteins.

      Response: Suggestion: Would it be possible to use a program like ImageJ to supplement the western blot observation? Qualitatively, In figure 3E, it appears that there is more signal in the DKO, but this could also be due to there being multiple cells clustered together or a particularly nicely stained region. Could you randomly sample 20-30 cells across a few experiments to see if this holds up. I am interested in whether the puncta in the KO image(s) is a very highly concentrated region and in the DKO this is more disperse. Also, the representative DKO seems to be cropped slightly wrong. (Please use puncta as a guide to make the cropping more precise)

      As suggested by the reviewer we have quantified the signals of 60 KO cells and 56 DKO cells in three different IF experiments by ImageJ. We measured a 1.4-fold higher expression level of L1 ORF1p in DKO cells. However, the difference is not statistically significant. This is most probably due to the change in cell size and protein content during the cell cycle with increasing protein contents from G1 to G2. Western blot analysis provides signals of comparable protein amounts representing an average expression levels over ten thousands of cells. Nevertheless, the quantification results reflect in principle the IF pictures shown in Figure 3E but IF is probably not the best method to quantify protein amounts. We have also corrected Figure 3E.

      Author response image 1.

      (9) The choice of ARMC1 and YY2 is unclear. What are the criteria for the selection?

      ARMC1 was one of the top hits in a pilot RIP-seq experiment (IP versus input and IP versus IgG IP). In the actual RIP-seq experiment with DKO HAP1 cells instead of IgG IP as a negative control, we found ARMC1 as an enriched hit, although it was not among the top 5 hits. The results from the 2nd RIP-seq further confirmed the validity of ARMC1 as an L1TD1interacting transcript. YY2 was of potential biological relevance as an L1TD1 target due to the fact that it is a processed pseudogene originating from YY1 mRNA as a result of retrotransposition. This is mentioned on page 6 of the revised manuscript.

      Response: Appreciated!

      (10) (P16) L1 is the only protein-coding transposon that is active in humans. This is perhaps too generalized of a statement as written. Other examples are readily found in the literature.

      Please clarify.

      We will tone down this statement in the revised manuscript.

      Response: Appreciated! To further clarify, the term "active" when it comes to transposable elements, has not been solidified. It can span "retrotransposition competent" to "transcripts can be recovered". There are quite a few reports of GAG transcripts and protein from various ERV/LTR subfamilies in various cells and tissues (in mouse and human at least), however whether they contribute to new insertions is actively researched.

      (11) In both the abstract and last sentence in the discussion section (P17), embryogenesis is mentioned, but this is not addressed at all in the manuscript. Please refrain from implying normal biological functions based on the results of this study unless appropriate samples are used to support them.

      Much of the published data on L1TD1 function are related to embryonic stem cells [3- 7].

      Therefore, it is important to discuss our findings in the context of previous reports.

      Response: It is well established that embryonic stem cells are not a perfect or direct proxies for the inner cell mass of embryos, as multiple reports have demonstrated transcriptomic, epigenetic, chromatin accessibility differences. The exact origin of ES cells is also considered controversial. We maintain that the distinction between embryos/embryogenesis and the results presented in the manuscript are not yet interchangeable. An important exception would be complex models of embryogenesis such as embryoids, (or synthetic/artificial embryo models that have been carefully been termed as such so as to not suggest direct implications to embryos). https://www.nature.com/articles/ncb2965  

      https://link.springer.com/article/10.1007/s00018-018-2965-y  

      https://www.cell.com/developmental-cell/abstract/S1534-5807(24)00363-0?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS1534580724003630%3Fshowall%3Dtrue

      We have deleted the corresponding paragraph in the Discussion.

      (12) Figure 3E: The format of Figures 1A and 3E are internally inconsistent. Please present similar data/images in a cohesive way throughout the manuscript. We show now consistent IF Figures in the revised manuscript.

      Response: Thanks

      Minor:

      In general:

      Still need checking for typos, mostly in Materials and Methods section; Please keep a consistent writing style throughout the whole manuscript. If you use L1 ORF1p, then please use L1 instead of LINE-1, or if you keep LINE-1 in your manuscript, then you should use LINE-1 ORF1p.

      A lab member from the US checked again the Materials and Methods section for typos. We keep the short version L1 ORF1p.

      (1) Intro:

      - Is L1Td1 in mice and Humans? How "conserved" is it and does this suggest function? Murine and human L1TD1 proteins share 44% identity on the amino acid level and it was suggested that the corresponding genes were under positive selection during evolution with functions in transposon control and maintenance of pluripotency [8].

      - Why HAP1? (Haploid?) The importance of this cell line is not clear.

      HAP1 is a nearly haploid human cancer cell line derived from the KBM-7 chronic myelogenous leukemia (CML) cell line [9, 10]. Due to its haploidy is perfectly suited and widely used for loss-of-function screens and gene editing. After gene editing cells can be used in the nearly haploid or in the diploid state. We usually perform all experiments with diploid HAP1 cell lines. Importantly, in contrast to other human tumor cell lines, this cell line tolerates ablation of DNMT1. We have included a corresponding explanation in the revised manuscript on page 5, first paragraph.

      - Global methylation status in DNMT1 KO? (Methylations near L1 insertions, for example?)

      The HAP1 DNMT1 KO cell line with a 20 bp deletion in exon 4 used in our study was validated in the study by Smits et al. [11]. The authors report a significant reduction in overall DNA methylation. However, we are not aware of a DNA methylome study on this cell line. We show now data on the methylation of L1 elements in HAP1 cells and upon DNMT1 deletion in the revised manuscript in Suppl. Figure S1B.

      Response: Looks great!

      (2) Figure 1:

      - Figure 1C. Why is LMNB used instead of Actin (Fig1D)?

      We show now beta-actin as loading control in the revised manuscript.

      - Figure 1G shows increased Caspase 3 in KO, while the matching sentence in the result section skips over this. It might be more accurate to mention this and suggest that the single KO has perhaps an intermediate phenotype (Figure 1F shows a slight but not significant trend).

      We fully agree with the reviewer and have changed the sentence on page 6, 2nd paragraph accordingly.

      - Would 96 hrs trend closer to significance? An interpretation is that L1TD1 loss could speed up this negative consequence.

      We thank the reviewer for the suggestion. We have performed a time course experiment with 6 biological replicas for each time point up to 96 hours and found significant changes in the viability upon loss of DNMT1 and again significant reduction in viability upon additional loss of L1TD1 (shown in Figure 1F). These data suggest that as expected loss of DNMT1 leads to significant reduction viability and that additional ablation of L1TD1 further enhances this effect.

      Response: Looks good!

      - What are the "stringent conditions" used to remove non-specific binders and artifacts (negative control subtraction?)

      Yes, we considered only hits from both analyses, L1TD1 IP in KO versus input and L1TD1 IP in KO versus L1TD1 IP in DKO. This is now explained in more detail in the revised manuscript on page 6, 3rd paragraph.

      (3) Figure 2:

      - Figure 2A is a bit too small to read when printed.

      We have changed this in the revised manuscript.

      - Since WT and DKO lack detectable L1TD1, would you expect any difference in RIP-Seq results between these two?

      Due to the lack of DNMT1 and the resulting DNA hypomethylation, DKO cells are more similar to KO cells than WT cells with respect to the expressed transcripts.

      - Legend says selected dots are in green (it appears blue to me). We have changed this in the revised manuscript.

      - Would you recover L1 ORF1p and its binding partners in the KO? (Is the antibody specific in the absence of L1TD1 or can it recognize L1?) I noticed an increase in ORF1p in the KO in Figure 3C.

      Thank you for the suggestion. Yes, L1 ORF1p shows slightly increased expression in the proteome analysis and we have marked the corresponding dot in the Volcano plot (Figure 3A).

      - Should the figure panel reference near the (Rosspopoff & Trono) reference instead be Sup S1C as well? Otherwise, I don't think S1C is mentioned at all.

      - What are the red vs. green dots in 2D? Can you highlight ERV and ALU with different colors?

      We added the reference to Suppl. Figure S1C (now S3C) in the revised manuscript. In Figure 2D L1 elements are highlighted in green, ERV elements in yellow, and other associated transposon transcripts in red.

      Response: Much better, thanks!

      - Which L1 subfamily from Figure 2D is represented in the qRT-PCR in 2E "LINE-1"? Do the primers match a specific L1 subfamily? If so, which? We used primers specific for the human L1.2 subfamily.

      - Pulling down SINE element transcripts makes some sense, as many insertions "borrow" L1 sequences for non-autonomous retro transposition, but can you speculate as to why ERVs are recovered? There should be essentially no overlap in sequence.

      In the L1TD1 evolution paper [8], a potential link between L1TD1 and ERV elements was discussed:

      "Alternatively, L1TD1 in sigmodonts could play a role in genome defense against another element active in these genomes. Indeed, the sigmodontine rodents have a highly active family of ERVs, the mysTR elements [46]. Expansion of this family preceded the death of L1s, but these elements are very active, with 3500 to 7000 speciesspecific insertions in the L1-extinct species examined [47]. This recent ERV amplification in Sigmodontinae contrasts with the megabats (where L1TD1 has been lost in many species); there are apparently no highly active DNA or RNA elements in megabats [48]. If L1TD1 can suppress retroelements other than L1s, this could explain why the gene is retained in sigmodontine rodents but not in megabats."

      Furthermore, Jin et al. report the binding of L1TD1 to repetitive sequences in transcripts [12]. It is possible that some of these sequences are also present in ERV RNAs.

      Response: Interesting, thanks for sharing

      - Is S2B a screenshot? (the red underline).

      No, it is a Powerpoint figure, and we have removed the red underline.

      (4) Figure 3:

      - Text refers to Figure 3B as a western blot. Figure 3B shows a volcano plot. This is likely 3C but would still be out of order (3A>3C>3B referencing). I think this error is repeated in the last result section.

      - Figure and legends fail to mention what gene was used for ddCT method (actin, gapdh, etc.).

      - In general, the supplemental legends feel underwritten and could benefit from additional explanations. (Main figures are appropriate but please double-check that all statistical tests have been mentioned correctly).

      Thank you for pointing this out. We have corrected these errors in the revised manuscript.

      (5) Discussion:

      - Aluy connection is interesting. Is there an "Alu retrotransposition reporter assay" to test whether L1TD1 enhances this as well?

      Thank you for the suggestion. There is indeed an Alu retrotransposition reporter assay reported be Dewannieux et al. [13]. The assay is based on a Neo selection marker. We have previously tested a Neo selection-based L1 retrotransposition reporter assay, but this system failed to properly work in HAP1 cells, therefore we switched to a blasticidin based L1 retrotransposition reporter assay. A corresponding blasticidin-based Alu retrotransposition reporter assay might be interesting for future studies (mentioned in the Discussion, page 11 paragraph 4 of the revised manuscript.

      (6) Material and Methods :

      - The number of typos in the materials and methods is too numerous to list. Instead, please refer to the next section that broadly describes the issues seen throughout the manuscript.

      Writing style

      (1) Keep a consistent style throughout the manuscript: for example, L1 or LINE-1 (also L1 ORF1p or LINE-1 ORF1p); per or "/"; knockout or knock-out; min or minute; 3 times or three times; media or medium. Additionally, as TE naming conventions are not uniform, it is important to maintain internal consistency so as to not accidentally establish an imprecise version.

      (2) There's a period between "et al" and the comma, and "et al." should be italic.

      (3) The authors should explain what the key jargon is when it is first used in the manuscript, such as "retrotransposon" and "retrotransposition".

      (4) The authors should show the full spelling of some acronyms when they use it for the first time, such as RNA Immunoprecipitation (RIP).

      (5) Use a space between numbers and alphabets, such as 5 μg. (6) 2.0 × 105 cells, that's not an "x".

      (7) Numbers in the reference section are lacking (hard to parse).

      (8) In general, there are a significant number of typos in this draft which at times becomes distracting. For example, (P3) Introduction: Yet, co-option of TEs thorough (not thorough, it should be through) evolution has created so-called domesticated genes beneficial to the gene network in a wide range of organisms. Please carefully revise the entire manuscript for these minor issues that collectively erode the quality of this submission. Thank you for pointing out these mistakes. We have corrected them in the revised manuscript. A native speaker from our research group has carefully checked the paper. In summary, we have added Supplementary Figure S7C and have changed Figures 1C, 1E, 1F, 2A, 2D, 3A, 4B, S3A-D, S4B and S6A based on these comments.

      Response: Thank you for taking these comments on board!

    1. Author response:

      Reviewer #1 (Public review):

      Wang et al., recorded concurrent EEG-fMRI in 107 participants during nocturnal NREM sleep to investigate brain activity and connectivity related to slow oscillations (SO), sleep spindles, and in particular their co-occurrence. The authors found SO-spindle coupling to be correlated with increased thalamic and hippocampal activity, and with increased functional connectivity from the hippocampus to the thalamus and from the thalamus to the neocortex, especially the medial prefrontal cortex (mPFC). They concluded the brain-wide activation pattern to resemble episodic memory processing, but to be dissociated from task-related processing and suggest that the thalamus plays a crucial role in coordinating the hippocampal-cortical dialogue during sleep.

      The paper offers an impressively large and highly valuable dataset that provides the opportunity for gaining important new insights into the network substrate involved in SOs, spindles, and their coupling. However, the paper does unfortunately not exploit the full potential of this dataset with the analyses currently provided, and the interpretation of the results is often not backed up by the results presented. I have the following specific comments.

      Thank you for your thoughtful and constructive feedback. We greatly appreciate your recognition of the strengths of our dataset and findings Below, we address your specific comments and provide responses to each point you raised to ensure our methods and results are as transparent and comprehensible as possible. We hope these revisions address your comments and further strengthen our manuscript. Thank you again for the constructive feedback.

      (1) The introduction is lacking sufficient review of the already existing literature on EEG-fMRI during sleep and the BOLD-correlates of slow oscillations and spindles in particular (Laufs et al., 2007; Schabus et al., 2007; Horovitz et al., 2008; Laufs, 2008; Czisch et al., 2009; Picchioni et al., 2010; Spoormaker et al., 2010; Caporro et al., 2011; Bergmann et al., 2012; Hale et al., 2016; Fogel et al., 2017; Moehlman et al., 2018; Ilhan-Bayrakci et al., 2022). The few studies mentioned are not discussed in terms of the methods used or insights gained.

      We acknowledge the need for a more comprehensive review of prior EEG-fMRI studies investigating BOLD correlates of slow oscillations and spindles. However, these articles are not all related to sleep SO or spindle. Articles (Hale et al., 2016; Horovitz et al., 2008; Laufs, 2008; Laufs, Walker, & Lund, 2007; Spoormaker et al., 2010) mainly focus on methodology for EEG-fMRI, sleep stages, or brain networks, which are not the focus of our study. Thank you again for your attention to the comprehensiveness of our literature review, and we will expand the introduction to include a more detailed discussion of the existing literature, ensuring that the contributions of previous EEG-fMRI sleep studies are adequately acknowledged.

      Introduction, Page 4 Lines 62-76

      “Investigating these sleep-related neural processes in humans is challenging because it requires tracking transient sleep rhythms while simultaneously assessing their widespread brain activation. Recent advances in simultaneous EEG-fMRI techniques provide a unique opportunity to explore these processes. EEG allows for precise event-based detection of neural signal, while fMRI provides insight into the broader spatial patterns of brain activation and functional connectivity (Horovitz et al., 2008; Huang et al., 2024; Laufs, 2008; Laufs, Walker, & Lund, 2007; Schabus et al., 2007; Spoormaker et al., 2010). Previous EEG-fMRI studies on sleep have focused on classifying sleep stages or examining the neural correlates of specific waves (Bergmann et al., 2012; Caporro et al., 2012; Czisch et al., 2009; Fogel et al., 2017; Hale et al., 2016; Ilhan-Bayrakcı et al., 2022; Moehlman et al., 2019; Picchioni et al., 2011). These studies have generally reported that slow oscillations are associated with widespread cortical and subcortical BOLD changes, whereas spindles elicit activation in the thalamus, as well as in several cortical and paralimbic regions. Although these findings provide valuable insights into the BOLD correlates of sleep rhythms, they often do not employ sophisticated temporal modeling (Huang et al., 2024), to capture the dynamic interactions between different oscillatory events, e.g., the coupling between SOs and spindles.”

      (2) The paper falls short in discussing the specific insights gained into the neurobiological substrate of the investigated slow oscillations, spindles, and their interactions. The validity of the inverse inference approach ("Open ended cognitive state decoding"), assuming certain cognitive functions to be related to these oscillations because of the brain regions/networks activated in temporal association with these events, is debatable at best. It is also unclear why eventually only episodic memory processing-like brain-wide activation is discussed further, despite the activity of 16 of 50 feature terms from the NeuroSynth v3 dataset were significant (episodic memory, declarative memory, working memory, task representation, language, learning, faces, visuospatial processing, category recognition, cognitive control, reading, cued attention, inhibition, and action).

      Thank you for pointing this out, particularly regarding the use of inverse inference approaches such as “open-ended cognitive state decoding.” Given the concerns about the indirectness of this approach, we decided to remove its related content and results from Figure 3 in the main text and include it in Supplementary Figure 7. We will refocus the main text on direct neurobiological insights gained from our EEG-fMRI analyses, particularly emphasizing the hippocampal-thalamocortical network dynamics underlying SO-spindle coupling, and we will acknowledge the exploratory nature of these findings and highlight their limitations.

      Discussion, Page 17-18 Lines 323-332

      “To explore functional relevance, we employed an open-ended cognitive state decoding approach using meta-analytic data (NeuroSynth: Yarkoni et al. (2011)). Although this method usefully generates hypotheses about potential cognitive processes, particularly in the absence of a pre- and post-sleep memory task, it is inherently indirect. Many cognitive terms showed significant associations (16 of 50), such as “episodic memory,” “declarative memory,” and “working memory.” We focused on episodic/declarative memory given the known link with hippocampal reactivation (Diekelmann & Born, 2010; Staresina et al., 2015; Staresina et al., 2023). Nonetheless, these inferences regarding memory reactivation should be interpreted cautiously without direct behavioral measures. Future research incorporating explicit tasks before and after sleep would more rigorously validate these potential functional claims.”

      (3) Hippocampal activation during SO-spindles is stated as a main hypothesis of the paper - for good reasons - however, other regions (e.g., several cortical as well as thalamic) would be equally expected given the known origin of both oscillations and the existing sleep-EEG-fMRI literature. However, this focus on the hippocampus contrasts with the focus on investigating the key role of the thalamus instead in the Results section.

      We appreciate your insight regarding the relative emphasis on hippocampal and thalamic activation in our study. We recognize that the manuscript may currently present an inconsistency between our initial hypothesis and the main focus of the results. To address this concern, we will ensure that our Introduction and Discussion section explicitly discusses both regions, highlighting the complementary roles of the hippocampus (memory processing and reactivation) and the thalamus (spindle generation and cortico-hippocampal coordination) in SO-spindle dynamics.

      Introduction, Page 5 Lines 87-103

      “To address this gap, our study investigates brain-wide activation and functional connectivity patterns associated with SO-spindle coupling, and employs a cognitive state decoding approach (Margulies et al., 2016; Yarkoni et al., 2011)—albeit indirectly—to infer potential cognitive functions. In the current study, we used simultaneous EEG-fMRI recordings during nocturnal naps (detailed sleep staging results are provided in the Methods and Table S1) in 107 participants. Although directly detecting hippocampal ripples using scalp EEG or fMRI is challenging, we expected that hippocampal activation in fMRI would coincide with SO-spindle coupling detected by EEG, given that SOs, spindles, and ripples frequently co-occur during NREM sleep. We also anticipated a critical role of the thalamus, particularly thalamic spindles, in coordinating hippocampal-cortical communication.

      We found significant coupling between SOs and spindles during NREM sleep (N2/3), with spindle peaks occurring slightly before the SO peak. This coupling was associated with increased activation in both the thalamus and hippocampus, with functional connectivity patterns suggesting thalamic coordination of hippocampal-cortical communication. These findings highlight the key role of the thalamus in coordinating hippocampal-cortical interactions during human sleep and provide new insights into the neural mechanisms underlying sleep-dependent brain communication. A deeper understanding of these mechanisms may contribute to future neuromodulation approaches aimed at enhancing sleep-dependent cognitive function and treating sleep-related disorders.”

      Discussion, Page 16-17 Lines 292-307

      “When modeling the timing of these sleep rhythms in the fMRI, we observed hippocampal activation selectively during SO-spindle events. This suggests the possibility of triple coupling (SOs–spindles–ripples), even though our scalp EEG was not sufficiently sensitive to detect hippocampal ripples—key markers of memory replay (Buzsáki, 2015). Recent iEEG evidence indicates that ripples often co-occur with both spindles (Ngo, Fell, & Staresina, 2020) and SOs (Staresina et al., 2015; Staresina et al., 2023). Therefore, the hippocampal involvement during SO-spindle events in our study may reflect memory replay from the hippocampus, propagated via thalamic spindles to distributed cortical regions.

      The thalamus, known to generate spindles (Halassa et al., 2011), plays a key role in producing and coordinating sleep rhythms (Coulon, Budde, & Pape, 2012; Crunelli et al., 2018), while the hippocampus is found essential for memory consolidation (Buzsáki, 2015; Diba & Buzsá ki, 2007; Singh, Norman, & Schapiro, 2022). The increased hippocampal and thalamic activity, along with strengthened connectivity between these regions and the mPFC during SO-spindle events, underscores a hippocampal-thalamic-neocortical information flow. This aligns with recent findings suggesting the thalamus orchestrates neocortical oscillations during sleep (Schreiner et al., 2022). The thalamus and hippocampus thus appear central to memory consolidation during sleep, guiding information transfer to the neocortex, e.g., mPFC.”

      (4) The study included an impressive number of 107 subjects. It is surprising though that only 31 subjects had to be excluded under these difficult recording conditions, especially since no adaptation night was performed. Since only subjects were excluded who slept less than 10 min (or had excessive head movements) there are likely several datasets included with comparably short durations and only a small number of SOs and spindles and even less combined SO-spindle events. A comprehensive table should be provided (supplement) including for each subject (included and excluded) the duration of included NREM sleep, number of SOs, spindles, and SO+spindle events. Also, some descriptive statistics (mean/SD/range) would be helpful.

      We appreciate your recognition of our sample size and the challenges associated with simultaneous EEG-fMRI sleep recordings. We acknowledge the importance of transparently reporting individual subject data, particularly regarding sleep duration and the number of detected SOs, spindles, and SO-spindle events. To address this, we will provide comprehensive tables in the supplementary materials, contains descriptive information about sleep-related characteristics (Table S1), as well as detailed information about sleep waves at each sleep stage for all 107 subjects(Table S2-S4), listing for each subject:(1)Different sleep stage duration; (2)Number of detected SOs; (3)Number of detected spindles; (4)Number of detected SO-spindle coupling events; (5)Density of detected SOs; (6)Density of detected spindles; (7)Density of detected SO-spindle coupling events.

      However, most of the excluded participants were unable to fall asleep or had too short a sleep duration, so they basically had no NREM sleep period, so it was impossible to count the NREM sleep duration, SO, spindle, and coupling numbers.

      Supplementary Materials, Page 42-54, Table S1-S4

      (Consider of the length, we do not list all the tables here. Please refer to the revised manuscript.)

      (5) Was the 20-channel head coil dedicated for EEG-fMRI measurements? How were the electrode cables guided through/out of the head coil? Usually, the 64-channel head coil is used for EEG-fMRI measurements in a Siemens PRISMA 3T scanner, which has a cable duct at the back that allows to guide the cables straight out of the head coil (to minimize MR-related artifacts). The choice for the 20-channel head coil should be motivated. Photos of the recording setup would also be helpful.

      Thank you for your comment regarding our choice of the 20-channel head coil for EEG-fMRI measurements. We acknowledge that the 64-channel head coil is commonly used in Siemens PRISMA 3T scanners; however, the 20-channel coil was selected due to specific practical and technical considerations in our study. In particular, the 20-channel head coil was compatible with our EEG system and ensured sufficient signal-to-noise ratio (SNR) for both EEG and fMRI acquisition. The EEG electrode cables were guided through the lateral and posterior openings of the head coil, secured with foam padding to reduce motion and minimize MR-related artifacts. Moreover, given the extended nature of nocturnal sleep recordings, the 20-channel coil allowed us to maintain participant comfort while still achieving high-quality simultaneous EEG-fMRI data.

      We have made this clearer in the revised manuscript.

      Methods, Page 20 Lines 385-392

      “All MRI data were acquired using a 20-channel head coil on a research-dedicated 3-Tesla Siemens Magnetom Prisma MRI scanner. Earplugs and cushions were provided for noise protection and head motion restriction. We chose the 20-channel head coil because it was compatible with our EEG system and ensured sufficient signal-to-noise ratio (SNR) for both EEG and fMRI acquisition. The EEG electrode cables were guided through the lateral and posterior openings of the head coil, secured with foam padding to reduce motion and minimize MR-related artifacts. Moreover, given the extended nature of nocturnal sleep recordings, the 20-channel coil helped maintain participant comfort while still achieving high-quality simultaneous EEG-fMRI data.”

      (6) Was the EEG sampling synchronized to the MR scanner (gradient system) clock (the 10 MHz signal; not referring to the volume TTL triggers here)? This is a requirement for stable gradient artifact shape over time and thus accurate gradient noise removal.

      Thank you for raising this important point. We confirm that the EEG sampling was synchronized to the MR scanner’s 10 MHz gradient system clock, ensuring a stable gradient artifact shape over time and enabling accurate artifact removal. This synchronization was achieved using the standard clock synchronization interface of the EEG amplifier, minimizing timing jitter and drift. As a result, the gradient artifact waveform remained stable across volumes, allowing for more effective artifact correction during preprocessing. We appreciate your attention to this critical aspect of EEG-fMRI data acquisition.

      We have made this clearer in the revised manuscript.

      Methods, Page 19-20 Lines 371-383

      “EEG was recorded simultaneously with fMRI data using an MR-compatible EEG amplifier system (BrainAmps MR-Plus, Brain Products, Germany), along with a specialized electrode cap. The recording was done using 64 channels in the international 10/20 system, with the reference channel positioned at FCz. In order to adhere to polysomnography (PSG) recording standards, six electrodes were removed from the EEG cap: one for electrocardiogram (ECG) recording, two for electrooculogram (EOG) recording, and three for electromyogram (EMG) recording. EEG data was recorded at a sample rate of 5000 Hz, the resistance of the reference and ground channels was kept below 10 kΩ, and the resistance of the other channels was kept below 20 kΩ. To synchronize the EEG and fMRI recordings, the BrainVision recording software (BrainProducts, Germany) was utilized to capture triggers from the MRI scanner. The EEG sampling was synchronized to the MR scanner’s 10 MHz gradient system clock, ensuring a stable gradient artifact shape over time and enabling accurate artifact removal. This was achieved via the standard clock synchronization interface of the EEG amplifier, minimizing timing jitter and drift.”

      (7) The TR is quite long and the voxel size is quite large in comparison to state-of-the-art EPI sequences. What was the rationale behind choosing a sequence with relatively low temporal and spatial resolution?

      We acknowledge that our chosen TR and voxel size are relatively long and large compared to state-of-the-art EPI sequences. This decision was made to optimize the signal-to-noise ratio (SNR) and reduce susceptibility-related distortions, which are particularly critical in EEG-fMRI sleep studies where head motion and physiological noise can be substantial. A longer TR allowed us to sample whole-brain activity with sufficient coverage, while a larger voxel size helped enhance BOLD sensitivity and minimize partial volume effects in deep brain structures such as the thalamus and hippocampus, which are key regions of interest in our study. We appreciate your concern and hope this clarification provides sufficient rationale for our sequence parameters.

      We have made this clearer in the revised manuscript.

      Methods, Page 20-21 Lines 398-408

      “Then, the “sleep” session began after the participants were instructed to try and fall asleep. For the functional scans, whole-brain images were acquired using k-space and steady-state T2*-weighted gradient echo-planar imaging (EPI) sequence that is sensitive to the BOLD contrast. This measures local magnetic changes caused by changes in blood oxygenation that accompany neural activity (sequence specification: 33 slices in interleaved ascending order, TR = 2000 ms, TE = 30 ms, voxel size = 3.5 × 3.5 × 4.2 mm<sup>3</sup>, FA = 90°, matrix = 64 × 64, gap = 0.7 mm). A relatively long TR and larger voxel size were chosen to optimize SNR and reduce susceptibility-related distortions, which are critical in EEG-fMRI sleep studies where head motion and physiological noise can be substantial. The longer TR allowed whole-brain coverage with sufficient temporal resolution, while the larger voxel size helped enhance BOLD sensitivity and minimize partial volume effects in deep brain structures (e.g., the thalamus and hippocampus), which are key regions of interest in this study.”

      (8) The anatomically defined ROIs are quite large. It should be elaborated on how this might reduce sensitivity to sleep rhythm-specific activity within sub-regions, especially for the thalamus, which has distinct nuclei involved in sleep functions.

      We appreciate your insight regarding the use of anatomically defined ROIs and their potential limitations in detecting sleep rhythm-specific activity within sub-regions, particularly in the thalamus. Given the distinct functional roles of thalamic nuclei in sleep processes, we acknowledge that using a single, large thalamic ROI may reduce sensitivity to localized activity patterns. To address this, we will discuss this limitation in the revised manuscript, acknowledging that our approach prioritizes whole-structure effects but may not fully capture nucleus-specific contributions.

      Discussion, Page 18 Lines 333-341

      “Despite providing new insights, our study has several limitations. First, our scalp EEG did not directly capture hippocampal ripples, preventing us from conclusively demonstrating triple coupling. Second, the combination of EEG-fMRI and the lack of a memory task limit our ability to parse fine-grained BOLD responses at the DOWN- vs. UP-states of SOs and link observed activations to behavioral outcomes. Third, the use of large anatomical ROIs may mask subregional contributions of specific thalamic nuclei or hippocampal subfields. Finally, without a memory task, we cannot establish a direct behavioral link between sleep-rhythm-locked activation and memory consolidation. Future studies combining techniques such as ultra-high-field fMRI or iEEG with cognitive tasks may refine our understanding of subregional network dynamics and functional significance during sleep.”

      (9) The study reports SO & spindle amplitudes & densities, as well as SO+spindle coupling, to be larger during N2/3 sleep compared to N1 and REM sleep, which is trivial but can be seen as a sanity check of the data. However, the amount of SOs and spindles reported for N1 and REM sleep is concerning, as per definition there should be hardly any (if SOs or spindles occur in N1 it becomes by definition N2, and the interval between spindles has to be considerably large in REM to still be scored as such). Thus, on the one hand, the report of these comparisons takes too much space in the main manuscript as it is trivial, but on the other hand, it raises concerns about the validity of the scoring.

      We appreciate your concern regarding the reported presence of SOs and spindles in N1 and REM sleep and the potential implications. Our detection method for detecting SO, spindle, and coupling were originally designed only for N2&N3 sleep data based on the characteristics of the data itself, and this method is widely recognized and used in the sleep research (Hahn et al., 2020; Helfrich et al., 2019; Helfrich et al., 2018; Ngo, Fell, & Staresina, 2020; Schreiner et al., 2022; Schreiner et al., 2021; Staresina et al., 2015; Staresina et al., 2023). While, because the detection methods for SO and spindle are based on percentiles, this method will always detect a certain number of events when used for other stages (N1 and REM) sleep data, but the differences between these events and those detected in stage N23 remain unclear. We will acknowledge the reasons for these results in the Methods section and emphasize that they are used only for sanity checks.

      Methods, Page 25 Lines 515-524

      “We note that the above methods for detecting SOs, spindles, and their couplings were originally developed for N2 and N3 sleep data, based on the specific characteristics of these stages. These methods are widely recognized in sleep research (Hahn et al., 2020; Helfrich et al., 2019; Helfrich et al., 2018; Ngo, Fell, & Staresina, 2020; Schreiner et al., 2022; Schreiner et al., 2021; Staresina et al., 2015; Staresina et al., 2023). However, because this percentile-based detection approach will inherently identify a certain number of events if applied to other stages (e.g., N1 and REM), the nature of these events in those stages remains unclear compared to N2/N3. We nevertheless identified and reported the detailed descriptive statistics of these sleep rhythms in all sleep stages, under the same operational definitions, both for completeness and as a sanity check. Within the same subject, there should be more SOs, spindles, and their couplings in N2/N3 than in N1 or REM (see also Figure S2-S4, Table S1-S4).”

      (10) Why was electrode F3 used to quantify the occurrence of SOs and spindles? Why not a midline frontal electrode like Fz (or a number of frontal electrodes for SOs) and Cz (or a number of centroparietal electrodes) for spindles to be closer to their maximum topography?

      We appreciate your suggestion regarding electrode selection for SO and spindle quantification. Our choice of F3 was primarily based on previous studies (Massimini et al., 2004; Molle et al., 2011), where bilateral frontal electrodes are commonly used for detecting SOs and spindles. Additionally, we considered the impact of MRI-related noise and, after a comprehensive evaluation, determined that F3 provided an optimal balance between signal quality and artifact minimization. We also acknowledge that alternative electrode choices, such as Fz for SOs and Cz for spindles, could provide additional insights into their topographical distributions.

      (11) Functional connectivity (hippocampus -> thalamus -> cortex (mPFC)) is reported to be increased during SO-spindle coupling and interpreted as evidence for coordination of hippocampo-neocortical communication likely by thalamic spindles. However, functional connectivity was only analysed during coupled SO+spindle events, not during isolated SOs or isolated spindles. Without the direct comparison of the connectivity patterns between these three events, it remains unclear whether this is specific for coupled SO+spindle events or rather associated with one or both of the other isolated events. The PPIs need to be conducted for those isolated events as well and compared statistically to the coupled events.

      We appreciate your critical perspective on our functional connectivity analysis and the interpretation of hippocampus-thalamus-cortex (mPFC) interactions during SO-spindle coupling. We acknowledge that, in the current analysis, functional connectivity was only examined during coupled SO-spindle events, without direct comparison to isolated SOs or isolated spindles. To address this concern, we have conducted PPI analyses for all three ROIs(Hippocampus, Thalamus, mPFC) and all three event types (SO-spindle couplings, isolated SOs, and isolated spindles). Our results indicate that neither isolated SOs nor isolated Spindles yielded significant connectivity changes in all three ROIs, as all failed to survive multiple comparison corrections. This suggests that the observed connectivity increase is specific to SO-spindle coupling, rather than being independently driven by either SOs or spindles alone.

      Results, Page 14 Lines 248-255

      “Crucially, the interaction between FC and SO-spindle coupling revealed that only the functional connectivity of hippocampus -> thalamus (ROI analysis, t<sub>(106)</sub> = 1.86, p = 0.0328) and thalamus -> mPFC (ROI analysis, t<sub>(106)</sub> = 1.98, p = 0.0251) significantly increased during SO-spindle coupling, with no significant changes in all other pathways (Fig. 4e). We also conducted PPI analyses for the other two events (SOs and spindles), and neither yielded significant connectivity changes in the three ROIs, as all failed to survive whole-brain FWE correction at the cluster level (p < 0.05). Together, these findings suggest that the thalamus, likely via spindles, coordinates hippocampal-cortical communication selectively during SO-spindle coupling, but not isolated SOs or spindle events alone.”

      (12) The limited temporal resolution of fMRI does indeed not allow for easily distinguishing between fMRI activation patterns related to SO-up- vs. SO-down-states. For this, one could try to extract the amplitudes of SO-up- and SO-down-states separately for each SO event and model them as two separate parametric modulators (with the risk of collinearity as they are likely correlated).

      We appreciate your insightful comment regarding the challenge of distinguishing fMRI activation patterns related to SO-up vs. SO-down states due to the limited temporal resolution of fMRI. While our current analysis does not differentiate between these two phases, we acknowledge that separately modeling SO-up and SO-down states using parametric modulators could provide a more refined understanding of their distinct neural correlates. However, as you notes, this approach carries the risk of collinearity, and there is indeed a high correlation between the two amplitudes across all subjects in our results (r=0.98). Future studies could explore more on leveraging high-temporal-resolution techniques. While implementing this in the current study is beyond our scope, we will acknowledge this limitation in the Discussion section.

      Discussion, Page 17 Lines 308-322

      “An intriguing aspect of our findings is the reduced DMN activity during SOs when modeled at the SO trough (DOWN-state). This reduced DMN activity may reflect large-scale neural inhibition characteristic of the SO trough. The DMN is typically active during internally oriented cognition (e.g., self-referential processing or mind-wandering) and is suppressed during external stimuli processing (Yeshurun, Nguyen, & Hasson, 2021). It is unlikely, however, that this suppression of DMN during SO events is related to a shift from internal cognition to external responses given it is during deep sleep time. Instead, it could be driven by the inherent rhythmic pattern of SOs, which makes it difficult to separate UP- from DOWN-states (the two temporal regressors were highly correlated, and similar brain activation during SOs events was obtained if modelled at the SO peak instead, Fig. S5). Since the amplitude at the SO trough is consistently larger than that at the SO peak, the neural activation we detected may primarily capture the large-scale inhibition from DOWN-state. Interestingly, no such DMN reduction was found during SO-spindle coupling, implying that coupling may involve distinct neural dynamics that partially re-engage DMN-related processes, possibly reflecting memory-related reactivation. Future research using high-temporal-resolution techniques like iEEG could clarify these possibilities.

      Discussion, Page 18 Lines 333-341

      “Despite providing new insights, our study has several limitations. First, our scalp EEG did not directly capture hippocampal ripples, preventing us from conclusively demonstrating triple coupling. Second, the combination of EEG-fMRI and the lack of a memory task limit our ability to parse fine-grained BOLD responses at the DOWN- vs. UP-states of SOs and link observed activations to behavioral outcomes. Third, the use of large anatomical ROIs may mask subregional contributions of specific thalamic nuclei or hippocampal subfields. Finally, without a memory task, we cannot establish a direct behavioral link between sleep-rhythm-locked activation and memory consolidation. Future studies combining techniques such as ultra-high-field fMRI or iEEG with cognitive tasks may refine our understanding of subregional network dynamics and functional significance during sleep.

      (13) L327: "It is likely that our findings of diminished DMN activity reflect brain activity during the SO DOWN-state, as this state consistently shows higher amplitude compared to the UP-state within subjects, which is why we modelled the SO trough as its onset in the fMRI analysis." This conclusion is not justified as the fact that SO down-states are larger in amplitude does not mean their impact on the BOLD response is larger.

      We appreciate your concern regarding our interpretation of diminished DMN activity reflecting the SO down-state. We acknowledge that the current expression is somewhat misleading, and our interpretation of it is: it could be driven by the inherent rhythmic pattern of SOs, which makes it difficult to separate UP- from DOWN-states (the two temporal regressors were highly correlated, and similar brain activation during SOs events was obtained if modelled at the SO peak instead). Since the amplitude at the SO trough is consistently larger than that at the SO peak, the neural activation we detected may primarily capture the large-scale inhibition from DOWN-state. And we will make this clear in the Discussion section.

      Discussion, Page 17 Lines 308-322

      “An intriguing aspect of our findings is the reduced DMN activity during SOs when modeled at the SO trough (DOWN-state). This reduced DMN activity may reflect large-scale neural inhibition characteristic of the SO trough. The DMN is typically active during internally oriented cognition (e.g., self-referential processing or mind-wandering) and is suppressed during external stimuli processing (Yeshurun, Nguyen, & Hasson, 2021). It is unlikely, however, that this suppression of DMN during SO events is related to a shift from internal cognition to external responses given it is during deep sleep time. Instead, it could be driven by the inherent rhythmic pattern of SOs, which makes it difficult to separate UP- from DOWN-states (the two temporal regressors were highly correlated, and similar brain activation during SOs events was obtained if modelled at the SO peak instead, Fig. S5). Since the amplitude at the SO trough is consistently larger than that at the SO peak, the neural activation we detected may primarily capture the large-scale inhibition from DOWN-state. Interestingly, no such DMN reduction was found during SO-spindle coupling, implying that coupling may involve distinct neural dynamics that partially re-engage DMN-related processes, possibly reflecting memory-related reactivation. Future research using high-temporal-resolution techniques like iEEG could clarify these possibilities.

      (14) Line 77: "In the current study, while directly capturing hippocampal ripples with scalp EEG or fMRI is difficult, we expect to observe hippocampal activation in fMRI whenever SOs-spindles coupling is detected by EEG, if SOs- spindles-ripples triple coupling occurs during human NREM sleep". Not all SO-spindle events are associated with ripples (Staresina et al., 2015), but hippocampal activation may also be expected based on the occurrence of spindles alone (Bergmann et al., 2012).

      We appreciate your clarification regarding the relationship between SO-spindle coupling and hippocampal ripples. We acknowledge that not all SO-spindle events are necessarily accompanied by ripples (Staresina et al., 2015). However, based on previous research, we found that hippocampal ripples are significantly more likely to occur during SO-spindle coupling events. This suggests that while ripple occurrence is not guaranteed, SO-spindle coupling creates a favorable network state for ripple generation and potential hippocampal activation. To ensure accuracy, we will revise the manuscript to delete this misleading sentence in the Introduction section and acknowledge in the Discussion that our results cannot conclusively directly observe the triple coupling of SO, spindle, and hippocampal ripples.

      Discussion, Page 18 Lines 333-341

      “Despite providing new insights, our study has several limitations. First, our scalp EEG did not directly capture hippocampal ripples, preventing us from conclusively demonstrating triple coupling. Second, the combination of EEG-fMRI and the lack of a memory task limit our ability to parse fine-grained BOLD responses at the DOWN- vs. UP-states of SOs and link observed activations to behavioral outcomes. Third, the use of large anatomical ROIs may mask subregional contributions of specific thalamic nuclei or hippocampal subfields. Finally, without a memory task, we cannot establish a direct behavioral link between sleep-rhythm-locked activation and memory consolidation. Future studies combining techniques such as ultra-high-field fMRI or iEEG with cognitive tasks may refine our understanding of subregional network dynamics and functional significance during sleep.”

      Reviewer #2 (Public review):

      In this study, Wang and colleagues aimed to explore brain-wide activation patterns associated with NREM sleep oscillations, including slow oscillations (SOs), spindles, and SO-spindle coupling events. Their findings reveal that SO-spindle events corresponded with increased activation in both the thalamus and hippocampus. Additionally, they observed that SO-spindle coupling was linked to heightened functional connectivity from the hippocampus to the thalamus, and from the thalamus to the medial prefrontal cortex-three key regions involved in memory consolidation and episodic memory processes.

      This study's findings are timely and highly relevant to the field. The authors' extensive data collection, involving 107 participants sleeping in an fMRI while undergoing simultaneous EEG recording, deserves special recognition. If shared, this unique dataset could lead to further valuable insights. While the conclusions of the data seem overall well supported by the data, some aspects with regard to the detection of sleep oscillations need clarification.

      The authors report that coupled SO-spindle events were most frequent during NREM sleep (2.46 [plus minus] 0.06 events/min), but they also observed a surprisingly high occurrence of these events during N1 and REM sleep (2.23 [plus minus] 0.09 and 2.32 [plus minus] 0.09 events/min, respectively), where SO-spindle coupling would not typically be expected. Combined with the relatively modest SO amplitudes reported (~25 µV, whereas >75 µV would be expected when using mastoids as reference electrodes), this raises the possibility that the parameters used for event detection may not have been conservative enough - or that sleep staging was inaccurately performed. This issue could present a significant challenge, as the fMRI findings are largely dependent on the reliability of these detected events.

      Thank you very much for your thorough and encouraging review. We appreciate your recognition of the significance and relevance of our study and dataset, particularly in highlighting how simultaneous EEG-fMRI recordings can provide complementary insights into the temporal dynamics of neural oscillations and their associated spatial activation patterns during sleep. In the sections that follow, we address each of your comments in detail. We have revised the text and conducted additional analyses wherever possible to strengthen our argument, clarify our methodological choices. We believe these revisions improve the clarity and rigor of our work, and we thank you for helping us refine it.

      We appreciate your insightful comments regarding the detection of sleep oscillations. Our methods for detecting SOs, spindles, and their couplings were originally developed for N2 and N3 sleep data, based on the specific characteristics of these stages. These methods are widely recognized in sleep research (Hahn et al., 2020; Helfrich et al., 2019; Helfrich et al., 2018; Ngo, Fell, & Staresina, 2020; Schreiner et al., 2022; Schreiner et al., 2021; Staresina et al., 2015; Staresina et al., 2023). However, because this percentile-based detection approach will inherently identify a certain number of events if applied to other stages (e.g., N1 and REM), the nature of these events in those stages remains unclear compared to N2/N3. We nevertheless identified and reported the detailed descriptive statistics of these sleep rhythms in all sleep stages, under the same operational definitions, both for completeness and as a sanity check. Within the same subject, there should be more SOs, spindles, and their couplings in N2/N3 than in N1 or REM. We will acknowledge the reasons for these results in the Methods section and emphasize that they are used only for sanity checks.

      Regarding the reported SO amplitudes (~25 µV), during preprocessing, we applied the Signal Space Projection (SSP) method to more effectively remove MRI gradient artifacts and cardiac pulse noise. While this approach enhances data quality, it also reduces overall signal power, leading to systematically lower reported amplitudes. Despite this, our SO detection in NREM sleep (especially N2/N3) remain physiologically meaningful and are consistent with previous fMRI studies using similar artifact removal techniques. We appreciate your careful evaluation and valuable suggestions.

      In addition, we will provide comprehensive tables in the supplementary materials, contains descriptive information about sleep-related characteristics (Table S1), as well as detailed information about sleep waves at each sleep stage for all 107 subjects(Table S2-S4), listing for each subject:(1)Different sleep stage duration; (2)Number of detected SOs; (3)Number of detected spindles; (4)Number of detected SO-spindle coupling events; (2)Density of detected SOs; (3)Density of detected spindles; (4)Density of detected SO-spindle coupling events.

      Methods, Page 25 Lines 515-524

      “We note that the above methods for detecting SOs, spindles, and their couplings were originally developed for N2 and N3 sleep data, based on the specific characteristics of these stages. These methods are widely recognized in sleep research (Hahn et al., 2020; Helfrich et al., 2019; Helfrich et al., 2018; Ngo, Fell, & Staresina, 2020; Schreiner et al., 2022; Schreiner et al., 2021; Staresina et al., 2015; Staresina et al., 2023). However, because this percentile-based detection approach will inherently identify a certain number of events if applied to other stages (e.g., N1 and REM), the nature of these events in those stages remains unclear compared to N2/N3. We nevertheless identified and reported the detailed descriptive statistics of these sleep rhythms in all sleep stages, under the same operational definitions, both for completeness and as a sanity check. Within the same subject, there should be more SOs, spindles, and their couplings in N2/N3 than in N1 or REM (see also Figure S2-S4, Table S1-S4).”

      Supplementary Materials, Page 42-54, Table S1-S4

      (Consider of the length, we do not list all the tables here. Please refer to the revised manuscript.)

      Reviewer #3 (Public review):

      Summary:

      Wang et al., examined the brain activity patterns during sleep, especially when locked to those canonical sleep rhythms such as SO, spindle, and their coupling. Analyzing data from a large sample, the authors found significant coupling between spindles and SOs, particularly during the upstate of the SO. Moreover, the authors examined the patterns of whole-brain activity locked to these sleep rhythms. To understand the functional significance of these brain activities, the authors further conducted open-ended cognitive state decoding and found a variety of cognitive processing may be involved during SO-spindle coupling and during other sleep events. The authors next investigated the functional connectivity analyses and found enhanced connectivity between the hippocampus, the thalamus, and the medial PFC. These results reinforced the theoretical model of sleep-dependent memory consolidation, such that SO-spindle coupling is conducive to systems-level memory reactivation and consolidation.

      Strengths:

      There are obvious strengths in this work, including the large sample size, state-of-the-art neuroimaging and neural oscillation analyses, and the richness of results.

      Weaknesses:

      Despite these strengths and the insights gained, there are weaknesses in the design, the analyses, and inferences.

      Thank you for your detailed and thoughtful review of our manuscript. We are delighted that you recognize our advanced analysis methods and rich results of neuroimaging and neural oscillations as well as the large sample size data. In the following sections, we provide detailed responses to each of your comments. And we have revised the text and conducted additional analyses to strengthen our arguments and clarify our methodological choices. We believe these revisions enhance the clarity and rigor of our work, and we sincerely appreciate your thoughtful feedback in helping us refine the manuscript.

      (1) A repeating statement in the manuscript is that brain activity could indicate memory reactivation and thus consolidation. This is indeed a highly relevant question that could be informed by the current data/results. However, an inherent weakness of the design is that there is no memory task before and after sleep. Thus, it is difficult (if not impossible) to make a strong argument linking SO/spindle/coupling-locked brain activity with memory reactivation or consolidation.

      We appreciate your suggestion regarding the lack of a pre- and post-sleep memory task in our study design. We acknowledge that, in the absence of behavioral measures, it is hard to directly link SO-spindle coupling to memory consolidation in an outcome-driven manner. Our interpretation is instead based on the well-established role of these oscillations in memory processes, as demonstrated in previous studies. We sincerely appreciate this feedback and will adjust our Discussion accordingly to reflect a more precise interpretation of our findings.

      Discussion, Page 18 Lines 333-341

      “Despite providing new insights, our study has several limitations. First, our scalp EEG did not directly capture hippocampal ripples, preventing us from conclusively demonstrating triple coupling. Second, the combination of EEG-fMRI and the lack of a memory task limit our ability to parse fine-grained BOLD responses at the DOWN- vs. UP-states of SOs and link observed activations to behavioral outcomes. Third, the use of large anatomical ROIs may mask subregional contributions of specific thalamic nuclei or hippocampal subfields. Finally, without a memory task, we cannot establish a direct behavioral link between sleep-rhythm-locked activation and memory consolidation. Future studies combining techniques such as ultra-high-field fMRI or iEEG with cognitive tasks may refine our understanding of subregional network dynamics and functional significance during sleep.”

      (2) Relatedly, to understand the functional implications of the sleep rhythm-locked brain activity, the authors employed the "open-ended cognitive state decoding" method. While this method is interesting, it is rather indirect given that there were no behavioral indices in the manuscript. Thus, discussions based on these analyses are speculative at best. Please either tone down the language or find additional evidence to support these claims.

      Moreover, the results from this method are difficult to understand. Figure 3e showed that for all three types of sleep events (SO, spindle, SO-spindle), the same mental states (e.g., working memory, episodic memory, declarative memory) showed opposite directions of activation (left and right panels showed negative and positive activation, respectively). How to interpret these conflicting results? This ambiguity is also reflected by the term used: declarative memory and episodic memories are both indexed in the results. Yet these two processes can be largely overlapped. So which specific memory processes do these brain activity patterns reflect? The Discussion shall discuss these results and the limitations of this method.

      We appreciate your critical assessment of the open-ended cognitive state decoding method and its interpretational challenges. Given the concerns about the indirectness of this approach, we decided to remove its related content and results from Figure 3 in the main text and include it in Supplementary Figure 7.

      Due to the complexity of memory-related processes, we acknowledge that distinguishing between episodic and declarative memory based solely on this approach is not straightforward. We will revise the Supplementary Materials to explicitly discuss these limitations and clarify that our findings do not isolate specific cognitive processes but rather suggest general associations with memory-related networks.

      Discussion, Page 17-18 Lines 323-332

      “To explore functional relevance, we employed an open-ended cognitive state decoding approach using meta-analytic data (NeuroSynth: Yarkoni et al. (2011)). Although this method usefully generates hypotheses about potential cognitive processes, particularly in the absence of a pre- and post-sleep memory task, it is inherently indirect. Many cognitive terms showed significant associations (16 of 50), such as “episodic memory,” “declarative memory,” and “working memory.” We focused on episodic/declarative memory given the known link with hippocampal reactivation (Diekelmann & Born, 2010; Staresina et al., 2015; Staresina et al., 2023). Nonetheless, these inferences regarding memory reactivation should be interpreted cautiously without direct behavioral measures. Future research incorporating explicit tasks before and after sleep would more rigorously validate these potenial functional claims.”

      (3) The coupling strength is somehow inconsistent with prior results (Hahn et al., 2020, eLife, Helfrich et al., 2018, Neuron). Specifically, Helfrich et al. showed that among young adults, the spindle is coupled to the peak of the SO. Here, the authors reported that the spindles were coupled to down-to-up transitions of SO and before the SO peak. It is possible that participants' age may influence the coupling (see Helfrich et al., 2018). Please discuss the findings in the context of previous research on SO-spindle coupling.

      We appreciate your concern regarding the temporal characteristics of SO-spindle coupling. We acknowledge that the SO-spindle coupling phase results in our study are not identical to those reported by Hahn et al. (2020); Helfrich et al. (2018). However, these differences may arise due to slight variations in event detection parameters, which can influence the precise phase estimation of coupling. Notably, Hahn et al. (2020) also reported slight discrepancies in their group-level coupling phase results, highlighting that methodological differences can contribute to variability across studies. Furthermore, our findings are consistent with those of Schreiner et al. (2021), further supporting the robustness of our observations.

      That said, we acknowledge that our original description of SO-spindle coupling as occurring at the "transition from the lower state to the upper state" was not entirely precise. The -π/2 phase represents the true transition point, while our observed coupling phase is actually closer to the SO peak rather than strictly at the transition. We will revise this statement in the manuscript to ensure clarity and accuracy in describing the coupling phase.

      Discussion, Page 16 Lines 283-291

      “Our data provide insights into the neurobiological underpinnings of these sleep rhythms. SOs, originating mainly in neocortical areas such as the mPFC, alternate between DOWN- and UP-states. The thalamus generates sleep spindles, which in turn couple with SOs. Our finding that spindle peaks consistently occurred slightly before the UP-state peak of SOs (in 83 out of 107 participants), concurs with prior studies, including Schreiner et al. (2021). Yet it differs from some results suggesting spindles might peak right at the SO UP-state (Hahn et al., 2020; Helfrich et al., 2018). Such discrepancies could arise from differences in detection algorithms, participant age (Helfrich et al., 2018), or subtle variations in cortical-thalamic timing. Nonetheless, these results underscore the importance of coordinated SO-spindle interplay in supporting sleep-dependent processes.”

      (4) The discussion is rather superficial with only two pages, without delving into many important arguments regarding the possible functional significance of these results. For example, the author wrote, "This internal processing contrasts with the brain patterns associated with external tasks, such as working memory." Without any references to working memory, and without delineating why WM is considered as an external task even working memory operations can be internal. Similarly, for the interesting results on SO and reduced DMN activity, the authors wrote "The DMN is typically active during wakeful rest and is associated with self-referential processes like mind-wandering, daydreaming, and task representation (Yeshurun, Nguyen, & Hasson, 2021). Its reduced activity during SOs may signal a shift towards endogenous processes such as memory consolidation." This argument is flawed. DMN is active during self-referential processing and mind-wandering, i.e., when the brain shifts from external stimuli processing to internal mental processing. During sleep, endogenous memory reactivation and consolidation are also part of the internal mental processing given the lack of external environmental stimulation. So why during SO or during memory consolidation, the DMN activity would be reduced? Were there differences in DMN activity between SO and SO-spindle coupling events?

      We appreciate your concerns regarding the brevity of the discussion and the need for clearer theoretical arguments. We will expand this section to provide more in-depth interpretations of our findings in the context of prior literature. Regarding working memory (WM), we acknowledge that our phrasing was ambiguous. We will modify this statement in the Discussion section.

      For the SO-related reduction in DMN activity, we recognize the need for a more precise explanation. This reduced DMN activity may reflect large-scale neural inhibition characteristic of the SO trough. The DMN is typically active during internally oriented cognition (e.g., self-referential processing or mind-wandering) and is suppressed during external stimuli processing (Yeshurun, Nguyen, & Hasson, 2021). It is unlikely, however, that this suppression of DMN during SO events is related to a shift from internal cognition to external responses given it is during deep sleep time. Instead, it could be driven by the inherent rhythmic pattern of SOs, which makes it difficult to separate UP- from DOWN-states (the two temporal regressors were highly correlated, and similar brain activation during SOs events was obtained if modelled at the SO peak instead). Since the amplitude at the SO trough is consistently larger than that at the SO peak, the neural activation we detected may primarily capture the large-scale inhibition from DOWN-state.

      To address your final question, we have conducted the additional post hoc comparison of DMN activity between isolated SOs and SO-spindle coupling events. Our results indicate that

      DMN activation during SOs was significantly lower than during SO-spindle coupling (t<sub>(106)</sub> = -4.17, p < 1e-4). This suggests that SO-spindle coupling may involve distinct neural dynamics that partially re-engage DMN-related processes, possibly reflecting memory-related reactivation. We appreciate your constructive feedback and will integrate these expanded analyses and discussions into our revised manuscript.

      Results, Page 11 Lines 199-208

      “Spindles were correlated with positive activation in the thalamus (ROI analysis, t<sub>(106)</sub> = 15.39, p < 1e-4), the anterior cingulate cortex (ACC), and the putamen, alongside deactivation in the DMN (Fig. 3c). Notably, SO-spindle coupling was linked to significant activation in both the thalamus (ROI analysis, t<sub>(106)</sub> \= 3.38, p = 0.0005) and the hippocampus (ROI analysis, t<sub>(106)</sub> \= 2.50, p = 0.0070, Fig. 3d). However, no decrease in DMN activity was found during SO-spindle coupling, and DMN activity during SO was significantly lower than during coupling (ROI analysis, t<sub>(106)</sub> \= -4.17, p < 1e-4). For more detailed activation patterns, see Table S5-S7. We also varied the threshold used to detect SO events to assess its effect on hippocampal activation during SO-spindle coupling and observed that hippocampal activation remained significant when the percentile thresholds for SO detection ranged between 71% and 80% (see Fig. S6).”

      Discussion, Page 17-18 Lines 308-332

      “An intriguing aspect of our findings is the reduced DMN activity during SOs when modeled at the SO trough (DOWN-state). This reduced DMN activity may reflect large-scale neural inhibition characteristic of the SO trough. The DMN is typically active during internally oriented cognition (e.g., self-referential processing or mind-wandering) and is suppressed during external stimuli processing (Yeshurun, Nguyen, & Hasson, 2021). It is unlikely, however, that this suppression of DMN during SO events is related to a shift from internal cognition to external responses given it is during deep sleep time. Instead, it could be driven by the inherent rhythmic pattern of SOs, which makes it difficult to separate UP- from DOWN-states (the two temporal regressors were highly correlated, and similar brain activation during SOs events was obtained if modelled at the SO peak instead, Fig. S5). Since the amplitude at the SO trough is consistently larger than that at the SO peak, the neural activation we detected may primarily capture the large-scale inhibition from DOWN-state. Interestingly, no such DMN reduction was found during SO-spindle coupling, implying that coupling may involve distinct neural dynamics that partially re-engage DMN-related processes, possibly reflecting memory-related reactivation. Future research using high-temporal-resolution techniques like iEEG could clarify these possibilities.

      To explore functional relevance, we employed an open-ended cognitive state decoding approach using meta-analytic data (NeuroSynth: Yarkoni et al. (2011)). Although this method usefully generates hypotheses about potential cognitive processes, particularly in the absence of a pre- and post-sleep memory task, it is inherently indirect. Many cognitive terms showed significant associations (16 of 50), such as “episodic memory,” “declarative memory,” and “working memory.” We focused on episodic/declarative memory given the known link with hippocampal reactivation (Diekelmann & Born, 2010; Staresina et al., 2015; Staresina et al., 2023). Nonetheless, these inferences regarding memory reactivation should be interpreted cautiously without direct behavioral measures. Future research incorporating explicit tasks before and after sleep would more rigorously validate these potential functional claims.”

      Reviewing Editor Comment:

      The reviewers think that you are working on a relevant and important topic. They are praising the large sample size used in the study. The reviewers are not all in line regarding the overall significance of the findings, but they all agree the paper would strongly benefit from some extra work, as all reviewers raise various critical points that need serious consideration.

      We appreciate your recognition of the relevance and importance of our study, as well as your acknowledgment of the large sample size as a strength of our work. We understand that there are differing perspectives regarding the overall significance of our findings, and we value the constructive critiques provided. We are committed to addressing the key concerns raised by all reviewers, including refining our analyses, clarifying our interpretations, and incorporating additional discussions to strengthen the manuscript. Below, we address your specific recommendations and provide responses to each point you raised to ensure our methods and results are as transparent and comprehensible as possible. We believe that these revisions will significantly enhance the rigor and impact of our study, and we sincerely appreciate your thoughtful feedback in helping us improve our work.

      Reviewer #1 (Recommendations for the authors):

      (1) The phrase "overnight sleep" suggests an entire night, while these were rather "nocturnal naps". Please rephrase.

      Thank you for pointing this out. We have revised the phrasing in our manuscript to "nocturnal naps" instead of "overnight sleep" to more accurately reflect the duration of the sleep recordings.

      (2) Sleep staging results (macroscopic sleep architecture) should be provided in more detail (at least min and % of the different sleep stages, sleep onset latency, total sleep duration, total recording duration), at least mean/SD/range.

      Thank you for this suggestion. We will provide comprehensive tables in the supplementary materials, contains descriptive information about sleep-related characteristics. This information will help provide a clearer overview of the macroscopic sleep architecture in our dataset.

      Supplementary Materials, Page 42, Table S1

      Author response table 1.

      Descriptive results of demographic information and sleep characteristics. Note: The total recorded time is equal to the awake time plus the total sleep time. The sleep onset latency is the time taken to reach the first sleep epoch. The Sleep Efficiency is the ratio of actual sleep time to total recording time.

      Reviewer #2 (Recommendations for the authors):

      In order to allow for a better estimation of the reliability of the detected sleep events, please:

      (1) Provide densities and absolute numbers of all detected SOs and spindles (N1, NREM, and REM sleep).

      Thank you for pointing this out. We will provide comprehensive tables in the supplementary materials, contains detailed information about sleep waves at each sleep stage for all 107 subjects (Table S2-S4), listing for each subject:1) Different sleep stage duration; 2) Number of detected SOs; 3) Number of detected spindles; 4) Number of detected SO-spindle coupling events; 5) Density of detected SOs; 6) Density of detected spindles; 7) Density of detected SO-spindle coupling events.

      Supplementary Materials, Page 43-54, Table S2-S4

      (Consider of the length, we do not list all the tables here. Please refer to the revised manuscript.)

      (2) Show ERPs for all detected SOs and spindles (per sleep stage).

      Thank you for the suggestion. We will provide ERPs for all detected SOs and spindles, separated by sleep stage (N1, N2&N3, and REM) in supplementary Fig. S2-S4. These ERP waveforms will help illustrate the characteristic temporal profiles of SOs and spindles across different sleep stages.

      Methods, Page 25, Line 525-532

      “Event-related potentials (ERP) analysis. After completing the detection of each sleep rhythm event, we performed ERP analyses for SOs, spindles, and coupling events in different sleep stages. Specifically, for SO events, we took the trough of the DOWN-state of each SO as the zero-time point, then extracted data in a [-2 s to 2 s] window from the broadband (0.1–30 Hz) EEG and used [-2 s to -0.5 s] for baseline correction; the results were then averaged across 107 subjects (see Fig. S2a). For spindle events, we used the peak of each spindle as the zero-time point and applied the same data extraction window and baseline correction before averaging across 107 subjects (see Fig. S2b). Finally, for SO-spindle coupling events, we followed the same procedure used for SO events (see Fig. 2a, Figs. S3–S4).”

      Supplementary Materials, Page 36-38, Fig. S2-S4

      Author response image 1.

      ERPs of SOs and spindles coupling during different sleep stages across all 107 subjects. a. ERP of SOs in different sleep stages using the broadband (0.1–30 Hz) EEG data. We align the trough of the DOWN-state of each SO at time zero (see Methods for details). The orange line represents the SO ERP in the N1 stage, the black line represents the SO ERP in the N2&N3 stage, and the green line represents the SO ERP in the REM stage. b. ERP of spindles in different sleep stages using the broadband (0.1–30 Hz) EEG data. We align the peak of each spindle at time zero (see Methods for details). The color scheme is the same as in panel a.

      Author response image 2.

      ERP and time-frequency patterns of SO-spindle coupling in the N1 stage. The averaged temporal frequency pattern and ERP across all instances of SO-spindle coupling, computed over all subjects, following the same procedure as in Fig. 2a, but for N1 stage.

      Author response image 3.

      ERP and time-frequency patterns of SO-spindle coupling in the REM stage. The averaged temporal frequency pattern and ERP across all instances of SO-spindle coupling, computed over all subjects, again following the same procedure as in Fig. 2a, but for REM stage.

      (3) Provide detailed info concerning sleep characteristics (time spent in each sleep stage etc.).

      Thank you for this suggestion. Same as the response above, we will provide comprehensive tables in the supplementary materials, contains descriptive information about sleep-related characteristics.

      Supplementary Materials, Page 42, Table S1 (same as above)

      (4) What would happen if more stringent parameters were used for event detection? Would the authors still observe a significant number of SO spindles during N1 and REM? Would this affect the fMRI-related results?

      Thank you for this suggestion. Our methods for detecting SOs, spindles, and their couplings were originally developed for N2 and N3 sleep data, based on the specific characteristics of these stages. These methods are widely recognized in sleep research (Hahn et al., 2020; Helfrich et al., 2019; Helfrich et al., 2018; Ngo, Fell, & Staresina, 2020; Schreiner et al., 2022; Schreiner et al., 2021; Staresina et al., 2015; Staresina et al., 2023). However, because this percentile-based detection approach will inherently identify a certain number of events if applied to other stages (e.g., N1 and REM), the nature of these events in those stages remains unclear compared to N2/N3. We nevertheless identified and reported the detailed descriptive statistics of these sleep rhythms in all sleep stages, under the same operational definitions, both for completeness and as a sanity check. Within the same subject, there should be more SOs, spindles, and their couplings in N2/N3 than in N1 or REM (see also Figure S2-S4, Table S1-S4).

      Furthermore, in order to explore the impact of this on our fMRI results, we conducted an additional sensitivity analysis by applying different detection parameters for SOs. Specifically, we adjusted amplitude percentile thresholds for SO detection (the parameter that has the greatest impact on the results). We used the hippocampal activation value during N2&N3 stage SO-spindle coupling as an anchor value and found that when the parameters gradually became stricter, the results were similar to or even better than the current results. However, when we continued to increase the threshold, the results began to gradually decrease until the threshold was increased to 80%, and the results were no longer significant. This indicates that our results are robust within a specific range of parameters, but as the threshold increases, the number of trials decreases, ultimately weakening the statistical power of the fMRI analysis.

      Thank you again for your suggestions on sleep rhythm event detection. We will add the results in Supplementary and revise our manuscript accordingly.

      Results, Page 11, Line 199-208

      “Spindles were correlated with positive activation in the thalamus (ROI analysis, t<sub>(106)</sub> = 15.39, p < 1e-4), the anterior cingulate cortex (ACC), and the putamen, alongside deactivation in the DMN (Fig. 3c). Notably, SO-spindle coupling was linked to significant activation in both the thalamus (ROI analysis, t<sub>(106)</sub> \= 3.38, p = 0.0005) and the hippocampus (ROI analysis, t<sub>(106)</sub> \= 2.50, p = 0.0070, Fig. 3d). However, no decrease in DMN activity was found during SO-spindle coupling, and DMN activity during SO was significantly lower than during coupling (ROI analysis, t<sub>(106)</sub> \= -4.17, p < 1e-4). For more detailed activation patterns, see Table S5-S7. We also varied the threshold used to detect SO events to assess its effect on hippocampal activation during SO-spindle coupling and observed that hippocampal activation remained significant when the percentile thresholds for SO detection ranged between 71% and 80% (see Fig. S6).”

      Supplementary Materials, Page 40, Fig. S6

      Author response image 4.

      Influence of the percentile threshold for SO detection on hippocampal activation (ROI) during SO-spindle coupling. We changed the percentile threshold for SO event detection in the EEG data analysis and then reconstructed the GLM design matrix based on the SO events detected at each threshold. The brain-wide activation pattern of SO-spindle couplings in the N2/3 stage was extracted using the same method as shown in Fig. 3. The gray horizontal line represents the significant range (71%–80%). * p < 0.05.

      Finally, we sincerely thank all again for your thoughtful and constructive feedback. Your insights have been invaluable in refining our analyses, strengthening our interpretations, and improving the clarity and rigor of our manuscript. We appreciate the time and effort you have dedicated to reviewing our work, and we are grateful for the opportunity to enhance our study based on your recommendations.

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    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer 1 (Public reviews):

      Summary

      Howard et al. performed deep mutational scanning on the MC4R gene, using a reporter assay to investigate two distinct downstream pathways across multiple experimental conditions. They validated their findings with ClinVar data and previous studies. Additionally, they provided insights into the application of DMS results for personalized drug therapy and differential ligand responses across variant types.

      Strengths

      They captured over 99% of variants with robust signals and investigated subtle functionalities, such as pathway-specific activities and interactions with different ligands, by refining both the experimental design and analytical methods.

      Weaknesses

      While the study generated informative results, it lacks a detailed explanation regarding the input library, replicate correlation, and sequencing depth for a given number of cells. Additionally, there are several questions that it would be helpful for authors to clarify.

      (1) It would be helpful to clarify the information regarding the quality of the input library and experimental replicates. Are variants evenly represented in the library? Additionally, have the authors considered using long-read sequencing to confirm the presence of a single intended variant per construct? Finally, could the authors provide details on the correlation between experimental replicates under each condition?

      Are variants evenly represented in the library?

      We strive to achieve as evenly balanced library as possible at every stage of the DMS process (e.g., initial cloning in E. coli through integration into human cells). Below is a representative plot showing the number of barcodes per amino acid variant at each position in a given ~60 amino acid subregion of MC4R, which highlights how evenly variants are represented at the E. coli cloning stage.

      Author response image 1.

      We also make similar measurements after the library is integrated into HEK293T cell lines, and see similarly even coverage across all variants, as shown in the plot below:

      Author response image 2.

      Additionally, have the authors considered using long-read sequencing to confirm the presence of a single intended variant per construct?

      We agree long-read sequencing would be an excellent way to confirm that our constructs contain a single intended variant. However, we elected for an alternate method (outlined in more detail in Jones et al. 2020) that leverages multiple layers of validation. First, the oligo chip-synthesized portions of the protein containing the variants are cloned into a sequence-verified plasmid backbone, which greatly decreases the chances of spuriously generating a mutation in a different portion of the protein. We then sequence both the oligo portion and random barcode using overlapping paired end reads during barcode mapping to avoid sequencing errors and to help detect DNA synthesis errors. At this stage, we computationally reject any constructs that have more than one variant. Given this, the vast majority of remaining unintended variants would come from somatic mutations introduced by the E. coli cloning or replication process, which should be low frequency. We have used our in-house full plasmid sequencing method, OCTOPUS, to sample and spot check this for several other DMS libraries we have generated using the same cloning methods. We have found variants in the plasmid backbone in only ~1% of plasmids in these libraries. Our statistical model also helps correct for this by accounting for barcode-specific variation. Finally we believe this provides further motivation for having multiple barcodes per variant, which dilutes the effect of any unintended additional variants.

      Finally, could the authors provide details on the correlation between experimental replicates under each condition?

      Certainly! In general, the Gs reporter had higher correlation between replicates than the Gq system (r ~ 0.5 vs r ~ 0.4). The plots below, which have been added as a panel to Supplementary Figure 1, show two representative correlations at the RNA-seq stage of read counts for barcodes between the low a-MSH conditions.

      We added the following text to reference this panel:

      (see Methods > Sequence processing for barcode expression): “The correlation (r) of barcode readcounts between replicates was ~0.5 and ~0.4 for the Gs and Gq assays, respectively (Supplementary Fig. 1E).”

      One important advantage of our statistical model is that it’s able to leverage information from barcodes regardless of the number of replicates they appear in.

      (2) Since the functional readout of variants is conducted through RNA sequencing, it seems crucial to sequence a sufficient number of cells with adequate sequencing saturation. Could the authors clarify the coverage depth used for each RNA-seq experiment and how this depth was determined? Additionally, how many cells were sequenced in each experiment?

      The text has been added in the manuscript as follows:

      (in Methods > Running DMS Assays): “Given the seeding density (~17x10<sup>6</sup> cells per 150 mm replicate dish), time from seeding to collection, and doubling time of HEK293T cells, approximately 25.5x10<sup>6</sup> cells were collected per replicate. This translates to approximately 30-60x cellular coverage per amino acid variant in each replicate.”

      (in Methods > Sequence processing for barcode expression): “Total mapped reads per replicate at the RNA-seq stage were as follows:

      - Gs/CRE: 9.1-18.2 million mapped reads, median=12.3

      - Gq/UAS: 8.6-24.1 million mapped reads, median=14.5

      - Gs/CRE+Chaperone: 6.4-9.5 million mapped reads, median=7.5”

      The median read counts per sample per barcode were 8, 10, and 6 reads for Gs/CRE, Gq/UAS, and Gs/CRE+Chaperone assays, respectively. The median number of barcodes per variant across all samples (the “median of medians”) were 56 for Gs/CRE, 28 for Gq/UAS, and 44 for Gs/CRE+Chaperone.”

      (3) It appears that the frequencies of individual RNA-seq barcode variants were used as a proxy for MC4R activity. Would it be important to also normalize for heterogeneity in RNA-seq coverage across different cells in the experiment? Variability in cell representation (i.e., the distribution of variants across cells) could lead to misinterpretation of variant effects. For example, suppose barcode_a1 represents variant A and barcode_b1 represents variant B. If the RNA-seq results show 6 reads for barcode_a1 and 7 reads for barcode_b1, it might initially appear that both variants have similar effect sizes. However, if these reads correspond to 6 separate cells each containing 1 copy of barcode_a1, and only 1 cell containing 7 copies of barcode_b1, the interpretation changes significantly. Additionally, if certain variants occupy a larger proportion of the cell population, they are more likely to be overrepresented in RNA sequencing.

      We account for this heterogeneity in several ways. First, as shown above (see Response to Reviewer 1, Question 1), we aim to have even representation of variants within our libraries. Second, we utilize compositional control conditions like forskolin or unstimulated conditions to obtain treatment-independent measurements of barcode abundance and, consequently, of mutant-vs-WT effects that are due to compositional rather than biological variability. We expect that variability observed under these controls is due to subtle effects of molecular cloning, gene expression, and stochasticity. Using these controls, we observe that mutant-vs-WT effects are generally close to zero in these normalization conditions (e.g., in untreated Gq, see Supplementary Figure 3) as compared to treated conditions. For example, pre-mature stops behave similar to WT in normalization conditions. This indicates that mutant abundance is relatively homogenous. Where there are barcode-dependent effects on abundance, we can use information from these conditions to normalize that effect. Finally, our mixed-effect model accounts for barcode-specific deviations from the expected mutant effect (e.g., a “high count” barcode consistently being high relative to the mean).

      (4) Although the assay system appears to effectively represent MC4R functionality at the molecular level, we are curious about the potential disparity between the DMS score system and physiological relevance. How do variants reported in gnomAD distribute within the DMS scoring system?

      Figure 2D shows DMS scores (variant effect on Gs signaling) relative to human population frequency for all MC4R variants reported in gnomAD as of January 8, 2024.

      (5) To measure Gq signaling, the authors used the GAL4-VPR relay system. Is there additional experimental data to support that this relay system accurately represents Gq signaling?

      The full Gq reporter uses an NFAT response element from the IL-2 promoter to regulate the expression of the GAL4-VPR relay. In this system, the activation of Gq signaling results in the activation of the NFAT response element, and this signal is then amplified by the GAL4-VPR relay. The NFAT response element has been previously well-validated to respond to the activation of Gq signaling (e.g., Boss, Talpade, and Murphy 1996). We will have added this reference to the text (see Results> Assays for disease-relevant mechanisms) to further support the use of the Gq assay.

      (6) Identifying the variants responsive to the corrector was impressive. However, we are curious about how the authors confirmed that the restoration of MC4R activity was due to the correction of the MC4R protein itself. Is there a possibility that the observed effect could be influenced by other factors affected by the corrector? When the corrector was applied to the cells, were any expected or unexpected differential gene expression changes observed?

      While we do not directly measure whether Ipsen-17 has effects on other signaling processes, previous work has shown that Ipsen-17 treatment does not indirectly alter signaling kinetics such as receptor internalization (Wang et al., 2014). Furthermore, our analysis methods inherently account for this by normalizing variant effects to WT signaling levels. Any observed rescue of a given variant inherently means that the variant is specifically more responsive to Ipsen-17 than WT, and the fact that different variants exhibit different levels of rescue is reassuring that the mechanism is on target to MC4R. Lastly, Ipsen-17 is known to be an antagonist of alpha-MSH activity and is thought to bind directly to the same site on MC4R (Wang et al., 2014).

      We have revised text in the Methods section as follows (see Running DMS Assays) to better articulate this : “For chaperone experiments, cells were washed 3x with 10 mL DMEM to remove Ipsen 17 prior to agonist stimulation as it has been shown to be an antagonist of α-MSH activity and is thought to bind directly to the same site on MC4R (Wang et al. 2014).”

      (7) As mentioned in the introduction, gain-of-function (GoF) variants are known to be protective against obesity. It would be interesting to see further studies on the observed GoF variants. Do the authors have any plans for additional research on these variants?

      We agree this would be an excellent line of inquiry, but due to changes in company priorities we unfortunately do not have any plans for additional research on these variants.

      Reviewer 2 (Public reviews):

      Overview

      In this manuscript, the authors use deep mutational scanning to assess the effect of ~6,600 protein-coding variants in MC4R, a G protein-coupled receptor associated with obesity. Reasoning that current deep mutational scanning approaches are insufficiently precise for some drug development applications, they focus on articulating new, more precise approaches. These approaches, which include a new statistical model and innovative reporter assay, enable them to probe molecular phenotypes directly relevant to the development of drugs that target this receptor with high precision and statistical rigor.

      They use the resulting data for a variety of purposes, including probing the relationship between MC4R's sequence and structure, analyzing the effect of clinically important variants, identifying variants that disrupt downstream MC4R signaling via one but not both pathways, identifying loss of function variants are amenable to a corrector drug and exploring how deep mutational scanning data could guide small molecule drug optimization.

      Strengths

      The analysis and statistical framework developed by the authors represent a significant advance. In particular, the study makes use of barcode-level internally replicated measurements to more accurately estimate measurement noise.

      The framework allows variant effects to be compared across experimental conditions, a task that is currently hard to do with rigor. Thus, this framework will be applicable to a large number of existing and future deep mutational scanning experiments.

      The authors refine their existing barcode transcription-based assay for GPCR signaling, and develop a clever "relay" new reporter system to boost signaling in a particular pathway. They show that these reporters can be used to measure both gain of function and loss of function effects, which many deep mutational scanning approaches cannot do.

      The use of systematic approaches to integrate and then interrogate high-dimensional deep mutational scanning data is a big strength. For example, the authors applied PCA to the variant effect results from reporters for two different MC4R signaling pathways and were able to discover variants that biased signaling through one or the other pathway. This approach paves the way for analyses of higher dimensional deep mutational scans.

      The authors use the deep mutational scanning data they collect to map how different variants impact small molecule agonists activate MC4R signaling. This is an exciting idea, because developing small-molecule protein-targeting therapeutics is difficult, and this manuscript suggests a new way to map small-molecule-protein interactions.

      Weaknesses

      The authors derive insights into the relationship between MC4R signaling through different pathways and its structure. While these make sense based on what is already known, the manuscript would be stronger if some of these insights were validated using methods other than deep mutational scanning.

      Likewise, the authors use their data to identify positions where variants disrupt MC4R activation by one small molecule agonist but not another. They hypothesize these effects point to positions that are more or less important for the binding of different small molecule agonists. The manuscript would be stronger if some of these insights were explored further.

      Impact

      In this manuscript, the authors present new methods, including a statistical framework for analyzing deep mutational scanning data that will have a broad impact. They also generate MC4R variant effect data that is of interest to the GPCR community.

      Recommendations for the authors:

      (1) Page 7 - the Gq reporter relay system is clever. Could the authors include the original data showing that the simpler design didn't work at all, or at least revise the text to say more precisely what "not suitable due to weak SNR" means?

      We added a panel (D) to Supplementary Figure 2 showing that the native NFAT reporter was ~10x weaker than the CRE reporter, and the relay system amplified the NFAT signal to be comparable to the CRE reporter:

      (2) Page 7 - Even though the relay system gives some signal, it's clearly less sensitive/higher background than Gs. How does that play out in the quantitative analysis?

      —AND—

      (4) Page 10 - The Gq library had fewer barcodes per variant, and, as noted above, the Gq reporter doesn't work quite as well as the Gs one. It would be nice if the authors could comment on how these aspects of the Gq experiments affected data quality/power to detect effects.

      Due to the reviewer's excellent suggestion, we updated Supplementary Figure 2B to better contextualize the quantitative effects of the difference in signal to noise ratio of the Gq versus the Gs reporter system (see changes below). These distributions show the Z-statistic for testing either each stop mutation (red) or all possible coding variants against WT. Thus, a |Z| > 1.96 corresponds to a p = 0.05 in a two-sided Wald Test. We can see that in the Gs reporter, 95% of the stops are nominally significantly different from WT (visualized above with the majority of the red distribution being < -1.96). Alternatively, only 64% of stops are nominally significantly different from WT in Gq. This implies that it will be more difficult to detect effects in the Gq system, especially those less severe than stops.

      In addition to the overall signal to noise ratio being less in the Gq system, there were also less barcodes per variant (28 vs 56 barcodes per variant on average for Gq vs Gs). As demonstrated in Supplementary Figure 2C, the error bars on our estimates are related to the number of barcodes per variant (Standard Error ~ 1 / sqrt(Number of Barcodes), as shown in the plot below). This suggests that our estimates of mutant effects will be less certain in the Gq library than the Gs library. For example, the average standard error in the Gq library was 0.260 which was ~1.58 times larger than the Gs library's 0.165. Finally, we believe this further reiterates the power of our statistical framework, as it naturally enables formalized hypothesis testing that takes these errors into account when making comparisons both within reporters and across reporters.

      (3) Page 9 - it would be nice to see the analysis framework applied to a few existing datasets from other types of assays, to really judge its performance. That's not the main point of this paper, and it's fine, but it would be lovely!

      We agree with the reviewer and hope others apply our framework to their problems to further refine its utility and applicability! To that end, we’ve open-sourced it under a permissive license to help encourage the community to use it. Part of the challenge in applying it to other existing datasets is that few DMS experiments leverage variant-level replication through barcodes. While we re-analyzed an older DMS data from Jones et al. 2020 to produce the distributions in Supplementary Figure 2b, a more thorough comparison is outside the scope of this paper. That said, we have two additional manuscripts in preparation that leverage this framework to analyze DMS data in different proteins and assay types.

      (5) Page 10 - In discussing the relationship of the data to ClinVar and AM, the authors use qualitative comparisons like "majority" and "typically." Just giving numbers would better help the reader appreciate how the data compare.

      We added specific proportions for these statements to the text for the ClinVar and AlphaMissense comparisons as follows:

      (See Results > Comprehensive Deep Mutational Scanning of MC4R): “For example, the majority (63.3%, 31/49) of human MC4R variants classified as pathogenic or likely pathogenic in ClinVar (Landrum et al., 2014) lead to a significant reduction of Gs signaling under low α-MSH stimulation conditions (significance threshold: false discovery rate (FDR) < 1%; Fig. 2C). Variants that are significantly loss-of-function in this condition are rarer in the human population, and more common human variants have no significant effect on MC4R function (significance threshold: FDR < 1%; Fig. 2D). Loss-of-function variants by our DMS assay are also typically (e.g., AlphaMissense: 93.4%, 1894/2028) predicted to be deleterious by commonly used variant effect predictors like AlphaMissense (Cheng et al., 2023) and popEVE (Orenbuch et al., 2023) (Supplementary Fig. 5).”

      (6) Pages 10-12, Figures 2C, E. The data look really nice, but the correlation with clinvar and the Huang data is not perfect (e.g. many pathogenic variants are classified as WT and partial LoF variants too). Can the authors comment on this discrepancy? For ClinVar, they should say when ClinVar was accessed and also how they filtered variants. I would recommend using variants with at least 1 star. Provided they did use high-quality clinical classifications, do they think the classifications are wrong, or their data? The same goes for Huang.

      —AND—

      (7) Page 13 - similar to previous comments, I'm curious about the 5 path/likely path ClinVar variants that are not LoF in the assay. Are they high noise/fewer barcodes? Or does the assay just miss some aspect of human biology?

      ClinVar data was accessed on January 5, 2024 (see Methods: Comparison to human genetics data and variant effect predictors). No annotation quality filtering was performed, and we have revised the text as follows to clarify this:

      (see Methods > Comparison to Human Genetics Data and Variant Effect Predictors): “Pathogenicity classifications of MC4R missense and nonsense variants were obtained from ClinVar (Landrum et al., 2014) on January 5, 2024, and all available annotations were included in the analysis regardless of ClinVar review status metric.”

      A substantial proportion of the discrepancy between our data and ClinVar is, as the reviewer suggests, likely due to low quality ClinVar annotations. Of the five variants that the reviewer notes were reported as pathogenic/likely pathogenic but did not result in loss of protein function in any of our DMS assays, two (V50M and V166I) have been reclassified in ClinVar to uncertain or conflicting interpretation since we accessed annotations in early 2024. An additional two of the five discrepant variants (Q43K and S58C) currently have 0 star ratings to support their pathogenic/likely pathogenic annotation. The remaining discrepant variant (S94N) has a 1 star rating supporting an annotation of “likely pathogenic.

      The Huang et al. paper did an admirably thorough job of aggregating variant annotations from more than a dozen primary literature sources that each reported functional validation data for small panels of variants. However, one inherent limitation of this approach is that the resulting annotation classes are based on experiments that were carried out using inconsistent methods and/or scoring criteria. For example, classifications in the Huang et al. paper are based on an inconsistent mix of functional assay types (e.g., Gs signaling, Gq signaling, protein cell surface expression, etc.), and different variants were tested in different cell types (e.g., HEK293T, CHO, Cos-7, etc.). In principle, DMS assays should provide a more accurate assessment of the relative quantitative differences between alleles since each variant was tested using identical experimental conditions and analysis parameters.

      That being said, while very good, our assays are likely missing or only indirectly reporting on at least some aspects of MC4R biology. For example, in addition to Gs and Gq signaling, MC4R interfaces with β-arrestin. Variants that are protective against obesity-related phenotypes have been shown to increase recruitment of β-arrestin to MC4R, and we did not directly assess this function.

      (8) Page 15, Fig 3C - The three variants they highlight all have paradoxical changes in bias as a-MSH dose is increased (e.g. the bias inverts). I'm not a GPCR expert, but this seems interesting and a little weird. Perhaps the authors could comment on it?

      We agree this is an interesting observation that deserves further study, but unfortunately is outside the scope of our priorities at the moment. As noted, all three highlighted variants in this region have a biased basal activity, and this bias inverts upon stimulation. While we don’t have a good explanation for why this would be the case, this phenomenon has been previously observed for 158R (Paisdzior et al., 2020). Our DMS data emphasizes how diverse biased effects can be and further highlights the importance of characterizing these effects. It would be interesting if further studies could elucidate the mechanistic basis for this behavior and how it may be related to G protein coupling in this region.

      (9) Page 16 - I'm not familiar with the A21x1 formalism. For the general reader, maybe the authors could introduce this formalism.

      Given the shared structural topology of GPCRs, others have developed a variety of numbering schemes to refer to where various variants are to allow more direct comparisons between different GPCRs. We use the GPCRDB.org numbering scheme (e.g., F202<sup>5x4</sup>) as it takes experimentally determined structures into account. Roughly speaking, the number preceding the “x” corresponds to which transmembrane domain (one through seven) or region the residue is located in. The numbers following the “x” correspond to where that residue is located in that region relative to a structurally conserved residue that is always assigned 50. For example F202<sup>5x48</sup> means that F202 is located in the 5th transmembrane helix and is 2 residues before the most conserved M204<sup>5x50</sup>. We updated the text to clarify this accordingly:

      (see Results > Structural Insights into Biased Signaling): “Upon ligand binding, W258 (W258<sup>6x48</sup> in https://gpcrdb.org/ nomenclature, where 6 corresponds to the 6th transmembrane helix and 48 denotes 258 is 2 residues before the most conserved residue in that helix (Isberg et al., 2015)) of the conserved CWxP motif undergoes a conformational rearrangement that is translated to L133<sup>3x36</sup> and I137<sup>3x40</sup>, of the conserved PIF motif (MIF in melanocortin receptors).”

      (10) Page 17, Figure 3A - Since 137, 254, and 140 are not picked out on the structure, I have no idea where they are. If the authors want to show readers these residues, perhaps they could be annotated or a panel added. Since ~1 entire page of the manuscript is dedicated to this cascade, it might make sense to add a panel. Just amplifying the comment above as regards position 79, others were discussed in that paragraph but not highlighted.

      We updated Supplementary Fig. 6C,D to label all of the listed residues on the protein structure for easy reference.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This manuscript describes an important study of the giant virus Jyvaskylavirus. The characterisation presented is solid, although, in the current form, it is not clear to what extent these findings change our perception of how giant viruses, especially those isolated from a cold environment, function. The work will be of interest to virologists working on giant viruses as well as those working with other members of the PRD1/Adenoviridae lineage.

      Thank you for the revision and positive comments. We decided to submit our revised version of the manuscript with changes made in light of the comments made by the editorial team and the reviewers. We hope that now the manuscript is in a better shape and satisfies all comments received. Major changes made were:

      - We changed the author order considering reviewer 2 comments (point 11). Note that no author was added or removed, we just rearranged the order of authorship.

      - We included a new supplementary table with the Jyvaskylavirus genome annotation. This is now supplementary table 2.

      - We included a supplementary figure 9 to support our changes based on reviewer 2 comments (point 6).

      - Figures 2,5,6,7 and the supplementary figure 2 were updated to accommodate our answers to different reviewer comments.

      - Three new references were added to support some of our changes.

      Below you will find our responses to each specific point raised by the reviewers.

      Public Reviews:

      Reviewer #1 (Public review):

      This study presents Jyvaskylavirus, a new member of the Marseilleviridae family, infecting Acanthamoeba castellanii. The study provides a detailed and comprehensive genomic and structural analysis of Jyvaskylavirus. The authors identified ORF142 as the capsid penton protein and additional structural proteins that comprise the virion. Using a combination of imaging techniques the authors provide new insights into the giant virus architecture and lifecycle. The study could be improved by providing atomic coordinates and refinement statistics, comparisons with available giant virus structures could be expanded, and the novelty in terms of the first isolated example of a giant virus from Finland could be expounded upon.

      The study contributes new structural and genomic diversity to the Marseilleviridae family, hinting at a broader distribution and ecological significance of giant viruses than previously thought.

      Thank you for your constructive comments. We have addressed each point raised in our rebuttal letter and revised the manuscript accordingly. By following your specific comments, we improved the manuscript regarding atomic coordinates, refinement statistics and novelty of finding a Finnish marseillevirus. Details are provided in the specific answers to your points.

      Reviewer #2 (Public review):

      Summary:

      This paper describes the molecular characterisation of a new isolate of the giant virus Jyvaskylavirus, a member of the Marseilleviridae family infecting Acanthamoeba castellanii. The isolate comes from a boreal environment in Finland, showcasing that giant viruses can thrive in this ecological niche. The authors came up with a non-trivial isolation procedure that can be applied to characterise other members of the family and will be beneficial for the virology field. The genome shows typical Marseilleviridae features and phylogenetically belongs to their clade B. The structural characterisation was performed on the level of isolated virion morphology by negative stain EM, virions associated with cells either during the attachment or release by helium microscopy, the visualisation of the virus assembly inside cells using stained thin sections, and lastly on the protein secondary structure level by reconstructing ~6 A icosahedral map of the massive virion using cryoEM. The cryoEM density combined with gene product structure prediction enabled the identification and functional assessment of various virion proteins.

      Strengths:

      The detailed description of the virus isolation protocol is the largest strength of the paper and this reviewer believes it can be modified for isolating various viruses infecting small eukaryotes. The cryoEM map allows us to understand how exceptionally large virions of these viruses are stabilised by minor capsid proteins and nicely demonstrates the integration of medium-resolution cryoEM with protein structure prediction in deciphering virion protein function. The visualisation of ongoing virus assembly inside virus factories brings interesting hypotheses about the process that; however, needs to be verified in the next studies.

      Weaknesses:

      The conclusions from helium microscopy images are overinterpreted, as the native membrane structure cannot be preserved in a fixed and dehydrated sample. In the image, there are many other parts of the curved membrane and a lot of virions, to me it seems the specific position of the highlighted virion could arise by a random chance. The claim that the cells were imaged in the near-original state by this method should be therefore omitted. Also, no mass spectrometry data are presented that would supplement and confirm the identity of virion proteins which predicted models were fitted into the cryoEM density. For a general virology reader outside of the giant virus field, the results presented in the current state might not have enough influence and the section should be rewritten to better showcase the novelty of findings.

      Thank you for your constructive comments. We thank reviewer #2 for highlighting these weaknesses, giving us the opportunity to improve our study. We have removed the claim that the cells were imaged in a near-original state. Additionally, we agree that the positions of the virions on the cell surface could result from a random distribution. However, the specific virion in panel 3C is situated halfway into a crevice, and it cannot be ruled out that this particular one could be in the process of being endocytotically uptaken. This is why we used the term "probably" while referring to this finding. Regarding the mass spectrometry data, while we understand that MS data would provide an additional layer of evidence to validate the specific proteins present in the virion, they would not confirm the precise location or role of these proteins within the virion.

      We have addressed each point raised in our rebuttal letter and revised the manuscript accordingly.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      I have only minor comments which should be relatively simple to address:

      (1) Atomic coordinates should be deposited in the PDB, and refinement statistics for the models provided, for example by expanding Table S2.

      We thank reviewer #1 for the suggestion. In the original submission in the ‘Data availability’ statement we stated that ‘Predicted Jyvaskylavirus PDB models using ModelAngelo and Alphafold have been deposited at BioStudies under the accession number S-BSST1654’. So, atomic coordinates of all predicted models are publicly available at the https://www.ebi.ac.uk/biostudies/ ; for additional clarity we also added the link in the ‘Data availability’ statement in the revised version.

      Our reasoning of not depositing them in the Protein Data Bank associated to our EMD-51613 entry is because they remain predicted models rigid-body fitted into the Jyvaskylavirus density map of 6.3 Å resolution. However, we have added into our BioStudies deposition (BSST1654) the whole Jyvaskylavirus pentameric assembly model (including all identified and predicted major and minor capsid proteins) rigid-body fitted into the Jyvaskylavirus map, and it can be easily downloaded.

      We did not to perform the real-space ‘minimization_global’ refinement of the predicted models corresponding to the ORFs of Melbournevirus (or Jyvaskylavirus) into the corresponding Melbournevirus available densities with entries EMD-37188, 37189, 37190 at ~ 3.5 Å resolution (by block-based reconstruction methods) as these maps were generated and deposited by other authors. Instead, we performed the rigid-body fit-into-map procedure of the individual predicted Jyvaskylavirus models into the previously deposited Melbournevirus maps using ChimeraX, demonstrating a fold-map alignment and assignment (see for example the individual stereo views in Supplementary Figure 6).

      In the revised version, we now provide the refinement statistics for the complete Jyvaskylavirus pentameric assembly (inclusive of peripentonal major capsid and minor capsid proteins) rigid-body fitted as a whole into the Melbournevirus 5-block reconstruction map using PHENIX, resulting into a CC<sub>mask</sub> of 57.3% (this is also stated in Supplementary Figure 7). The same pentameric assembly model was then placed into our lower-resolution 6.3 Å Jyvaskylavirus 3D density map in ChimeraX and rigid-body refined as a whole in PHENIX, yielding a predictably lower CC<sub>mask</sub> of 33%. This pentameric assembly model has now also been included into BioStudies entry.

      The procedure for this rigid body fitting and refinement has been clarified and added to the 'Materials and Methods' section as follows:

      “Then, the corresponding full 3D models were predicted using AlphaFold3 and fitted into the Melbournevirus and Jyvaskylavirus cryoEM density using the fit-into-map routine in ChimeraX together with the peripentonal capsomers (Meng et al 2023). To assess the metric of this fitting (Supplementary Figure 7), the 3.5 Å five-fold Melbournevirus block 3D density (EMDB-37190) was boxed around the pentameric assembly model and refined as a whole using rigid-body refinement in PHENIX, yielding a CC<sub>mask</sub> of 57.3%. The same pentameric model was subsequently fitted into the 6.3 Å Jyvaskylavirus 3D cryo-EM density (previously boxed around the model), resulting in a lower CC<sub>mask</sub> of 33%, consistent with the limited resolution of the capsid map and below regions.”

      (2) The results section 'Jyvaskylavirus three-dimensional architecture' could be expanded to compare and contrast with other giant virus structures, in terms of T-number, diameter, and features on and inside the capsid. This is not essential but would help focus claims of novelty with regard to structure.

      We have added a few lines as indicated by reviewer#1 to contextualize in morphological terms Jyvaskylavirus with other NCLDV viruses as follows:

      “Both the capsid organization and virion size are similar to those of other Marseilleviruses, such as Melbournevirus and Tokyovirus. Pacmanvirus, considered to be at the crossroads between Asfarviridae and Faustoviruses, also possesses the same T number (309) and a comparable diameter to Jyvaskylavirus. In contrast, other giant viruses, such as African swine fever virus (ASFV), representative of the Asfarviridae family, have a T number of 277 and a diameter of approximately 2,100 Å, while PBCV-1, a member of the Phycodnaviridae family, has a T number of 169 and an average diameter of 1,900 Å. All of the above-mentioned viruses have been shown to possess a major capsid protein with a vertical double jelly-roll fold that composes the capsid shell, along with an internal membrane bilayer. Minor capsid proteins have been identified and structurally modelled for the smaller virions ASFV and PBCV-1 (Wang et al. 2019; Shao et al. 2022).”

      (3) The authors highlight one of the main novelties of the virus as being the first to be isolated from Finland. The first isolation of a giant virus from the region is indeed a success but reported isolation experiments for giant viruses are still relatively few. To help shed light on the likely distribution of Jyvaskylavirus-like viruses in the region, and further afield, the genome of Jyvaskylavirus could be searched against relevant available metagenomes.

      In the last decade the interest on finding giant viruses by metagenomics has increased. However, the focus has been on marine environments, where these viruses are shown to be prevalent. Besides the few isolates from the Northern hemisphere mentioned in the manuscript, northern giant viruses were detected in metagenome datasets from glacier samples, epishelf lakes, the permafrost, the Nordic seas and in a deep-sea hydrothermal vent. Most of the genomic hits are for mimivirus-like or phycodnavirus-like sequences. A few marseilleviruses were found in the Loki’s castle deep sea vent, and we have already included these sequences in the analysis shown by the supplementary figure 3. In this case the deep-sea vent viruses clusters outside the conventional clades of the marseilleviridae family, evidencing their uniqueness.

      In response to the suggestion of exploring the distribution of Jyvaskylavirus, we utilized the MGnify-database to search for DNA polymerase (DNApol) and major capsid protein (MCP) sequences. Our findings revealed multiple hits with significantly low E-values (< 1e-80), where both DNApol and MCP were detected from the same studies, indicating the presence of similar virus-like particles (VLPs) globally. Of particular interest was the detection of similar sequences in metagenomes and transcriptomes obtained from drinking water distribution systems of ground and surface waterworks in central and eastern Finland (https://www.ebi.ac.uk/metagenomics/studies/MGYS00005650#overview). We have acknowledged this in the manuscript and cited the appropriated references, as follows:

      Results: “Searching the Jyvaskylavirus major capsid protein and DNA polymerase sequences in the MGnify-database (Richardson et al 2023) yields multiple hits with significantly low E-values (< 1e-80), as expected from the apparent ubiquity of marseilleviruses. Of note was the detection of similar sequences in metagenomes and transcriptomes obtained from drinking water distribution systems of ground and surface waterworks in central and eastern Finland, evidencing that marseilleviruses are prevalent but still unexplored in this region (Tiwari et al 2022)”.

      Discussion: “Marseillevirus DNA polymerase sequences are present in metagenomes from Finnish drinking water distribution systems (Tiwari et al 2022), hinting to a wide distribution of these viruses and still unknown ecological role in Central and Eastern Finland.”

      Reviewer #2 (Recommendations for the authors):

      Apart from the major comments in the weaknesses section, I have these additional minor comments to the authors:

      (1) I do not understand why the authors emphasized the uniqueness of isolating a giant virus from Finland. I think the manuscript would benefit if they rather emphasize that the virus comes from a boreal environment.

      The first giant virus, APMV, was described in 2003. In the following years the apparent ubiquity of these viruses was evidenced by two fronts. Metagenomics made clear that giant viruses are found almost everywhere, biased towards the oceans. Isolation efforts brought new virus groups in evidence but has been so far biased towards central Europe and South America samples. The closest isolated giant viruses to Jyvaskylavirus would be either an uncharacterized Swedish cedratvirus or a few microalgae-infecting mimivirus-like and phycodnaviruses-like isolates from Norway. Among marseilleviruses, Jyvaskylavirus is the northernmost isolate so far. Other marseilleviruses from the northern hemisphere were found in France, India, Japan and Algeria only.

      We still believe that finding a giant virus in Finland is relevant, considering that no other is known to date, be as an isolate or detected by genomics. We have made these observations clearer in the manuscript, giving emphasis to the boreal environment as well.

      (2) All discussed AlphaFold models should be added as Supplementary PDB data.

      We thank reviewer #2 for the suggestion. In the original submission in the ‘Data availability’ statement we stated that ‘Predicted Jyvaskylavirus PDB models using ModelAngelo and Alphafold have been deposited at BioStudies under the accession number S-BSST1654’. So, atomic coordinates of all predicted models are publicly available at the https://www.ebi.ac.uk/biostudies/ ; for additional clarity we also added the link in the ‘Data availability’ statement in the revised version.

      Our reasoning of not depositing them in the Protein Data Bank associated to our EMD-51613 entry is because they remain predicted models rigid-body fitted into the Jyvaskylavirus density map of 6.3 Å resolution. However, we have added into our BioStudies deposition (BSST1654) the whole Jyvaskylavirus pentameric assembly model (including all identified and predicted major and minor capsid proteins) rigid-body fitted into the Jyvaskylavirus map, and it can be easily downloaded.

      We did not to perform the real-space ‘minimization_global’ refinement of the predicted models corresponding to the ORFs of Melbournevirus (or Jyvaskylavirus) into the corresponding Melbournevirus available densities with entries EMD-37188, 37189, 37190 at ~ 3.5 Å resolution (by block-based reconstruction methods) as these maps were generated and deposited by other authors. Instead, we performed the rigid-body fit-into-map procedure of the individual predicted Jyvaskylavirus models into the previously deposited Melbournevirus maps using ChimeraX, demonstrating a fold-map alignment and assignment (see for example the individual stereo views in Supplementary Figure 6).

      In the revised version, we now provide the refinement statistics for the complete Jyvaskylavirus pentameric assembly (inclusive of peripentonal major capsid and minor capsid proteins) rigid-body fitted as a whole into the Melbournevirus 5-block reconstruction map using PHENIX, resulting into a CC<sub>mask</sub> of 57.3% (this is also stated in Supplementary Figure 7).

      The same pentameric assembly model was then placed into our lower-resolution 6.3 Å Jyvaskylavirus 3D density map in ChimeraX and rigid-body refined as a whole in PHENIX, yielding a predictably lower CC<sub>mask</sub> of 33%. This pentameric assembly model has now also been included into BioStudies entry.

      The procedure for this rigid body fitting and refinement has been clarified and added to the 'Materials and Methods' section as follows:

      “Then, the corresponding full 3D models were predicted using AlphaFold3 and fitted into the Melbournevirus and Jyvaskylavirus cryoEM density using the fit-into-map routine in ChimeraX together with the peripentonal capsomers (Meng et al 2023). To assess the metric of this fitting (Supplementary Figure 7), the 3.5 Å five-fold Melbournevirus block 3D density (EMDB-37190) was boxed around the pentameric assembly model and refined as a whole using rigid-body refinement in PHENIX, yielding a CC<sub>mask</sub> of 57.3%. The same pentameric model was subsequently fitted into the 6.3 Å Jyvaskylavirus 3D cryo-EM density (previously boxed around the model), resulting in a lower CC<sub>mask</sub> of 33%, consistent with the limited resolution of the capsid map and below regions.”

      (3) Figure 2A: Could ORFs that encode structural proteins discussed in the paper, be somehow highlighted?

      We have updated Figure2A to include this information.

      (4) Figure 2C: Could be somehow highlighted from these members on which there was conducted structural characterisation (e.g. by some symbol next to the name)?

      We have updated Figure2C to include this information.

      (5) Figure 5A: Could the central bid be shown in a lower threshold (you can retain the threshold for the protein shell)? It would be interesting to see some details of the interior, rather than a massive blob.

      We have decreased the threshold level of the map as suggested.

      (6) Figure 6: the density corresponding to MCPs, minor capsid, and penton proteins respectively could be colour-zoned in Chimera(X). This would better visualise where each entity lies.

      About ORF142 - what other virus protein possesses this fold? Is it similar to the penton protein in other PRD1/Adenoviridae viruses? Maybe some comparison could be presented?

      We have incorporated the feedback from reviewer_#_2 by modifying the corresponding panel A in Figure 6. We have colour-zoned the penton (ORF142), some of the density region corresponding to the MCPs (ORF184) and to the minor cap proteins (ORF121). We have kept in grey the density corresponding to other minor proteins, and those we were able to identify are logically introduced later and shown as individual coloured cartoon tube models fitted into the density in panel A of Figure 7.

      Regarding ORF142, we have included a reference in the Discussion section to a new Supplementary Figure 9, where we provide a side-by-side comparison of the predicted Jyvaskylavirus penton protein model with experimentally derived penton protein models of PRD1 and HCIV-1. In light of this comparison, we have also added a brief clarification in the Discussion as follows:

      “However, in ORF142, the CHEF strands are predicted to be tilted relative to the BIDG strands, with an estimated angle of approximately 60° based on visual inspection (Supplementary Figure 9).”

      (7) Figure 7B: Could the density around the protein be zoned (rather than side view clipped), as this would better showcase how it fits the density?

      Initially, we presented a side view of the clipped surface to highlight the correspondence between the wall-shaped density, characteristic of a low-resolution beta-barrel, and the beta-barrel of the predicted model. Following the Reviewer’s suggestion, we have now surface-zoned the density and provided a stereo view of the density with the model fitted into the map using ChimeraX. While we recognize that stereo views are no longer commonly used in main text figures, we believe they remain valuable for visually assessing the overall match in low-resolution 3D density maps.

      (8) The authors did not try to reconstruct the asymmetric feature of the virion by classifying pentons, which may have identified a special vertex, one they claim might be required for genome packaging in "open particles". I understand the number of particles is low, but even low-resolution classification in C5 might be of interest in the field.

      We thank reviewer #2 for this valuable comment. The potential existence of a unique vertex in Marseilleviruses remains an open and intriguing question. Further investigations, including a significant increase in the number of particles, may help clarify this issue, and we plan to explore this topic in future structural studies.

      (9) Supplementary Figure 2: It would be interesting how the titre changes after the 12 hours, will it plateau? Could you add a bar showing the original titre to the chart showing stability after 109 days? I like the data in this figure and think it should be transferred to the main text.

      The titre at the 12h time point is very close to the titre we often get in our stocks, indicating that indeed it is close to peaking. For comparison: the titre of the 12-hour time point was 10<sup>11.55</sup> TCID50/ml, whereas our stock has a titre of 10<sup>11.66</sup> TCID50/ml. Our growth curve had more time points up to 48h and we lost the later time points due to a higher viral load than predicted, which led to us not being able to count these time points with the dilutions used. Showing the first 12 hours was enough for our initial purpose, which was to show a quick replication cycle for Jyvaskylavirus, in accordance with the other marseilleviruses in which the timing of the replication cycle was observed (see the answer for point 10 below).

      We have added a bar representing the original titre of the stock used for the stability experiment as suggested.

      While preparing the draft we were divided into having the growth and stability figure in the main text or in the supplementary material. Our decision was to move this data to the supplementary material and keep the focus of the main text on the discovery, genome analysis and structural data, as these are the main findings of our work. The specifics regarding stability, growth and other uncharacterized VLPs went to the supplementary material for those in the field who are interested in looking deeper. That being said, we will decide to keep this data as supplementary material if you and the editor agrees.

      (10) In the Discussion, the authors should focus on how our perception of giant viruses changes by this study - compare with other growth curves, stability assays, and structures of giant viruses, showcasing how prevalent those stabilising minor capsid proteins are, etc. My impression is that in the current form, it is just not clear if/how substantial these findings are and such a comparison and putting the results in a bigger picture would considerably increase the impact of the paper.

      Our comparisons with other marseilleviruses were based on genomic and structural characteristics, the two fronts we had data from the literature and databases to compare to. Sadly there is not too much information regarding stability and growth of other isolates that could be used for an in-depth comparison. For example: although marseilleviruses are known to have a fast replication cycle, this has been measured by DAPI staining of DNA inside infected cells to evaluate viral factory formation (Boyer et al 2009), or by time-series observations of viral cycle stages by electron microscopy (Fabre et al 2017), and not by viral titration as done here. We included a mention to these references in the results:

      “A fast replication cycle is a feature also shown for other marseilleviruses (Boyer et al 2009 ; Fabre et al 2017).”

      The literature also does not show virion stability of other isolates, making it impossible to have a comparison with jyvaskylavirus. A comparative study testing different isolates side by side is definitely of relevance and interest, but this would be difficult to be done in a short time due to obtaining other isolates. We believe the results in this manuscript might set some parameters to be used for comparing with other marseilleviruses, by our groups and others, in the future.

      Regarding the prevalence of the minor capsid proteins, we have expanded and clarified the identification of ORFs in Melbournevirus in the ‘Results’ and ‘Discussion’ sections. The revised Supplementary Table 4 has been updated accordingly and referenced in the results to clarify that the identification of Melbourne ORFs was carried out in BLASTp by querying the Jyvaskylavirus minor protein sequences exclusively against the Melbournevirus isolate 1 (NCBI Reference Sequence: NC_025412.1). BLASTp was then performed against the full sequence database, and homologous sequences were primarily retrieved from other marseillaviruses. These results have been compiled in a new Supplementary Table 5.

      However, Supplementary Table 5 also shows that the hits for Melbournevirus are not ranked at the top, and in some cases, they do not appear among the top hits.

      The ‘Results’ section now contains the following text:

      “To this end, we identified the corresponding Jyvaskylavirus ORFs in Melbournevirus through sequence comparison with Melbournevirus isolate 1 (NCBI Reference Sequence: NC_025412.1) (Supplementary Table 34). However, when the identified Jyvaskylavirus ORF sequences were analyzed using BLASTp without restricting the search to the Melbournevirus reference, many hits were observed in other giant viruses, primarily marseillevirus. Remarkably, some of these hits scored higher than those for Melbournevirus, supporting the presence of homologous proteins in these viruses (Supplementary Table 5).”

      The ‘Discussion’ section now contains the following text:

      “Additionally, the observation that the identified Jyvaskylavirus minor capsid protein sequences are shared across other marseillaviruses supports their essential structural and stabilizing roles in these viruses.”

      At the same time, we have modified the ‘Materials and Methods’ section to include a reference to Supplementary Figure 5, where the use of ModelAngelo is mentioned. Additionally, a new Supplementary Figure 10 has been included to clarify how the residues built into the Melbournevirus density using ModelAngelo (without prior knowledge of any sequence) are subsequently matched with the Jyvaskylavirus sequences.

      (11) Based on the author's statement, Iker Arriaga did all the cryoEM experiments. It is strange to me they are not placed higher on the author's list.

      We thank you for this observation and agree with your comment. This manuscript has been in preparation for a few years, and the first draft had the author order defined before the structural data collection and analyses were completed. Iker participation was indeed important and substantial from the first draft to the submitted version and he definitely deserves a better author placement. We have modified the author order to accommodate this. Note that only the author order changed and that no author has been included or removed.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this manuscript, the authors provide strong evidence that the cell surface E3 ubiquitin ligases RNF43 and ZNRF3, which are well known for their role in regulating cell surface levels of WNT receptors encoded by FZD genes, also target EGFR for degradation. This is a newly identified function for these ubiquitin ligases beyond their role in regulating WNT signaling. Loss of RNF43/ZNRF3 expression leads to elevated EGFR levels and signaling, suggesting a potential new axis to drive tumorigenesis, whereas overexpression of RNF43 or ZNRF3 decreases EGFR levels and signaling. Furthermore, RNF43 and ZNRF3 directly interact with EGFR through their extracellular domains.

      Strengths:

      The data showing that RNF43 and ZNRF3 interact with EGFR and regulate its levels and activity are thorough and convincing, and the conclusions are largely supported.

      Weaknesses:

      While the data support that EGFR is a target for RNF43/ZNRF3, some of the authors' interpretations of the data on EGFR's role relative to WNT's roles downstream of RNF43/ZNRF3 are overstated. The authors, perhaps not intentionally, promote the effect of RNF43/ZNRF3 on EGFR while minimizing their role in WNT signaling. This is the case in most of the biological assays (cell and organoid growth and mouse tumor models). For example, the conclusion of "no substantial activation of Wnt signaling" (page 14) in the prostate cancer model is currently not supported by the data and requires further examination. In fact, examination of the data presented here indicates effects on WNT/b-catenin signaling, consistent with previous studies.

      Cancers in which RNF43 or ZNRF3 are deleted are often considered to be "WNT addicted", and inhibition of WNT signaling generally potently inhibits tumor growth. In particular, treatment of WNT-addicted tumors with Porcupine inhibitors leads to tumor regression. The authors should test to what extent PORCN inhibition affects tumor (and APC-min intestinal organoid) growth. If the biological effects of RNF43/ZNRF3 loss are mediated primarily or predominantly through EGFR, then PORCN inhibition should not affect tumor or organoid growth.

      We thank the reviewer’s appreciation of the key strength of our study. We fully agree with the reviewer that RNF43/ZNRF3 play key roles in restraining WNT signaling and their deletions activate WNT signaling that leads  to cancer promotion, as discussed and cited in our manuscript (Hao et al, 2012; Koo et al, 2012). We have revised the language in this manuscript to avoid any confusion or appearance of downplaying this known signaling pathway in cancer progression.

      What we would like to highlight in this work is that our study uncovered an effect of RNF43/ZNRF3 on EGFR, leading to biological impact in multiple model systems. In particular, we included the APC-mutated human cancer cell line HT29 and Apc min mouse intestinal tumor organoids. In the context of APC mutations, β-catenin stabilization and the activation of WNT target genes are essentially decoupled from upstream WNT ligand binding to WNT receptors, thus we could primarily focus on the effect of RNF43/ZNRF3 on EGFR. Our statement of “no substantial activation of WNT signaling” as cited by the reviewer was made in describing the data in Fig. 7E where we did not observe β-catenin accumulation in the nucleus and reasoned no substantial activation of canonical WNT signaling. We agree that further examination would help strengthen the conclusion and appreciate the reviewer’s suggestion of PORCN inhibition experiments. While PORCN inhibition is a valuable experiment in models with abundance of WNT ligands/receptors and non-mutationally activated regulators of WNT signaling (Yu et al, 2020), in biological scenarios with existing APC mutations, another group has previously demonstrated that PORCN inhibition had no observable effect on WNT signaling in APC-deficient cells (PMID: 29533772). In our initial submission, we confirmed this predicted low response to manipulation of WNT signaling components upstream of a mutated APC. We showed that addition of RSPO1 in Apc min mouse intestinal tumor organoids failed to further activate WNT target expression (Fig. 6G). Furthermore, in this revised manuscript, we added new data on EGFR inhibition and PORCN inhibition in WT and Znrf3 KO MEFs (Fig. 6L). PORCN inhibition had no impact on cell growth in neither WT nor Znrf3 KO MEFs, suggesting that Znrf3 KO promoting MEF growth is WNT independent. In contrast, inhibition of EGFR downstream signaling components (Fig. 6L) significantly blocked MEF growth and abolished the impact of Znrf3 KO in MEF growth. This new evidence further supports our main conclusion that RNF43/ZNRF3 controls EGFR signaling to regulate cell growth.

      Reviewer #2 (Public Review):

      Using proteogenomic analysis of human cancer datasets, Yu et al, found that EGFR protein levels negatively correlate with ZNFR3/RNF43 expression across multiple cancers. Interestingly, they found that CRC harbouring the frequent RNF43 G659Vfs*41 mutation exhibits higher levels of EGFR when compared to RNF43 wild-type tumors. This is highly interesting since this mutation is generally not thought to influence Frizzled levels and Wnt-bcatenin pathway activity. Using CRISPR knockouts and overexpression experiments, the authors show that EGFR levels are modulated by ZNRF3/RNF43. Supporting these findings, modulation of ZNRF3/RNF43 activity using Rspondin also leads to increased EGFR levels. Mechanistically, the authors, show that ZNRF3/RNF43 ubiquitinate EGFR and leads to degradation. Finally, the authors present functional evidence that loss of ZNRF3/RNF43 unleashes EGFR-mediated cell growth in 2D culture and organoids and promotes tumor growth in vivo.

      Overall, the conclusions of the manuscript are well supported by the data presented, but some aspects of the mechanism presented need to be reinforced to fully support the claims made by the authors. Additionally, the title of the paper suggests that ZNRF3 and RNF43 loss leads to the hyperactivity of EGFR and that its signalling activity contributes to cancer initiation/progression. I don't think the authors convincingly showed this in their study.

      We thank the reviewer commenting that our “conclusions of the manuscript are well supported by the data presented.”  We address the concerns raised by this reviewer in an itemized way as detailed below:

      Major points:

      (1) EGFR ubiquitination. All of the experiments supporting that ZNFR3/RNF43 mediates EGFR ubiquitination are performed under overexpression conditions. A major caveat is also that none of the ubiquitination experiments are performed under denaturing conditions. Therefore, it is impossible to claim that the ubiquitin immunoreactivity observed on the western blots presented in Figure 4 corresponds to ubiquitinated-EGFR species. Another issue is that in Figure 4A, the experiments suggest that the RNF43-dependent ubiquitination of EGFR is promoted by EGF. However, there is no control showing the ubiquitination of EGFR in the absence of EGF but under RNF43 overexpression. According to the other experiments presented in Figures 4B, 4C, and 4F, there seems to be a constitutive ubiquitination of EGFR upon overexpression. How do the authors reconcile the role of ZNRF3/RNF43 vs c-cbl?

      We agree with this reviewer of the limitation of overexpression experiments. In this manuscript, we actually leveraged both overexpression and knockout systems to demonstrate that ZNRF3/RNF43 regulates EGFR ubiquitination: in Fig 4A, we showed that overexpression of RNF43 increased EGFR ubiquitination; in Fig 4B&C and Fig S3A, we showed that RNF43 knockout decreased EGFR ubiquitination; in Fig 4F, we showed that overexpression of ZNRF3 WT increased EGFR ubiquitination but overexpression of ZNRF3 RING domain deletion mutant failed to increase EGFR ubiquitination.

      We also appreciate the rigor with which the reviewer has approached our methodology. We acknowledge that denaturing conditions can provide additional validation, but the technical challenges associated with denaturing conditions include the potential disruption of epitope structures recognized by these antibodies. Our methodology was chosen to balance the need for accurate detection with the preservation of protein structure and function, which are crucial for understanding the biological implications of EGFR ubiquitination. Moreover, our immunoprecipitation and subsequent Western blotting were stringent with high SDS and 2-ME, optimized to minimize non-specific binding and enhance the specificity of detection. We believe that the data presented are robust and contribute significantly to the existing body of knowledge on EGFR ubiquitination.

      CBL is a well-known E3 ligase of EGFR, and it induces EGFR ubiquitination upon EGF ligand stimulation. Therefore, in order to have a fair comparison of RNF43 and CBL on EGFR ubiquitination, we designed Fig 4A and related experiments in the setting of EGF stimulation. We observed that RNF43 overexpression increased EGFR ubiquitination as potently as CBL did. Following this result, we further demonstrated that knockout of RNF43 decreased endogenous ubiquitinated EGFR level in the unstimulated/basal condition (Fig 4B) as well as in the EGF-stimulated condition (Fig 4C). We acknowledge the importance and interest in fully understanding how ZNRF3/RNF43 interplays with the functions of CBL in regulating EGFR ubiquitination. This line of investigation indeed holds the potential to uncover novel regulatory mechanisms in detail. However, the primary focus of the current study was to establish a foundational understanding of ZNRF3/RNF43 role in regulating EGFR ubiquitination. We look forward to exploring further in future work.

      (2) EGFR degradation vs internalization. In Figure 3C, the authors show experiments that demonstrate that RNF43 KO increases steady-state levels of EGFR and prevents its EGF-dependent proteolysis. Using flow cytometry they then present evidence that the reduction in cell surface levels of EGFR mediated by EGF is inhibited in the absence of RNF43. The authors conclude that this is due to inhibition of EGF-induced internalization of surface EGF. However, the experiments are not designed to study internalization and rather merely examine steady-state levels of surface EGFR pre and post-treatment. These changes are an integration of many things (retrograde and anterograde transport mechanisms presumable modulated by EGF). What process(es) is/are specifically affected by ZNFR3/RNF43? Are these processes differently regulated by c-cbl? If the authors are specifically interested in internalization/recycling, the use of cell surface biotinylation experiments and time courses are needed to examine the effect of EGF in the presence or absence of the E3 ligases.

      We agree that our study design primarily assesses EGFR levels on the cell surface before and after EGF treatment and does not comprehensively measure the whole internalization process. In response to the reviewer’s comments, we have revised the relevant sections of manuscript to clarify that our current findings are focused on changes in cell surface EGFR and do not extend to the detailed mechanisms of EGF-induced internalization or recycling.

      (3) RNF43 G659fs*41. The authors make a point in Figure 1D that this mutant leads to elevated EGFR in cancers but do not present evidence that this mutant is ineffective in mediated ubiquitination and degradation of EGFR. As this mutant maintains its ability to promote Frizzled ubiquitination and degradation, it would be important to show side by side that it does not affect EGFR. This would perhaps imply differential mechanisms for these two substrates.

      Fig 1D is based on bioinformatic analysis of colon cancer patient samples, showing that RNF43 G659Vfs*41 mutant tumors exhibited significantly higher levels of EGFR protein compared to RNF43 WT tumors. Following this lead, we investigated whether this RNF43 G659fs*41 hotspot mutation lost its role in downregulating EGFR. To this end, we transfected the same amount of control vector, RNF43 WT, RING deletion mutant, G659fs*41 mutant DNA into 293T cells and measured the level of EGFR (co-transfected). As shown in Author response image 1, overexpression of RNF43 WT decreased EGFR level while overexpression of RING deletion mutant had no impact on EGFR level as compared with the Vector group, which is consistent with our findings in the manuscript. Cells transfected with the RNF43 G659Vfs*41 mutant exhibited nearly normal levels of EGFR; however, we also observed that RNF43 G659Vfs*41 was less expressed than WT, even though the same amounts of DNA were transfected. Therefore, the insubstantial impact on EGFR levels could be attributed to both functional loss or compromised stability of RNF43 G659Vfs*41 mRNA or protein. Further investigation on RNF43 G659Vfs*41 mRNA and protein stability vs. RNF43 G659Vfs*41 protein function is needed to draw a solid conclusion.

      Author response image 1.

      (4) "Unleashing EGFR activity". The title of the paper implies that ZNRF3/RNF43 loss leads to increased EGFR expression and hence increased activity that underlies cancer. However, I could find only one direct evidence showing that increased proliferation of the HT29 cell line mutant for RNF43 could be inhibited by the EGFR inhibitor Erlotinib. All the other evidence presented that I could find is correlative or indirect (e.g. RPPA showing increased phosphorylation of pathway members upon RNF43 KO, increased proliferation of a cell line upon ZNRF3/ RNF43 KO, decreased proliferation of a cell line upon ZNRF3/RNF43 OE in vitro or in xeno...). Importantly, the authors claim that cancer initiation/ progression in ZNRF3/RNF43 mutants may in some contexts be independent of their regulation of Wnt-bcatenin signaling and relying on EGFR activity upregulation. However, this has not been tested directly. Could the authors leverage their znrf3/RNF43 prostate cancer model to test whether EGFR inhibition could lead to reduced cancer burden whereas a Frizzled or Wnt inhibitor does not?

      More broadly, if EGFR signaling were to be unleashed in cancer, then one prediction would be that these cells would be more sensitive to EGFR pathway inhibition. Could the authors provide evidence that this is the case? Perhaps using isogenic cell lines or a panel of patient-derived organoids (with known genotypes).

      We appreciate the reviewer’s suggestion to provide more direct evidence demonstrating the importance of the ZNRF3/RNF43-EGFR axis in cancer cell proliferation.   In this revised manuscript, we further studied this issue in the WT vs. Znrf3 KO MEF cells. We observed that treatment with the EGFR inhibitor erlotinib did not affect WT MEF but stunted the growth advantage of Znrf3 KO MEF cells (Fig. 6L). On the other hand, treatment with the porcupine inhibitor C59 did not impact either WT or Znrf3 KO MEF cells (Fig. 6L), suggesting a more important role of the ZNRF3/RNF43-EGFR axis in mediating the enhanced cell growth of MEF caused by Znrf3 knockout. Furthermore, considering EGFR is often mutated in human cancer, to increase the clinical relance of our study, we also tested the effect of RNF43 knockout on EGFR L858R (Fig. 2D), a common oncogenic EGFR mutant, and found that RNF43 knockout in HT29 boosted levels of this EGFR mutant detected by its FLAG tag, suggesting that RNF43 degrades both WT and mutated EGFR and its loss can enhance signaling of both WT EGFR and its oncogenic mutant .  However, we emphasize again that this manuscript is in no way written to diminish the proven importance of ZNRF3/RNF43-WNT-β-catenin axis in cancer and development.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      The main conclusion that EGFR is targeted for degradation by RNF43 and ZNRF3 is well supported and documented. Figures 1-5 and associated supplemental figures contain largely convincing data. Figures 6 and 7, however, require some modifications, as follows in order of appearance:

      Figure 6C: Growth of intestinal tumor organoids from Apcmin mice does not require Rspo, however, the authors show that these organoids grow larger in the presence of Rspo, an effect they attribute to increased EGFR activity, rather than increased WNT activity. While this conclusion may be correct, the authors should address this possibility by treating the organoids with PORCN inhibitor. The prediction would be that Rspo treatment still increases organoid size in the presence of PORCN inhibition. A further prediction would be that blocking EGFR (e.g. with Cetuximab) will abrogate the RSPO1 effect.

      Yes, we attributed the impact of Rspo on Apc min organoid growth to enhanced EGFR activity because we observed increased EGFR levels (Fig 6F) but no detectable increase in eight WNT target genes assayed. We agree that further pharmacologic experiments would further boost our conclusion, but our few attempts at treating organoids encountered technical difficulties. Hence, we switched to testing PORCN inhibition vs EGFR inhibition in WT and Znfr33 KO MEFs. As shown in the revised Fig. 6L, EGFR inhibition significantly reversed the growth advantage caused by Znrf3 KO but C59 did not.

      Figure 6G: It is unclear why the authors provide "8-day RSPO1 treatment" data. Here, EGFR mRNA appears to be elevated 2-fold (perhaps not statistically significant), and the Wnt targets Lef1 and Axin2 are decreased, as indicated by the statistical significance. What point is being made here?

      Our observation of increased size of APC min mouse intestinal tumor organoids and increased the EGFR protein levels were at 8 days of RSPO1 treatment. Therefore, we measured mRNA levels at the same time point with the 2-day time point also included for comparison. The goal of this qPCR experiment was to detect the contribution of WNT signaling, and we did not detect an increased transcriptional readout. We included EGFR mRNA levels for comparison, and we did not detect a statistically significant increase, consistent with our experiments concluding that ZNRF3/RNF43 regulate EGFR at the protein level. As stated in the preceding response, these data led us to attribute the impact of Rspo on Apc min organoid growth to enhanced EGFR activity.

      Figure 7A: This requires quantitation. How many mice were used per cell line? The data shown is not particularly convincing, with ZNRF3 overexpressing HT29 cells growing detectably. Showing representative mice is fine, but this should be supplemented with quantitation of all mice.

      We had provided this data. The BLI signal quantification was shown below the representative BLI images. Seven mice were used per cell line, as annotated at the top of the graph.

      Figure 7B: The authors assert that "canonical WNT signaling, based on levels of active-β-Catenin (non-phosphorylated at Ser33/37/Thr41; Figure 7B), remained unaffected". As shown, 2 of the 3 Myc-Znrf3 tumors have increased active-b-catenin signal over the GFP tumors. This indicates to me that canonical Wnt signaling was affected. The authors either need to present quantitative data that supports this claim or modify their conclusions. As presented, I don't think it is appropriate to decouple the effect of Znrf3 overexpression on EGFR from its effect on WNT.

      As requested, we have quantified the level of non-phospho β-Catenin at Ser33/37/Thr41 and found no significant differences (p > 0.05) between the control group vs. ZNRF3 overexpression group. We once again note that our manuscript was not meant to dispute the proven signaling and biological significance of WNT signaling regulation by ZNRF3/RNF43, and we have proof-read the manuscript multiple times to ensure that we did not make any generalized or misleading statements in this aspect.

      Author response image 2.

      Figure 7E: Here the authors assert that "no substantial activation of canonical Wnt signaling" in the Z&R KO tumors, however, the figure shows a substantial increase in active b-catenin staining. The current resolution is insufficient to claim that there is no increase in nuclear b-catenin. The authors' claim that WNT signaling is not involved here is not supported by the data presented here. One way to demonstrate that this effect is through EGFR activation and not through WNT activation is to treat mice with PORCN inhibitor. WNT-addicted tumors, such as by Rnf43 or Znrf3 deletion, regress upon PORCN inhibition. In this case, if the effect of Z&R KO is mediated through EGFR rather than WNT, then there should be no effect on tumor growth upon PORCN inhibition. This is a critical experiment in order to make this point.

      We appreciate the reviewer’s comments and suggestion of experiments. We based our initial statement on insubstantial nuclear β-catenin staining, but we agree that immunohistochemical staining lacks the resolution suitable for quantification. We could not generate the adequate number of KO animals for these in vivo experiments in the window of time planned for this revision. Rather, as shown in the newly added Fig. 6L, we tested EGFR inhibition and PORCN inhibition in Znrf3 KO MEFs and obtained strong data further supporting EGFR in mediating Znrf3 KO promotion of MEF growth. Notwithstanding, we have carefully revised our description of the in vivo data in Fig 7E to avoid any confusion or over-interpretation.

      Minor points:

      Figure 2A: provide quantitation of this immunoblot.

      We have revised manuscript with quantification result shown next to the immunoblot.

      Figure 2B: provide more detail in the figure legend and in the Materials and Methods section on how the KO MEFs were generated. Confirmation that Znrf3 (or in cases of Rnf43 KO) expression is lost in KO would be advisable.

      We have confirmed Znrf3 KO by genotyping and RNF43 KO by immunofluorescent staining. We have also tested multiple commercial anti-ZNRF3 antibodies and anti-RNF43 antibodies for Western blotting, but they all failed.

      Figure 4C is a little misleading. The schematic indicates that ECD-TM and TM-ICD truncations were analyzed for both ZNRF3 and RNF43. However, Figure 4 only shows data for ZNRF3, and the corresponding Figure S4 lacks data for the TM-ICD of Rnf43. A recommendation is to show only those schematics for which data is presented in that figure. On a related topic, the results using the deltaRING constructs (Figure S5) are not mentioned/described in the text.

      We think that the reviewer meant Fig 5C. We have revised the Fig 5C by removing the RNF43 label, and we confirm that  Results section does include the data in Fig S5.

      Figure S4A: Only ZNRF3 is indicated in this figure. Please explain why RNF43 is not represented here. Also, indicate what is plotted along the x-axis.

      We only detected the endogenous ZNRF3-EGFR interaction, possibly because the RNF43 protein level is relatively low in the cell line we used for the mass spec experiment. X-axis is the proteins ordered based on Y-axis values as detailed in the figure legend  -- each data point was arranged along the x axis based on the fold change of iBAQ of EGFR-associated proteins identified in EGF-stimulated vs. control in the log2 scale, from low to high (from left to right on x axis). We have added the phrase “Proteins detected by Mass-Spec” for X-axis.

      Reviewer #2 (Recommendations For The Authors):

      Minor Points.

      (1) In Figure 2B, the authors claim that Znrf3 KO enhanced both EGFR and p-EGFR levels both in the absence and presence of EGF. Although it is clear in the presence of EGF, the increased in p-EGFR in the absence of EGF is less than clear.

      We have revised the manuscript to more clearly state the result in Fig 2B.

      (2) Importantly the authors validated their findings using three independent RNF43 gRNA (fig S2D) but they do not show the editing efficiency obtained with the gRNA.

      We did not include RNF43 IB in this Figure due to lack of specific antibodies for detecting RNR43 in IB. We have no reasons to doubt adequate efficiency of knockout since EGFR was increased compared to the control group. As a result, we did not perform deep sequencing to validate knockout efficacy.

      (3) In S2E, the authors show that KO of either ZNRF3 or RNF43 enhance HER2 levels. This suggests that there is no redundancy between these E3 ligases, at least in this context. How do the authors reconcile that?

      The reviewer raised an interesting issue. Due to the lack of WB antibodies for these two proteins, we would not easily assess the feedback impact of knockout of either gene on the protein levels of the other gene. We speculate that there may be a threshold level of the sum of the two proteins that is needed for adequate degradation of HER2, leading to HER2 increase when either gene is knocked out. Detailed studies of this issue is beyond the scope of this current work.

      (4) Experiments performed in Fig 3C are performed in only one clone. The authors need to repeat in an additional clone or rescue this phenotype using a RNF43 cDNA.

      Our RNF43 KO HT29 line is a pool of KO cells, not a single clone.

      (5) In Figure 7E, the authors suggest that the absence of nuclear bcatenin means that canonical Wnt signaling is unaffected. It is widely known that nuclear bcatenin is often not correlating with pathway activity.

      As stated above, we have revised the manuscript to avoid confusion and misinterpretation.

      (6) What is the nature of the error bars in Fig 3c? Are the differences statistically significant?

      As mentioned in the figure legend, the error bars are SEM. The result is statistically significant, and p-value is noted in the graph.

      (7) In the Figure legends, it should be stated clearly how many biological replicates were performed for each experiment and single data points should be plotted where applicable (e.g. qPCR data). It would be helpful if the uncropped and unprocessed Western blot membranes and replicates that are not shown would be accessible to allow the reader a more comprehensive view of the acquired data, especially for blots that were quantified (e.g. Figure 2F, Figure 3C, there is clearly some defect on the blot).

      For WB representation, it would be helpful to include more size markers on the Western blots (especially on the Ips that show ubiquitin smear) and in general to use a reference protein (GAPDH, Actin, Vinculin) that is closer to the protein being accessed.

      More details should be added in the Methods section to explain how protocols were performed in detail. For example, it should be explained how the viruses used for infecting cells were produced (which plasmids were transfected using which transfection reagent, how long was the virus collected for, etc). Then, it should be stated how long the cells were undergoing selection before being harvested. Because the expression of the viral constructs potentially has an effect on cell proliferation through EGFR, this information is quite relevant. This is just an example, there are details missing in nearly every section (Flow: washing protocols, gating protocols (Live/dead stain?), WB: RIPA lysis buffer composition? How much protein was loaded on blots? How was protein quantification done? IP: how were washes performed and how often repeated?)

      Missing: antibody dilutions for IF, IHC, and WB, plasmid backbones, sequences and availability, qPCR primer sequences from Origene.

      Incucyte experiments are not described.

      We have revised the relevant sections to include more details.

      (8) Line 141: revise text: 2x mRNA abundance in the same sentence.

      Line 162: define intermediate expression better.

      Line 197/198: revise text ('the predominant one'?).

      Line 218/219: revise text (Internalisation of surface EGFR?).

      Line 245: clarify in text that it is endogenous EGFR that is being pulled down.

      Line 264: typo: conserved instead of conservative.

      Line 324: revise text (What does 'unknown significance' mean).

      Line 396/397: revise text: 2x Co-IP in the same sentence.

      Figure 3 D/E: more details on the Method in the figure legend.

      We have revised them accordingly.

    1. Author response:

      We thank the reviewers and editors for these careful and constructive comments. Based on these comments, we plan to perform new experiments and revised analysis, summarized as follows:

      (1) A more thorough analysis and experimental test of the effects of YW->SR variants on baseline AP excitability in neurons in the absence of any pharmacology.

      (2) More details on modeling of selective block of Na<sub>V</sub>1.2 and Na<sub>V</sub>1.6.

      (3) Revisions to text, figure contents, and figure order to better convey key points and better frame these findings in the context of current clinically available anti-seizure medications that interact with sodium channels.

    1. Author response:

      We thank both reviewers for their comments on our manuscript. We are pleased that the value of this research has been communicated effectively, and that the reviewers agree that whilst our sample size of individuals is relatively small, it offers a unique perspective for understanding the effects of aging for wild chimpanzees’ technological behaviors. Whilst only yielding data on a few individuals, the Bossou archive is the only available data source with which we can currently address these questions over extended timescales, and is key for understanding longitudinal effects of aging for specific individuals. This is particularly true if we are to understand the life-long dynamics of chimpanzees’ technical skills during tasks which require the organization of multiple movable elements. Bossou is the only community where chimpanzees both perform nut cracking with moveable hammer and anvil stones, and have been systematically studied over a period of decades. Moreover, given the dwindling population at Bossou (N = 3 as of 2025), we must make every effort to understand these effects with existing data. We agree that this work will likely form a valuable foundation for future studies, which may aim to either replicate our results, or use our findings to design more specific research questions and approaches.

      In the next iteration of the manuscript, we will elaborate on our choice of field seasons more clearly. However, this was a logistical tradeoff between needing to sample across a long lifespan using fine-granularity behavior coding, versus the time constraints for our project and the likely yield of data collection. We sampled from the middle of individuals’ prime age, up until the oldest recorded ages of individuals lifespans (17 years). Where possible we aimed to use consistent time intervals (approximately 4 years); however, this was not always possible, as in some years data was not collected by researchers at Bossou (for example, during years where there were Ebola outbreaks affecting the region). In such instances, we sampled the closest available year that offered sufficient data to meet our sampling requirements).

      Reviewer 2 raises that there may be a disconnect between how human observers and chimpanzees conceive of efficiency when nut cracking, and support this idea with a citation to previous work on efficiency of Oldowan stone knapping. We agree that knowing precisely how chimpanzees perceive their own efficiency during tool use is not available through observation alone, nor can we assess the true extent to which chimpanzees are concerned about the efficiency of their nut-cracking. However, following previous studies, it is reasonable to assume that adult chimpanzees embody some level of efficiency, given that adults often select tools which aid efficient nut cracking (Braun et al. 2025, J. Hum. Evol.; Carvalho et al. 2008, J. Hum. Evol.; Sirianni et al. 2015, Animal Behav.); perform nut cracking using more streamlined combinations of actions than less experienced individuals (Howard-Spink et al. 2024, Peer J; Inoue-Nakamura & Matsuzawa 1997, J. Comp. Psychol.), and consequently end up cracking nuts using fewer hammer strikes, indicating a higher level of skill (Biro et al. 2003, Animal Cogn.; Boesch et al. 2019, Sci. Rep.). Ultimately, these factors suggest that across adulthood, experienced chimpanzees perform nut cracking with a level of efficiency which exceeds novice individuals, including across the chaine operatoire.

      To account for the multiple ways in which reduced efficiency may manifest later in life, we provide one of the most flexible measures of efficiency in wild chimpanzee tool use to date, which incorporates more classical measures of time and hammer strikes (see previous examples of Biro et al. 2003, Animal Cogn.; Boesch et al. 2019 Sci. Rep.) as well as additional variables which aim to characterize how streamlined behavioral sequences are (tool rotations, tool swaps, nut replacements, etc. see Berdugo et al. 2024 Nat. Hum. Behav for other analyses using similar metrics). In the case of swapping out tools, Reviewer 2 suggests that some of these tool swaps may in fact be to aid nut cracking, by maintaining kernel integrity (a key result relating to Yo’s coula nut cracking efficiency). This however seems unlikely, given that these behaviors were performed extremely rarely by chimpanzees in early field seasons, and were not performed more frequently by other individuals with aging. We will provide additional information behind our metrics for measuring efficiency, with reference to earlier work, and also will incorporate the points raised by Reviewer 2 concerning the limitations with which we can infer chimpanzees’ goals, and how efficiently they meet them.

      Reviewer 1 questioned why we did not sample efficiency data for younger individuals, and compare this data with older individuals to detect the effects of aging. Throughout our manuscript, we compared aging individuals’ nut-cracking efficiency with their efficiency in previous years (thus, at younger ages). This offered each individual personalized benchmark of efficiency in early life, and allowed us to identify aging effects whilst controlling for long-term interindividual variation in skill levels. Indeed, previous analyses at Bossou find that across the majority of adulthood, efficiency varies between individuals, but is relatively stable within individuals (see Berdugo et al. 2024, Nat. Hum. Behav.). As focal aging chimpanzees cracked multiple nuts each field season (and each encounter), we had ample data to fit models that examine individuals’ efficiency over field seasons, using random slopes to model correlations for each individual. By taking this approach, our paper offers a novel perspective by being able to report the longitudinal effects of aging on tool-using efficiency, rather than averaged cross-sectional effects between young and old cohorts. As random slope models (and not just random intercept models) offered the best explanation for variation in aging individuals’ efficiency over our sample period, this implies that focal chimpanzees were experiencing individual-level changes in efficiency over time, thus giving us key evidence that interindividual variation in tool-using efficiency can be compounded by aging.

      We argue that the reductions in efficiency observed for some individuals (e.g. Yo & Velu) are unlikely to be due to environmental changes (e.g. nuts becoming harder in later field seasons), as if this was the case, these effects would be detected across the behaviors of all individuals (which was not observed). Additionally, in the specific case of the hardness of nuts, nuts used in our experiment were sourced from local communities, and were moderately aged. This avoided the use of young nuts which are harder to crack, or older nuts which are often worm-eaten or can be empty (Sakura & Matsuzawa, 1991; Ethology). We will update our manuscript with this information.

      Whilst other factors may introduce general variation into our efficiency data (such as different stones used on different encounters, or more general variation in nut hardness across encounters), very few of these factors predict directional long-term changes in efficiency. Rather, if these factors were driving the majority of variation in our data, we would expect them to lead to variation across visits during earlier field seasons (such as 1999-2008) and later field seasons (2011 onwards) equally, and in a way which does not necessarily correlate with age. This does not match the pattern we observed in our data, where for some individuals (e.g. Yo & Velu), efficiency in nut cracking reduced in later field seasons only, and was relatively consistent across field seasons prior to 2011. Moreover, for Yo – the individual who exhibited the greatest reductions in tool-using efficiency - efficiency continued to decrease across the three of the latest sampled field seasons. Thus, it is more likely Yo was experiencing deleterious effects of aging. We do however agree that additional data on these variables would help us to remove the possibility of compounding factors more rigorously – we will include recommendations for this data to be collected in future studies.

      When modelling the effect of aging on attendance at the outdoor laboratory, we could not use the same approach we used when modelling tool-using efficiency, as we could only acquire one datapoint (attendance rate) per individual for each field season. We therefore had to adapt our analysis, and introduce attendance rates for younger individuals as a baseline to compare against the attendance rates of older individuals across years. We observed a significant interaction effect, where across field seasons, attendance dropped significantly more rapidly for older individuals than younger ones. Reviewer 2 has asked why we do not consider inter-annual variability across this time period, and suggested that we ignored intervening years. This is not the case. When fitting models that examined the effects of aging on attendance, we used all data across all field seasons. We reported an approximate effect size for this significant correlation using a digestible comparison of the attendance rates in the initial and final field seasons sampled. We will ensure that this is clear in the next iteration of our manuscript.

      Reviewer 2 noted that many factors may have influenced the decision for chimpanzees to attend the outdoor laboratory in older field seasons, and the current data may not be used to make strong arguments for changes in attendance rates being due to dietary preferences. We agree that many factors may have influenced these attendance rates, and that is what we have aimed to transparently report within our discussion where we raise an extensive, non-exhaustive list of hypotheses for why we have observed this age-related change in our data. We will aim to ensure that this is exceptionally clear prior to resubmission, and where relevant, will further emphasize points raised by Reviewer 2. We consider some points raised by Reviewer 2 to be unlikely to apply for our study; for example, it is unlikely neophobia has influenced the behaviors of chimpanzees, as these chimpanzees habitually attended the outdoor laboratory at their own accord for over a decade prior to the earliest year we sampled in this study (reflecting extremely high levels of habituation to the experimental set up). Previous studies at Bossou have surveyed the ecology of stone tool use across the home range, and confirm that the outdoor laboratory is visited by chimpanzees during ranging as a food patch (Almeida-Warren et al. 2022 Int. J. Primatol.).

      Reviewer 2 suggested that it would be helpful to have additional data on variables such as hand grip, as this may reveal further information about how cognitive and physiological senescence influences reductions in tool-using efficiency. We agree that whilst further data on hand grips are not required to detect reductions in efficiency per say per se, it would be profitable for future analyses to collect similar data – we will add this as a recommendation to our discussion.

      Finally, Reviewer 2 commented that they found our discussion of coula-nut cracking disruptive to the flow of the manuscript, given that we could not compare with coula-nut cracking in earlier years. We reported the coula nut cracking of Yo in 2011 as it was part of our sampled data, and we felt that the comparison with other individuals in the same year was an interesting discussion point, however we acknowledge this limitation. We will move all data and discussion of coula-nut cracking to the Supplementary Materials, which we will present as an interesting additional observation which may warrant further investigation using additional data from the Bossou archive. Data collection for this future project could include collecting data on the additional variables raised by both reviewers (e.g. hand grips).

      We thank both reviewers for their comments. We believe that their feedback will improve the quality of our reporting, and the validity of our interpretations.

    1. Author response:

      eLife Assessment

      The conclusions of this work are based on valuable simulations of a detailed model of striatal dopamine dynamics. Establishing that a lower dopamine uptake rate can lead to a 'tonic' level of dopamine in the ventral but not dorsal striatum, and that dopamine concentration changes at short delays can be tracked by D1 but not D2 receptor activation, is of value and will be of interest to dopamine aficionados. However, the simulations are incomplete, providing only partial support for the key claims. Several things can be done to strengthen the conclusions, including, for example, but not exclusively, a demonstration of how the results would change as a function of changes in D2 affinity.

      We sincerely thank the Editors and Reviewers for their insightful comments on our manuscript. We are pleased that our simulations are recognized as interesting, sophisticated and valuable. Moreover, we fully agree that many of the findings will be of particular interest to dopamine aficionados. While we maintain that our simulations provide a solid basis for the key claims, we acknowledge that the conclusions can be further strengthened by the revisions suggested below.

      Reviewer #1 (Public review):

      Ejdrup, Gether, and colleagues present a sophisticated simulation of dopamine (DA) dynamics based on a substantial volume of striatum with many DA release sites. The key observation is that a reduced DA uptake rate in the ventral striatum (VS) compared to the dorsal striatum (DS) can produce an appreciable "tonic" level of DA in VS and not DS. In both areas they find that a large proportion of D2 receptors are occupied at "baseline"; this proportion increases with simulated DA cell phasic bursts but has little sensitivity to simulated DA cell pauses. They also examine, in a separate model, the effects of clustering dopamine transporters (DAT) into nanoclusters and say this may be a way of regulating tonic DA levels in VS. I found this work of interest and I think it will be useful to the community. At the same time, there are a number of weaknesses that should be addressed, and the authors need to more carefully explain how their conclusions are distinct from those based on prior models.

      (1) The conclusion that even an unrealistically long (1s) and complete pause in DA firing has little effect on DA receptor occupancy is potentially important. The ability to respond to DA pauses has been thought to be a key reason why D2 receptors (may) have high affinity. This simulation instead finds evidence that DA pauses may be useless. This result should be highlighted in the abstract and discussed more.

      We appreciate that the reviewer finds our work interesting and useful to the community. However, we acknowledge that in the revised version we to need to better describe how our conclusions are different from those reached based on previous models.

      We will also carry out new simulations across a range of D2R affinities to assess how this will affect the finding that even a long pause in DA firing has little effect on DR2 receptor occupancy. As also suggested, the results will be highlighted and further discussed.

      (2) The claim of "DAT nanoclustering as a way to shape tonic levels of DA" is not very well supported at present. None of the panels in Figure 4 simply show mean steady-state extracellular DA as a function of clustering. Perhaps mean DA is not the relevant measure, but then the authors need to better define what is and why. This issue may be linked to the fact that DAT clustering is modeled separately (Figure 4) to the main model of DA dynamics (Figures 1-3) which per the Methods assumes even distribution of uptake. Presumably, this is because the spatial resolution of the main model is too coarse to incorporate DAT nanoclusters, but it is still a limitation.

      We will improve our definitions and descriptions relating to nanoclustering of DAT in the revised version of the manuscript. We fully agree that the spatial resolution of the main model is a limitation and, ideally, that the nanoclustering should be combined with the large-scale release simulations. Unfortunately, this would require many orders of magnitude more computational power than currently available.

      As it stands it is convincing (but too obvious) that DAT clustering will increase DA away from clusters, while decreasing it near clusters. I.e. clustering increases heterogeneity, but how this could be relevant to striatal function is not made clear, especially given the different spatial scales of the models.

      Thank you for raising this important point. While it is true that DAT clustering increases heterogeneity in DA distribution at the microscopic level, the diffusion rate is, in most circumstances, too fast to permit concentration differences on a spatial scale relevant for nearby receptors. Accordingly, we propose that the primary effect of DAT nanoclustering is to decrease the overall uptake capacity, which in turn increases overall extracellular DA concentrations. Thus, homogeneous changes in extracellular DA concentrations can arise from regulating heterogenous DAT distribution. An exception to this would be the circumstance where the receptor is located directly next to a dense cluster – i.e. within nanometers. In such cases, local DA availability may be more directly influenced by clustering effects. This will be further discussed in the revised manuscript.

      (3) I question how reasonable the "12/40" simulated burst firing condition is, since to my knowledge this is well outside the range of firing patterns actually observed for dopamine cells. It would be better to base key results on more realistic values (in particular, fewer action potentials than 12).

      We fully agree that this typically is outside the physiological range. The values are included to showcase what extreme situations would look like.

      (4) There is a need to better explain why "focality" is important, and justify the measure used.

      We will expand on the intention of this measure in the revised manuscript. Thank you for pointing out this lack of clarification.

      (5) Line 191: " D1 receptors (-Rs) were assumed to have a half maximal effective concentration (EC50) of 1000 nM" The assumptions about receptor EC50s are critical to this work and need to be better justified. It would also be good to show what happens if these EC50 numbers are changed by an order of magnitude up or down.

      We agree that these assumptions are critical. Simulations on effective off-rates across a range of EC50 values will be included in the revised version.

      (6) Line 459: "we based our receptor kinetics on newer pharmacological experiments in live cells (Agren et al., 2021) and properties of the recently developed DA receptor-based biosensors (Labouesse & Patriarchi, 2021). Indeed, these sensors are mutated receptors but only on the intracellular domains with no changes of the binding site (Labouesse & Patriarchi, 2021)”

      This argument is diminished by the observation that different sensors based on the same binding site have different affinities (e.g. in Patriarchi et al. 2018, dLight1.1 has Kd of 330nM while dlight1.3b has Kd of 1600nM).

      We sincerely thank the reviewer for highlighting this important point. We fully recognize the fundamental importance of absolute and relative DA receptor kinetics for modeling DA actions and acknowledge that differences in affinity estimates from sensor-based measurements highlight the inherent uncertainty in selecting receptor kinetics parameters. While we have based our modeling decisions on what we believe to be the most relevant available data, we acknowledge that the choice of receptor kinetics is a topic of ongoing debate. Importantly, we are making our model available to the research community, allowing others to test their own estimates of receptor kinetics and assess their impact on the model’s behavior. In our revised manuscript, we will further discuss the rationale behind our parameter choices, including: Our selection of a Kd value of 1000 nM for D1R (based on the observed affinities for D1R sensors) and an extrapolated Koff of 19.5 s<sup>-1</sup> (Labouesse & Patriarchi, 2021). Our use of a Kd value of 7 nM and an extrapolated Koff of 0.2 s<sup>-1</sup> for D2R, consistent with recent binding studies (Ågren et al., 2021).

      (7) Estimates of Vmax for DA uptake are entirely based on prior fast-scan voltammetry studies (Table S2). But FSCV likely produces distorted measures of uptake rate due to the kinetics of DA adsorption and release on the carbon fiber surface.

      We fully agree that this is a limitation of FSCV. However, most of the cited papers attempt to correct for this by way of fitting the output to a multi-parameter model for DA kinetics. If newer literature brings the Vmax values estimated into question, we have made the model publicly available to rerun the simulations with new parameters.

      (8) It is assumed that tortuosity is the same in DS and VS - is this a safe assumption?

      The original paper cited does not specify which region the values are measured in. However, a separate paper estimates the rat cerebellum has a comparable tortuosity index (Nicholson and Phillips, J Physiol. (1981)), suggesting it may be a rather uniform value across brain regions.

      (9) More discussion is needed about how the conclusions derived from this more elaborate model of DA dynamics are the same, and different, to conclusions drawn from prior relevant models (including those cited, e.g. from Hunger et al. 2020, etc).

      As part of our revision, we will expand the current discussion of our finding in the context of previous models in the manuscript

      Reviewer #2 (Public review):

      The work presents a model of dopamine release, diffusion, and reuptake in a small (100 micrometer^2 maximum) volume of striatum. This extends previous work by this group and others by comparing dopamine dynamics in the dorsal and ventral striatum and by using a model of immediate dopamine-receptor activation inferred from recent dopamine sensor data. From their simulations, the authors report two main conclusions. The first is that the dorsal striatum does not appear to have a sustained, relatively uniform concentration of dopamine driven by the constant 4Hz firing of dopamine neurons; rather that constant firing appears to create hotspots of dopamine. By contrast, the lower density of release sites and lower rate of reuptake in the ventral striatum creates a sustained concentration of dopamine. The second main conclusion is that D1 receptor (D1R) activation is able to track dopamine concentration changes at short delays but D2 receptor activation cannot.

      The simulations of the dorsal striatum will be of interest to dopamine aficionados as they throw some doubt on the classic model of "tonic" and "phasic" dopamine actions, further show the disconnect between dopamine neuron firing and consequent release, and thus raise issues for the reward-prediction error theory of dopamine.

      There is some careful work here checking the dependence of results on the spatial volume and its discretisation. The simulations of dopamine concentration are checked over a range of values for key parameters. The model is good, the simulations are well done, and the evidence for robust differences between dorsal and ventral striatum dopamine concentration is good.

      However, the main weakness here is that neither of the main conclusions is strongly evidenced as yet. The claim that the dorsal striatum has no "tonic" dopamine concentration is based on the single example simulation of Figure 1 not the extensive simulations over a range of parameters. Some of those later simulations seem to show that the dorsal striatum can have a "tonic" dopamine concentration, though the measurement of this is indirect. It is not clear why the reader should believe the example simulation over those in the robustness checks, for example by identifying which range of parameter values is more realistic.

      We appreciate that the reviewer finds our work interesting and carefully performed. The reviewer is correct that DA dynamics, including the presence and level of tonic DA, are parameter-dependent in both the dorsal striatum (DS) and ventral striatum (VS). Indeed, our simulations across a broad range of biological parameters were intended to help readers understand how such variation would impact the model’s outcomes, particularly since many of the parameters remain contested. Naturally, altering these parameters results in changes to the observed dynamics. However, to derive possible conclusions, we selected a subset of parameters that we believe best reflect the physiological conditions, as elaborated in the manuscript. This is eventually required in computational modelling of biological systems. In response to the reviewer’s comment, we will place greater emphasis on clarifying which parameter regimes produce a "tonic" versus "non-tonic" DA state in the DS. Additionally, we will underscore that the distinction between tonic and non-tonic states is not a binary outcome but a parameter-dependent continuum—one that our model now allows researchers to explore systematically. Finally, we will highlight how our simulations across parameter space not only capture this continuum but also identify the regimes that produce the most heterogeneous DA signaling, both within and across striatal regions.

      The claim that D1Rs can track rapid changes in dopamine is not well supported. It is based on a single simulation in Figure 1 (DS) and 2 (VS) by visual inspection of simulated dopamine concentration traces - and even then it is unclear that D1Rs actually track dynamics because they clearly do not track rapid changes in dopamine that are almost as large as those driven by bursts (cf Figure 1i).

      We would like to draw the attention also to Fig. S1, where the claim that D1R track rapid changes is supported in more depth. According to this figure, upon coordinated burst firing, the D1R occupancy rapidly increased as diffusion no longer equilibrated the extracellular concentrations on a timescale faster than the receptors – and D1R receptor occupancy closely tracked extracellular DA with a delay on the order of tens of milliseconds. Note that the brief increases in [DA] from uncoordinated stochastic release events from tonic firing in Fig. 1i are too brief to drive D1 signaling, as the DA concentration diffuses into the remaining extracellular space on a timescale of 1-5 ms. This is faster than the receptors response rate, and does not lead to any downstream signaling according to our simulations. This means D1 kinetics are rapid enough to track coordinated signaling on a ~50 ms timescale and slower, but not fast enough to respond to individual release events from tonic activity. In our revised manuscript we will expand the discussion of this topic to provide greater clarity.

      The claim also depends on two things that are poorly explained. First, the model of binding here is missing from the text. It seems to be a simple bound-fraction model, simulating a single D1 or D2 receptor. It is unclear whether more complex models would show the same thing.

      We realize that this is not made clear in the methods and, accordingly, we will update the method section to elaborate on how we model receptor binding. The model simulates occupied fraction of D1R and D2R in every single voxel of the simulation space.

      Second, crucial to the receptor model here is the inference that D1 receptor unbinding is rapid; but this inference is made based on the kinetics of dopamine sensors and is superficially explained - it is unclear why sensor kinetics should let us extrapolate to receptor kinetics, and unclear how safe is the extrapolation of the linear regression by an order of magnitude to get the D1 unbinding rate.

      We chose to use the sensors because it was possible to estimate precise affinities/off-rates from the fluorescent measurements. Although there might some variation in affinities that could be attributable to the mutations introduced in the sensors, the data clearly separated D1R and D2R with a D1R affinity of ~1000 nM and a D1R affinity of ~7 nM (Labouesse & Patriarchi, 2021) consistent with earlier predictions of receptor affinities. From our assessment of the literature we found that this was the most reasonable way to estimate affinities and thereby off-rates. Importantly, the model has been made publicly available, so should new measurements arise, the simulations can be rerun with tweaks to the input parameters.

    1. Author response:

      We thank the reviewers for their thoughtful feedback. Below we provide an initial response to the central concerns that they have raised. In general, as part of our revisions, we plan to perform additional analyses to strengthen our conclusions, tone down more speculative interpretations, and clarify the novel contributions of our work. A full, point-by-point reply will follow alongside the revised manuscript.

      Briefly, the reviewers’ central concerns are that some of the conclusions are not sufficiently supported by the experimental evidence, specifically (1) the involvement of sharp-wave ripple (SWR)-unmodulated PFC neurons in signaling upcoming choice and (2) the absence of SWR time-locking of PFC non-local representations. They further suggest that (3) the spatial tuning in the PFC may reflect other cognitive processes rather than encoding spatial information; and (4) the manuscript is ambiguous as to which results are novel or corroborating previous work.

      (1) SWR-unmodulated PFC neurons signaling upcoming choice

      Reviewer 1 suggests that our finding that SWR-modulated neurons relate to hippocampal non-local representations contradicts the manuscript’s main conclusion. However, in our view, there is no contradiction and the finding highlights the distinction between the two sub-populations, namely the SWR-modulated neurons linked to hippocampal non-local representations, and the SWR-unmodulated neurons that are more active during prefrontal non-local representations.

      We do agree with the reviewer that the observation of higher firing rates of SWR-unmodulated neurons in the expression of non-local representations does not mean that these neurons are the sole or even main contributors to the non-local decoding. To address both comments, we will perform additional analyses to further disentangle the contributions of SWR-modulated and SWR-unmodulated PFC neurons to the non-local representations of upcoming choice.

      (2) Time-locking of PFC non-local representations to hippocampal SWRs

      Reviewer 1 comments that in the analysis of time-locking to hippocampal SWRs and theta phase, the behavior of the animals needs to be taken into account (i.e., immobility or running). We confirm that this was indeed done in our analysis and we will clarify this point in the revised manuscript.

      The reviewer further requested that PFC decoding during SWRs be performed at shorter timescales as in previous studies. We like to point out that (1) we found no increase in non-local decoding in the PFC around SWR onset (see Fig 5a), and (2) most of the non-local representations in the PFC occurred during the expression of local representations in the hippocampus (see Fig 4d). These data suggest that the non-local representations in both brain regions are expressed independently. To further strengthen this idea, we plan to (1) include the result of decoding PFC activity during SWRs at fine timescales as the reviewer suggested, and (2) look at the firing rates of PFC neurons during non-local representations exclusively when the hippocampus is encoding the actual (local) position.

      Following a suggestion by reviewer 2, we will also add a statistical assessment of how strongly the data supports the absence of time-locking.

      (3) Spatial tuning in the mPFC

      Reviewer 2 points out that the spatial tuning in the prefrontal cortex may be related to cognitive processes (e.g., attention or decision-making) rather than spatial encoding. However, our results show that decoded mPFC activity reliably differentiates between the two start and goal arms (Fig 4a), rate maps show little evidence of mirroring (Fig 3a), and the activity predicts turns in the cue-based task during which goal arms switch pseudo-randomly (meaning that the non-local representations encode the North and South arm alternatingly and correctly, rather than encoding a general rewarded goal arm; Fig. 4b). While it is likely that mPFC encodes several task-related variables, our data suggest that it also encodes distinct locations.

      The reviewer further claims that the results of Jadhav et al. (2016) contradict our findings because they supposedly showed that mPFC neurons unmodulated by SWRs are less tuned to space. However, this is incorrect, as Jadhav et al. (2016) showed that SWR-unmodulated PFC neurons have lower spatial coverage and consequentially are more spatially selective, which is consistent with our observations. We will rephrase this in the text to improve clarity.

      (4) Novelty

      We thank reviewer 2 for pointing out the significance of several novel findings in our work that deserve to be highlighted. This includes the dorsal-ventral profile of SWR-modulation and theta phase locking in the PFC and our observation that the neural representations in the PFC precede the behavioral switch in reversal learning. In our revised manuscript, we will rewrite the text to better emphasize our novel contributions, clearly distinguish new findings from confirmatory observations, and add missing citations where appropriate.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Hippocampal place cells display a sequence of firing activities when the animal travels through a spatial trajectory at a behavioral time scale of seconds to tens of seconds. Interestingly, parts of the firing sequence also occur at a much shorter time scale: ~120 ms within individual cycles of theta oscillation. These so-called theta sequences are originally thought to naturally result from the phenomenon of theta phase precession. However, there is evidence that theta sequences do not always occur even when theta phase precession is present, for example, during the early experience of a novel maze. The question is then how they emerge with experience (theta sequence development). This study presents evidence that a special group of place cells, those tuned to fast-gamma oscillations, may play a key role in theta sequence development.

      The authors analyzed place cells, LFPs, and theta sequences as rats traveled a circular maze in repeated laps. They found that a group of place cells were significantly tuned to a particular phase of fast-gamma (FG-cells), in contrast to others that did not show such tunning (NFG-cells). The authors then omitted FG-cells or the same number of NFG-cells, in their algorithm of theta sequence detection and found that the quality of theta sequences, quantified by a weighted correlation, was worse with the FG-cell omission, compared to that with the NFG-cell omission, during later laps, but not during early laps. What made the FG-cells special for theta sequences? The authors found that FG-cells, but not NFG-cells, displayed phase recession to slow-gamma (25 - 45 Hz) oscillations (within theta cycles) during early laps (both FG- and NFG-cells showed slow-gamma phase precession during later laps). Overall, the authors conclude that FG-cells contribute to theta sequence development through slow-gamma phase precession during early laps.

      How theta sequences are formed and developed during experience is an important question, because these sequences have been implicated in several cognitive functions of place cells, including memory-guided spatial navigation. The identification of FG-cells in this study is straightforward. Evidence is also presented for the role of these cells in theta sequence development. However, given several concerns elaborated below, whether the evidence is sufficiently strong for the conclusion needs further clarification, perhaps, in future studies.

      We thank the reviewer for these positive comments.

      (1) The results in Figure 3 and Figure 8 seems contradictory. In Figure 8, all theta sequences displayed a seemingly significant weighted correlation (above 0) even in early laps, which was mostly due to FG-cell sequences but not NFG-cell sequences (correlation for NFG-sequences appeared below 0). However, in Figure 3H, omitting FG-cells and omitting NFG-cells did not produce significant differences in the correlation. Conversely, FG-cell and NFG-cell sequences were similar in later laps in Figure 8 (NFG-cell sequences appeared even better than FG-cell sequences), yet omitting NFG-cells produced a better correlation than omitting FG-cells. This confusion may be related to how "FG-cell-dominant sequences" were defined, which is unclear in the manuscript. Nevertheless, the different results are not easy to understand.

      We thank the reviewer for pointing out this important problem.  The potential contradictory can be interpreted by different sequence dataset included in Fig3 and Fig8, described as follows.

      (1) In Fig 3, all sequences decoded without either FG or NFG cells were included, defined as exFG-sequences and exNFG sequences, so that we couldn’t observe sequence development at early phase and thus the weighted correlation was low.  (2) In Fig8, however, the sequences with either FG or NFG cells firing across at least 3 slow gamma cycles were included, defined as FG-cell sequences and NFG-cell sequences.  This criterion ensures to investigate the relationship between sequence development and slow gamma phase precession, so that these sequences were contributed by cells likely to show slow gamma phase precession.  These definitions have been updated to the “Theta sequences detection” section of the Methods (Line 606-619).

      At early phase, there’s still no difference of weighted correlation between FG-cell sequences and NFG-cell sequences (Author response image 1A, Student’s t test, t(65)=0.2, p=0.8, Cohen's D=0.1), but the FG-cell sequences contained high proportion of slow gamma phase precession (Fig8F).  At late phase, both FG-cell sequences and NFG-cell sequences exhibited slow gamma phase precession, so that their weighted correlation were high with no difference (Author response image 1B, Student’s t test, t(62)=-1.1, p=0.3, Cohen's D=0.3).  This result further indicates that the theta sequence development requires slow gamma phase precession, especially for FG cells during early phase.

      Author response image 1.

      (2) The different contributions between FG-cells and NFG-cells to theta sequences are supposed not to be caused by their different firing properties (Figure 5). However, Figure 5D and E showed a large effect size (Cohen's D = 07, 0.8), although not significant (P = 0.09, 0.06). But the seemingly non-significant P values could be simply due to smaller N's (~20). In other parts of the manuscript, the effect sizes were comparable or even smaller (e.g. D = 0.5 in Figure 7B), but interpreted as positive results: P values were significant with large N's (~480 in Fig. 7B). Drawing a conclusion purely based on a P value while N is large often renders the conclusion only statistical, with unclear physical meaning. Although this is common in neuroscience publications, it makes more sense to at least make multiple inferences using similar sample sizes in the same study.

      We thank the reviewer for this kind suggestion.  We made multiple inferences using similar sample sizes as much as possible.  In Fig7B, we did the statistical analysis with sessions as samples, and we found the significant conclusion was maintained.  These results have been updated to the revised manuscript (Lines 269-270).and the Fig7B has been replaced correspondingly.

      (3) In supplementary Figure 2 - S2, FG-cells displayed stronger theta phase precession than NFG-cells, which could be a major reason why FG-cells impacted theta sequences more than NFG cells. Although factors other than theta phase precession may contribute to or interfere with theta sequences, stronger theta phase precession itself (without the interference of other factors), by definition, can lead to stronger theta sequences.

      This is a very good point.  The finding that FG-cells displayed stronger theta phase precession than NFG-cells was consistent with the finding of Guardamagna et al., 2023 Cell Rep, that the theta phase precession pattern emerged with strong fast gamma.  Since slow gamma phase precession occurred within theta cycles, it is hard to consider the contribution of these factors to theta sequences development, without taking theta phase precession into account.  But one should be noted that the theta sequences could not be developed even if theta phase precession existed from the very beginning of the exploration (Feng et al., 2025 J Neurosci).  These findings suggest that theta phase precession, together with other factors, impact theta sequence development.  However, the weight of each factor and their interaction still need to be further investigated.  We have discussed this possibility in the Discussion section (Lines 361- 373).

      (4) The slow-gamma phase precession of FG-cells during early laps is supposed to mediate or contribute to the emergence of theta sequences during late laps (Figure 1). The logic of this model is unclear. The slow-gamma phase precession was present in both early and late laps for FG-cells, but only present in late laps for NFG-cells. It seems more straightforward to hypothesize that the difference in theta sequences between early and later laps is due to the difference in slow-gamma phase precession of NFG cells between early and late laps. Although this is not necessarily the case, the argument presented in the manuscript is not easy to follow.

      We thank the reviewer for pointing this out.  The slow gamma phase precession was first found in my previous publication (Zheng et al., 2016 Neuron), which indicates a temporally compressed manner for coding spatial information related to memory retrieval.  In this case, we would expect that slow gamma phase precession occurred in all cells during late laps, because spatial information was retrieved when rats have been familiar with the environment.  However, during early laps when novel information was just encoded, there would be balance between fast gamma and slow gamma modulation of cells for upcoming encoding-retrieval transition.  A possibility is that FG-cells support this balance by receiving modulation of both fast gamma and slow gamma, but with distinct phase-coding modes (fast gamma phase locking and slow gamma phase precession) in a temporally coordinated manner.  We have discussed this possibility in the Discussion section (Lines 415- 428).

      (5) There are several questions on the description of methods, which could be addressed to clarify or strengthen the conclusions.

      (i) Were the identified fast- and slow-gamma episodes mutually exclusive?

      Yes, the fast- and slow-gamma episodes are mutually exclusive. We have added descriptions in the “Detection of gamma episodes” section in the Methods part (Lines 538-550).

      (ii) Was the task novel when the data were acquired? How many days (from the 1st day of the task) were included in the analysis? When the development of the theta sequence was mentioned, did it mean the development in a novel environment, in a novel task, or purely in a sense of early laps (Lap 1, 2) on each day?

      We thank the reviewer for pointing this out.  The task was not novel to rats in this dataset, because only days with good enough recording quality for sequence decoding were included in this paper, which were about day2-day10 for each rat.  However, we still observed the process of sequence formation because of the rat’s exploration interest during early laps.  Thus, when the development of the theta sequence was mentioned, it meant a sense of early laps on each day.

      (iii) How were the animals' behavioral parameters equalized between early and later laps? For example, speed or head direction could potentially produce the differences in theta sequences.

      This is a very good point.  In terms of the effect of running speed on theta sequences, we quantified the running speeds during theta sequences across trials 1-5.  We found that the rats were running at stable running speed, which has been reported in Fig.3F.  In terms of the effect of head direction on theta sequences, we measured the angle difference between head direction and running direction.  We found that the angle difference for each lap was distributed around 0, with no significant difference across laps (Fig.S3, Watson-Williams multi-sample test, F(4,55)=0.2, p=0.9, partial η<sup>2</sup>= 0.01).  These results indicate that the differences in theta sequences across trials cannot be interpreted by the variability of behavioral parameters.  We have updated these results and corresponding methods in the revised manuscript (Lines 172-175, Lines 507-511, with a new Fig.S3).

      Reviewer #2 (Public Review):

      This manuscript addresses an important question that has not yet been solved in the field, what is the contribution of different gamma oscillatory inputs to the development of "theta sequences" in the hippocampal CA1 region? Theta sequences have received much attention due to their proposed roles in encoding short-term behavioral predictions, mediating synaptic plasticity, and guiding flexible decision-making. Gamma oscillations in CA1 offer a readout of different inputs to this region and have been proposed to synchronize neuronal assemblies and modulate spike timing and temporal coding. However, the interactions between these two important phenomena have not been sufficiently investigated. The authors conducted place cell and local field potential (LFP) recordings in the CA1 region of rats running on a circular track. They then analyzed the phase locking of place cell spikes to slow and fast gamma rhythms, the evolution of theta sequences during behavior, and the interaction between these two phenomena. They found that place cells with the strongest modulation by fast gamma oscillations were the most important contributors to the early development of theta sequences and that they also displayed a faster form of phase precession within slow gamma cycles nested with theta. The results reported are interesting and support the main conclusions of the authors. However, the manuscript needs significant improvement in several aspects regarding data analysis, description of both experimental and analytical methods, and alternative interpretations, as I detail below.

      • The experimental paradigm and recordings should be explained at the beginning of the Results section. Right now, there is no description whatsoever which makes it harder to understand the design of the study.

      We thank the reviewer for this kind suggestion.  The description of experimental paradigm and recordings has been added to the beginning of the results section (Lines 114-119).

      • An important issue that needs to be addressed is the very small fraction of CA1 cells phased-locked to slow gamma rhythms (3.7%). This fraction is much lower than in many previous studies, that typically report it in the range of 20-50%. However, this discrepancy is not discussed by the authors. This needs to be explained and additional analysis considered. One analysis that I would suggest, although there are also other valid approaches, is to, instead of just analyzing the phase locking in two discrete frequency bands, compute the phase locking will all LFP frequencies from 25-100 Hz. This will offer a more comprehensive and unbiased view of the gamma modulation of place cell firing. Alternative metrics to mean vector length that is less sensitive to firing rates, such as pairwise phase consistency index (Vinck et a., Neuroimage, 2010), could be implemented. This may reveal whether the low fraction of phase-locked cells could be due to a low number of spikes entering the analysis.

      We thank the reviewer for this constructive suggestion.  A previous work also on Long-Evans rats showed that the proportion of slow gamma phase-locked cells during novelty exploration was ~20%, however it dropped to ~10% during familiar exploration (Fig.4E, Kitanishi et al., 2015 Neuron).  This suggests that the proportion of slow gamma phase-locked cells may decreased with familiarity of the environment, which supports our data.  In addition, we also calculated the pairwise phase consistency index in terms of the effect of spike counts on MVL.  We could observe that the tendency of PPC (Author response image 2A) and MVL (Author response image 2B) along frequency bands were consistent across different subsets of cells, suggesting that the determination of cell subsets by MVL metric was not biased by the low number of spikes.  These results further shed light to the contribution of slow gamma phase precession of place cells to theta sequence development.

      Author response image 2.

      • From the methods, it is not clear to me whether the reference LFP channel was consistently selected to be a different one that where the spikes analyzed were taken. This is the better practice to reduce the contribution of spike leakage that could substantially inflate the coupling with faster gamma frequencies. These analyses need to be described in more detail.

      We thank the reviewer for pointing this out.  In the main manuscript, we used local LFPs as the cells were recorded from the same tetrode.  In addition, we selected an individual tetrode which located at stratum pyramidale and at the center of the drive bundle for each rat.  We detected a similar proportion of FG-cells by using LFPs on this tetrode, compared with that using local LFPs (Author response image 3A-B, Chi-squared test, χ<sup>2</sup>= 0.9, p=0.4, Cramer V=0.03).  We further found that the PPC measurement of FG- and NFG-cells were different at fast gamma band by using central LFPs (Author response image 3D), consistent with that by using local LFPs (Author response image 3C).  Therefore, these results suggest that the findings related to fast gamma was not due to the contribution of spike leakage in the local LFPs.  We have updated the description in the manuscript (Lines 553-557, 566-568).

      Author response image 3.

      • The initial framework of the authors of classifying cells into fast gamma and not fast gamma modulated implies a bimodality that may be artificial. The authors should discuss the nuances and limitations of this framework. For example, several previous work has shown that the same place cell can couple to different gamma oscillations (e.g., Lastoczni et al., Neuron, 2016; Fernandez-Ruiz et al., Neuron, 2017; Sharif et al., Neuron,2021).

      We thank the reviewer for this kind suggestion.  We have cited these references and discussed the possibility of bimodal phase-locking in the manuscript (Lines 430-433).

      • It would be useful to provide a more thorough characterization of the physiological properties of FG and NFG cells, as this distinction is the basis of the paper. Only very little characterization of some place cell properties is provided in Figure 5. Important characteristics that should be very feasible to compare include average firing rate, burstiness, estimated location within the layer (i.e., deep vs superficial sublayers) and along the transverse axis (i.e., proximal vs distal), theta oscillation frequency, phase precession metrics (given their fundamental relationship with theta sequences), etc.

      We thank the reviewer for this constructive suggestion.  In addition to the characterizations shown in Fig5, we also analyzed firing rate, anatomical location and theta modulation to compare the physiological properties of FG- and NFG-cells.

      In terms of the firing properties of both types of cells, we found that the mean firing rate of FG-cell was higher than NFG-cell (Fig. 5A, Student's t-test, t(22) = 2.1, p = 0.04, Cohen's D = 0.9), which was consistent with the previous study that the firing rate was higher during fast gamma than during slow gamma (Zheng et al., 2015 Hippocampus).  However, the spike counts of excluded FG- and NFG-cells for decoding were similar (Fig. 5B, Student's t-test, t(22) = 1.2, p = 0.3, Cohen's D = 0.5), suggesting that the differences found in theta sequences cannot be accounted for by different decoding quality related to spike counts.  In addition, we measured the burstiness based on the distribution of inter-spike-intervals, and we found that the bursting probability of spikes was not significantly different between FG and NFG cells (Author response image 4A, Student's t-test, t(22) = 0.6, p=0.5, Cohen's d=0.3).

      In terms of theta modulation of cells, we first compared the theta frequency related to the firing of FG and NFG cells.  We detected the instantaneous theta frequency at each spike timing of FG and NFG cells, and found that it was not significantly different between cell types (Author response image 4B, Student's t-test, t(22) = -0.5, p=0.6, Cohen's d=0.2).  In addition, we found the proportion of cells with significant theta phase precession was greater in FG-cells than in NFG-cells (Fig. S2E).  However, the slope and starting phase of theta phase precession was not significantly different between FG and NFG cells (Author response image 4C, Student's t-test, t(21) = 0.3, p=0.8, Cohen's d=0.1; Author response image 4D, Watson-Williams test, F(1,21)=0.5, p=0.5, partial η<sup>2</sup>=0.02).

      In terms of the anatomical location of FG and NFG cells, we identified tetrode traces in slices for each cell.  We found that both FG and NFG cells were recorded from the deep layer of dorsal CA1, with no difference of proportions between cell types (Author response image 4E, Chi-squared test, χ<sup>2</sup>=0.5, p=0.5, Cramer V=0.05).  The distribution of FG-cells he NFG-cells along the transverse axis was also similar between cell types (Author response image 4F, χ<sup>2</sup>=0.08, p=0.8, Cramer V=0.02).

      Author response image 4.

      • It is not clear to me how the analysis in Figure 6 was performed. In Figure 6B I would think that the grey line should connect with the bottom white dot in the third panel, which would be the interpretation of the results.

      We thank the reviewer for raising this good point.  The grey line was just for intuitional observation, not a quantitative analysis.  We have removed the grey lines from all heat maps in Fig.6.

      Reviewer #3 (Public Review):

      [Editors' note: This review contains many criticisms that apply to the whole sub-field of slow/fast gamma oscillations in the hippocampus, as opposed to this particular paper. In the editors' view, these comments are beyond the scope of any single paper. However, they represent a view that, if true, should contextualise the interpretation of this paper and all papers in the sub-field. In doing so, they highlight an ongoing debate within the broader field.]

      Summary:

      The authors aimed to elucidate the role of dynamic gamma modulation in the development of hippocampal theta sequences, utilizing the traditional framework of "two gammas," a slow and a fast rhythm. This framework is currently being challenged, necessitating further analyses to establish and secure the assumed premises before substantiating the claims made in the present article.

      The results are too preliminary and need to integrate contemporary literature. New analyses are required to address these concerns. However, by addressing these issues, it may be possible to produce an impactful manuscript.

      We thank the reviewer for raising these important questions in the hippocampal gamma field.  We have done a lot of new analyses according to the comments to strengthen our manuscript.

      I. Introduction

      Within the introduction, multiple broad assertions are conveyed that serve as the premise for the research. However, equally important citations that are not mentioned potentially contradict the ideas that serve as the foundation. Instances of these are described below:

      (1) Are there multiple gammas? The authors launched the study on the premise that two different gamma bands are communicated from CA3 and the entorhinal cortex. However, recent literature suggests otherwise, offering that the slow gamma component may be related to theta harmonics:

      From a review by Etter, Carmichael and Williams (2023)

      "Gamma-based coherence has been a prominent model for communication across the hippocampal-entorhinal circuit and has classically focused on slow and fast gamma oscillations originating in CA3 and medial entorhinal cortex, respectively. These two distinct gammas are then hypothesized to be integrated into hippocampal CA1 with theta oscillations on a cycle-to-cycle basis (Colgin et al., 2009; Schomburg et al., 2014). This would suggest that theta oscillations in CA1 could serve to partition temporal windows that enable the integration of inputs from these upstream regions using alternating gamma waves (Vinck et al., 2023). However, these models have largely been based on correlations between shifting CA3 and medial entorhinal cortex to CA1 coherence in theta and gamma bands. In vivo, excitatory inputs from the entorhinal cortex to the dentate gyrus are most coherent in the theta band, while gamma oscillations would be generated locally from presumed local inhibitory inputs (Pernía-Andrade and Jonas, 2014). This predominance of theta over gamma coherence has also been reported between hippocampal CA1 and the medial entorhinal cortex (Zhou et al., 2022). Another potential pitfall in the communication-through-coherence hypothesis is that theta oscillations harmonics could overlap with higher frequency bands (Czurkó et al., 1999; Terrazas et al., 2005), including slow gamma (Petersen and Buzsáki, 2020). The asymmetry of theta oscillations (Belluscio et al., 2012) can lead to harmonics that extend into the slow gamma range (Scheffer-Teixeira and Tort, 2016), which may lead to a misattribution as to the origin of slow-gamma coherence and the degree of spike modulation in the gamma range during movement (Zhou et al., 2019)."

      And from Benjamin Griffiths and Ole Jensen (2023)

      "That said, in both rodent and human studies, measurements of 'slow' gamma oscillations may be susceptible to distortion by theta harmonics [53], meaning open questions remain about what can be attributed to 'slow' gamma oscillations and what is attributable to theta."

      This second statement should be heavily considered as it is from one of the original authors who reported the existence of slow gamma.

      Yet another instance from Schomburg, Fernández-Ruiz, Mizuseki, Berényi, Anastassiou, Christof Koch, and Buzsáki (2014):

      "Note that modulation from 20-30 Hz may not be related to gamma activity but, instead, reflect timing relationships with non-sinusoidal features of theta waves (Belluscio et al., 2012) and/or the 3rd theta harmonic."

      One of this manuscript's authors is Fernández-Ruiz, a contemporary proponent of the multiple gamma theory. Thus, the modulation to slow gamma offered in the present manuscript may actually be related to theta harmonics.

      With the above emphasis from proponents of the slow/fast gamma theory on disambiguating harmonics from slow gamma, our first suggestion to the authors is that they A) address these statements (citing the work of these authors in their manuscript) and B) demonstrably quantify theta harmonics in relation to slow gamma prior to making assertions of phase relationships (methodological suggestions below). As the frequency of theta harmonics can extend as high as 56 Hz (PMID: 32297752), overlapping with the slow gamma range defined here (25-45 Hz), it will be important to establish an approach that decouples the two phenomena using an approach other than an arbitrary frequency boundary.

      We agree with the reviewer that the theta oscillations harmonics could overlap with higher frequency bands including slow gamma, as the above reviews discussed.  In order to rule out the possibility of theta harmonics effects in this study, we added new analyses in this letter (see below).

      (2) Can gammas be segregated into different lamina of the hippocampus? This idea appears to be foundational in the premise of the research but is also undergoing revision.

      As discussed by Etter et al. above, the initial theory of gamma routing was launched on coherence values. However, the values reported by Colgin et al. (2009) lean more towards incoherence (a value of 0) rather than coherence (1), suggesting a weak to negligible interaction. Nevertheless, this theory is coupled with the idea that the different gamma frequencies are exclusive to the specific lamina of the hippocampus.

      Recently, Deschamps et al. (2024) suggested a broader, more nuanced understanding of gamma oscillations than previously thought, emphasizing their wide range and variability across hippocampal layers. This perspective challenges the traditional dichotomy of gamma sub-bands (e.g., slow vs. medium gamma) and their associated cognitive functions based on a more rigid classification according to frequency and phase relative to the theta rhythm. Moreover, they observed all frequencies across all layers.

      Similarly, the current source density plots from Belluscio et al. (2012) suggest that SG and FG can be observed in both the radiatum and lacunosum-moleculare.

      Therefore, if the initial coherence values are weak to negligible and both slow and fast gamma are observed in all layers of the hippocampus, can the different gammas be exclusively related to either anatomical inputs or psychological functions (as done in the present manuscript)? Do these observations challenge the authors' premise of their research? At the least, please discuss.

      We thank the reviewer for raising this point, which I believe still remains controversial in this field.  We also thank the reviewer for providing detailed proofs of existence forms of gamma rhythms.  The reviewer was considering 2 aspects of gamma: 1) the reasonability of dividing slow and fast gamma by specific frequency bands; 2) the existence of gamma across all hippocampal layers, which challenged the functional significance of different types of gamma rhythms.  Although the results in Douchamps et al., 2024 challenged the idea of rigid gamma sub-bands, we still could see separate slow and fast gamma components exclusively occurred along time course, with central frequency of slow gamma lower than ~60Hz and central frequency of fast gamma higher than ~60Hz (Fig.1b of Douchamps et al., 2024).  This was also seen in the rat dataset of this reference (Fig. S3).  Since their behavioral test required both memory encoding and retrieval processes, it was hard to distinguish the role of different gamma components as they may dynamically coordinate during complex memory process.  Thus, although the behavioral performance can be decoded from broad range of gamma, we still cannot deny the existence of difference gamma rhythms and their functional significance during difference memory phases.

      (3) Do place cells, phase precession, and theta sequences require input from afferent regions? It is offered in the introduction that "Fast gamma (~65-100Hz), associated with the input from the medial entorhinal cortex, is thought to rapidly encode ongoing novel information in the context (Fernandez-Ruiz et al., 2021; Kemere, Carr, Karlsson, & Frank, 2013; Zheng et al., 2016)".

      CA1 place fields remain fairly intact following MEC inactivation include Ipshita Zutshi, Manuel Valero, Antonio Fernández-Ruiz , and György Buzsáki (2022)- "CA1 place cells and assemblies persist despite combined mEC and CA3 silencing" and from Hadas E Sloin, Lidor Spivak, Amir Levi, Roni Gattegno, Shirly Someck, Eran Stark (2024) - "These findings are incompatible with precession models based on inheritance, dual-input, spreading activation, inhibition-excitation summation, or somato-dendritic competition. Thus, a precession generator resides locally within CA1."

      These publications, at the least, challenge the inheritance model by which the afferent input controls CA1 place field spike timing. The research premise offered by the authors is couched in the logic of inheritance, when the effect that the authors are observing could be governed by local intrinsic activity (e.g., phase precession and gamma are locally generated, and the attribution to routed input is perhaps erroneous). Certainly, it is worth discussing these manuscripts in the context of the present manuscript.

      We thank the review for this discussion.  The main purpose of our current study is to investigate the mechanism of theta sequence development along with learning, which may or may not dependent on theta phase precession of single place cells as it remains controversial in this field.  Also, there is a limitation in this study that all gamma components were recorded from stratum pyramidale, thus we cannot make any conclusion on the originate of gamma in modulating sequence development.

      II. Results

      (1) Figure 2-

      a. There is a bit of a puzzle here that should be discussed. If slow and fast frequencies modulate 25% of neurons, how can these rhythms serve as mechanisms of communication/support psychological functions? For instance, if fast gamma is engaged in rapid encoding (line 72) and slow gamma is related to the integration processing of learned information (line 84), and these are functions of the hippocampus, then why do these rhythms modulate so few cells? Is this to say 75% of CA1 neurons do not listen to CA3 or MEC input?

      The proportion ~25% was the part of place cells phase-locked to either slow or fast gamma.  However, one of the main findings in this study was that most cells were modulated by slow gamma as they fired at precessed slow gamma phase within a theta cycle (Figs 6-8), which would promote information compression for theta sequence development.  Therefore, we didn’t mean that only a small proportion of cells were modulated by gamma rhythms and contributed to this process.

      b. Figure 2. It is hard to know if the mean vector lengths presented are large or small. Moreover, one can expect to find significance due to chance. For instance, it is challenging to find a frequency in which modulation strength is zero (please see Figure 4 of PMID: 30428340 or Figure 7 of PMID: 31324673).

      i. Please construct the histograms of Mean Vector Length as in the above papers, using 1 Hz filter steps from 1-120Hz and include it as part of Figure 2 (i.e., calculate the mean vector length for the filtered LFP in steps of 1-2 Hz, 2-3 Hz, 3-4 Hz,... etc). This should help the authors portray the amount of modulation these neurons have relative to the theta rhythm and other frequencies. If the theta mean vector length is higher, should it be considered the primary modulatory influence of these neurons (with slow and fast gammas as a minor influence)?

      We thank the review for this suggestion.  We measured the mean vector length at 5Hz step (equivalent to 1Hz step), and we found that the FG-cells were phase-locked to fast gamma rhythms even stronger than that to theta (Author response image 2B, mean MVL of theta=0.126±0.007, mean MVL of theta=0.175±0.006, paired t-test, t(112)=-5.9, p=0.01, Cohen's d=0.7).  In addition, in some previous studies with significant fast gamma phase locking, the MVL values were around 0.15 by using broad gamma band (Kitanishi et al., 2015 Neuron, Lasztóczi et al., 2016 Neuron, Tomar et al., 2021 Front Behav Neurosci, and Asiminas et al., 2022 Molecular Autism), which was consistent with the value in this study.  Therefore, we don’t believe that fast gamma was only a minor influence of these neurons.

      ii. It is possible to infer a neuron's degree of oscillatory modulation without using the LFP. For instance, one can create an ISI histogram as done in Figure 1 here (https://www.biorxiv.org/content/10.1101/2021.09.20.461152v3.full.pdf+html; "Distinct ground state and activated state modes of firing in forebrain neurons"). The reciprocal of the ISI values would be "instantaneous spike frequency". In favor of the Douchamps et al. (2024) results, the figure of the BioRXiV paper implies that there is a single gamma frequency modulate as there is only a single bump in the ISIs in the 10^-1.5 to 10^-2 range. Therefore, to vet the slow gamma results and the premise of two gammas offered in the introduction, it would be worth including this analysis as part of Figure 2.

      By using suggested method, we calculated the ISI distribution on log scale for FG-cells and NFG-cells during behavior (Author response image 5).  We could observe that the ISI distribution of FG-cells had a bump in the 10<sup>-1.5</sup>= to 10<sup>-2</sup>= range (black bar), in particular in the fast gamma range (10<sup>-2</sup>= to 10<sup>-1.8</sup>=).

      Author response image 5.

      c. There are some things generally concerning about Figure 2.

      i. First, the raw trace does not seem to have clear theta epochs (it is challenging to ascertain the start and end of a theta cycle). Certainly, it would be worth highlighting the relationship between theta and the gammas and picking a nice theta epoch.

      We thank the review for this suggestion.  We've updated this figure with a nice theta epoch in the revised manuscript.

      ii. Also, in panel A, there looks to be a declining amplitude relationship between the raw, fast, and slow gamma traces, assuming that the scale bars represent 100uV in all three traces. The raw trace is significantly larger than the fast gamma. However, this relationship does not seem to be the case in panel B (in which both the raw and unfiltered examples of slow and fast gamma appear to be equal; the right panels of B suggest that fast gamma is larger than slow, appearing to contradict the A= 1/f organization of the power spectral density). Please explain as to why this occurs. Including the power spectral density (see below) should resolve some of this.

      We thank the review for pointing this out.  The scales of y-axis of LFPs tracs in Fig.2B was not consistent, which mislead the comparison of amplitude between slow and fast gamma.  We have unified y axis scales across different gamma types in the revised manuscript.  Moreover, we also have replaced these examples with more typical ones (also see the response below).

      iii. Within the example of spiking to phase in the left side of Panel B (fast gamma example)- the neuron appears to fire near the trough twice, near the peak twice, and somewhere in between once. A similar relationship is observed for the slow gamma epoch. One would conclude from these plots that the interaction of the neuron with the two rhythms is the same. However, the mean vector lengths and histograms below these plots suggest a different story in which the neuron is modulated by FG but not SG. Please reconcile this.

      We thank the review for pointing this out.  We found that the fast gamma phase locking was robust across FG-cells with fast gamma peak as the preferred phase.  Therefore, we have replaced these examples with more typical ones, so that the examples were consistent with the group effect.

      iv. For calculating the MVL, it seems that the number of spikes that the neuron fires would play a significant role. Working towards our next point, there may be a bias of finding a relationship if there are too few spikes (spurious clustering due to sparse data) and/or higher coupling values for higher firing rate cells (cells with higher firing rates will clearly show a relationship), forming a sort of inverse Yerkes-Dodson curve. Also, without understanding the magnitude of the MVL relative to other frequencies, it may be that these values are indeed larger than zero, but not biologically significant.

      - Please provide a scatter plot of Neuron MVL versus the Neuron's Firing Rate for 1) theta (7-9 Hz), 2) slow gamma, and 3) fast gamma, along with their line of best fit.

      - Please run a shuffle control where the LFP trace is shifted by random values between 125-1000ms and recalculate the MVL for theta, slow, and fast gamma. Often, these shuffle controls are done between 100-1000 times (see cross-correlation analyses of Fujisawa, Buzsaki et al.).

      - To establish that firing rate does not play a role in uncovering modulation, it would be worth conducting a spike number control, reducing the number of spikes per cell so that they are all equal before calculating the phase plots/MVL.

      We thank the review for raising this point.  Beside of the MVL value, we also calculated the pairwise phase consistency (PPC) as suggested by Reviewer2, which is not sensitive to the spike counts.  We found that the phase locking strength to either rhythm (theta or gamma) was comparable between MVL and PPC measurements (Author response image 2).  Moreover, we quantified the relationship between MVL and mean firing rate, as suggested.  We found that the MVL value for theta, slow gamma and fast gamma was negatively correlated with mean firing rate (Author response image 6, Pearson correlation, theta: R<sup>2</sup>= 0.06, Pearson’s r=-0.3, p=1.3×10<sup>-8</sup>=; slow gamma: R<sup>2</sup>= 0.1, Pearson’s r=-0.4, p=2.4×10<sup>-17</sup>=; fast gamma: R<sup>2</sup>= 0.03, Pearson’s r=-0.2, p=4.3×10<sup>-5</sup>=).  These results help us rule out the concerns of the effect of spikes counts on the phase modulation measurement.

      Author response image 6.

      (2) Something that I anticipated to see addressed in the manuscript was the study from Grosmark and Buzsaki (2016): "Cell assembly sequences during learning are "replayed" during hippocampal ripples and contribute to the consolidation of episodic memories. However, neuronal sequences may also reflect preexisting dynamics. We report that sequences of place-cell firing in a novel environment are formed from a combination of the contributions of a rigid, predominantly fast-firing subset of pyramidal neurons with low spatial specificity and limited change across sleep-experience-sleep and a slow-firing plastic subset. Slow-firing cells, rather than fast-firing cells, gained high place specificity during exploration, elevated their association with ripples, and showed increased bursting and temporal coactivation during postexperience sleep. Thus, slow- and fast-firing neurons, although forming a continuous distribution, have different coding and plastic properties."

      My concern is that much of the reported results in the present manuscript appear to recapitulate the observations of Grosmark and Buzsaki, but without accounting for differences in firing rate. A parsimonious alternative explanation for what is observed in the present manuscript is that high firing rate neurons, more integrated into the local network and orchestrating local gamma activity (PING), exhibit more coupling to theta and gamma. In this alternative perspective, it's not something special about how the neurons are entrained to the routed fast gamma, but that the higher firing rate neurons are better able to engage and entrain their local interneurons and, thus modulate local gamma. However, this interpretation challenges the discussion around the importance of fast gamma routed from the MEC.

      a. Please integrate the Grosmark & Buzsaki paper into the discussion.

      b. Also, please provide data that refutes or supports the alternative hypothesis in which the high firing rate cells are just more gamma modulated as they orchestrate local gamma activity through monosynaptic connections with local interneurons (e.g., Marshall et al., 2002, Hippocampal pyramidal cell-interneuron spike transmission is frequency dependent and responsible for place modulation of interneuron discharge). Otherwise, the attribution to a MEC routed fast gamma routing seems tenuous.

      c. It is mentioned that fast-spiking interneurons were removed from the analysis. It would be worth including these cells, calculating the MVL in 1 Hz increments as well as the reciprocal of their ISIs (described above).

      We thank the review for this suggestion.  Because we found the mean firing rate of FG-cells was higher than that of NFG-cells, it would be possible that the FG-cells are mainly overlapped with fast-firing cells (rigid cells) in Grosmark et al., 2016 Science.  Actually, in this study, we aimed to investigate how fast and slow gamma rhythms modulated neurons dynamically during learning, rather than defining new cell types.  Thus, we don’t think this work was just a replication of the previous publication.  We have added this description in the Discussion part (Lines 439-441).  In addition, we don’t have enough number of interneurons to support the analysis between interneurons and place cells.  Therefore, we couldn’t make any statement about where was the fast gamma originated (CA1 locally or routed from MEC) in this study.

      (3) Methods - Spectral decomposition and Theta Harmonics.

      a. It is challenging to interpret the exact parameters that the authors used for their multi-taper analysis in the methods (lines 516-526). Tallon-Baudry et al., (1997; Oscillatory γ-Band (30-70 Hz) Activity Induced by a Visual Search Task in Humans) discuss a time-frequency trade-off where frequency resolution changes with different temporal windows of analysis. This trade-off between time and frequency resolution is well known as the uncertainty principle of signal analysis, transcending all decomposition methods. It is not only a function of wavelet or FFT, and multi-tapers do not directly address this. (The multitaper method, by using multiple specially designed tapers -like the Slepian sequences- smooths the spectrum. This smoothing doesn't eliminate leakage but distributes its impact across multiple estimates). Given the brevity of methods and the issues of theta harmonics as offered above, it is worth including some benchmark trace testing for the multi-taper as part of the supplemental figures.

      i. Please spectrally decompose an asymmetric 8 Hz sawtooth wave showing the trace and the related power spectral density using the multiple taper method discussed in the methods.

      ii. Please also do the same for an elliptical oscillation (perfectly symmetrical waves, but also capable of casting harmonics). Matlab code on how to generate this time series is provided below:

      A = 1; % Amplitude

      T = 1/8; % Period corresponding to 8 Hz frequency

      omega = 2*pi/T; % Angular frequency

      C = 1; % Wave speed

      m = 0.9; % Modulus for the elliptic function (0<m<1 for cnoidal waves)

      x = linspace(0, 2*pi, 1000); % temporal domain

      t = 0; % Time instant

      % Calculate B based on frequency and speed

      B = sqrt(omega/C);

      % Cnoidal wave equation using the Jacobi elliptic function

      u = A .* ellipj(B.*(x - C*t), m).^2;

      % Plotting the cnoidal wave

      figure;

      plot(x./max(x), u);

      title('8 Hz Cnoidal Wave');

      xlabel('time (x)');

      ylabel('Wave amplitude (u)');

      grid on;

      The Symbolic Math Toolbox needs to be installed and accessible in your MATLAB environment to use ellipj. Otherwise, I trust that, rather than plotting a periodic orbit around a circle (sin wave) the authors can trace the movement around an ellipse with significant eccentricity (the distance between the two foci should be twice the distance between the co-vertices).

      We thank the review for this suggestion.  In the main text of manuscript, we only applied Morlet's wavelet method to calculate the time varying power of rhythms.  Multitaper method was used for the estimation of power spectra across running speeds, which was shown in the manuscript.  Therefore, we removed the description of Multitaper method and updated the Morlet's wavelet power spectral analysis in the Methods (Lines 541-544).

      As suggested, we estimated the power spectral densities of 8 Hz sawtooth and elliptical oscillation by using these methods, and compared them with the results from FFT.  We found that both the Multitaper's and Morlet's wavelet methods could well capture the 8Hz oscillatory components (Author response image 7).  However, we could observe harmonic components from FFT spectrum.

      Author response image 7.

      iii. Line 522: "The power spectra across running speeds and absolute power spectrum (both results were not shown).". Given the potential complications of multi-taper discussed above, and as each convolution further removes one from the raw data, it would be the most transparent, simple, and straightforward to provide power spectra using the simple fft.m code in Matlab (We imagine that the authors will agree that the results should be robust against different spectral decomposition methods. Otherwise, it is concerning that the results depend on the algorithm implemented and should be discussed. If gamma transience is a concern, the authors should trigger to 2-second epochs in which slow/fast gamma exceeds 3-7 std. dev. above the mean, comparing those resulting power spectra to 2-second epochs with ripples - also a transient event). The time series should be at least 2 seconds in length (to avoid spectral leakage issues and the issues discussed in Talon-Baudry et al., 1997 above).

      Please show the unmolested power spectra (Y-axis units in mV2/Hz, X-axis units as Hz) as a function of running speed (increments of 5 cm/s) for each animal. I imagine three of these PSDs for 3 of the animals will appear in supplemental methods while one will serve as a nice manuscript figure. With this plot, please highlight the regions that the authors are describing as theta, slow, and fast gamma. Also, any issues should be addressed should there be notable differences in power across animals or tetrodes (issues with locations along proximal-distal CA1 in terms of MEC/LEC input and using a local reference electrode are discussed below).

      As suggested, we firstly estimated the power spectra as a function of running speeds in each running lap, and showed them separately for each rat, by using the multitaper spectral analysis (Author response image 8).  In addition, to achieve unmolested power spectra, the short-time Fourier transform (STFT) was used for this analysis at the same frequency resolution (Author response image 9).  We could see that the power spectra were consistent between these two methods.  Notably, there seems no significant theta harmonic component in the slow gamma band range.

      The multitaper spectral analysis was performed as follows.  The power spectra were measured across different running speeds as described previously (Ahmed et al., 2012 J Neurosci; Zheng et al., 2015 Hippocampus; Zheng et al., 2016 eNeuro).  Briefly, the absolute power spectrum was calculated for 0.5s moving window and 0.2s step size of the LFPs recordings each lap, using the multitaper spectral analysis in the Chronux toolbox (Mitra and Bokil, 2008, http://chronux.org/) and STFT spectral analysis in Matlab script stft.m.  In the multitaper method, the time-bandwidth product parameter (TW) was set at 3, and the number of tapers (K) was set at 5.  In the STFT method, the FFT length was set at 2048, which was equivalent with the parameters used in multitaper method.  Running speed was calculated (see “Estimation of running speed and head direction” section in the manuscript) and averaged within each 0.5s time window corresponding to the LFP segments.  Then, the absolute power at each frequency was smoothed with a Gaussian kernel centered on given speed bin.  The power spectral as a function of running speed and frequency were plotted in log scale.  Also, the colormap was in log scale, allowing for comparisons across different frequencies that would otherwise be difficult due to the 1/f decay of power in physiological signals.

      Author response image 8.

      Author response image 9.

      iv. Schomberg and colleagues (2014) suggested that the modulation of neurons in the slow gamma range could be related to theta harmonics (see above). Harmonics can often extend in a near infinite as they regress into the 1/f background (contributing to power, but without a peak above the power spectral density slope), making arbitrary frequency limits inappropriate. Therefore, in order to support the analyses and assertions regarding slow gamma, it seems necessary to calculate a "theta harmonic/slow gamma ratio". Aru et al. (2015; Untangling cross-frequency coupling in neuroscience) offer that: " The presence of harmonics in the signal should be tested by a bicoherence analysis and its contribution to CFC should be discussed." Please test both the synthetic signals above and the raw LFP, using temporal windows of greater than 4 seconds (again, the large window optimizes for frequency resolution in the time-frequency trade-off) to calculate the bicoherence. As harmonics are integers of theta coupled to itself and slow gamma is also coupled to theta, a nice illustration and contribution to the field would be a method that uses the bispectrum to isolate and create a "slow gamma/harmonic" ratio.

      We thank the reviewer for providing the method regarding on the theta harmonics.  We firstly measured the theta harmonics on the synthesized signal by using the biphasic coherence method, and we could clearly observe the nonlinear coupling between theta rhythm and its harmonics (Author response image 10).

      Author response image 10.

      In addition, we also measured the bicoherence on raw traces during slow gamma episodes.  We did not see nonlinear coupling between slow gamma and theta bands in this real data (mean bicoherence=0.1±0.0002) compared with that in the synthesized signal (mean bicoherence=0.7 for elliptical waves and 0.5 for sawtooth waves), suggesting that the slow gamma detected in this study was not pure theta harmonic (Author response image 11C, F, I, in red boxes).  Therefore, we believe that the contribution of theta harmonic in slow gamma is not significant.

      Author response image 11.

      (4) I appreciate the inclusion of the histology for the 4 animals. Knerim and colleagues describe a difference in MEC projection along the proximal-distal axis of the CA1 region (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3866456/)- "There are also differences in their direct projections along the transverse axis of CA1, as the LEC innervates the region of CA1 closer to the subiculum (distal CA1), whereas the MEC innervates the region of CA1 closer to CA2 and CA3 (proximal CA1)" From the histology, it looks like some of the electrodes are in the part of CA1 that would be dominated by LEC input while a few are closer to where the MEC would project.

      a. How do the authors control for these differences in projections? Wouldn't this change whether or not fast gamma is observed in CA1?

      b. I am only aware of one manuscript that describes slow gamma in the LEC which appeared in contrast to fast gamma from the MEC (https://www.science.org/doi/10.1126/science.abf3119). One would surmise that the authors in the present manuscript would have varying levels of fast gamma in their CA1 recordings depending on the location of the electrodes in the Proximal-distal axis, to the extent that some of the more medial tetrodes may need to be excluded (as they should not have fast gamma, rather they should be exclusively dominated by slow gamma). Alternatively, the authors may find that there is equal fast gamma power across the entire proximal-distal axis. However, this would pose a significant challenge to the LEC/slow gamma and MEC/fast gamma routing story of Fernandez-Ruiz et al. and require reconciliation/discussion.

      c. Is there a difference in neuron modulation to these frequencies based on electrode location in CA1?

      We thank the reviewer for this concern, which was also raised by Reviewer2.  We aligned the physical location of LFP channels in the proximal-distal axis based on histology.  In our dataset, only 2 rats were recorded from both distal and proximal hippocampus, so we calculated the gamma power from both sites in these rats.  We found that slow power was higher from proximal tetrodes than that from distal tetrodes (Author response image 12, repeated measure ANOVA, F(1,7)=10.2, p=0.02, partial η <sup>2</sup>=0.8).  However, fast gamma power were similar between different recording sites (F(1,7)=0.008, p=0.9, partial η <sup>2</sup>=0.001).  These results are partially consistent with the LEC/slow gamma and MEC/fast gamma routing story of Fernandez-Ruiz’s work.  The main reason would be that all LFPs were recorded from tetrodes in stratum pyramidale, deep layer in particular (Author response image 4E), so that it was hard to precisely identify their distance to distal/proximal apical dendrites.

      Author response image 12.

      In terms of the anatomical location of FG and NFG cells, we identified tetrode traces in slices for each cell.  We found that both FG and NFG cells were recorded from the deep layer of dorsal CA1, with no difference of proportions between cell types (Author response image 4E, Chi-squared test, χ<sup>2</sup>=0.5, p=0.5, Cramer V=0.05).  The distribution of FG-cells he NFG-cells along the transverse axis was also similar between cell types (Author response image 4F, χ<sup>2</sup>=0.08, p=0.8, Cramer V=0.02).

      (5) Given a comment in the discussion (see below), it will be worth exploring changes in theta, theta harmonic, slow gamma, and fast gamma power with running speed as no changes were observed with theta sequences or lap number versus. Notably, Czurko et al., report an increase in theta and harmonic power with running speed (1999) while Ahmed and Mehta (2012) report a similar effect for gamma.

      a. Please determine if the oscillations change in power and frequency of the rhythms discussed above change with running speed using the same parameters applied in the present manuscript. The specific concern is that how the authors calculate running speed is not sensitive enough to evaluate changes.

      We thank the reviewer for this suggestion.  The description of running speed quantification has been updated in the Method (see “Estimation of running speed and head direction” section, Lines 501-511).  Overall, the sample frequency of running speed was25Hz which would be sensitive enough to evaluate the behavioral changes.

      By measuring the rhythmic power changing as a function of running speed (Author response image 8 and Author response image 9), we could observe that theta power was increased as running speed getting higher.  Consistent with the results in (Ahmed and Mehta, 2012) and our previous study (Zheng et al., 2015), the fast gamma power was increasing and slow gamma power was decreasing when running speed was getting high.

      In addition, we also estimated the rhythmic frequency as a function of running speed in the slow and fast episodes respectively.  We found that fast gamma frequency was increased with running speed (Author response image 13, linear regression, R<sup>2</sup>=0.4, corr=0.6, p=9.9×10<sup>-15</sup>), whereas slow gamma frequency was decreased with running speed (R<sup>2</sup>=0.2, corr=-0.4, p=8.8×10<sup>-6</sup>).  Although significant correlation was found between gamma frequency and running speed, consistent with the previous studies, the frequency change (~70-75Hz for fast gamma and ~30-28Hz for slow gamma) was not big enough to affect the sequence findings in this study.  In additiontheta frequency was maintained in either slow episodes (R<sup>2</sup>=0.02, corr=-0.1, p=0.1) or fast episodes (R<sup>2</sup>=0.004, corr=0.06, p=0.5), consistent with results in Fig.1G of Kropff et al., 2021 Neuron.

      Author response image 13.

      b. It is astounding that animals ran as fast as they did in what appears to be the first lap (Figure 3F), especially as rats' natural proclivity is thigmotaxis and inquisitive exploration in novel environments. Can the authors expand on why they believe their rats ran so quickly on the first lap in a novel environment and how to replicate this? Also, please include the individual values for each animal on the same plot.

      We thank the reviewer for pointing this out.  The task was not brand new to rats in this dataset, because only days with good enough recording quality for sequence decoding were included in this paper, which were about day2-day10 for each rat.  However, we still observed the process of sequence formation because of the rat’s exploration interest during early laps.  Thus, in terms exploration behaviors, the rats ran at relative high speeds across laps (Author response image 14, each gray line represents the running speed within an individual session).

      Author response image 14.

      c. Can the authors explain how the statistics on line 169 (F(4,44)) work? Specifically, it is challenging to determine how the degrees of freedom were calculated in this case and throughout if there were only 4 animals (reported in methods) over 5 laps (depicted in Figure 3F. Given line 439, it looks like trials and laps are used synonymously). Four animals over 5 laps should have a DOF of 16.

      This statistic result was performed with each session/day as a sample (n=12 sessions/days).  The statistics were generated by repeated measures ANOVA on 5 trials in 12 sessions, with a DOF of 44.

      (6) Throughout the manuscript, I am concerned about an inflation of statistical power. For example on line 162, F(2,4844). The large degrees of freedom indicate that the sample size was theta sequences or a number of cells. Since multiple observations were obtained from the same animal, the statistical assumption of independence is violated. Therefore, the stats need to be conducted using a nested model as described in Aarts et al. (2014; https://pubmed.ncbi.nlm.nih.gov/24671065/). A statistical consult may be warranted.

      We thank the reviewer for this suggestion.  We have replaced this statistic result by using generalized linear mixed model with ratID being a covariate.  These results have been updated in the revised manuscript (Lines 164-167).

      (7) It is stated that one tetrode served as a quiet recording reference. The "quiet" part is an assumption when often, theta and gamma can be volume conducted to the cortex (e.g., Sirota et al., 2008; This is often why laboratories that study hippocampal rhythms use the cerebellum for the differential recording electrode and not an electrode in the corpus callosum). Generally, high frequencies propagate as well as low frequencies in the extracellular milieu (https://www.eneuro.org/content/4/1/ENEURO.0291-16.2016). For transparency, the authors should include a limitation paragraph in their discussion that describes how their local tetrode reference may be inadvertently diminishing and/or distorting the signal that they are trying to isolate. Otherwise, it would be worth hearing an explanation as to how the author's approach avoids this issue.

      In terms of the locations of references, we had 2 screws above the cerebellum in the skull connected to the recording drive ground, and 1 tetrode in a quiet area of the cortex serving as the recording reference.  We agree that the theta and gamma can be volume conducted to the cortex which may affect the power of these rhythms in the stratum pyramidale.  However, we didn’t mean to measure or compare the absolute theta or gamma power in this study, as we only cared about the phase modulation of gamma to place cells.  Therefore, we believe the location of recording reference would not make significant effect on our conclusion.

      Apologetically, this review is already getting long. Moreover, I have substantial concerns that should be resolved prior to delving into the remainder of the analyses. e.g., the analyses related to Figure 3-5 assert that FG cells are important for sequences. However, the relationship to gamma may be secondary to either their relationship to theta or, based on the Grosmark and Buzsaki paper, it may just be a phenomenon coupled to the fast-firing cells (fast-firing cells showing higher gamma modulation due to a local PING dynamic). Moreover, the observation of slow gamma is being challenged as theta harmonics, even by the major proponents of the slow/fast gamma theory. Therefore, the report of slow gamma precession would come as an unsurprising extension should they be revealed to be theta harmonics (however, no control for harmonics was implemented; suggestions were made above). Following these amendments, I would be grateful for the opportunity to provide further feedback.

      III. Discussion.

      a. Line 330- it was offered that fast gamma encodes information while slow gamma integrates in the introduction. However, in a task such as circular track running (from the methods, it appears that there is no new information to be acquired within a trial), one would guess that after the first few laps, slow gamma would be the dominant rhythm. Therefore, one must wonder why there are so few neurons modulated by slow gamma (~3.7%).

      The proportion of ~3.7% was the part of place cells phase-locked to slow gamma.  However, we aimed to find that the slow gamma phase precession of place cells promoted the theta sequence development.  We would not expect the cells phase-locked to slow gamma if phase precession occurred.

      b. Line 375: The authors contend that: "...slow gamma, related to information compression, was also required to modulate fast gamma phase-locked cells during sequence development. We replicated the results of slow gamma phase precession at the ensemble level (Zheng et al., 2016), and furthermore observed it at late development, but not early development, of theta sequences." In relation to the idea that slow gamma may be coupled to - if not a distorted representation of - theta harmonics, it has been observed that there are changes in theta relative to novelty.

      i. A. Jeewajee, C. Lever, S. Burton, J. O'Keefe, and N. Burgess (2008) report a decrease in theta frequency in novel circumstances that disappears with increasing familiarity.

      ii. One could surmise that this change in frequency is associated with alterations in theta harmonics (observed here as slow gamma), challenging the author's interpretation.

      iii. Therefore, the authors have a compelling opportunity to replicate the results of Jeewajee et al., characterizing changes of theta along with the development of slow gamma precession, as the environment becomes familiar. It will become important to demonstrate, using bicoherence as offered by Aru et al., how slow gamma can be disambiguated from theta harmonics. Specifically, we anticipate that the authors will be able to quantify A) theta harmonics (the number, and their respective frequencies and amplitudes), B) the frequency and amplitude of slow gamma, and C) how they can be quantitatively decoupled. Through this, their discussion of oscillatory changes with novelty-familiarity will garner a significant impact.

      We think we have demonstrated that the slow gamma observed in this study was not purely theta harmonics.  We didn’t focus on the frequency change of slow gamma or theta rhythms in this study.  Further investigation will be carried out on this topic in the future.

      c. Broadly, it is interesting that the authors emphasize the gamma frequency throughout the discussion. Given that the power spectral density of the Local Field Potential (LFP) exhibits a log-log relationship between amplitude and frequency, as described by Buzsáki (2005) in "Rhythms of the Brain," and considering that the LFP is primarily generated through synaptic transmembrane currents (Buzsáki et al., 2012), it seems parsimonious to consider that the bulk of synaptic activity occurs at lower frequencies (e.g., theta). Since synaptic transmission represents the most direct form of inter-regional communication, one might wonder why gamma (characterized by lower amplitude rhythms) is esteemed so highly compared to the higher amplitude theta rhythm. Why isn't the theta rhythm, instead, regarded as the primary mode of communication across brain regions? A discussion exploring this question would be beneficial.

      We thank the reviewer for this deep thinking.  When stating the conclusion on gamma rhythms, we didn’t mean to weaken the role of theta rhythm.  Conversely, the fast or slow gamma episodes were detected riding on theta rhythms, and we believe that the information compression should occur at a finer scale within a theta cycle scale.  More investigation will be carried out on this topic in the future.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) It is helpful to clearly define "FG-cell sequences" before the relevant results are described in the Results section. More importantly, the seemingly conflicting results between Figure 3 and Figure 8 may need to be clarified.

      The “exFG-sequences and exNFG sequences”, “FG-cell sequences and NFG-cell sequences” have been defined clearly in the revised manuscript.  Moreover, the seemingly conflicting results between Figure 3 and Figure 8 have been interpreted properly.

      (2) It is helpful to clearly state the N and what defines a sample whenever a result is described.

      In each statistical results, the N and what defines a sample have been clarified in the revised manuscript.

      (3) Addressing the questions regarding the methods (#5) would clarify some of the results.

      The questions regarding the Methods part has addressed in the revised manuscript.

      (4) Line #244: "successful" should be "successive"?

      Fixed.

      Reviewer #2 (Recommendations For The Authors):

      - The writing of the manuscript can be substantially improved.

      The manuscript can be substantially revised and updated.

      - I noticed that the last author of the manuscript is not the lead or corresponding and has only provided a limited contribution to this work (according to the detailed author contributions). The second to last author seems to be the main senior intellectual contributor and supervisor, together with the third to last author. This speaks of potential bad academic practices where a senior person whose intellectual contribution to the study is relatively minor takes the last author position, against the standard conventions on authorship worldwide. I strongly suggest that this is corrected.

      We thank the reviewer for raising this problem.  The last author Dr. Ming was also a senior author and supervised this project with large contribution.  We have fixed his role as a co-corresponding author in the revised manuscript.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This paper concerns mechanisms of foraging behavior in C. elegans. Upon removal from food, C. elegans first executes a stereotypical local search behavior in which it explores a small area by executing many random, undirected reversals and turns called "reorientations." If the worm fails to find food, it transitions to a global search in which it explores larger areas by suppressing reorientations and executing long forward runs (Hills et al., 2004). At the population level, the reorientation rate declines gradually. Nevertheless, about 50% of individual worms appear to exhibit an abrupt transition between local and global search, which is evident as a discrete transition from high to low reorientation rate (Lopez-Cruz et al., 2019). This observation has given rise to the hypothesis that local and global search correspond to separate internal states with the possibility of sudden transitions between them (Calhoun et al., 2014). The main conclusion of the paper is that it is not necessary to posit distinct internal states to account for discrete transitions from high to low reorientation rates. On the contrary, discrete transitions can occur simply because of the stochastic nature of the reorientation behavior itself.

      Strengths:

      The strength of the paper is the demonstration that a more parsimonious model explains abrupt transitions in the reorientation rate.

      Weaknesses:

      (1) Use of the Gillespie algorithm is not well justified. A conventional model with a fixed dt and an exponentially decaying reorientation rate would be adequate and far easier to explain. It would also be sufficiently accurate - given the appropriate choice of dt - to support the main claims of the paper, which are merely qualitative. In some respects, the whole point of the paper - that discrete transitions are an epiphenomenon of stochastic behavior - can be made with the authors' version of the model having a constant reorientation rate (Figure 2f).

      We apologize, but we are not sure what the reviewer means by “fixed dt”. If the reviewer means taking discrete steps in time (dt), and modeling whether a reorientation occurs, we would argue that the Gillespie algorithm is a better way to do this because it provides floating-point precision time resolution, rather than a time resolution limited by dt, which we hopefully explain in the comments below.

      The reviewer is correct that discrete transitions are an epiphenomenon of stochastic behavior as we show in Figure 2f. However, abrupt stochastic jumps that occur with a constant rate do not produce persistent changes in the observed rate because it is by definition, constant. The theory that there are local and global searches is based on the observation that individual worms often abruptly change their rates. But this observation is only true for a fraction of worms. We are trying to argue that the reason why this is not observed for all, or even most worms is because these are the result of stochastic sampling, not a sudden change in search strategy.

      (2) In the manuscript, the Gillespie algorithm is very poorly explained, even for readers who already understand the algorithm; for those who do not it will be essentially impossible to comprehend. To take just a few examples: in Equation (1), omega is defined as reorientations instead of cumulative reorientations; it is unclear how (4) follows from (2) and (3); notation in (5), line 133, and (7) is idiosyncratic. Figure 1a does not help, partly because the notation is unexplained. For example, what do the arrows mean, what does "*" mean?

      We apologize for this, you are correct,  is cumulative reorientations, and we will edit the text as follows:

      Experimentally, reorientation rate is measured as the number of reorientation events that occurred in an observational window. However, these are discrete stochastic events, so we should describe them in terms of propensity, i.e. the probability of observing a transitional event (in this case, a reorientation) is:

      Here, P(W+1,t) is the probability of observing a reorientation event at time t, and a<sub>1</sub> is the propensity for this event to occur. Observationally, the frequency of reorientations observed decays over time, so we can define the propensity as:

      Where α is the initial propensity at t=0.

      We can model this decay as the reorientation propensity coupled to a decaying factor (M):

      Where the propensity of this event (a<sub>2</sub>) is:

      Since M is a first-order decay process, when integrated, the cumulative M observed is:

      We can couple the probability of observing a reorientation to this decay by redefining (a<sub>1</sub> as:

      So that now:

      A critical detail should be noted. While reorientations are modeled as discrete events, the amount of M at time t\=0 is chosen to be large (M<sub>0</sub>←1,000), so that over the timescale of 40 minutes, the decay in M is practically continuous. This ensures that sudden changes in reorientations are not due to sudden changes in M, but due to the inherent stochasticity of reorientations.

      To model both processes, we can create the master equation:

      Since these are both Poisson processes, the probability density function for a state change i occurring in time t is:

      The probability that an event will not occur in time interval t is:

      The probability that no events will occur for ALL transitions in this time interval is:

      We can draw a random number (r<sub>1</sub> ∈[0,1]) that represents the probability of no events in time interval t, so that this time interval can be assigned by rearranging equation 11:

      where:

      This is the time interval for any event (W+1 or M-1) happening at t + t. The probability of which event occurs is proportional to its propensity:

      We can draw a second number (r<sub>2</sub> ∈[0,1]) that represents this probability so that which event occurs at time t + t is determined by the smallest n that satisfies:

      so that:

      The elegant efficiency of the Gillespie algorithm is two-fold. First, it models all transitions simultaneously, not separately. Second, it provides floating-point time resolution. Rather than drawing a random number, and using a cumulative probability distribution of interval-times to decide whether an event occurs at discrete steps in time, the Gillespie algorithm uses this distribution to draw the interval-time itself. The time resolution of the prior approach is limited by step size, whereas the Gillespie algorithm’s time resolution is limited by the floating-point precision of the random number that is drawn.

      We are happy to add this text to improve clarity.

      We apologize for the arrow notation confusion. Arrow notation is commonly used in pseudocode to indicate variable assignment, and so we used it to indicate variable assignment updates in the algorithm.

      We added Figure 2a to help explain the Gillespie algorithm for people who are unfamiliar with it, but you are correct, some notation, like probabilities, were left unexplained. We will address this to improve clarity.

      (3) In the model, the reorientation rate dΩ⁄dt declines to zero but the empirical rate clearly does not. This is a major flaw. It would have been easy to fix by adding a constant to the exponentially declining rate in (1). Perhaps fixing this obvious problem would mitigate the discrepancies between the data and the model in Figure 2d.

      You are correct that the model deviates slightly at longer times, but this result is consistent with Klein et al. that show a continuous decline of reorientations. However, we could add a constant to the model, since an infinite run length is likely not physiological.

      (4) Evidence that the model fits the data (Figure 2d) is unconvincing. I would like to have seen the proportion of runs in which the model generated one as opposed to multiple or no transitions in reorientation rate; in the real data, the proportion is 50% (Lopez). It is claimed that the "model demonstrated a continuum of switching to non-switching behavior" as seen in the experimental data but no evidence is provided.

      We should clarify that the 50% proportion cited by López-Cruz was based on an arbitrary difference in slopes, and by assessing the data visually. We sought to avoid this subjective assessment by plotting the distribution of slopes and transition times produced by the method used in López-Cruz. We should also clarify by what we meant by “a continuum of switching and non-switching” behavior. Both the transition time distributions and the slope-difference distributions do not appear to be the result of two distributions. This is unlike roaming and dwelling on food, where two distinct distributions of behavioral metrics can be identified based on speed and angular speed (Flavell et al, 2009, Fig S2a). We will add a permutation test to verify the mean differences in slopes and transition times between the experiment and model are not significant.

      (5) The explanation for the poor fit between the model and data (lines 166-174) is unclear. Why would externally triggered collisions cause a shift in the transition distribution?

      Thank you, we should rewrite the text to clarify this better. There were no externally triggered collisions; 10 animals were used per experiment. They would occasionally collide during the experiment, but these collisions were excluded from the data that were provided. However, worms are also known to increase reorientations when they encounter a pheromone trail, and it is unknown (from this dataset) which orientations may have been a result of this phenomenon.

      (6) The discussion of Levy walks and the accompanying figure are off-topic and should be deleted.

      Thank you, we agree that this topic is tangential, and we will remove it.

      Reviewer #2 (Public review):

      Summary:

      In this study, the authors build a statistical model that stochastically samples from a time-interval distribution of reorientation rates. The form of the distribution is extracted from a large array of behavioral data, and is then used to describe not only the dynamics of individual worms (including the inter-individual variability in behavior), but also the aggregate population behavior. The authors note that the model does not require assumptions about behavioral state transitions, or evidence accumulation, as has been done previously, but rather that the stochastic nature of behavior is "simply the product of stochastic sampling from an exponential function".

      Strengths:

      This model provides a strong juxtaposition to other foraging models in the worm. Rather than evoking a behavioral transition function (that might arise from a change in internal state or the activity of a cell type in the network), or evidence accumulation (which again maps onto a cell type, or the activity of a network) - this model explains behavior via the stochastic sampling of a function of an exponential decay. The underlying model and the dynamics being simulated, as well as the process of stochastic sampling, are well described and the model fits the exponential function (Equation 1) to data on a large array of worms exhibiting diverse behaviors (1600+ worms from Lopez-Cruz et al). The work of this study is able to explain or describe the inter-individual diversity of worm behavior across a large population. The model is also able to capture two aspects of the reorientations, including the dynamics (to switch or not to switch) and the kinetics (slow vs fast reorientations). The authors also work to compare their model to a few others including the Levy walk (whose construction arises from a Markov process) to a simple exponential distribution, all of which have been used to study foraging and search behaviors.

      Weaknesses:

      This manuscript has two weaknesses that dampen the enthusiasm for the results. First, in all of the examples the authors cite where a Gillespie algorithm is used to sample from a distribution, be it the kinetics associated with chemical dynamics, or a Lotka-Volterra Competition Model, there are underlying processes that govern the evolution of the dynamics, and thus the sampling from distributions. In one of their references, for instance, the stochasticity arises from the birth and death rates, thereby influencing the genetic drift in the model. In these examples, the process governing the dynamics (and thus generating the distributions from which one samples) is distinct from the behavior being studied. In this manuscript, the distribution being sampled is the exponential decay function of the reorientation rate (lines 100-102). This appears to be tautological - a decay function fitted to the reorientation data is then sampled to generate the distributions of the reorientation data. That the model performs well and matches the data is commendable, but it is unclear how that could not be the case if the underlying function generating the distribution was fit to the data.

      Thank you, we apologize that this was not clearer. In the Lotka-Volterra model, the density of predators and prey are being modeled, with the underlying assumption that rates of birth and death are inherently stochastic. In our model, the number of reorientations are being modeled, with the assumption (based on the experiments), that the occurrence of reorientations is stochastic, just like the occurrence (birth) of a prey animal is stochastic. However, the decay in M is phenomenological, and we speculate about the nature of M later in the manuscript.

      You are absolutely right that the decay function for M was fitted to the population average of reorientations and then sampled to generate the distributions of the reorientation data. This was intentional to show that the parameters chosen to match the population average would produce individual trajectories with comparable stochastic “switching” as the experimental data. All we’re trying to show really is that observed sudden changes in reorientation that appear persistent can be produced by a stochastic process without resorting to binary state assignments. In Calhoun, et al 2014 it is reported all animals produced switch-like behavior, but in Klein et al, 2017 it is reported that no animals showed abrupt transitions. López-Cruz et al seem to show a mix of these results, which can be easily explained by an underlying stochastic process.

      The second weakness is somewhat related to the first, in that absent an underlying mechanism or framework, one is left wondering what insight the model provides. Stochastic sampling a function generated by fitting the data to produce stochastic behavior is where one ends up in this framework, and the authors indeed point this out: "simple stochastic models should be sufficient to explain observably stochastic behaviors." (Line 233-234). But if that is the case, what do we learn about how the foraging is happening? The authors suggest that the decay parameter M can be considered a memory timescale; which offers some suggestion, but then go on to say that the "physical basis of M can come from multiple sources". Here is where one is left for want: The mechanisms suggested, including loss of sensory stimuli, alternations in motor integration, ionotropic glutamate signaling, dopamine, and neuropeptides are all suggested: these are basically all of the possible biological sources that can govern behavior, and one is left not knowing what insight the model provides. The array of biological processes listed is so variable in dynamics and meaning, that their explanation of what governs M is at best unsatisfying. Molecular dynamics models that generate distributions can point to certain properties of the model, such as the binding kinetics (on and off rates, etc.) as explanations for the mechanisms generating the distributions, and therefore point to how a change in the biology affects the stochasticity of the process. It is unclear how this model provides such a connection, especially taken in aggregate with the previous weakness.

      Providing a roadmap of how to think about the processes generating M, the meaning of those processes in search, and potential frameworks that are more constrained and with more precise biological underpinning (beyond the array of possibilities described) would go a long way to assuaging the weaknesses.

      Thank you, these are all excellent points. We should clarify that in López-Cruz et al, they claim that only 50% of the animals fit a local/global search paradigm. We are simply proposing there is no need for designating local and global searches if the data don’t really support it. The underlying behavior is stochastic, so the sudden switches sometimes observed can be explained by a stochastic process where the underlying rate is slowing down, thus producing the persistently slow reorientation rate when an apparent “switch” occurs. What we hope to convey is that foraging doesn’t appear to follow a decision paradigm, but instead a gradual change in reorientations which for individual worms, can occasionally produce reorientation trajectories that appear switch-like.

      As for M, you are correct, we should be more explicit. A decay in reorientation rate, rather than a sudden change, is consistent with observations made by López-Cruz et al.  They found that the neurons AIA and ADE redundantly suppress reorientations, and that silencing either one was sufficient to restore the large number of reorientations during early foraging. The synaptic output of AIA and ADE was inhibited over long timescales (tens of minutes) by presynaptic glutamate binding to MGL-1, a slow G-Protein coupled receptor expressed in AIA and ADE. Their results support a model where sensory neurons suppress the synaptic output of AIA and ADE, which in turn leads to a large number of reorientations early in foraging. As time passes, glutamatergic input from the sensory neurons decrease, which leads to disinhibition of AIA and ADE, and a subsequent suppression of reorientations.

      The sensory inputs into AIA and ADE are sequestered into two separate circuits, with AIA receiving chemosensory input and ADE receiving mechanosensory input. Since the suppression of either AIA or ADE is sufficient to increase reorientations, the decay in reorientations is likely due to the synaptic output of both of these neurons decaying in time. This correlates with an observed decrease in sensory neuron activity as well, so the timescale of reorientation decay could be tied to the timescale of sensory neuron activity, which in turn is influencing the timescale of AIA/ADE reorientation suppression. This implies that our factor “M” is likely the sum of several different sensory inputs decaying in time.

      The molecular basis of which sensory neuron signaling factors contribute to decreased AIA and ADE activity is made more complicated by the observation that the glutamatergic input provided by the sensory neurons was not essential, and that additional factors besides glutamate contribute to the signaling to AIA and ADE. In addition to this, it is simply not the sensory neuron activity that decays in time, but also the sensitivity of AIA and ADE to sensory neuron input that decays in time. Simply depolarizing sensory neurons after the animals had starved for 30 minutes was insufficient to rescue the reorientation rates observed earlier in the foraging assay. This observation could be due to decreased presynaptic vesicle release, and/or decreased receptor localization on the postsynaptic side.

      In summary, there are two neuronal properties that appear to be decaying in time. One is sensory neuron activity, and the other is decreased potentiation of presynaptic input onto AIA and ADE. Our factor “M” is a phenomenological manifestation of these numerous decaying factors.

      Reviewer #3 (Public review):

      Summary:

      This intriguing paper addresses a special case of a fundamental statistical question: how to distinguish between stochastic point processes that derive from a single "state" (or single process) and more than one state/process. In the language of the paper, a "state" (perhaps more intuitively called a strategy/process) refers to a set of rules that determine the temporal statistics of the system. The rules give rise to probability distributions (here, the probability for turning events). The difficulty arises when the sampling time is finite, and hence, the empirical data is finite, and affected by the sampling of the underlying distribution(s). The specific problem being tackled is the foraging behavior of C. elegans nematodes, removed from food. Such foraging has been studied for decades, and described by a transition over time from 'local'/'area-restricted' search'(roughly in the initial 10-30 minutes of the experiments, in which animals execute frequent turns) to 'dispersion', or 'global search' (characterized by a low frequency of turns). The authors propose an alternative to this two-state description - a potentially more parsimonious single 'state' with time-changing parameters, which they claim can account for the full-time course of these observations.

      Figure 1a shows the mean rate of turning events as a function of time (averaged across the population). Here, we see a rapid transient, followed by a gradual 4-5 fold decay in the rate, and then levels off. This picture seems consistent with the two-state description. However, the authors demonstrate that individual animals exhibit different "transition" statistics (Figure 1e) and wish to explain this. They do so by fitting this mean with a single function (Equations 1-3).

      Strengths:

      As a qualitative exercise, the paper might have some merit. It demonstrates that apparently discrete states can sometimes be artifacts of sampling from smoothly time-changing dynamics. However, as a generic point, this is not novel, and so without the grounding in C. elegans data, is less interesting.

      Weaknesses:

      (1) The authors claim that only about half the animals tested exhibit discontinuity in turning rates. Can they automatically separate the empirical and model population into these two subpopulations (with the same method), and compare the results?

      Thank you, we should clarify that the observation that about half the animals exhibit discontinuity was not made by us, but by López-Cruz et al. The observed fraction of 50% was based on a visual assessment of the dual regression method we described. To make the process more objective, we decided to simply plot the distributions of the metrics they used for this assessment to see if two distinct populations could be observed. However, the distributions of slope differences and transition times do not produce two distinct populations. Our stochastic approach, which does not assume abrupt state-transitions, also produces comparable distributions. To quantify this, we will perform permutation tests on the means and variances differences between experimental and model data.

      (2) The equations consider an exponentially decaying rate of turning events. If so, Figure 2b should be shown on a semi-logarithmic scale.

      We are happy to add this panel as well.

      (3) The variables in Equations 1-3 and the methods for simulating them are not well defined, making the method difficult to follow. Assuming my reading is correct, Omega should be defined as the cumulative number of turning events over time (Omega(t)), not as a "turn" or "reorientation", which has no derivative. The relevant entity in Figure 1a is apparently <Omega (t)>, i.e. the mean number of events across a population which can be modelled by an expectation value. The time derivative would then give the expected rate of turning events as a function of time.

      Thank you, you are correct. Please see response to Reviewer #1.

      (4) Equations 1-3 are cryptic. The authors need to spell out up front that they are using a pair of coupled stochastic processes, sampling a hidden state M (to model the dynamic turning rate) and the actual turn events, Omega(t), separately, as described in Figure 2a. In this case, the model no longer appears more parsimonious than the original 2-state model. What then is its benefit or explanatory power (especially since the process involving M is not observable experimentally)?

      Thank you, yes we see how as written this was confusing. In our response to Reviewer #1, we added an important detail:

      While reorientations are modeled as discrete events, which is observationally true, the amount of M at time t\=0 is chosen to be large (M<sub>0</sub>←1,000), so that over the timescale of 40 minutes, the decay in M is practically continuous. This ensures that sudden changes in reorientations are not due to sudden changes in M, but due to the inherent stochasticity of reorientations.

      However you are correct that if M was chosen to have a binary value of 0 or 1, then this would indeed be the two state model. Adding this as an additional model would be a good idea to compare how this matches the experimental data, and we are happy to add it.

      (5) Further, as currently stated in the paper, Equations 1-3 are only for the mean rate of events. However, the expectation value is not a complete description of a stochastic system. Instead, the authors need to formulate the equations for the probability of events, from which they can extract any moment (they write something in Figure 2a, but the notation there is unclear, and this needs to be incorporated here).

      Thank you, yes please see our response to Reviewer #1.

      (6) Equations 1-3 have three constants (alpha and gamma which were fit to the data, and M0 which was presumably set to 1000). How does the choice of M0 affect the results?

      Thank you, this is a good question. We will test this down to a binary state of M as mentioned in comment #4.

      (7) M decays to near 0 over 40 minutes, abolishing omega turns by the end of the simulations. Are omega turns entirely abolished in worms after 30-40 minutes off food? How do the authors reconcile this decay with the leveling of the turning rate in Figure 1a?

      Yes, reviewer #1 recommended adding a baseline reorientation rate which is likely more biologically plausible. However, we should also note that in Klein et al they observed a continuous decay over 50 minutes.

      (8) The fit given in Figure 2b does not look convincing. No statistical test was used to compare the two functions (empirical and fit). No error bars were given (to either). These should be added. In the discussion, the authors explain the discrepancy away as experimental limitations. This is not unreasonable, but on the flip side, makes the argument inconclusive. If the authors could model and simulate these limitations, and show that they account for the discrepancies with the data, the model would be much more compelling. To do this, I would imagine that the authors would need to take the output of their model (lists of turning times) and convert them into simulated trajectories over time. These trajectories could be used to detect boundary events (for a given size of arena), collisions between individuals, etc. in their simulations and to see their effects on the turn statistics.

      Thank you, we will add error bars and perform a permutation test on the mean and variance differences between experiment and model over the 40 minute window.

      (9) The other figures similarly lack any statistical tests and by eye, they do not look convincing. The exception is the 6 anecdotal examples in Figure 2e. Those anecdotal examples match remarkably closely, almost suspiciously so. I'm not sure I understood this though - the caption refers to "different" models of M decay (and at least one of the 6 examples clearly shows a much shallower exponential). If different M models are allowed for each animal, this is no longer parsimonious. Are the results in Figure 2d for a single M model? Can Figure 2e explain the data with a single (stochastic) M model?

      Thank you, yes, we will perform permutation tests on the mean and variance differences in the observed distributions in figure 2d. We certainly don’t want the panels in Figure 2e to be suspicious! These comparisons were drawn from calculating the correlations between all model traces and all experimental traces, and then choosing the top hits. Every time we run the simulation, we arrive at a different set of examples. Since it was recommended we add a baseline rate, these examples will be a completely different set when we run the simulation, again.

      We apologize for the confusion regarding M. Since the worms do not all start out with identical reorientation rates, we drew the initial M value from a distribution centered on M0 and a variance to match the initial distribution of observed experimental rates.

      (10) The left axes of Figure 2e should be reverted to cumulative counts (without the normalization).

      Thank you, we will add this. We want to clarify that we normalized it because we chose these examples based on correlation to show that the same types of sudden changes in search strategy can occur with a model that doesn’t rely on sudden rate changes.

      (11) The authors give an alternative model of a Levy flight, but do not give the obvious alternative models:

      a) the 1-state model in which P(t) = alpha exp (-gamma t) dt (i.e. a single stochastic process, without a hidden M, collapsing equations 1-3 into a single equation).

      b) the originally proposed 2-state model (with 3 parameters, a high turn rate, a low turn rate, and the local-to-global search transition time, which can be taken from the data, or sampled from the empirical probability distributions). Why not? The former seems necessary to justify the more complicated 2-process model, and the latter seems necessary since it's the model they are trying to replace. Including these two controls would allow them to compare the number of free parameters as well as the model results. I am also surprised by the Levy model since Levy is a family of models. How were the parameters of the Levy walk chosen?

      Thank you, we will remove this section completely, as it is tangential to the main point of the paper.

      (12) One point that is entirely missing in the discussion is the individuality of worms. It is by now well known that individual animals have individual behaviors. Some are slow/fast, and similarly, their turn rates vary. This makes this problem even harder. Combined with the tiny number of events concerned (typically 20-40 per experiment), it seems daunting to determine the underlying model from behavioral statistics alone.

      Thank you, yes we should have been more explicit in the reasoning behind drawing the initial M from a distribution (response to comment #9). We assume that not every worm starts out with the same reorientation rate, but that some start out fast (high M) and some start out slow (low M). However, we do assume M decays with the same kinetics, which seems sufficient to produce the observed phenomena.

      (13) That said, it's well-known which neurons underpin the suppression of turning events (starting already with Gray et al 2005, which, strangely, was not cited here). Some discussion of the neuronal predictions for each of the two (or more) models would be appropriate.

      Thank you, yes we will add Gray et al, but also the more detailed response to Reviewer #2.

      (14) An additional point is the reliance entirely on simulations. A rigorous formulation (of the probability distribution rather than just the mean) should be analytically tractable (at least for the first moment, and possibly higher moments). If higher moments are not obtainable analytically, then the equations should be numerically integrable. It seems strange not to do this.

      Thank you for suggesting this, we will add these analyses.

      In summary, while sample simulations do nicely match the examples in the data (of discontinuous vs continuous turning rates), this is not sufficient to demonstrate that the transition from ARS to dispersion in C. elegans is, in fact, likely to be a single 'state', or this (eq 1-3) single state. Of course, the model can be made more complicated to better match the data, but the approach of the authors, seeking an elegant and parsimonious model, is in principle valid, i.e. avoiding a many-parameter model-fitting exercise.

      As a qualitative exercise, the paper might have some merit. It demonstrates that apparently discrete states can sometimes be artifacts of sampling from smoothly time-changing dynamics. However, as a generic point, this is not novel, and so without the grounding in C. elegans data, is less interesting.

      Thank you, we agree that this is a generic phenomenon, which is partly why we did this. The data from López-Cruz seem to agree in part with Calhoun et al, that claim abrupt transitions occur, and Klein et al, which claim they do not occur. Since the underlying phenomenon is stochastic, we propose the mixed observations of sudden and gradual changes in search strategy are simply the result of a stochastic process, which can produce both phenomena for individual observations.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      This study investigated the mechanism by which PGE2 inhibits the release of insulin from pancreatic beta cells in response to glucose. The researchers used a combination of cell line experiments and studies in mice with genetic ablation of the Kv2.2 channel. Their findings suggest a novel pathway where PGE2 acts through EP2/EP4 receptors to activate PKA, which directly phosphorylates a specific site (S448) on the Kv2.2 channel, inhibiting its activity and reducing GSIS.

      Strengths:

      - The study elegantly demonstrates a potential pathway connecting PGE2, EP2/EP4 receptors, PKA, and Kv2.2 channel activity, using embryonic cell line.

      - Additional experiments in INS1 and primary mouse beta cells with altered Kv2.2 function partially support the inhibitory role of PGE2 on GSIS through Kv2.2 inhibition.

      Weaknesses:

      - A critical limitation is the use of HEK293T cells, which are not pancreatic beta cells. Functional aspects can differ significantly between these cell types.

      - The study needs to address the apparent contradiction of PKA activating insulin secretion in beta cells, while also inhibiting GSIS through the proposed mechanism.

      - A more thorough explanation is needed for the discrepancies observed between the effects of PGE2 versus Kv2.2 knockdown/mutation on the electrical activity of beta cells and GSIS.

      Thank you for your positive evaluation and constructive feedback on our study. We appreciate the concern regarding the use of HEK293T cells, which are not pancreatic beta cells and may exhibit functional differences. In response, we have repeated our key experiments using INS1 cells and primary mouse beta cells, which are more representative of the native beta cell environment. These additional experiments confirm our hypothesis and further support the role of Kv2.2 in PGE2-induced inhibition of GSIS. In beta cells, glucose-induced PKA activation is highly localized. As a result, while some PKA pathways promote insulin secretion, others may inhibit it. To directly demonstrate that PGE2-induced PKA phosphorylation of Kv2.2 is involved in the inhibitory effect on GSIS, we overexpressed the S448A mutant Kv2.2 channel in INS-1(832/13) cells. Our results show that Kv2.2-S448A channels significantly attenuate the inhibitory effect of PGE2 on GSIS, further supporting the critical role of Kv2.2 phosphorylation at S448. These data have been added to the revised Figure 7C.

      Reviewer #2 (Public Review):

      The authors identified new target elements for prostaglandin E2 (PGE2) through which insulin release can be regulated in pancreatic beta cells under physiological conditions. In vitro extracellular exposure to PGE2 could directly and dose-dependently inhibit the potassium channel Kv2.2. In vitro pharmacology revealed that this inhibition occurs through the EP2/4 receptors, which activate protein kinase A (PKA). By screening specific sites of the Kv2.2 channel, the target phosphorylation site (S448) for PKA regulation was found. The physiological relevance of the described signaling cascade was investigated and confirmed in vivo, using a Kv2.2 knockdown mouse model.

      The strength of this manuscript is the novelty of the (EP2/4-PKA-Kv2.2 channel) molecular pathway described and the comprehensive methodological toolkit the authors have relied upon.

      The introduction is detailed and contains all the information necessary to place the claims in context. Although the dataset is comprehensive and a logical lead is consistently built, there is one important point to consider: to clarify that the described signaling pathway is characteristic of normal physiological conditions and thus differs from pathological changes. It would be useful to carry out basic experiments in a diabetes model (regardless of whether this is in mice or rats).

      Thank you for your positive evaluation and insightful comment. We have clarified in the Discussion section that our findings pertain specifically to physiological conditions. We acknowledge the importance of investigating the signaling pathway in a pathological context and plan to conduct experiments using a diabetes model in future studies to explore how this pathway may differ under such conditions.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) Figure 3A-C: PKA activation regulates different functional aspects in beta cells and HEK293T cells. It is well known that PKA activation enhances insulin secretion in beta cells, therefore the mechanisms that allow the same pathway at the same time to inhibit GSIS are not clear and should be addressed by experiments in beta cells.

      Thank you for your insightful comment. Specificity and versatility in cAMP-PKA signaling are governed by the spatial localization and temporal dynamics of the signal. In beta cells, glucose-induced PKA activation is highly localized (Tengholm and Gylfe, 2017). As a result, while some PKA pathways promote insulin secretion, others may inhibit it. For example, a global increase in cAMP, such as through treatment with Db-cAMP, can simultaneously activate both stimulatory and inhibitory PKA pathways, reflecting a more integrated, complex response. In previous studies, 1 mM Db-cAMP was shown to enhance GSIS in INS-1 cells (Dezaki et al., 2011). We observed that 1 mM Db-cAMP increased GSIS, but lower concentrations (10 mM) decreased GSIS (as shown in Author response image 1). These findings suggest that not all PKA signaling events increase GSIS. To further investigate the role of PGE2-induced PKA phosphorylation of Kv2.2 in the inhibition of GSIS, we overexpressed the S448A mutant of Kv2.2 in INS-1 (832/13) cells. Our results showed that the Kv2.2-S448A mutant significantly attenuated the inhibitory effect of PGE2 on GSIS. These new data have been incorporated into the revised Figure 7C.

      Author response image 1.

      Effect of Db-cAMP on GSIS in INS-1 cells. Statistics for the effect of different concentrations of Db-cAMP on GSIS in INS-1(832/13) cells. One-way ANOVA with Bonferroni post hoc test. *p < 0.05; ***p < 0.001; ****p < 0.0001; n.s., not significant.

      (2) Figure 3G: One would expect that the phospho-mimetic mutation, S448D, will have an opposite effect to S448A and a similar effect as PGE2 or PKA activator in Figure 3B. There is no explanation by the authors for having the same effect in S448A and S448D.

      Thank you for your thoughtful comment. Indeed, the S448D mutation exhibited a similar effect to PGE2 on Kv2.2 channels, as we observed significantly smaller currents compared to wild-type Kv2.2 (Figure 3F). The S448D mutation mimics the phosphorylated state of S448, and since PGE2 regulates Kv2.2 channels by phosphorylating this residue, it has no further effect on the S448D mutant (Figure 3G). In contrast, the S448A mutation prevents phosphorylation at this site, which explains why PGE2 has no effect on the currents of S448A mutant Kv2.2 channels (Figure 3H). These results confirm that PGE2 modulates Kv2.2 channels specifically through phosphorylation of S448, as evidenced by the lack of effect on both the S448A and S448D mutants.

      (3) Figure 4E: Since both PGE2 and Kv2.2 KD inhibit the activity of the channel, it doesn't definitively prove whether PGE2 acts through Kv2.2 in INS-1 cells. A complementary experiment should be done in which overactivation of Kv2.2 rescues the effect of PGE2. For example, with the S448A form of the channel.

      We appreciate your comment and valuable suggestion. Knockdown of Kv2.2 abrogated the inhibitory effect of PGE2 on I<sub>K</sub> currents in INS-1 cells (Figure 4E and F), which strongly indicates that PGE2 acts through Kv2.2. While we agree that the suggested complementary experiment with Kv2.2 overactivation (e.g., using the S448A mutant) could provide additional insights, we believe the current data sufficiently support our conclusion, as the knockdown of Kv2.2 eliminates the observed PGE2 effect, providing direct evidence of the channel's involvement.

      (4) Figure 5C: This result requires further explanation. If PGE2 downregulates Kv2.2 activity and has an inhibitory effect on GSIS, why does Kv2.2 KD have the opposite effect?

      The knockdown of Kv2.2 (Fig. 5C) reduced action potential (AP) firing rates compared to the scramble control (Fig. 5B), which is expected because Kv2.2 is critical for maintaining AP firing. When Kv2.2 is knocked down, the reduced AP firing diminishes the system’s responsiveness to further modulation by PGE2. This is because PGE2 exerts its effects primarily through Kv2.2 channels. Therefore, in the Kv2.2 knockdown condition, PGE2 does not exert an additional inhibitory effect on AP firing rates, as the channels critical for its action are already impaired.

      (5) Figure 5D - The EP1-EP4 receptor antibodies should be validated at least in INS-1(832/13) cells using knockdowns.

      Thank you for your suggestion. We have validated the EP1-EP4 receptor antibodies in INS-1(832/13) cells using knockdown experiments. The validation results, including confirmation of specificity and knockdown efficiency, are provided in Supplemental Figure S2.

      (6) Figure 7B - These experiments don't necessarily prove that PGE2 acts directly through Kv2.2 inhibition. Using the S448A mutation in these experiments could prove this point.

      Thank you for this valuable suggestion. We have now overexpressed the S448A mutant Kv2.2 channels in INS-1(832/13) cells, and the results demonstrate that Kv2.2-S448A channels significantly reduce the inhibitory effect of PGE2 on GSIS. These new data have been incorporated into the revised Figure 7C.

      Reviewer #2 (Recommendations For The Authors):

      (1) Deficiencies and inaccuracies in the description of the methods (animal numbers, name of vendors, abbreviations) and the typos in the figures (axis label) require correction.

      Thank you for pointing this out. We have carefully reviewed the manuscript and the figures, making the necessary corrections to address the deficiencies in the methods section and the typos in the figure axis labels.

      (2) Reducing the number of figures (Figures 7/C-E: knockout mouse line test and Figure1/HEK cell experiments could be part of supplementary) and paragraphs would make the manuscript more compact and powerful. It would also ease its reading for non-experts.

      Thank you for your suggestion. We have moved Figures 7C-E to the supplementary data (Supplemental Figure S1) to streamline the main manuscript.

      (3) Multiple immunostainings for EP receptors in insulinoma cells or pancreatic islets would be representative.

      Due to the rabbit-derived nature of the antibodies (EP1, EP2, EP4), performing multiple immunostainings on the same samples is not feasible due to potential cross-reactivity. However, the immunohistochemistry images demonstrate that each antibody labels more than 90% of the cells, indicating that β-cell express different subtypes of EP receptors simultaneously.

      (4) The antagonists chosen (AH6809, AH23848) are non-specific. Experiments should be re-run (at least some) under more stringent conditions.

      Thank you for your suggestion. AH6809 and AH23848 are well-documented, widely used antagonists in the literature. To further strengthen our findings, we have included additional, widely-used antagonists: the EP2-specific antagonist TG4155 and the EP4-specific antagonist GW627368. The results obtained with these new antagonists were consistent with those observed using AH6809 and AH23848. These updated data are now included in the revised Figure 4I and 4J.

      (5) It would be very helpful to indeed emphasise that this work is for physiological conditions and that it is (or is not) modified in diabetes. Maybe even irrelevant for diabetes (?). This needs to be clarified and supported by data even if one could assume the authors intend to have a follow-up entirely dedicated to pathological changes, perhaps.

      Thank you for this insightful comment. We have clarified in the Discussion that our findings are specific to physiological conditions. To address this point, we have added the following statement:

      "Importantly, our findings pertain to physiological conditions. While we demonstrate the inhibitory effects of PGE2 on Kv2.2 channels in normal b-cells, the role of this pathway under diabetic conditions remains to be investigated and will be the focus of future studies."

      Dezaki K, Damdindorj B, Sone H, Dyachok O, Tengholm A, Gylfe E, Kurashina T, Yoshida M, Kakei M, Yada T (2011) Ghrelin attenuates cAMP-PKA signaling to evoke insulinostatic cascade in islet beta-cells. Diabetes 60:2315-2324.

      Tengholm A, Gylfe E (2017) cAMP signalling in insulin and glucagon secretion. Diabetes Obes Metab 19 Suppl 1:42-53.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer 1 (Public Review):

      O’Neill et al. have developed a software analysis application, miniML, that enables the quantification of electrophysiological events. They utilize a supervised deep learned-based method to optimize the software. miniML is able to quantify and standardize the analyses of miniature events, using both voltage and current clamp electrophysiology, as well as optically driven events using iGluSnFR3, in a variety of preparations, including in the cerebellum, calyx of held, Golgi cell, human iPSC cultures, zebrafish, and Drosophila. The software appears to be flexible, in that users are able to hone and adapt the software to new preparations and events. Importantly, miniML is an open-source software free for researchers to use and enables users to adapt new features using Python.

      Overall this new software has the potential to become widely used in the field and an asset to researchers. However, the authors fail to discuss or even cite a similar analysis tool recently developed (SimplyFire), and determine how miniML performs relative to this platform. There are a handful of additional suggestions to make miniML more user-friendly, and of broad utility to a variety of researchers, as well as some suggestions to further validate and strengthen areas of the manuscript:

      (1) miniML relative to existing analysis methods: There is a major omission in this study, in that a similar open source, Python-based software package for event detection of synaptic events appears to be completely ignored. Earlier this year, another group published SimplyFire in eNeuro (Mori et al., 2024; doi: 10.1523/eneuro.0326-23.2023). Obviously, this previous study needs to be discussed and ideally compared to miniML to determine if SimplyFire is superior or similar in utility, and to underscore differences in approach and accuracy.

      We thank the reviewer for bringing this interesting publication to our attention. We have included SimplyFire in our benchmarking for comprehensive comparison with miniML. The approach taken by SimplyFire differs from miniML in a number of ways. Our results show that miniML provides higher recall and precision than SimplyFire (revised Figure 3). We appreciate that SimplyFire provides a user-interface similar to the commonly used MiniAnalysis software. In addition, the peak-finding-based approach of SimplyFire makes it relatively robust to event shape, which facilitates analysis of diverse data. However, we noted a strong threshold-dependence and long run time of SimplyFire (revised Figure 3 and Figure 3—figure supplement 1). In addition, SimplyFire is not robust against various types of noise typically encountered in electrophysiological recordings. Our extended benchmark analysis thus indicates that AI-based event detection is superior to existing algorithmic approaches, including SimplyFire.

      (2) The manuscript should comment on whether miniML works equally well to quantify current clamp events (voltage; e.g. EPSP/mEPSPs) compared to voltage clamp (currents, EPSC/mEPSCs), which the manuscript highlights. Are rise and decay time constants calculated for each event similarly?

      miniML works equally well for current- and voltage events (Figure 5, Figure 9). In general, events of opposite polarity can be analyzed by simply inverting the data. Transfer learning models may further improve the detection.

      For each detected event, independent of data/recording type, rise times are calculated as 10–90% times (baseline–peak), and decay times are calculated as time to 50% of the peak. In addition, event decay time constants are calculated from a fit to the event average. With miniML being open-source, researchers can adapt the calculations of event statistics to their needs, if desired. In the revised manuscript, we have expanded the Methods section that describes the quantification of event statistics (Methods, Quantification).

      (3) The interface and capabilities of miniML appear quite similar to Mini Analysis, the free software that many in the field currently use. While the ability and flexibility for users to adapt and adjust miniML for their own uses/needs using Python programming is a clear potential advantage, can the authors comment, or better yet, demonstrate, whether there is any advantage for researchers to use miniML over Mini Analysis or SimplyFire if they just need the standard analyses?

      Following the reviewer’s suggestion, we developed a graphical user interface (GUI) for miniML to enhance its usability (Figure 2—figure supplement 2), which is provided on the GitHub repository. Our comprehensive benchmark analysis demonstrated that miniML outperforms existing tools such as MiniAnalysis and SimplyFire. The main advantages are (i) increased reliability of results, which eliminates the need for visual inspection; (ii) fast runtime and easy automation; (iii) superior detection performance as demonstrated by higher recall in both synthetic and real data; (iv) open-source Python-based design. We believe that these advantages make miniML a valuable tool for researchers recording various types of synaptic events, offering a more efficient and reliable solution compared to existing methods.

      (4) Additional utilities for miniML: The authors show miniML can quantify miniature electrophysiological events both current and voltage clamp, as well as optical glutamate transients using iGluSnFR. As the authors mention in the discussion, the same approach could, in principle, be used to quantify evoked (EPSC/EPSP) events using electrophysiology, Ca2+ events (using GCaMP), and AP waveforms using voltage indicators like ASAP4. While I don’t think it is reasonable to ask the authors to generate any new experimental data, it would be great to see how miniML performs when analysing data from these approaches, particularly to quantify evoked synaptic events and/or Ca2+ (ideally postsynaptic Ca2+ signals from miniature events, as the Drosophila NMJ have developed nice approaches).

      In the revised manuscript, we have extended the application examples of miniML. We applied miniML to detect mEPSPs recorded with the novel voltage-sensitive indicator ASAP5 (Figure 9 and Figure 9—figure supplement 1). We performed simultaneous recordings of membrane voltage through electrophysiology and ASAP5 voltage imaging in rat cultured neurons at physiological temperature. Data were analyzed using miniML, with electrophysiology data being used as ground-truth for assessing detection performance in imaging data. Our results demonstrate that miniML robustly detects mEPSPs in current-clamp, and can localize corresponding transients in imaging data. Furthermore, we observed that miniML performs better than template matching and deconvolution on ASAP5 imaging data (Figure 9 and Figure 9—figure supplement 2).

      Reviewer 2 (Public Review):

      This paper presents miniML as a supervised method for the detection of spontaneous synaptic events. Recordings of such events are typically of low SNR, where state-of-the-art methods are prone to high false positive rates. Unlike current methods, training miniML requires neither prior knowledge of the kinetics of events nor the tuning of parameters/thresholds.

      The proposed method comprises four convolutional networks, followed by a bi-directional LSTM and a final fully connected layer which outputs a decision event/no event per time window. A sliding window is used when applying miniML to a temporal signal, followed by an additional estimation of events’ time stamps. miniML outperforms current methods for simulated events superimposed on real data (with no events) and presents compelling results for real data across experimental paradigms and species. Strengths:

      The authors present a pipeline for benchmarking based on simulated events superimposed on real data (with no events). Compared to five other state-of-the-art methods, miniML leads to the highest detection rates and is most robust to specific choices of threshold values for fast or slow kinetics. A major strength of miniML is the ability to use it for different datasets. For this purpose, the CNN part of the model is held fixed and the subsequent networks are trained to adapt to the new data. This Transfer Learning (TL) strategy reduces computation time significantly and more importantly, it allows for using a substantially smaller data set (compared to training a full model) which is crucial as training is supervised (i.e. uses labeled examples).

      Weaknesses:

      The authors do not indicate how the specific configuration of miniML was set, i.e. number of CNNs, units, LSTM, etc. Please provide further information regarding these design choices, whether they were based on similar models or if chosen based on performance.

      The data for the benchmark system was augmented with equal amounts of segments with/without events. Data augmentation was undoubtedly crucial for successful training.

      (1) Does a balanced dataset reflect the natural occurrence of events in real data? Could the authors provide more information regarding this matter?

      In a given recording, the event frequency determines the ratio of event-containing vs. nonevent-containing data segments. Whereas many synapses have a skew towards non-events, high event frequencies as observed, e.g., in pyramidal cells or Purkinje neurons, can shift the ratio towards event-containing data.

      For model training, we extracted data segments from mEPSC recordings in cerebellar granule cells, which have a low mEPSC frequency (about 0.2 Hz, Delvendahl et al. 2019). Unbalanced training data may complicate model training (Drummond and Holte 2003; Prati et al. 2009; Tyagi and Mittal 2020). We therefore decided to balance the training dataset for miniML by down-sampling the majority class (i.e., non-event segments), so that the final datasets for model training contained roughly equal amounts of events and non-events.

      (2) Please provide a more detailed description of this process as it would serve users aiming to use this method for other sub-fields.

      We thank the reviewer for raising this point. In the revised manuscript, we present a systematic analysis of the impact of imbalanced training data on model training (Figure 1—figure supplement 2). In addition, we have revised the description of model training and data augmentation in the Methods section (Methods, Training data and annotation).

      The benchmarking pipeline is indeed valuable and the results are compelling. However, the authors do not provide comparative results for miniML for real data (Figures 4-8). TL does not apply to the other methods. In my opinion, presenting the performance of other methods, trained using the smaller dataset would be convincing of the modularity and applicability of the proposed approach.

      Quantitative comparison of synaptic detection methods on real-world data is challenging because the lack of ground-truth data prevents robust, quantitative analyses. Nevertheless, we compared miniML to common template-based and finite-threshold based methods on four different types of synapses. We noted that miniML generally detects more events, whereas other methods are susceptible to false-positives (Figure 4—figure supplement 1). In addition, we analyzed the performance of miniML on voltage imaging data (Figure 9). Simultaneous recordings of electrophysiological and imaging data allowed a quantitative comparison of detection methods in this dataset. Our results demonstrate that miniML provides higher recall for optical minis recorded using ASAP5 (Figure 9 and Figure 9—figure supplement 2; F1 score, Cohen’s d 1.35 vs. template matching and 5.1 vs. deconvolution).

      Impact:

      Accurate detection of synaptic events is crucial for the study of neural function. miniML has a great potential to become a valuable tool for this purpose as it yields highly accurate detection rates, it is robust, and is relatively easily adaptable to different experimental setups.

      Additional comments:

      Line 73: the authors describe miniML as "parameter-free". Indeed, miniML does not require the selection of pulse shape, rise/fall time, or tuning of a threshold value. Still, I would not call it "parameter-free" as there are many parameters to tune, starting with the number of CNNs, and number of units through the parameters of the NNs. A more accurate description would be that as an AI-based method, the parameters of miniML are learned via training rather than tuned by the user.

      We agree that a deep learning model is not parameter-free, and this term may be misleading. We have therefore changed this sentence in the introduction as follows: "The method is fast, robust to threshold choice, and generalizable across diverse data types [...]"

      Line 302: the authors describe miniML as "threshold-independent". The output trace of the model has an extremely high SNR so a threshold of 0.5 typically works. Since a threshold is needed to determine the time stamps of events, I think a better description would be "robust to threshold choice".

      To detect event localizations, a peak search is performed on the model output, which uses a minimum peak height parameter (or threshold). Extreme values for this parameter do indeed have a small impact on detection performance (Figure 3J). We have changed the description in the introduction and discussion according to the reviewer’s suggestion.

      Reviewer 3 (Public Review):

      miniML as a novel supervised deep learning-based method for detecting and analyzing spontaneous synaptic events. The authors demonstrate the advantages of using their methods in comparison with previous approaches. The possibility to train the architecture on different tasks using transfer learning approaches is also an added value of the work. There are some technical aspects that would be worth clarifying in the manuscript:

      (1) LSTM Layer Justification: Please provide a detailed explanation for the inclusion of the LSTM layer in the miniML architecture. What specific benefits does the LSTM layer offer in the context of synaptic event detection?

      Our model design choice was inspired by similar approaches in the literature (Donahue et al. 2017; Islam et al. 2020; Passricha and Aggarwal 2019; Tasdelen and Sen 2021; Wang et al. 2020). Convolutional and recurrent neural networks are often combined for time-series classification problems as they allow learning spatial and temporal features, respectively. Combining the strengths of both network architectures can thus help improve the classification performance. Indeed, a CNN-LSTM architecture proved to be superior in both training accuracy and detection performance (Figure 1—figure supplement 2). Further, this architecture requires fewer free parameters than comparable model designs using fully connected layers instead. The revised manuscript shows a comparison of different model architectures (Figure 1—figure supplement 2), and we added the following description to the text (Methods, Deep learning model architecture):

      "The combination of convolutional and recurrent neural network layers helps to improve the classification performance for time-series data. In particular, LSTM layers allow learning temporal features."

      (2) Temporal Resolution: Can you elaborate on the reasons behind the lower temporal resolution of the output? Understanding whether this is due to specific design choices in the model, data preprocessing, or post-processing will clarify the nature of this limitation and its impact on the analysis.

      When running inference on a continuous recording, we choose to use a sliding window approach with stride. Therefore, the model output has a lower temporal resolution than the raw data, which is determined by the stride length (i.e., how many samples to advance the sliding window). While using a stride is not required, it significantly reduces inference time (cf. Figure 2—figure supplement 1). We recommend a stride of 20 samples, which does not impact the detection of events. Any subsequent quantification of events (amplitude, area, risetimes, etc.) is performed on raw data. Based on the reviewer’s comment, we have adapted the code to resample the prediction trace to the sampling rate of the original data. This maintains temporal precision and avoids confusion.

      The Methods now include the following statement:

      "To maintain temporal precision, the prediction trace is resampled to the sampling frequency of the raw data."

      (3) Architecture optimization: how was the architecture CNN+LSTM optimized in terms of a number of CNN layers and size?

      We performed a Bayesian optimization over a defined range of hyperparameters in combination with empirical hyperparameter tuning. We now describe this in the Methods section as follows:

      "To optimise the model architecture, we performed a Bayesian optimisation of hyperparameters. Hyperparameter ranges were chosen for the free parameters of all layers. Optimisation was then performed with a maximum number of trials of 50. Models were evaluated using the validation dataset. Because higher number of free parameters tended to increase inference times, we then empirically tuned the chosen hyperparameter combination to achieve a trade-off between number of free parameters and accuracy."

      Recommendations For The Authors

      Reviewing Editor (Recommendations For The Authors):

      Overall suggestions to the authors:

      (1) Directly compare miniML with SimplyFire (which was not cited or discussed in the original manuscript), with both idealized and actual data. Discuss the pros/cons of each software.

      We have conducted an extensive comparison between miniML and SimplyFire using both simulated and actual experimental data. This analysis is now presented in the revised Figure 3, Figure 3—figure supplement 1, and Figure 4—figure supplement 1. In addition, we have included relevant citations for SimplyFire in our manuscript. These additions provide a more comprehensive and balanced view of the available tools in the field, positioning our work within the broader context of existing solutions.

      (2) Generate a better user interface akin to MiniAnalysis or SimplyFire.

      We thank the editor and reviewers for the suggestion to improve the user interface. We have created a user-friendly graphical user interface (GUI) for miniML that is available on our GitHub repository. This GUI is now showcased in Figure 2—figure supplement 2 of the manuscript. The new interface allows users to load and analyze data through an intuitive point-and-click system, visualize results in real-time, and adjust parameters easily without coding knowledge. We have incorporated user feedback to refine the interface and improve user experience. These improvements significantly enhance the accessibility of miniML, making it more user-friendly for researchers with varying levels of programming expertise.

      Reviewer 1 (Recommendations For The Authors):

      Related to point (1) of the Public Review, we have taken the liberty to compare electrophysiological data using miniAnalysis, SimiplyFire, and miniML. In our comparison, we note the following in our experience:

      (1.1) In contrast to both SimplyFire and miniAnalysis, miniML does not currently have a user-friendly interface where the user can directly control or change the parameters of interest, nor does miniML have a user control center, so the user cannot simply type or select the mini manually. Rather, if any parameter needs to be changed, the user needs to read, understand, and change the original source code to generate the preferred change. This level of "activation energy" and required user coding expertise in computer science, which many researchers do not have, renders miniML much less accessible when directly compared to SimplyFire and miniAnalysis. Hence, unless miniML’s interface can be made more user-friendly, this is a major disadvantage, especially when compared to SimplyFire, which has many of the same features as miniML but with a much easier interface and user controls.

      As suggested by the reviewer, we have created a graphical user interface (GUI) for miniML. The GUI allows easy data loading, filtering, analysis, event inspection, and saving of results without the need for writing Python code. Figure 2—figure supplement 2 illustrates the typical workflow for event analysis with miniML using the GUI and a screenshot of the user interface. Code to use miniML via the GUI is now included in the project’s GitHub repository. The GUI provides a simple and intuitive way to analyze synaptic events, whereas running miniML as Python script allows for more customization and a high degree of automatization.

      (1.2) We compared electrophysiological miniature events between miniML, SimplyFire, and miniAnalysis. All three achieved similar mean amplitudes in "wild type" conditions, and conditions in which mini events were enhanced and diminished, so the overall means and utilities are similar, with miniML and SimplyFire being preferred given the flexibility and much faster analysis. We did note a few differences, however. SimplyFire tends to capture a high number of mini-events over miniML, especially in conditions of diminished mini amplitude (e.g., miniML found 76 events, while SimplyFire 587). The mean amplitudes, however, were similar. It seems that in data with low SNR, SimplyFire captures many more events as real minis that are probably noise, while miniML is more selective, which might be an advantage in miniML. That being said, we found SimplyFire to be superior in many respects, not least of which the user interface and experience.

      We appreciate the reviewer’s thorough comparison of miniML, SimplyFire, and MiniAnalysis. While we acknowledge SimplyFire’s user-friendly interface, our study highlights several advantages of AI-based event analysis over conventional algorithmic approaches. Our updated benchmark analysis revealed better detection performance of miniML compared with SimplyFire (revised Figure 3), which had similar performance to deconvolution. As already noted by the reviewer, high false positive rates are a major issue of the SimplyFire approach. Although a minimum amplitude cutoff can partially resolve this problem, detection performance is highly sensitive to threshold setting (revised Figure 3). Another apparent disadvantage of SimplyFire is its relatively slow runtime (Figure 3—figure supplement 1). Finally, we have enhanced miniML’s accessibility by providing a graphical user interface that is easy to use and provides additional functionality.

      Some technical comments:

      (1) Improvements to the dependence version of miniML: There is a need to clarify the dependence version of the python and tensor flow used in this study and in the GitHub. We used Python version 3.8.19 to load the miniML model. However, if Python versions >=3.9, as described on the GitHub provided, it is difficult to have a matched h5py version installed. It is also inaccurate to say using Python >=3.9, because tensor flow version for this framework needs to be around 2.13. However, if using Python >=3.10, it will only allow 2.16 version tensor flow to be the download choice. Therefore, as a Python framework, the dependency version needs to be specified on GitHub to allow researchers to access the model using the entire work.

      Thank you for highlighting this issue. We have now included specific version numbers in the requirements to avoid version conflicts and to ensure proper functioning of the code.

      (2) Due to the intrinsic characteristics of the trained model, every model is only suitable for analyzing data with similar attributes. It is hard for researchers without a strong computer science background to train a new model themselves for their specific data. Therefore, it would be preferred if there were more available transfer learning models on GitHub accessible for researchers to adapt to their data.

      We would like to thank the reviewer for this feedback. Trained models (such as the default model) can often be used on different data (see, e.g., Figure 4, where data from four distinct synaptic preparations were analyzed with the base model, and Figure 5—figure supplement 1). However, changes in event waveform and/or noise characteristics may necessitate transfer learning to obtain optimal results with miniML. We have revised the description and tutorial for model training on the project’s GitHub repository to provide more guidance in this process. In addition, we now provide a tutorial on how to use existing models on out-of-sample data with distinct kinetics, using resampling. We hope these updates to the miniML GitHub repository will facilitate the use of the method.

      Following the suggestion by the reviewer, we have provided the transfer learning models used for the manuscript on the project’s GitHub repository to increase the number of available machine learning models for event detection. In addition, users of miniML are encouraged to supply their custom models. We hope that this will facilitate model exchange between laboratories in the future.

      Reviewer 3:

      I congratulate all authors for the convincing demonstration of their methodology, I do not have additional recommendations.

      We would like to thank the reviewer for the positive assessment of our manuscript.

      References

      Delvendahl, I., Kita, K., & Müller, M. (2019). Rapid and sustained homeostatic control of presynaptic exocytosis at a central synapse. Proceedings of the National Academy of Sciences, 116(47), 23783–23789. https://doi.org/10.1073/pnas.1909675116

      Donahue, J., Hendricks, L. A., Rohrbach, M., Venugopalan, S., Guadarrama, S., Saenko, K., & Darrell, T. (2017). Long-term recurrent convolutional networks for visual recognition and description. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 677–691. https://doi.org/10.1109/tpami.2016.2599174

      Drummond, C., & Holte, R. C. (2003). C4.5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling. https: //api.semanticscholar.org/CorpusID:204083391

      Islam, M. Z., Islam, M. M., & Asraf, A. (2020). A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using x-ray images. Informatics in Medicine Unlocked, 20, 100412. https://doi.org/10.1016/j.imu.2020.100412

      Passricha, V., & Aggarwal, R. K. (2019). A hybrid of deep CNN and bidirectional LSTM for automatic speech recognition. Journal of Intelligent Systems, 29(1), 1261–1274. https://doi.org/10.1515/jisys-2018-0372

      Prati, R. C., Batista, G. E. A. P. A., & Monard, M. C. (2009). Data mining with imbalanced class distributions: Concepts and methods. Indian International Conference on Artificial Intelligence. https://api.semanticscholar.org/CorpusID:16651273

      Tasdelen, A., & Sen, B. (2021). A hybrid CNN-LSTM model for pre-miRNA classification. Scientific Reports, 11(1). https://doi.org/10. 1038/s41598-021-93656-0

      Tyagi, S., & Mittal, S. (2020). Sampling approaches for imbalanced data classification problem in machine learning. In P. K. Singh, A. K. Kar, Y. Singh, M. H. Kolekar, & S. Tanwar (Eds.), Proceedings of icric 2019 (pp. 209–221). Springer International Publishing.

      Wang, H., Zhao, J., Li, J., Tian, L., Tu, P., Cao, T., An, Y., Wang, K., & Li, S. (2020). Wearable sensor-based human activity recognition using hybrid deep learning techniques. Security and Communication Networks, 2020, 1–12. https://doi.org/10.1155/2020/ 2132138

    1. Author response:

      Reviewer #1 (Public review):

      Summary:

      This is a new and important system that can efficiently train mice to perform a variety of cognitive tasks in a flexible manner. It is innovative and opens the door to important experiments in the neurobiology of learning and memory.

      Strengths:

      Strengths include: high n's, a robust system, task flexibility, comparison of manual-like training vs constant training, circadian analysis, comparison of varying cue types, long-term measurement, and machine teaching.

      Weaknesses:

      I find no major problems with this report.

      (1) Line 219: Water consumption per day remained the same, but number of trails triggered was more as training continued. First, is this related to manual-type training? Also, I'm trying to understand this result quantitatively, since it seems counter-intuitive: I would assume that with more trials, more water would be consumed since accuracy should go up over training (so more water per average trial). Am I understanding this right? Can the authors give more detail or understanding to how more trials can be triggered but no more water is consumed despite training?

      Thanks for the thoughtful comment. We would like to clarify the phenomenon described in Line 219: As the training advanced, the number of trials triggered by mice per day decreased (rather than increased as you mentioned in the comment) gradually for both manual and autonomous groups of mice (Fig. 2H left). The performance as you mentioned, improved over time, leading to an increased probability of obtaining water and thus relatively stable daily water intake (Fig. 2H left). We believe the stable daily intake is the minimum amount of water required by the mice under circumstance of autonomous behavioral training.

      (2) Figure 2J: The X-axis should have some label: at least "training type". Ideally, a legend with colors can be included, although I see the colors elsewhere in the figure. If a legend cannot be added, then the color scheme should be explained in the caption.

      (3) Figure 2K: What is the purple line? I encourage a legend here. The same legend could apply to 2J.

      (4) Supplementary Figure S2 D: I do not think the phrase "relying on" is correct. Instead, I think "predicted by" or "correlating with" might be better.

      We thank the reviewer for the valuable suggestion. We will address all these points and make the necessary revisions in the next version of our manuscript.

      Reviewer #2 (Public review):

      Summary:

      The manuscript by Yu et al. describes a novel approach for collecting complex and different cognitive phenotypes in individually housed mice in their home cage. The authors report a simple yet elegant design that they developed for assessing a variety of complex and novel behavioral paradigms autonomously in mice.

      Strengths:

      The data are strong, the arguments are convincing, and I think the manuscript will be highly cited given the complexity of behavioral phenotypes one can collect using this relatively inexpensive ($100/box) and high throughput procedure (without the need for human interaction). Additionally, the authors include a machine learning algorithm to correct for erroneous strategies that mice develop which is incredibly elegant and important for this approach as mice will develop odd strategies when given complete freedom.

      Weaknesses:

      (1) A limitation of this approach is that it requires mice to be individually housed for days to months. This should be discussed in depth.

      Thank you for raising this important point. We agree that the requirement for individual housing of mice during the training period is a limitation of our approach, and we appreciate the opportunity to discuss this in more depth. In the revised manuscript, we will add a dedicated section to the Discussion to address this limitation, including the potential impact of individual housing on the mice, the rationale for individual housing in our study, and efforts or alternatives made to mitigate the effects of individual housing.

      (2) A major issue with continuous self-paced tasks such as the autonomous d2AFC used by the authors is that the inter-trial intervals can vary significantly. Mice may do a few trials, lose interest, and disengage from the task for several hours. This is problematic for data analysis that relies on trial duration to be similar between trials (e.g., reinforcement learning algorithms). It would be useful to see the task engagement of the mice across a 24-hour cycle (e.g., trials started, trials finished across a 24-hour period) and approaches for overcoming this issue of varying inter-trial intervals.

      Thank you for your insightful comment regarding the variability in inter-trial intervals and its potential impact on data analysis. We agree that this is an important consideration for continuous self-paced tasks like the autonomous d2AFC paradigm used in our study. In the original manuscript, we have showed the general task engagement across 24-hour cycle (Fig. 2K). The distribution of inter-trial interval was also illustrated (Fig. S3H), which actually shows that most of trials have short intervals (though with extreme long ones). We will include more detailed analysis and discuss the challenges for data analysis.

      Regarding the approaches to mitigate the issue of varying inter-trial interval, we will also discuss strategies to account for and mitigate the effects, including: trial selection, incorporating engagement period (e.g., open only during a fixed 2-hour period each day), etc.

      (3) Movies - it would be beneficial for the authors to add commentary to the video (hit, miss trials). It was interesting watching the mice but not clear whether they were doing the task correctly or not.

      Thanks for the reminder. We will add subtitles to the videos in the next version.

      (4) The strength of this paper (from my perspective) is the potential utility it has for other investigators trying to get mice to do behavioral tasks. However, not enough information was provided about the construction of the boxes, interface, and code for running the boxes. If the authors are not willing to provide this information through eLife, GitHub, or their own website then my evaluation of the impact and significance of this paper would go down significantly.

      Thanks for this important comment. We would like to clarify that the construction methods, GUI, code for our system, PCB and CAD files (newly uploaded) have already been made publicly available on https://github.com/Yaoyao-Hao/HABITS. Additionally, we have open-sourced all the codes and raw data for all training protocols (https://doi.org/10.6084/m9.figshare.27192897). We will continue to maintain these resources in the future.

      Minor concerns:

      Learning rate is confusing for Figure 3 results as it actually refers to trials to reach the criterion, and not the actual rate of learning (e.g., slope).

      Thanks for pointing this out. We will make the revision in the next version.

      Reviewer #3 (Public review):

      Summary:

      In this set of experiments, the authors describe a novel research tool for studying complex cognitive tasks in mice, the HABITS automated training apparatus, and a novel "machine teaching" approach they use to accelerate training by algorithmically providing trials to animals that provide the most information about the current rule state for a given task.

      Strengths:

      There is much to be celebrated in an inexpensively constructed, replicable training environment that can be used with mice, which have rapidly become the model species of choice for understanding the roles of distinct circuits and genetic factors in cognition. Lingering challenges in developing and testing cognitive tasks in mice remain, however, and these are often chalked up to cognitive limitations in the species. The authors' findings, however, suggest that instead, we may need to work creatively to meet mice where they live. In some cases, it may be that mice may require durations of training far longer than laboratories are able to invest with manual training (up to over 100k trials, over months of daily testing) but the tasks are achievable. The "machine teaching" approach further suggests that this duration could be substantially reduced by algorithmically optimizing each trial presented during training to maximize learning.

      Weaknesses:

      (1) Cognitive training and testing in rodent models fill a number of roles. Sometimes, investigators are interested in within-subjects questions - querying a specific circuit, genetically defined neuron population, or molecule/drug candidate, by interrogating or manipulating its function in a highly trained animal. In this scenario, a cohort of highly trained animals that have been trained via a method that aims to make their behavior as similar as possible is a strength.

      However, often investigators are interested in between-subjects questions - querying a source of individual differences that can have long-term and/or developmental impacts, such as sex differences or gene variants. This is likely to often be the case in mouse models especially, because of their genetic tractability. In scenarios where investigators have examined cognitive processes between subjects in mice who vary across these sources of individual difference, the process of learning a task has been repeatedly shown to be different. The authors do not appear to have considered individual differences except perhaps as an obstacle to be overcome.

      The authors have perhaps shown that their main focus is highly-controlled within-subjects questions, as their dataset is almost exclusively made up of several hundred young adult male mice, with the exception of 6 females in a supplemental figure. It is notable that these female mice do appear to learn the two-alternative forced-choice task somewhat more rapidly than the males in their cohort.

      Thank you for your insightful comments and for highlighting the importance of considering both within-subject and between-subject questions in cognitive training and testing in rodent models.

      We acknowledge that our study primarily focused on highly controlled within-subject questions. However, the datasets we provided have showed some evidences for the ‘between-subject’ questions. For example, the large variability in learning rates among mice observed in Fig. 2I, the overall learning rate difference between male and female subjects (Fig. 2D vs. Fig. S2G, as the reviewer already mentioned), the varying nocturnal behavioral patterns (Fig. 2K), etc. While our primary focus was on highly controlled within-subjects questions, we recognize the value of exploring between-subjects differences. In the revised version, we will discuss these points more systematically.

      (2) Considering the implications for mice modeling relevant genetic variants, it is unclear to what extent the training protocols and especially the algorithmic machine teaching approach would be able to inform investigators about the differences between their groups during training. For investigators examining genetic models, it is unclear whether this extensive training experience would mitigate the ability to observe cognitive differences, or select the animals best able to overcome them - eliminating the animals of interest. Likewise, the algorithmic approach aims to mitigate features of training such as side biases, but it is worth noting that the strategic uses of side biases in mice, as in primates, can benefit learning, rather than side biases solely being a problem. However, the investigators may be able to highlight variables selected by the algorithm that are associated with individual strategies in performing their tasks, and this would be a significant contribution.

      Thank you for the insightful comments. We acknowledge that the extensive training experience, particularly through the algorithmic machine teaching approach, could potentially influence the ability to observe cognitive differences between groups of mice with relevant genetic variants. However, our study design and findings suggest that this approach can still provide valuable insights into individual differences and strategies used by the animals during training. First, the behavioral readout (including learning rate, engagement pattern, etc.) as mentioned above, could tell certain number of differences among mice. Second, detailed modelling analysis (with logistical regression modelling) could further dissect the strategy that mouse use along the training process (Fig. S2B). We have actually highlighted some variables selected by the regression that are associated with individual strategies in performing their tasks (Fig. S2C) and these strategies could be different between manual and autonomous training groups (Fig. S2D). We will discuss these points more in the next version of the manuscript.

      (3) A final, intriguing finding in this manuscript is that animal self-paced training led to much slower learning than "manual" training, by having the experimenter introduce the animal to the apparatus for a few hours each day. Manual training resulted in significantly faster learning, in almost half the number of trials on average, and with significantly fewer omitted trials. This finding does not necessarily argue that manual training is universally a better choice because it leads to more limited water consumption. However, it suggests that there is a distinct contribution of experimenter interactions and/or switching contexts in cognitive training, for example by activating an "occasion setting" process to accelerate learning for a distinct period of time. Limiting experimenter interactions with mice may be a labor-saving intervention, but may not necessarily improve performance. This could be an interesting topic of future investigation, of relevance to understanding how animals of all species learn.

      Thank you for your insightful comments. We agree that the finding that manual training led to significantly faster learning compared to self-paced training is both intriguing and important. One of the possible reasons we think is due to the limited duration of engagement provided by the experimenter in the manual training case, which forced the mice to concentrate more on the trails (thus with fewer omitting trials) than in autonomous training. Your suggestion that experimenter interactions might activate an "occasion setting" process is particularly interesting. In the context of our study, we could actually introduce, for example, a light, serving as the cue that prompt the animals to engage; and when the light is off, the engagement was not accessible any more for the mice to simulate the manual training situation. We agree that this could be an interesting topic for future investigation that might create a more conducive environment for learning, thereby accelerating the learning rate.

    1. Author response:

      Reviewer #1:

      Summary:

      The authors aim to predict ecological suitability for the transmission of highly pathogenic avian influenza (HPAI) using ecological niche models. This class of models identify correlations between the locations of species or disease detections and the environment. These correlations are then used to predict habitat suitability (in this work, ecological suitability for disease transmission) in locations where surveillance of the species or disease has not been conducted. The authors fit separate models for HPAI detections in wild birds and farmed birds, for two strains of HPAI (H5N1 and H5Nx) and for two time periods, pre- and post-2020. The authors also validate models fitted to disease occurrence data from pre-2020 using post-2020 occurrence data.

      Strengths:

      The authors follow the established methods of Dhingra et al., 2016 to provide an updated spatial assessment of HPAI transmission suitability for two time periods, pre- and post-2020. They explore further methods of model cross-validation and consider the diversity of the bird species that HPAI has been detected in.

      Weaknesses:

      The precise ecological niche that the authors are modelling here is ambiguous: if we treat the transmission of HPAI in the wild bird population and in poultry populations as separate transmission cycles, linked by spillover events, then these transmission cycles are likely to have fundamentally different ecological niches.

      We apologise if this aspect was not clear enough in the previous version of our manuscript but our analyses do not treat or make the assumption of distinct transmission cycles between wild and domestic bird species; those transmission cycles being indeed interconnected by frequent spillover events. Yet, we indeed conduct independent ecological niche modelling analyses to estimate both the ecological suitability for the risk of local circulation in domestic birds as well as the ecological suitability for the risk of local circulation in wild birds. This distinction does not imply that the virus circulates exclusively within one of these populations but rather allows us to identify potential differences in the environmental conditions associated with virus occurrences in each context.

      Our results indicate that these two ecological niche models capture distinct environmental patterns. Virus occurrences in wild birds were primarily associated with factors such as open water and proximity to urban areas, while occurrences in domestic birds were more strongly linked to variables like poultry density and cultivated vegetation. This finding supports the existence of two distinct ecological niches for the virus, corresponding to virus circulation in wild and domestic bird populations. We thank the Reviewer for their feedback and we will take this opportunity to further clarify this aspect in the text.

      While an "index case" in farmed poultry is relevant to the wildlife transmission cycle, further within-farm and farm-to-farm transmission is likely to be contingent on anthropogenic factors, rather than the environment. Similarly, we would expect "index cases" in outbreaks of HPAI in mammals to be relevant to transmission risk in wild birds - this data is not included in this manuscript. Such "index cases" in farmed poultry occur under separate ecological conditions to subsequent transmission in farmed poultry, so should be separated if possible. Some careful editing of the language used in the manuscript may elucidate some of my questions related to model conceptualisation.

      We agree, but index cases are particularly difficult to separate from secondary spread in the absence of field investigation. Identification of index cases based on space-time filtering have been previously investigated but are strongly dependent on the quality of the surveillance, i.e. an “apparent” primary case can be a secondary case of previously undetected ones, and constant surveillance quality cannot be assumed to be homogeneous across countries. Our ecological niche modelling approach is based on HPAI cases reported in the EMPRES-i database, which includes all documented outbreaks without distinguishing primary introductions from subsequent farm-to-farm transmissions. Thus, our ecological niche models are trained on confirmed cases that result from a combination of different transmission dynamics, including introduction events in poultry populations (which can be impacted by ecological factors) and persistence within and between poultry populations (which can be impacted by anthropogenic factors).

      For clarity, we will revise the manuscript to clarify that, while our study primarily aims to assess the environmental suitability for HPAI occurrences, the dataset does not exclude cases resulting from farm-to-farm spread. This means that our models can capture the environmental variables associated with the risk of cases associated with both primary introductions (e.g., spillover from wild birds) and secondary transmission events within poultry systems, although the latter is also influenced by anthropogenic factors such as biosecurity practices and poultry trade networks. These latter factors are not included in our models, which will be highlighted in the limitations (Discussion section) of the revised manuscript.

      In addition, we note the Reviewer's comment regarding the relevance of “index cases” in mammalian outbreaks to understanding the risk of HPAI transmission in wild birds. Although these data are not included in our current study, we will highlight the potential value of incorporating these cases into future models in order to refine risk predictions, provided that they can be identified with some reasonable level of certainty.

      The authors' handling of sampling bias in disease detection data in poultry is possibly inappropriate: one would expect the true spatial distribution of disease surveillance in poultry to be more closely correlated with poultry farming density, in contrast to human population density. This shortcoming in the modelling workflow possibly dilutes a key finding of the Results, that the transmission risk of HPAI in poultry is greatest in areas where poultry farming density is high.

      The Reviewer raises a valid point that poultry surveillance efforts can also be considered as correlated with poultry farm density than with human population density. While human population density can serve as a reasonable proxy for surveillance intensity — given that disease detection is often more active in areas with stronger veterinary notification systems — we acknowledge that poultry disease surveillance can also be influenced by the spatial distribution of poultry farms, as high-density poultry areas could be prioritised for monitoring. Please note that in our study, we followed a previously established approach (Dhingra et al. 2016) and weighted pseudo-absence sampling based on human population density to account for general surveillance biases. However, we do not agree with the Reviewer’s point. In fact, assuming a sampling bias correlated with poultry density would result in reducing its effect as a risk factor. The current approach does not.

      Reviewer #2:

      Summary:

      This study aimed to determine which spatial factors (conceived broadly as environmental, agronomic and socio-economic) explain greater avian influenza case numbers reported since 2020 (2020--2022) by comparing similar models built with data from the period 2015--2020. The authors have chosen an environmental niche modelling approach, where detected infections are modelled as a function of spatial covariates extracted at the location of each case. These covariates are available over the entire world so that the predictions can be projected back to space in the form of a continuous map.

      Strengths:

      The authors use boosted regression trees as the main analytical tool, which always feature among the best-performing models for environmental niche models (also known as habitat suitability models). They run replicate sets of the analysis for each of their model targets (wild/domestic x pathogen variant), which can help produce stable predictions. The authors take steps to ameliorate some forms of expected bias in the detection of cases, such as geographic variation in surveillance efforts, and in general more detections near areas of higher human population density.

      Weaknesses:

      The study is not altogether coherent with respect to time. Data sets for the response (N5H1 or N5Hx case data in domestic or wild birds) are divided into two periods; 2015-2020, and 2020-2022. Each set is modelled using a common suite of covariates that are not time-varying. That suggests that causation is inferred by virtue of cases being in different geographic areas in those two time periods. Furthermore, important predictors such as chicken density appear to be informed (in the areas of high risk) from census data from before 2010. The possibility for increased surveillance effort *through time* is overlooked, as is the possibility that previously high-burden locations may implement practice changes to reduce vulnerability.

      We acknowledge the Reviewer's comments regarding the consistency of time periods in our study. Our approach is to divide the HPAI case data into two time periods (2015-2020 and 2020-2022) and ecological niche models using a common set of covariates that do not explicitly account for temporal variation. We will further clarify these aspects in the revised version of our manuscript:

      (1) Our primary objective is to assess changes in ecological suitability over time rather than infer direct causation. By comparing models trained on pre-2020 data with post-2020 occurrences, we evaluate whether pre-2020 environmental conditions can predict recent HPAI suitability. However, we acknowledge that this does not capture dynamic changes in surveillance efforts, biosecurity measures, or host-pathogen interactions over time.

      (2) Regarding predictor variables, we used poultry density data from 2015, rather than pre-2010 data. However, this dataset is not based on a single census year; instead, it represents a median estimate derived from subnational poultry census data collected between 2000 and 2019. This median year approach provides a more stable representation of poultry density than any single-year snapshot. Furthermore, while poultry production systems may exhibit some temporal variation, these changes are generally minor compared to the inter-annual variability observed in HPAI occurrence, which is largely driven by epidemic dynamics. Given the current limitations of global poultry data, distinguishing distributions from different years is not feasible with the available GLW dataset. We will clarify these points in the manuscript.

      (3) We recognise that increased surveillance efforts and adaptive changes in poultry farming practices could influence the observed HPAI case distribution. While our current models do not incorporate time-varying surveillance intensity or biosecurity policies, we will address this limitation in the Discussion section and suggest that future work integrates dynamic surveillance data to improve risk assessments.

    1. Author response:

      Reviewer #1 (Public review):

      Wang et al. investigated how sexual failure influences sweet taste perception in male Drosophila. The study revealed that courtship failure leads to decreased sweet sensitivity and feeding behavior via dopaminergic signaling. Specifically, the authors identified a group of dopaminergic neurons projecting to the suboesophageal zone that interacts with sweet-sensing Gr5a+ neurons. These dopaminergic neurons positively regulate the sweet sensitivity of Gr5a+ neurons via DopR1 and Dop2R receptors. Sexual failure diminishes the activity of these dopaminergic neurons, leading to reduced sweet-taste sensitivity and sugar-feeding behavior in male flies. These findings highlight the role of dopaminergic neurons in integrating reproductive experiences to modulate appetitive sensory responses.

      Previous studies have explored the dopaminergic-to-Gr5a+ neuronal pathways in regulating sugar feeding under hunger conditions. Starvation has been shown to increase dopamine release from a subset of TH-GAL4 labeled neurons, known as TH-VUM, in the suboesophageal zone. This enhanced dopamine release activates dopamine receptors in Gr5a+ neurons, heightening their sensitivity to sugar and promoting sucrose acceptance in flies. Since the function of the dopaminergic-to-Gr5a+ circuit motif has been well established, the primary contribution of Wang et al. is to show that mating failure in male flies can also engage this circuit to modulate sugar-feeding behavior. This contribution is valuable because it highlights the role of dopaminergic neurons in integrating diverse internal state signals to inform behavioral decisions.

      An intriguing discrepancy between Wang et al. and earlier studies lies in the involvement of dopamine receptors in Gr5a+ neurons. Prior research has shown that Dop2R and DopEcR, but not DopR1, mediate starvation-induced enhancement of sugar sensitivity in Gr5a+ neurons. In contrast, Wang et al. found that DopR1 and Dop2R, but not DopEcR, are involved in the sexual failure-induced decrease in sugar sensitivity in these neurons. I wish the authors had further explored or discussed this discrepancy, as it is unclear how dopamine release selectively engages different receptors to modulate neuronal sensitivity in a context-dependent manner.

      Our immunostaining experiments showed that three dopamine receptors, DopR1, Dop2R, and DopEcR were expressed in Gr5a<sup>+</sup> neurons in the proboscis, which was consistent with previous findings by using RT-PCR (Inagaki et al 2012). As the reviewer pointed out, we found that DopR1 and Dop2R were required for courtship failure-induced suppression of sugar sensitivity, whereas Marella et al 2012 and Inagaki et al 2012 found that Dop2R and DopEcR were required for starvation-induced enhancement of sugar sensitivity. These results may suggest different internal states (courtship failure vs. starvation) modulate peripheral sensory system via different signaling pathways (e.g. different subsets of dopaminergic neurons; different dopamine release mechanisms; and different dopamine receptors). We will further discuss these possibilities in the revised manuscript.

      The data presented by Wang et al. are solid and effectively support their conclusions. However, certain aspects of their experimental design, data analysis, and interpretation warrant further review, as outlined below.

      (1) The authors did not explicitly indicate the feeding status of the flies, but it appears they were not starved. However, the naive and satisfied flies in this study displayed high feeding and PER baselines, similar to those observed in starved flies in other studies. This raises the concern that sexually failed flies may have consumed additional food during the 4.5-hour conditioning period, potentially lowering their baseline hunger levels and subsequently reducing PER responses. This alternative explanation is worth considering, as an earlier study demonstrated that sexually deprived males consumed more alcohol, and both alcohol and food are known rewards for flies. To address this concern, the authors could remove food during the conditioning phase to rule out its influence on the results.

      We think this is a valid concern. We will conduct courtship conditioning in the absence of food and test if courtship failure can still suppress sugar sensitivity in the revised manuscript.

      (2) Figure 1B reveals that approximately half of the males in the Failed group did not consume sucrose yet Figure 1-S1A suggests that the total volume consumed remained unchanged. Were the flies that did not consume sucrose omitted from the dataset presented in Figure 1-S1A? If so, does this imply that only half of the male flies experience sexual failure, or that sexual failure affects only half of males while the others remain unaffected? The authors should clarify this point.

      Here is a brief clarification of our experimental design and we will further clarify the details in the revised manuscript:

      After the behavioral conditioning, male flies were divided for two assays. On the one hand, we quantified PER responses of individual flies. As shown in Figure 1C, Failed males exhibited decreased sweet sensitivity (as demonstrated by the right shift of the response curve).

      On the other hand, we sought to quantify food consumption of individual flies by using the MAFE assay (Qi et al 2005). When presented with 400 mM sucrose, approximately 100% of the flies in the Naïve and Satisfied groups, and 50% of the flies in the Failed group, extended their proboscis and started feeding (Figure 1B). For these flies, we could quantify the consumed volumes and found there was no change (Figure 1, S1A). We should also note the consistency of these two experiments, e.g. in Figure 1C, only 50-60% of Failed males responded to 400 mM stimulation.  

      These two experiments in combination suggest that sexual failure suppressed sweet sensitivity of the Failed males. Meanwhile, as long as they still initiated feeding, the volume of food consumption remained unchanged. These results led us to focus on the modulatory effect of sexual failure on the sensory system, the main topic of this present study.

      In addition, to further clarify the potential misunderstanding, we plan to examine food consumption by using 800 mM sucrose in the revised manuscript. As shown in Figure 1C, 800 mM sucrose was adequate to induce feeding in ~100% of the flies.

      (3) The evidence linking TH-GAL4 labeled dopaminergic neurons to reduced sugar sensitivity in Gr5a+ neurons in sexually failed males could be further strengthened. Ideally, the authors would have activated TH-GAL4 neurons and observed whether this restored GCaMP responses in Gr5a+ neurons in sexually failed males. Instead, the authors performed a less direct experiment, shown in Figures 3-S1C and D. The manuscript does not describe the condition of the flies used in this experiment, but it appears that they were not sexually conditioned. I have two concerns with this experiment. First, no statistical analysis was provided to support the enhancement of sucrose responses following activation of TH-GAL4 neurons. Second, without performing this experiment in sexually failed males, the authors lack direct evidence to confirm that the dampened response of Gr5a+ neurons to sucrose results from decreased activity in TH-GAL4 neurons.

      We think this is also a valid suggestion. We will directly examine whether activating TH<sup>+</sup> neurons in sexually conditioned males would enhance sugar responses of Gr5a<sup>+</sup> neurons in sexually failed males. We will also add in statistical analysis.

      Nevertheless, we would still argue our current experiments using Naive males (Figure 3, S1C-D) are adequate to show a functional link between TH<sup>+</sup> neurons and Gr5a<sup>+</sup> neurons. Combining with the results that these neurons form active synapses (Figure 3, S1B) and that the activity of TH<sup>+</sup> neurons was dampened in sexually failed males (Figure 3G-I), our current data support the notion that sexual failure suppresses sweet sensitivity via TH-Gr5a circuity.

      (4) The statistical methods used in this study are poorly described, making it unclear which method was used for each experiment. I suggest that the authors include a clear description of the statistical methods used for each experiment in the figure legends. Furthermore, as I have pointed out, there is a lack of statistical comparisons in Figures 3-S1C and D, a similar problem exists for Figures 6E and F.

      We will add detailed information of statistical analysis in each figure legend.

      (5) The experiments in Figure 5 lack specificity. The target neurons in this study are Gr5a+ neurons, which are directly involved in sugar sensing. However, the authors used the less specific Dop1R1- and Dop2R-GAL4 lines for their manipulations. Using Gr5a-GAL4 to specifically target Gr5a+ neurons would provide greater precision and ensure that the observed effects are directly attributable to the modulation of Gr5a+ neurons, rather than being influenced by potential off-target effects from other neuronal populations expressing these dopamine receptors.

      We agree with the reviewer that manipulating Dop1R1 and Dop2R genes (Figure 4) and the neurons expressing them (Figure 5) might have broader impacts. In fact, we have also tested the role of Dop1R1 and Dop2R in Gr5a<sup>+</sup> neurons by RNAi experiments (Figure 6). As shown by both behavioral and calcium imaging experiments, knocking down Dop1R1 and Dop2R in Gr5a<sup>+</sup> neurons both eliminated the effect of sexual failure to dampen sweet sensitivity, further confirming the role of these two receptors in Gr5a<sup>+</sup> neurons.

      (6) I found the results presented in Fig. 6F puzzling. The knockdown of Dop2R in Gr5a+ neurons would be expected to decrease sucrose responses in naive and satisfied flies, given the role of Dop2R in enhancing sweet sensitivity. However, the figure shows an apparent increase in responses across all three groups, which contradicts this expectation. The authors may want to provide an explanation for this unexpected result.

      We agree that there might be some potential discrepancies. However, our current data are not adequate for the clarification given the experiments shown in Figure 6E-F and the apparent control (Figure 3C) were not conducted under identical settings at the same (that’s why we did not directly compare these results). One way to address the issues is to conduct these calcium imaging experiments again with a head-to-head comparison with the control group (Gr5a-GCaMP, +/- Dop1R1 and Dop2R RNAi). We will conduct the experiments and present the data in the revised manuscript.

      (7) In several instances in the manuscript, the authors described the effects of silencing dopamine signaling pathways or knocking down dopamine receptors in Gr5a neurons with phrases such as 'no longer exhibited reduced sweet sensitivity' (e.g., L269 and L288), 'prevent the reduction of sweet sensitivity' (e.g., L292), or 'this suppression was reversed' (e.g. L299). I found these descriptions misleading, as they suggest that sweet sensitivity in naive and satisfied groups remains normal while the reduction in failed flies is specifically prevented or reversed. However, this is not the case. The data indicate that these manipulations result in an overall decrease in sweet sensitivity across all groups, such that a further reduction in failed flies is not observed. I recommend revising these descriptions to accurately reflect the observed phenotypes and avoid any confusion regarding the effects of these manipulations.

      We will change our expressions in the revised manuscript. In brief, we think that these manipulations (suppressing Dop1R1<sup>+</sup> and Dop2R<sup>+</sup> neurons) have two consequences: suppressing the overall sweet sensitivity and eliminating the effect of sexual failure.

      Reviewer #2 (Public review):

      Summary:

      The authors exposed naïve male flies to different groups of females, either mated or virgin. Male flies can successfully copulate with virgin females; however, they are rejected by mated females. This rejection reduces sugar preference and sensitivity in males. Investigating the underlying neural circuits, the authors show that dopamine signaling onto GR5a sensory neurons is required for reduced sugar preference. GR5a sensory neurons respond less to sugar exposure when they lack dopamine receptors.

      Strengths:

      The findings add another strong phenotype to the existing dataset about brain-wide neuromodulatory effects of mating. The authors use several state-of-the-art methods, such as activity-dependent GRASP to decipher the underlying neural circuitry. They further perform rigorous behavioral tests and provide convincing evidence for the local labellar circuit.

      Weaknesses:

      The authors focus on the circuit connection between dopamine and gustatory sensory neurons in the male SEZ. Therefore, it is still unknown how mating modulates dopamine signaling and what possible implications on other behaviors might result from a reduced sugar preference.

      We agree with the reviewer that in the current study, we did not examine how mating experience suppressed the activity of dopaminergic neurons in the SEZ. The current study mainly focused on the behavioral characterization (sexual failure suppresses sweet sensitivity) and the downstream mechanism (TH-Gr5a pathway). We think that examining the upstream modulatory mechanism may be more suitable for a separate future study.

      We believe that a sustained reduction in sweet sensitivity (not limited to sucrose but extend to other sweet compounds, Figure 1, S1B-C) upon sexual failure suggests a generalized and sustained consequence on reward-related behaviors. Sexual failure may thus resemble a state of “primitive emotion” in fruit flies. We will further discuss this possibility in the revised manuscript.

      Reviewer #3 (Public review):

      Summary

      In this work, the authors asked how mating experience impacts reward perception and processing. For this, they employ fruit flies as a model, with a combination of behavioral, immunostaining, and live calcium imaging approaches.

      Their study allowed them to demonstrate that courtship failure decreases the fraction of flies motivated to eat sweet compounds, revealing a link between reproductive stress and reward-related behaviors. This effect is mediated by a small group of dopaminergic neurons projecting to the SEZ. After courtship failure, these dopaminergic neurons exhibit reduced activity, leading to decreased Gr5a+ neuron activity via Dop1R1 and Dop2R signaling, and leading to reduced sweet sensitivity. The authors therefore showed how mating failure influences broader behavioral outputs through suppression of the dopamine-mediated reward system and underscores the interactions between reproductive and reward pathways.

      Concern

      My main concern regarding this study lies in the way the authors chose to present their results. If I understood correctly, they provided evidence that mating failure induces a decrease in the fraction of flies exhibiting PER. However, they also showed that food consumption was not affected (Fig. 1, supplement), suggesting that individuals who did eat consumed more. This raises questions about the analysis and interpretation of the results. Should we consider the group as a whole, with a reduced sensitivity to sweetness, or should we focus on individuals, with each one eating more? I am also concerned about how this could influence the results obtained using live imaging approaches, as the flies being imaged might or might not have been motivated to eat during the feeding assays. I would like the authors to clarify their choice of analysis and discuss this critical point, as the interpretation of the results could potentially be the opposite of what is presented in the manuscript.

      Here is a brief clarification of our experimental design and we will further clarify the details in the revised manuscript:

      After the behavioral conditioning, male flies were divided for two assays. On the one hand, we quantified PER responses of individual flies. As shown in Figure 1C, Failed males exhibited decreased sweet sensitivity (as demonstrated by the right shift of the response curve).

      On the other hand, we sought to quantify food consumption of individual flies by using the MAFE assay (Qi et al 2005). When presented with 400 mM sucrose, approximately 100% of the flies in the Naïve and Satisfied groups, and 50% of the flies in the Failed group, extended their proboscis and started feeding (Figure 1B). For these flies, we could quantify the consumed volumes and found there was no change (Figure 1, S1A). We should also note the consistency of these two experiments, e.g. in Figure 1C, only 50-60% of Failed males responded to 400 mM stimulation.  

      These two experiments in combination suggest that sexual failure suppressed sweet sensitivity of the Failed males. Meanwhile, as long as they still initiated feeding, the volume of food consumption remained unchanged. These results led us to focus on the modulatory effect of sexual failure on the sensory system, the main topic of this present study.

      In addition, to further clarify the potential misunderstanding, we plan to examine food consumption by using 800 mM sucrose instead. As shown in Figure 1C, 800 mM sucrose was adequate to induce feeding in ~100% of the flies.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This valuable study combined whole-head magnetoencephalography (MEG) and subthalamic (STN) local field potential (LFP) recordings in patients with Parkinson's disease undergoing deep brain stimulation surgery. The paper provides solid evidence that cortical and STN beta oscillations are sensitive to movement context and may play a role in the coordination of movement redirection.

      We are grateful for the expert assessment by the editor and the reviewers. Below we provide pointby-point replies to both public and private reviews. We have tried to keep the answers in the public section short and concise, not citing the changed passages unless the point does not re-appear in the recommendations. There, we did include all of the changes to the manuscript, such that the reviewers need not go back and forth between replies and manuscript.

      The reviewer comments have not only led to numerous improvements of the text, but also to new analyses, such as Granger causality analysis, and to methodological improvements e.g. including numerous covariates in the statistical analyses. We believe that the article improved substantially through the feedback, and we thank the reviewers and the editor for their effort.

      Public Reviews

      Reviewer #1 (Public review):

      Summary:

      Winkler et al. present brain activity patterns related to complex motor behaviour by combining wholehead magnetoencephalography (MEG) with subthalamic local field potential (LFP) recordings from people with Parkinson's disease. The motor task involved repetitive circular movements with stops or reversals associated with either predictable or unpredictable cues. Beta and gamma frequency oscillations are described, and the authors found complex interactions between recording sites and task conditions. For example, they observed stronger modulation of connectivity in unpredictable conditions. Moreover, STN power varied across patients during reversals, which differed from stopping movements. The authors conclude that cortex-STN beta modulation is sensitive to movement context, with potential relevance for movement redirection.

      Strengths:

      This study employs a unique methodology, leveraging the rare opportunity to simultaneously record both invasive and non-invasive brain activity to explore oscillatory networks.

      Weaknesses:

      It is difficult to interpret the role of the STN in the context of reversals because no consistent activity pattern emerged.

      We thank the reviewer for the valuable feedback to our study. We agree that the interpretation of the role of the STN during reversals is rather difficult, because reversal-related STN activity was highly variable across patients. Although there seem to be consistent patterns in sub-groups of the current cohort, with some patients showing event-related increases (Fig. 3b) and others showing decreases, the current dataset is not large enough to substantiate or even explain the existence of such clusters. Thus, we limit ourselves to acknowledging this limitation and discussing potential reasons for the high variability, namely variability in electrode placement and insufficient spatial resolution for the separation of specialized cell ensembles within the STN (see Discussion, section Limitations and future directions).

      Reviewer #2 (Public review):

      Summary:

      This study examines the role of beta oscillations in motor control, particularly during rapid changes in movement direction among patients with Parkinson's disease. The researchers utilized magnetoencephalography (MEG) and local field potential (LFP) recordings from the subthalamic nucleus to investigate variations in beta band activity within the cortex and STN during the initiation, cessation, and reversal of movements, as well as the impact of external cue predictability on these dynamics. The primary finding indicates that beta oscillations more effectively signify the start and end of motor sequences than transitions within those sequences. The article is well-written, clear, and concise.

      Strengths:

      The use of a continuous motion paradigm with rapid reversals extends the understanding of beta oscillations in motor control beyond simple tasks. It offers a comprehensive perspective on subthalamocortical interactions by combining MEG and LFP.

      Weaknesses:

      (1) The small and clinically diverse sample size may limit the robustness and generalizability of the findings. Additionally, the limited exploration of causal mechanisms reduces the depth of its conclusions and focusing solely on Parkinson's disease patients might restrict the applicability of the results to broader populations.

      We thank the reviewer for the insightful feedback. We address these issues one by one in our responses to points 2, 4 and 6, respectively.

      (2) The small sample size and variability in clinical characteristics among patients may limit the robustness of the study's conclusions. It would be beneficial for the authors to acknowledge this limitation and propose strategies for addressing it in future research. Additionally, incorporating patient-specific factors as covariates in the ANOVA could help mitigate the confounding effects of heterogeneity.

      Thank you for this comment. The challenges associated with recording brain activity peri-operatively can be a limiting factor when it comes to sample size and cohort stratification. We now acknowledge this in the revised discussion (section Limitations and future directions). Furthermore, we suggest using sensing-capable devices in the future as a measure to increase sample sizes (Discussion, section Limitations and future directions). Lastly, we appreciate the idea of adding patient-specific factors as covariates to the ANOVAs and have thus included age, disease duration and pre-surgical UPDRS score into our models. This did not lead to any qualitative changes of statistical effects.

      (3) The author may consider using standardized statistics, such as effect size, that would provide a clearer picture of the observed effect magnitude and improve comparability.

      Thanks for the suggestion. As measures of effect size, we have added partial eta squared (η<sub>p</sub><sup2</sup>) to the results of all ANOVAs and Cohen’s d to all follow-up t-tests.

      (4) Although the study identifies relevance between beta activity and motor events, it lacks causal analysis and discussion of potential causal mechanisms. Given the valuable datasets collected, exploring or discussing causal mechanisms would enhance the depth of the study.

      We appreciate this idea and have conducted Granger causality analyses in response to this comment. This new analysis reveals that there is a strong cortical drive to the STN for all movements of interest and predictability conditions in the beta band. The detailed results can be viewed on p. 16 in the section on Granger causality. For statistical testing, we conducted an rmANCOVA, similar to those for power and coherence (see p. 46-48 and 54-56 for the corresponding tables), as well as t-tests assessing directionality (Figure 6-figure supplement 2 on p. 35). In the discussion section, we connect these results with prior findings suggesting that the frontal cortex drives the STN in the beta band, likely through hyperdirect pathway fibers (p. 17).

      (5) The study cohort focused on senior adults, who may exhibit age-related cortical responses during movement planning in neural mechanisms. These aspects were not discussed in the study.

      We appreciate the comment and agree that age may have impacted neural oscillatory activity of patients in the present study. We now acknowledge this in the limitations section, and point out that our approach to handling these effects was including age as a covariate in the statistical analyses.

      (6) Including a control group of patients with other movement disorders who also undergo DBS surgery would be beneficial. Because we cannot exclude the possibility that the observed findings are specific to PD or can be generalized. Additionally, the current title and the article, which are oriented toward understanding human motor control, may not be appropriate.

      We thank the reviewer for this comment and fully agree that it cannot be ruled out that the present findings are, in part, specific to PD. We acknowledge this limitation in the Limitations and future directions section (p. 20-21). Indeed, including a control group of patients with other disorders would be ideal, but the scarcity of patients with diseases other than PD who receive STN DBS in our centre makes this an unfeasible option in practical terms. We do suggest that future research may address this issue by extending our approach to different disorders or healthy participants on the cortical level (p. 21). Lastly, we appreciate the idea to adjust the title of the present article. The adjusted title is: “Context-Dependent Modulations of Subthalamo-Cortical Synchronization during Rapid Reversals of Movement Direction in Parkinson’s Disease”.

      That being said, we do believe that our findings at least approximate healthy functioning and are not solely related to PD. For one, patients were on their usual dopaminergic medication and dopamine has been found to normalize pathological alterations of beta activity. Further, the general pattern of movement-related beta and gamma oscillations reported here has been observed in numerous diseases and brain structures, including cortical beta oscillations measured non-invasively in healthy participants.

      Reviewer #3 (Public review):

      Summary:

      The study highlights how the initiation, reversal, and cessation of movements are linked to changes in beta synchronization within the basal ganglia-cortex loops. It was observed that different movement phases, such as starting, stopping briefly, and stopping completely, affect beta oscillations in the motor system.

      It was found that unpredictable cues lead to stronger changes in STN-cortex beta coherence. Additionally, specific patterns of beta and gamma oscillations related to different movement actions and contexts were observed. Stopping movements was associated with a lack of the expected beta rebound during brief pauses within a movement sequence.

      Overall, the results underline the complex and context-dependent nature of motor-control and emphasize the role of beta oscillations in managing movement according to changing external cues.

      Strengths:

      The paper is very well written, clear, and appears methodologically sound.

      Although the use of continuous movement (turning) with reversals is more naturalistic than many previous button push paradigms.

      Weaknesses:

      The generalizability of the findings is somewhat curtailed by the fact that this was performed perioperatively during the period of the microlesion effect. Given the availability of sensing-enabled DBS devices now and HD-EEG, does MEG offer a significant enough gain in spatial localizability to offset the fact that it has to be done shortly postoperatively with externalized leads, with an attendant stun effect? Specifically, for paradigms that are not asking very spatially localized questions as a primary hypothesis?

      We appreciate the reviewer’s feedback and acknowledge the valid point raised on the timing of our measurements. Indeed, sensing-enabled devices offer a valid alternative to peri-operative recordings, circumventing the stun effect. We acknowledge this in the revised discussion, section Limitations and future directions (p. 23): “Additionally, future research could capitalize on sensingcapable devices to circumvent the necessity to record brain activity peri-operatively, facilitating larger sample sizes and circumventing the stun effect, an immediate improvement in motor symptoms arising as a consequence of electrode implantation (Mann et al., 2009).” This alternative strategy, however, was not an option here because we did not have a sufficient number of patients implanted with sensing-enabled devices at the time when the data collection was initialized.

      That being said, we would like to highlight that in the present study, our goal was not to study pathology related to Parkinson’s disease. Rather, we aimed to learn about motor control in general. The stun effect may have facilitated motor performance in our patients, which is actually beneficial to the research goals at hand.

      Further investigation of the gamma signal seems warranted, even though it has a slightly lower proportional change in amplitude in beta. Given that the changes in gamma here are relatively wide band, this could represent a marker of neural firing that could be interestingly contrasted against the rhythm account presented.

      We appreciate the reviewer’s interest and we have extended the investigation of gamma oscillations. We now provide statistics regarding the influence of predictability on gamma power and gamma coherence (no significant effects) and explore Granger causality in the gamma (and beta) band (see comment 4 of reviewer 2). Unfortunately, we cannot measure spiking via the DBS electrode, and therefore we cannot investigate correlations between gamma oscillatory activity and action potentials. We do agree with the reviewer, however, that action potentials rather than oscillations form the basis of motor control in the brain. This view of ours is now reflected in the revised discussion, section Limitations and future directions (p. 21): “Lastly, given the present study’s focus on understanding movement-related rhythms, particularly in the beta range, future research could further explore the role of gamma oscillations in continuous movement and their relation to action potentials in motor areas (Fischer et al., 2020; Igarashi, Isomura, Arai, Harukuni, & Fukai, 2013), which form the basis of movement encoding in the brain.”

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      This is a well-conducted study and overall the results are clear. I only have one minor suggestion for improvement of the manuscript. I found the order of appearance of the results somewhat confusing, switching from predictability-related behavioral effects to primarily stopping and reversal-related neurophysiological effects, back to predictability but starting with coherence. I would suggest that the authors try to follow a systematic order focused on the questions at hand. E.g. perhaps readability could be improved if the results section is split into reversal vs. stopping related effects, reporting behavior, power, and coherence in this order, followed by a predictability section, again reporting behavior, power, and coherence. Obviously, this is an optional suggestion. Apart from that, I just missed a more direct message related to the absence of statistical significance related to STN power changes during reversal. I think this could be made more clear in the text.

      We thank the reviewer for the feedback to our study. In order to ease reading, we modified the order and further added additional sub-titles to the results section. We start with Behavior (p. 4) and then move on to Power (general movement effects on power – movement effects on STN power – movement effects on cortical power – predictability effects on power). Next, we move on to Connectivity (movement effects on connectivity – predictability effects on connectivity – Granger causality). We hope that these adaptations will help guide the reader.

      Additionally, we thank the reviewer for noting that we did not explicitly mention the lack of statistical significance of reversal-related beta power modulations in the STN. We have adapted the section on modulation of STN beta power associated with reversals (p. 8) to: “In the STN, reversals were associated with a brief modulation of beta power, which was weak in the group-average spectrum and did not reach significance (Fig. 3a).”

      Reviewer #2 (Recommendations for the authors):

      (1) The small sample size and variability in clinical characteristics among patients may limit the robustness of the study's conclusions. It would be beneficial for the authors to acknowledge this limitation and propose strategies for addressing it in future research. Additionally, incorporating patient-specific factors as covariates in the ANOVA could help mitigate the confounding effects of heterogeneity.

      Thank you for this comment. The challenges associated with recording brain activity peri-operatively can be a limiting factor when it comes to sample size. We now acknowledge this in the revised discussion, section Limitations and future directions (p. 20):

      “Invasive measurements of STN activity are only possible in patients who are undergoing or have undergone brain surgery. Studies drawing from this limited pool of candidate participants are typically limited in terms of sample size and cohort stratification, particularly when carried out in a peri-operative setting. Here, we had a sample size of 20, which is rather high for a peri-operative study, but still low in terms of absolute numbers.”

      Furthermore, we suggest using sensing-capable devices in the future as a measure to increase sample sizes (p. 21):

      “Additionally, future research could capitalize on sensing-capable devices to circumvent the necessity to record brain activity peri-operatively, facilitating larger sample sizes and circumventing the stun effect, an immediate improvement in motor symptoms arising as a consequence of electrode implantation (Mann et al., 2009).”

      Lastly, we appreciate the idea of adding patient-specific factors as covariates to the ANOVAs and have thus included age, disease duration and pre-surgical UPDRS score into our models. This did not lead to any qualitative changes of statistical effects.

      Revised article

      Methods, Statistical analysis:

      “To account for their potential influence on brain activity, we added age, pre-operative UPDRS score, and disease duration as covariates to all ANOVAs. Covariates were standardized by means of zscoring.”

      (2) The author may consider using standardized statistics, such as effect size, that would provide a clearer picture of the observed effect magnitude and improve comparability.

      Thanks for this useful suggestion. As measures of effect size, we have added partial eta squared (η<sub>p</sub><sup2</sup>) to the results of all ANOVAs and Cohen’s d to all follow-up _t-_tests.

      (3) Although the study identifies relevance between beta activity and motor events, it lacks causal analysis and discussion of potential causal mechanisms. Given the valuable datasets collected, exploring or discussing causal mechanisms would enhance the depth of the study.

      We appreciate this idea and have conducted Granger causality analyses in response to this comment. This new analysis reveals that there is a strong cortical drive to the STN for all movements of interest and predictability conditions in the beta band, but no directed interactions in the gamma band. For statistical testing, we conducted an rmANCOVA, similar to the analysis of power and coherence (see p. 46-48 and 54-56 for the corresponding tables), as well as t-tests assessing directionality (Figure 6 figure supplement 2 on p. 35). In the discussion section, we connect these results with prior findings suggesting that the frontal cortex drives the STN in the beta band, likely through hyperdirect pathway fibers (p. 17).

      Revised article

      Methods Section, Granger Causality Analysis

      “We computed beta and gamma band non-parametric Granger causality (Dhamala, Rangarajan, & Ding, 2008) between cortical ROIs and the STN in the hemisphere contralateral to movement for the post-event time windows (0 – 2 s with respect to start, reversal, and stop). Because estimates of Granger causality are often biased, we compared the original data to time-reversed data to suppress non-causal interactions. True directional influence is reflected by a higher causality measure in the original data than in its time-reversed version, resulting in a positive difference between the two, the opposite being the case for a signal that is “Granger-caused” by the other. Directionality is thus reflected by the sign of the estimate (Haufe, Nikulin, Müller, & Nolte, 2013). Because rmANCOVA results indicated no significant effects for predictability and movement type, and post-hoc tests did not detect significant differences between hemispheres, we averaged Granger causality estimates over movement types, hemispheres and predictability conditions in Figure 6-figure supplement 2.”

      Results, Granger causality

      “In general, cortex appeared to drive the STN in the beta band, regardless of the movement type and predictability condition. This was reflected in a main effect of ROI on Granger causality estimates (F<sub>ROI</sub>(7,9) = 3.443, p<sub>ROI</sub> = 0.044, η<sub>p</sub><sup2</sup> = 0.728; refer to Supplementary File 4 for the full results of the ANOVA). In the hemisphere contralateral to movement, follow-up t-tests revealed significantly higher Granger causality estimates from M1 to the STN (t = 3.609, one-sided p < 0.001, d = 0.807) and from MSMC to the STN (t = 2.051, one-sided p < 0.027, d = 0.459) than the other way around. The same picture emerged in the hemisphere ipsilateral to movement (M1 to STN: t = 3.082, one-sided p = 0.003, d = 0.689; MSMC to STN: t \= 1.833, one-sided p < 0.041, d = 0.410). In the gamma band, we did not detect a significant drive from one area to the other (F<sub>ROI</sub>(7,9) = 0.338, p<sub>ROI</sub> = 0.917, η<sub>p</sub><sup2</sup> = 0.208, Supplementary File 6). Figure 6-figure supplement 2 demonstrates the differences in Granger causality between original and time-reversed data for the beta and gamma band.”

      Discussion, The dynamics of STN-cortex coherence

      “Considering the timing of the increase observed here, the STN’s role in movement inhibition (Benis et al., 2014; Ray et al., 2012) and the fact that frontal and prefrontal cortical areas are believed to drive subthalamic beta activity via the hyperdirect pathway (Chen et al., 2020; Oswal et al., 2021) it seems plausible that the increase of beta coherence reflects feedback of sensorimotor cortex to the STN in the course of post-movement processing. In line with this idea, we observed a cortical drive of subthalamic activity in the beta band.”

      (4) The study cohort focused on senior adults, who may exhibit age-related cortical responses during movement planning in neural mechanisms. These aspects were not discussed in the study.

      We appreciate the comment and agree that age may have impacted neural oscillatory activity of patients in the present study. We now acknowledge this in the limitations section, and point out that our approach to handling these effects was including age as a covariate in the statistical analyses.

      Revised article

      Discussion, Limitations and Future Directions

      “Further, most of our participants were older than 60 years. To diminish any confounding effects of age on movement-related modulations of neural oscillations, such as beta suppression and rebound (Bardouille & Bailey, 2019; Espenhahn et al., 2019), we included age as a covariate in the statistical analyses.”

      (5) Including a control group of patients with other movement disorders who also undergo DBS surgery would be beneficial. Because we cannot exclude the possibility that the observed findings are specific to PD or can be generalized. Additionally, the current title and the article, which are oriented toward understanding human motor control, may not be appropriate.

      We thank the reviewer for this comment and fully agree that it cannot be ruled out that the present findings are, in part, specific to PD. We acknowledge this limitation in the Limitations and future directions section (p. 20-21). Indeed, including a control group of patients with other disorders would be ideal, but the scarcity of patients with diseases other than PD who receive STN DBS makes this an unfeasible option. We do suggest that future research may address this issue by extending our approach to different disorders or healthy participants on the cortical level (p. 21). Lastly, we appreciate the idea to adjust the title of the present article. The adjusted title is: “Context-Dependent Modulations of Subthalamo-Cortical Synchronization during Rapid Reversals of Movement Direction in Parkinson’s Disease”.

      That being said, we do believe that our findings at least approximate healthy functioning and are not solely related to PD. For one, patients were on their usual dopaminergic medication for the study and dopamine has been found to normalize pathological alterations of beta activity. More importantly, the general pattern of movement-related beta and gamma oscillations has been observed in numerous diseases and brain structures, including cortical beta oscillations measured non-invasively in healthy participants. Thus, it is not unlikely that the new aspects discovered here are also general features of motor processing.

      Revised article

      Discussion, Limitations and future directions

      “Furthermore, we cannot be sure to what extent the present study’s findings relate to PD pathology rather than general motor processing. We suggest that our approach at least approximates healthy brain functioning as patients were on their usual dopaminergic medication. Dopaminergic medication has been demonstrated to normalize power within the STN and globus pallidus internus, as well as STN-globus pallidus internus and STN-cortex coherence (Brown et al., 2001; Hirschmann et al., 2013). Additionally, several of our findings match observations made in other patient populations and healthy participants, who exhibit the same beta power dynamics at movement start and stop (Alegre et al., 2004) that we observed here. Notably, our finding of enhanced cortical involvement in face of uncertainty aligns well with established theories of cognitive processing, given the cortex' prominent role in managing higher cognitive functions (Altamura et al., 2010). Yet, transferring our approach and task to patients with different disorders, e.g. obsessive compulsive disorder, or examining young and healthy participants solely at the cortical level, could contribute to elucidating whether the synchronization dynamics reported here are indeed independent of PD and age.”

      Reviewer #3 (Recommendations for the authors):

      Despite the strengths of the "rhythm" account of cognitive processes, the paper could possibly be improved by making it less skewed to rhythms explaining all of the movement encoding.

      Thank you for this comment - the point is well taken. There is a large body of literature relating neural oscillations to spiking in larger neural populations, which itself is likely the most relevant signal with respect to motor control. In our eyes, it is this link that justifies the rhythm account, i.e. we agree with the reviewer that action potentials are the basis of movement encoding in the brain, not oscillations. Unfortunately, we cannot measure spiking with the method at hand.

      To better integrate this view into the current manuscript, we make the following suggestion for future research in the Limitations and future directions section (p. 21): “Lastly, given the present study’s focus on understanding movement-related rhythms, particularly in the beta range, future research could further explore the role of gamma oscillations in continuous movement and their relation to action potentials in motor areas (Fischer et al., 2020; Igarashi, Isomura, Arai, Harukuni, & Fukai, 2013), which form the basis of movement encoding in the brain.”

      In Figure 5 - is the legend correct? Is it really just a 0.2% change in power only? That would be a very surprisingly small effect size.

      We thank the reviewer for noting this. Indeed, the numbers on the scale quantify relative change (post - pre)/pre and should be multiplied by 100 to obtain %-change. We have adjusted the color bars accordingly.

      The dissociation between the effects of unpredictable cues in coherence versus raw power is interesting and could potentially be directly contrasted further in the discussion (here they are presented separately with separate discussions, but this seems like a pretty important and novel finding as beta coherence and power usually go in the same direction).

      We appreciate the reviewer’s interest in our findings on the predictability of movement instructions. In case of coherence, the difference between pre- and post-event was generally more positive in the unpredictable condition, meaning that suppressions (negative pre-post difference) were diminished whereas increases (positive pre-post difference) were enhanced. With respect to power, we also observed less suppression in the unpredictable condition at movement start. Therefore, the direction of change is in fact the same. We made this clearer in the revised version by adapting the corresponding sections of the abstract, results and discussion (see below).

      The only instance of coherence and power diverging (on a qualitative level) was observed during reversals: here, we noted post-event increases in coherence and post-event decreases in M1 power in the group-average spectra. However, when comparing the pre- and post-event epochs statistically by means of permutation testing, the coherence increase did not reach significance. Hence, we did not highlight this aspect.

      Revised version

      Abstract

      “… Event-related increases of STN-cortex beta coherence were generally stronger in the unpredictable than in the predictable condition. … “

      Results, Effects of predictability on beta power  

      “With respect to the effect of predictability of movement instructions on beta power dynamics (research aim 2), we observed an interaction between movement type and condition (F<sub>cond*mov</sub> (2,14) = 4.206, p<sub>cond*mov</sub> = 0.037, η<sub>p</sub><sup2</sup> = 0.375), such that the beta power suppression at movement start was generally stronger in the predictable (M = -0.170, SD = 0.065) than in the unpredictable (M \= -0.154, SD = 0.070) condition across ROIs (t = -1.888, one-sided p \= 0.037, d = -0.422). We did not observe any modulation of gamma power by the predictability of movement instructions (F<sub>cond</sub> (1,15) = 0.792, p<sub>cond</sub> = 0.388, η<sub>p</sub><sup2</sup> = 0.050, Supplementary File 5).”

      Effects of predictability on STN-cortex coherence

      “With respect to the effect of predictability of movement instructions on beta coherence (research aim 2), we found that the pre-post event differences were generally more positive in the unpredictable condition (main effect of predictability condition; F<sub>cond</sub>(1,15) = 8.684, p<sub>cond</sub> = 0.010, η<sub>p</sub><sup2</sup> = 0.367; Supplementary File 3), meaning that the suppression following movement start was diminished and the increases following stop and reversal were enhanced in the unpredictable condition (Fig. 6a). This effect was most pronounced in the MSMC (Fig. 6b). When comparing regionaverage TFRs between the unpredictable and the predictable condition, we observed a significant difference only for stopping (t<sub>clustersum</sub> = 142.8, p = 0.023), suggesting that the predictability effect was mostly carried by increased beta coherence following stops. When repeating the rmANCOVA for preevent coherence, we did not observe an effect of predictability (F<sub>cond</sub>(1,15) = 0.163, p<sub>cond</sub> = 0.692, η<sub>p</sub><sup2</sup> = 0.011), i.e. the effect was most likely not due to a shift of baseline levels. The increased tendency for upward modulations and decreased tendency for downward modulations rather suggests that the inability to predict the next cue prompted intensified event-related interaction between STN and cortex. STN-cortex gamma coherence was not modulated by predictability (F<sub>cond</sub>(1,15) = 0.005, p<sub>cond</sub> = 0.944, η<sub>p</sub><sup2</sup> = 0.000, Supplementary File 5).”

      Discussion, Beta coherence and beta power are modulated by predictability

      “In the present paradigm, patients were presented with cues that were either temporally predictable or unpredictable. We found that unpredictable movement prompts were associated with stronger upward modulations and weaker downward modulations of STN-cortex beta coherence, likely reflecting the patients adopting a more cautious approach, paying greater attention to instructive cues. Enhanced STN-cortex interactions might thus indicate the recruitment of additional neural resources, which might have allowed patients to maintain the same movement speed in both conditions. […]”

      With respect to power, we observed reduced beta suppression in the unpredictable condition at movement start, consistent with the effect on coherence, likely demonstrating a lower level of motor preparation.

      Given that you have a nice continuous data task here - the turning of the wheel, it might be interesting to cross-correlate the circular position (and separately - velocity) of the turning with the envelope of the beta signal. This would be a nice finding if you could also show that the beta is modulated continuously by the continuous movements. In the natural world, we rarely do a continuous movement with a sudden reversal, or stop, most of the time we are in continuous movement. Look at this might also be a strength of your dataset.

      We could not agree more. In fact, having a continuous behavioral output was a major motivation for choosing this particular task. We are very interested in state space models such as preferential subspace identification (Sani et al., 2021), for example. These models relate continuous brain signals to continuous behavioral target variables and should be of great help for questions such as: do oscillations relate to moment-by-moment adaptations of continuous movement? Which frequency bands and brain areas are important? Is angular position encoded by different brain areas/frequency bands than angular speed? These analyses are in fact ongoing. This project, however, is too large to fit into the current article.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This study is an important follow-up to their prior work - Wong et al. (2019), starting with clear questions and hypotheses, followed by a series of thoughtful and organized experiments. The method and results are convincing. Experiment 1 demonstrated the sensory preconditioned fear with few (8) or many (32) sound-light pairings. Experiments 2A and 2B showed the role of PRh NMDA receptors during conditioning for online integration, revealing that this contribution is present only after a few sound-light pairings, not after many sound-light pairings. Experiments 3A and 3B showed the contribution of PRh-BLA communication to online integration, again only after a few but not after many. Contrary to Experiments 3A and 3B, Experiments 4A and 4B showed the contribution of PRh-BLA communication to integration at test only after many but not few sound-light pairings.

      Strengths:

      Throughout the manuscript, the methods and results are clearly organized and described, and the use of statistics is solid, all contributing to the overall clarity of the research. The discussion section was also well-written, effectively comparing the current research with the prior work and offering insightful interpretations and potential future directions for this line of research. I have only a limited amount of concerns about some results and some details of experiments/statistics.

      We thank the reviewer for their positive assessment.

      Weaknesses:

      Could you provide further interpretation regarding line 171: the observation that sensory preconditioned fear increased with the number of sound-light pairings? Was this increase due to better sound-light association learning during Stage 1? Additionally, were there any experimental differences between Experiment 1 and the other experiments that might explain why freezing was higher in the P32 group compared to the P8 group? This pattern seemed to be absent in the other experiments. If we consider the hypothesis that the online integration mechanism is more active with fewer pairings and the chaining mechanism at the test is more prominent with many pairings, we wouldn't expect a difference between the P8 and P32 groups. Given the relatively small sample size in Experiment 1, the authors might consider conducting a cross-experiment analysis or something similar to investigate this further.

      We appreciate the reviewer’s point and thank them for the question. The heightened level of sensory preconditioned fear among rats that received many sound-light pairings in the initial control experiment (Group P32) may reflect the combined effects of both mediated learning and chaining at test. We are, however, reluctant to offer a strong interpretation of this result as it was not replicated in the subsequent experiments: i.e., the levels of freezing to the sensory preconditioned stimulus at test were almost identical among vehicle-injected controls that received either few (8) or many (32) sound-light pairings in Experiments 2A and 2B; and this was also true in Experiments 3A and 3B, and again in Experiments 4A and 4B. A key difference between the initial and subsequent experiments is that, in contrast to the initial experiment, rats in subsequent experiments underwent surgery for one reason or another (implantation of cannulas, lesion of the perirhinal cortex). The implication is that surgical interventions in the perirhinal cortex and/or basolateral amygdala might affect the way that rats integrate the sound-light and light-shock associations in sensory preconditioning: i.e., they may force rats to rely on one type of integration strategy or the other. This is, of course, purely speculative – it will be addressed in future research.

      Reviewer #2 (Public review):

      This manuscript builds on the authors' earlier work, most recently Wong et al. 2019, in which they showed the importance of the perirhinal cortex (PRh) during the first-order conditioning stage of sensory preconditioning. Sensory preconditioning requires learning between two neutral stimuli (S2-S1) and subsequent development of a conditioned response to one of the neutral stimuli after pairing of the other stimulus with a motivationally relevant unconditioned stimulus (S1-US). One highly debated question regarding the mechanisms of learning of sensory preconditioning has been whether conditioned responses evoked by the indirectly trained stimulus (S2) occur through a mediated representation at the time of the first-order US training, or whether the conditioned responses develop through a chained evoked representation (S2--> S1 --> US) at the time of test. The authors' prior findings provided strong evidence for PRh being involved in mediated learning during the first-order training. They showed that protein synthesis was required during the first-order S1-US learning to support the conditioned response to the indirectly trained stimulus (S2) at the test.

      One question remaining following the previous paper was whether certain conditions may promote a chaining mechanism over mediated learning, as there is some evidence for chained representations at the time of the test. In this paper, the authors directly address this important question and find unambiguous results that the extent of training during the preconditioning stage impacts the involvement of PRh during the first-order conditioning or stage 2. They show that putative blockade of synaptic changes in PRh, using an NMDA antagonist, disrupts responding to the preconditioned cue at test during shorter duration preconditioning training (8 trials), but not during extended training (32 trials). They also show that this is the case for communication between the PRh and BLA during the same stage of training using a contralateral inactivation approach. This confirms their previous findings in 2019 of connectivity between these regions for the short-duration training, while they observe here for the first time that this is not the case for extended training. Finally, they show that with extended training, communication between BLA and the PRh is required at the final test of the preconditioned stimulus, but not for the short duration training.

      The results are clear and extremely consistent across experiments within this paper as well as with earlier work. The experiments here are thorough, and well-conceived, and address an important and highly debated question in the field regarding the neural and psychological mechanisms underlying sensory preconditioning. This work is highly impactful for the field as the debate over mediated versus chaining mechanisms has been an important topic for more than 70 years.

      We thank the reviewer for their kind assessment.

      Reviewer #3 (Public review):

      The authors tested whether the number of stimulus-stimulus pairings alters whether preconditioned fear depends on online integration during the formation of the stimulus-outcome memory or during the probe test/mobilization phase, when the original stimulus, which was never paired with aversive events, elicits fear via chaining of stimulus-stimulus and stimulus-outcome memories. They found that sensory preconditioning was successful with either 8 or 32 stimulus-stimulus pairings. Perirhinal cortex NMDA receptor blockade during stimulus-outcome learning impaired preconditioning following 8 but not 32 pairings during preconditioning. Therefore, perirhinal cortex NMDA activity is required for online integration or mediated learning. Perirhinal-basolateral amygdala had nearly identical effects with the same interpretation: these areas communicate during stimulus-outcome learning, and this online communication is required for later expressing preconditioned fear. Disconnection prior to the probe test, when chaining might occur, had different effects: it impaired the expression of preconditioned fear in rats that received 32, but not 8, pairings during preconditioning. The study has several strengths and provides a thoughtful discussion of future experiments. The study is highly impactful and significant; the authors were successful in describing the behavioral and neurobiological mechanisms of mediated learning versus chaining in sensory preconditioning, which is often debated in the learning field. Therefore this study will have a significant impact on the behavioral neurobiology and learning fields.

      Strengths:

      Careful, rigorous experimental design and statistics.

      The discussion leaves open questions that are very much worth exploring. For example - why did perirhinal-amygdala disconnection prior to the probe have no effect in the 8-pairing group, when bilateral perirhinal inactivation did (in Wong et al, 2019)? The authors propose that perirhinal cortex outputs bypass the amygdala during the probe test, which is an excellent hypothesis to test.

      The authors provide evidence that both mediated learning and chaining occur.

      Thank you for the positive assessment – we fully intend to identify the circuitry that regulates retrieval/expression of sensory preconditioned fear when it is based on mediated learning in stage 2.

      Weaknesses:

      This is inherent to all neural interference and behavioral experiments: biological/psychological functions do not typically operate binarily. There is no single clear number or parameter at which mediated learning or chaining happens, and both probably happen to some extent. Addressing this is even more difficult given behavioral variability across subjects, implant sites, etc. Thus, this is not so much a weakness particular to this study as much as an existential problem, which the authors were able to work around with careful experimental design and appropriate controls.

      We completely agree with the point raised here and thank the reviewer for their assessment.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) It appears that the method description for Sensory Preconditioning was copied from their previous Wong et al. (2019) paper, which is fine, but in the current research, the authors use 8 or 32 presentations, which is not reflected in the description.

      Thank you for bringing this to our attention. This is now addressed in the method section on page 27 (beginning at line 655):

      “Rats received either eight presentations of the sound and eight of the light in a single session, or 32 presentations of the sound and 32 of the light across four daily sessions. On Day 3, all rats received eight presentations of the sound and eight of the light. Each presentation of the sound was 30 s in duration and each presentation of the light was 10 s in duration. The first stimulus presentation occurred five min after rats were placed into the chambers. The offset of one stimulus co-occurred with the onset of the other stimulus for groups that received paired presentations of the sound and the light, while these stimuli were presented separately for groups that received explicitly unpaired presentations. The interval between each paired presentation was five min while the interval between each separately presented stimulus was 150 s. After the last stimulus presentation, rats remained in the chambers for an additional one min. They were then returned to their home cages. This training was repeated on Days 4-6 for rats that received 32 presentations of the sound and 32 of the light. All rats proceeded to first-order conditioning (details below) the day after their final session of sound and light exposures, which was Day 4 for rats exposed to eight presentations of the sound and light and Day 7 for rats exposed to 32 presentations of the sound and light.”

      (2) Line 148: Could the authors clarify how the "significant linear increase" was assessed? From similar descriptions in later experiments, it seems it was based on a comparison of freezing across the four presentations, but the F(1,26) statistic suggests there seemed to be a half-split test. The same questions exist in all the experiments. Please clarify.

      Conditioning data were analysed using contrasts with repeated measures in ANOVA. The repeated measures (or within-subject) factor was “trial” as all rats were exposed to four light-shock pairings in this stage of training. We examined whether there was a significant linear increase in freezing across trials using a standard within-subject contrast. The specific coefficients for this contrast, given the four trials, were -3, -1, 1, and 3. The reason that the degrees of freedom remain 1 and 26 in this analysis is because the within-subject contrast is part of a set of planned orthogonal contrasts. That is, in any planned analysis of the sort conducted here, the df1 will always be 1, indicating the very nature of the analysis. There was no splitting of the data, or comparisons between the split halves.

      (3) Line 154: Could the authors clarify what is meant by "other main effects and their interactions"? It is not clearly inferable from the context.

      Apologies for the confusion here. “Other main effects” refer to the two between-subject factors in isolation: i.e., the overall comparison of freezing to the light (averaged across the four trials) between groups that received either paired or unpaired stimulus presentations in stage 1 (factor 1 à main effect 1), and between groups that received either eight or 32 sound and light exposures in stage 1 (factor 2 à main effect 2). “Their interaction” refers to the assessment of whether the overall difference in freezing to the light (averaged across the four trials) between Groups P8 and U8 differs from the overall difference in freezing to the light (averaged across the four trials) between Groups P32 and U32. We have edited the text near line 153 to indicate that:

      “The overall comparisons of freezing to the light (averaged across the four conditioning trials) between groups that received either paired or unpaired stimulus presentations in stage 1 (factor 1), and between groups that received either eight or 32 sound and light exposures in stage 1 (factor 2), were not significant (Fs < .45, p > .508). The interaction between these two between-subject factors was also not significant (F < .45, p > .508).”

      (4) The use of sound and light as preconditioned and conditioned cues are counterbalanced. Was there any difference in the increase of freezing during conditioning depending on the type of conditioned cues? Was there any difference in the preconditioned fear? While it is hard to assess statistical significance due to the sample size limit, even observing a trend could be interesting.

      We examined whether the levels of freezing to the conditioned and preconditioned stimuli depend on their physical identity. In general, there was a slight trend towards more freezing to the preconditioned stimulus when it was a tone, and less freezing to the conditioned stimulus when it was a tone. These are, however, simply indications. None of the statistical comparisons between rats for which the preconditioned stimulus was the tone (and, thereby, conditioned stimulus was the light) and rats for which the preconditioned stimulus was the light (and, thereby, conditioned stimulus was the tone) reached the conventional level of significance.

      (5) General suggestion on reporting non-significant statistics: the authors reported a small F statistic value a few times to suggest non-significance. But without clearly specifying degrees of freedom, it is hard to get a sense of statistical significance (e.g. Line 227, largest F<3.10). I recommend adding p values alongside the F statistics and reporting exact statistics whenever possible.

      Apologies for the omission. The p values have now been included alongside all non-significant F statistics.

      (6) Another general suggestion is to use non-parametric statistical testing with such small sample sizes. I recommend using the Kruskal-Wallis H test (the non-parametric equivalent of F-statistic) to replace the ANOVA result. Also, given many tests only involve comparing two independent groups, using Mann-Whitney U test (the non-parametric equivalent of independent t-test) would be sufficient.

      We understand that small sample sizes can occasionally lead to unequal variances between groups, which necessitates the use of non-parametric statistics. However, as non-parametric statistics raise a different set of issues for data analysis (e.g., power) and interpretation, our general view for the type of data collected in this study is that parametric analyses are appropriate and should be retained (particularly in the absence of unequal variances between groups). We hold this view for two reasons. First, the hypotheses tested in the present series were derived from past work in which parametric analyses revealed meaningful patterns of results at the same level of statistical power. Second, the application of these analyses then yielded results consistent with our hypotheses: for the most part, we observed between-group differences where we expected there to be such differences and did not observe between-group differences where we did not expect there to be such differences. As such, we have not switched from a parametric to non-parametric analysis strategy. We do, however, appreciate the suggestion and will apply a non-parametric approach where it is warranted in our future work.

      Reviewer #2 (Recommendations for the authors):

      I have a few very minor comments for the authors regarding the discussion and interpretation of the very nice experimental results.

      (1) In Figures 4 and 5, the authors provide a schematic of the experiment. It's very clearly indicated whether the BLA inactivation is ipsi- or contralateral, but the unilateral PRh lesion isn't mentioned. I'd recommend including that here so that someone reading through the figures can more easily understand the experiment. The hypothesis is clear and the experiment is so well designed that a read through of the figures can relay most information to an experienced reader.

      Thank you for this suggestion – we have included information about the unilateral PRh lesion in the schematic for Figures 4 and 5.

      (2) The authors have an extended description of backward conditioning in the discussion. It seems like the authors are suggesting this as an important future direction, but they never explicitly say this, resulting in a bit of confusion as to what this section refers to. Also, Ward-Robinson and Hall 1996 showed backward sensory preconditioning using a serial auditory-visual association and argued for a mediated solution based on their results. It may be worth citing that paper here.

      Apologies for the lack of clarity. We have revised this point in the discussion (page 18, beginning line 434) and referenced Ward-Robinson and Hall (1996):

      “Why does increasing the number of sound-light pairings change the way that rats integrate the sound-light and light-shock memories? One possibility is that increasing the number of sound-light pairings in stage 1 reduces the ability of each stimulus to activate the memory of the other. This is consistent with findings by Holland (1998), who showed that the likelihood of mediated learning in rats decreases with the amount of training (see also Holland, 2005); but inconsistent with our findings that, after extended training, rats continue to integrate the sound-light and light-shock associations through chaining at the time of testing (as chaining is predicated on the sound activating the memory of the light after extended training). Instead, we propose that the change in integration occurs because the increased number of sound-light pairings allows the rats to learn about the order in which the sound and light are presented (Figure 1; for evidence that rats acquire order information in sensory preconditioning, see Barnet et al., 1997; Hart et al., 2022; Leising et al., 2007; Miller & Barnet, 1993). This order hypothesis is consistent with evidence showing that the way in which animals represent an audio-visual compound changes across repeated compound exposures (e.g., Bellingham & Gillette, 1981; Holmes & Harris, 2009). It can be tested using a so-called “backward” sensory preconditioning protocol, which reverses the order of stimulus presentations in stage 1 (e.g., Ward-Robinson & Hall, 1996). That is, rather than rats being exposed to the “forward” sound-light pairings used here and by Wong et al. (2019), rats in a backward protocol are exposed to light-sound pairings. Increasing the number of light-sound pairings in this protocol should result in rats learning that the light is followed by the sound (light→sound) and that the sound is followed by nothing (sound→nothing). Hence, during the session of light-shock pairings in stage 2, the light should continue to activate the memory of the sound, resulting in formation of the mediated sound-shock association (e.g., Ward-Robinson & Hall, 1996). That is, if our order hypothesis is correct, increasing the number of light-sound pairings in the backward protocol should preserve the likelihood of mediated learning in stage 2 and, if anything, diminish the likelihood of chaining at test in stage 3 (as the sound is never followed by a light). Hence, PRh manipulations that fail to affect fear of the sound when administered after many sound-light pairings (e.g., infusion of DAP5) should disrupt that fear when administered after many light-sound pairings in the backward protocol. This will be assessed in future work.”

      (3) Line 467 in the discussion suggests that the results are surprising that PRh-BLA communication is not needed at test when learning putatively occurs through a mediated mechanism during first-order conditioning. I was a bit surprised by this comment since I was under the assumption that only BLA was required at this point after consolidation of the mediated learning. Holmes et al., 2013 showed that BLA is required for extinction to S2 after first-order conditioning. In that experiment they inactivated BLA during S2- presentations (typically considered the extinction test), and showed that reduction to S2 did not occur the subsequent day, indicating the memory was stored in BLA and may not necessarily require PRh-BLA communication.

      The result noted here was somewhat surprising as our past studies showed that silencing activity in the PRh prior to testing attenuates freezing to a sensory preconditioned stimulus (i.e., an S2). We took this to mean that the PRh is necessary for retrieval/expression of fear to S2 and supposed that this retrieval/expression would be achieved through communication between the PRh and BLA. However, the results of the PRh-BLA disconnection at test show that this communication is not required, leaving us to speculate that retrieval/expression of fear to S2 may be achieved through communication between the PRh and CeA.

      We have edited the opening of the relevant paragraph to clarify why the result noted here was surprising (page 20, beginning line 485):

      “While the PRh and BLA clearly communicate to support mediated learning about the sound, this communication is not required for retrieval/expression of the mediated sound-shock association at the time of testing. This result is somewhat surprising as activity in the PRh is needed for expression of fear to the sound (Holmes et al., 2013; Wong et al., 2019) and raises the question: how does the PRh-dependent sound-shock association come to be expressed in fear responses?”

      (4) The authors reference Holland 1981 and 1998, yet there's not much discussion of these findings. I think there should be a bit more emphasis on these studies since they show how mediated learning greatly depends on the extent of training. Also, it may be worth considering Holland's theory of why mediated conditioning is more effective with shorter training. His theory may be consistent with the authors, but I believe he suggests that early in training a stronger mediated representation is evoked which tends to dissipate with time. I think this is a valid hypothesis to consider in this paper.

      The Holland papers show that rats form mediated associations (Holland, 1981) and that the likelihood of them doing so decreases with the amount of training (Holland, 1998). These findings are paralleled by those reported in the present series of experiments. However, the protocols used by Holland were very different to those used in the present study; and the explanation for his 1998 findings (which is the more relevant of the two papers) simply does not apply to the case of sensory preconditioning.

      To be clear: Holland (1998) exposed rats to either “few” or “many” tone-food pairings in stage 1, tone-lithium chloride pairings in stage 2 and, finally, tested rats with the food alone in stage 3. He predicted and showed that those exposed to few tone-food pairings showed an aversion to the food at test (i.e., they consumed less of the food than controls) whereas those exposed to many tone-food pairings showed no such aversion (i.e., they consumed the same amount of food as the controls). This was taken to mean that, across the series of tone-lithium pairings, the tone activated the memory of food among rats in the few condition, resulting in a mediated food-lithium association; but failed to do so among rats in the many condition, resulting in no food-lithium association. According to Holland, the tone failed to activate the memory of food in the many condition because, by the end of training in stage 1, it was not needed for them to know what to do when the tone was presented: they simply had to run to the magazine to collect the food when delivered. That is, the tone eventually associated with the responses that rats emitted in the training situation, thereby obviating any need for activation of the food memory.

      While this explanation is both elegant and interesting, it cannot be applied to the results obtained in the present study where the initial stage of training involved few or many sound-light pairings. That is, unlike in the Holland study where rats in the many condition eventually learned a stimulus-“run to magazine” association that maintained performance in the absence of any mental image of food, in the present study, any stimulus-response association acquired in stage 1 (e.g., orienting responses towards the sources of the auditory and visual stimuli) cannot have contributed to the expression of sensory preconditioned fear at test. Hence, stimulus-response learning in the many condition cannot be invoked to explain the pattern of results in the present study, even if it adequately explains what-appears-to-be a similar finding in the Holland study.

      Nonetheless, we have included a reference to the general style of explanation that was considered and rejected by Holland in his 1998 and 2005 papers. This appears on page 18 (beginning line 434) and reads:

      “Why does increasing the number of sound-light pairings change the way that rats integrate the sound-light and light-shock memories? One possibility is that increasing the number of sound-light pairings in stage 1 reduces the ability of each stimulus to activate the memory of the other. This is consistent with findings by Holland (1998), who showed that the likelihood of mediated learning in rats decreases with the amount of training (see also Holland, 2005); but inconsistent with our findings that, after extended training, rats continue to integrate the sound-light and light-shock associations through chaining at the time of testing (as chaining is predicated on the sound activating the memory of the light after extended training). Instead, we propose that the change in integration occurs because the increased number of sound-light pairings allows the rats to learn about the order in which the sound and light are presented (Figure 1; for evidence that rats acquire order information in sensory preconditioning, see Barnet et al., 1997; Hart et al., 2022; Leising et al., 2007; Miller & Barnet, 1993)…”

      (5) There is also a Holland 2005 paper in which he tests whether extended training of the initial stimulus associations may result in a reduced associability of those stimuli. This would potentially result in lower mediated learning due to a decreased associability of the mediated representation, thereby explaining why extended training reductions in mediated learning occur. Using a probabilistic design, Holland shows that this reduction in mediated learning is likely not due to a change in associability.

      We appreciate the note re Holland (2005) and have included a reference to it in our General Discussion. We agree with Holland that the reduction in mediated learning across extended training is not due to reduced associability of the retrieved stimulus representation. If this were the case, it would remain to explain why stimulus representations continue to be activated at test, which must occur for successful chaining of the sound-light and light-shock associations upon presentations of the sound alone. This is included in the modified text on page 18 (beginning line 434), which is part of our response to point 4.

      Reviewer #3 (Recommendations for the authors):

      (1) I think the 4th intro paragraph is essentially saying that more pairings during preconditioning encourage chaining as opposed to mediated learning - I might recommend clarifying this a bit. It took me a while to put it together.

      Apologies for the confusion. We have clarified the argument at this point in the Introduction with the following insertion on page 4 (beginning line 84):

      “That is, increasing the number of sound-light pairings may allow rats to encode information about stimulus order in stage 1 and, thereby, shift the locus of integration from mediated conditioning in stage 2 to chaining at test in stage 3 (Holmes et al., 2022).”

      (2) In analyzing test data I am assuming percent freezing is the average of the entire 30s or 10s CS period - could this be clarified?

      This is correct and has been clarified in the section for ‘Scoring and Statistics’ on page 29 (beginning line 708):

      “Freezing data were collected using a time-sampling procedure in which each rat was scored as either ‘freezing’ or ‘not freezing’ every two seconds by an observer blind to the rat’s group allocation. A percentage score was then calculated by dividing the number of samples scored as freezing by the total number of samples. The baseline level of freezing was established by scoring the first two min at the start of each experimental session: i.e., we divided the total number of samples scored as freezing by the total number of observed samples, which was 60. The levels of freezing to the 10 s conditioned stimulus and 30 s preconditioned stimulus were established in a similar manner: we scored the entire period of each stimulus presentation and divided the number of samples scored as freezing by the total number of observed samples, which was 5 for each presentation of the conditioned stimulus and 15 for each presentation of the preconditioned stimulus.”

      (3) Complementary to the above - during the probe test is there a difference during the first/last 2s of the CS? This would be interesting with respect to understanding the associative structure encoded.

      We have previously examined whether freezing responses change across the duration of a 30 s preconditioned stimulus and a 10 s conditioned stimulus. We have never seen any such changes: in our past work and in the present series of experiments, the expression of freezing is largely uniform across each presentation of a preconditioned or conditioned stimulus.

      (4) It is sort of unclear to me why more CS-CS pairings produced stronger preconditioned fear - is it that both mediated learning and chaining occur and giving 32 pairings permits both processes more than 8 pairings?

      This is a very reasonable explanation for the heightened level of sensory preconditioned fear among rats that received many sound-light pairings in the initial control experiment. We are, however, reluctant to offer a strong interpretation of this result as it was not replicated across subsequent experiments in the series: i.e., the levels of freezing to the sensory preconditioned stimulus at test were largely the same among vehicle-injected controls that received either few (8) or many (32) sound-light pairings in Experiments 2A and 2B, and again in Experiments 3A and 3B as well as Experiments 4A and 4B.

      (5) I would suggest individual data points overlaid on the bars, violin plots, or box and whisker plots to provide a better visualization of the data.

      We appreciate the suggestion – these have been included overlaid on bars in each histogram_._

      (6) There are other citations that would strengthen arguments for the idea that unidirectional/temporal associative structure can be acquired during (appetitive) sensory preconditioning: Leising 2007 Learning and Behavior, Hart 2022 Current Biology, for example.

      Thank you for these citations. We have included references to the Leising et al (2007) and Hart et al (2022) papers in our discussion on page 18-19 (beginning line 442):

      “Instead, we propose that the change in integration occurs because the increased number of sound-light pairings allows the rats to learn about the order in which the sound and light are presented (Figure 1; for evidence that rats acquire order information in sensory preconditioning, see Barnet et al., 1997; Hart et al., 2022; Leising et al., 2007; Miller & Barnet, 1993)…”

      Editor's note:

      We agree with the suggestions about full statistical reporting for non-significant results and about putting individual data points, perhaps coded to identify sex, on top of the bar graphs. Both will increase the transparency of the rigor of the work for readers.

      We thank the editors and authors for their suggestions. We have included full statistical reporting for non-significant results and overlaid individual data points on the bars in each histogram.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Joint Public Review:

      Summary:

      The behavioral switch between foraging and mating is important for resource allocation in insects. This study investigated the role of the neuropeptide, sulfakinin, and of its receptor, the sulfakinin receptor 1 (SkR1), in mediating this switch in the oriental fruit fly, Bactrocera dorsalis. The authors use genetic disruption of sulfakinin and of SkR1 to provide strong evidence that changes in sulfakinin signaling alter odorant receptor expression profiles and antennal responses and that these changes mediate the behavioral switch. The combination of molecular and physiological data is a strength of the study. Additional work would be needed to determine whether the physiological and molecular changes observed account for the behavioral changes observed.

      Strengths:

      (1) The authors show that sulfakinin signaling in the olfactory organ mediates the switch between foraging and mating, thereby providing evidence that peripheral sensory inputs contribute to this important change in behavior.

      (2) The authors' development of an assay to investigate the behavioral switch and their use of different approaches to demonstrate the role of sulfakinin and SkR1 in this process provides strong support for their hypothesis.

      (3) The manuscript is overall well-organized and documented.

      Weaknesses:

      (1) The authors claim that sulfakinin acts directly on SkR1-positive neurons to modulate the foraging and mating behaviors in B. dorsalis. The authors also indicated in the schematic that satiation suppresses SkR1 expression. Additional experiments and more a detailed discussion of the results would help support these claims.

      (2) The findings reported could be strengthened with additional experimental details regarding time of day versus duration of starvation effects and additional genetic controls, amongst others.

      Recommendations for the authors:

      Major issues

      (1) As written the introduction is somewhat fragmented and does not lay out a clear rationale for the current study in the species used by the authors. Others, including Guo et al. (2021) and Wang et al. (2022), have previously shown that sulfakinin signaling pathways are important for feeding and receptivity regulation in D. melanogaster. Thus, the novelty of this study should be more clearly articulated.

      The introduction in the revision is significantly changed to improve the description for the rationale of study (lines 60-66 in the revision).

      (2) In addition, the Introduction should provide more specific background information on the pheromonal activity of oriental fruit fly body extract, the odor-preferences, and the sex pheromone of this species compared to that of model insects such as Drosophila melanogaster.

      The revision contains a paragraph of introduction for chemical ecology of oriental fruit fly that is related to this study (lines 67-75).

      (3) It isn't clear what the first image in Figure 1C represents - is this a schematic of the area or does it represent data?

      The Fig 1C and the associated figure caption are revised. The figure is more visible by changing the track colors. The figure caption is revised as “Representative foraging trajectories in the 100 mm diameter arenas within a 15-min observation period of flies starved for different durations.”

      (4) The authors should include examples of the EAG recordings following the stimulation with food volatiles or pheromones, not only the results of their analyses. This could be included in the main figures or even in supporting information.

      As suggested, we added the examples of the EAG recordings following the stimulation with food odors and body extracts in the Figure 1 and Figure 3.

      (5) The demonstration that removal of the antennae severely impairs mating is dispensable because the antennae are required for other functions in addition to olfaction.

      We agree that the roles of the antennae are likely more than the olfactory function. As suggested, we removed the data.

      (6) It is currently difficult to understand how the authors measured successful rates of foraging. Please provide more details.

      In the revision, we added a sentence describing the method for measuring in detail. See line 269-273.

      (7) The expression of sulfakinin does not change significantly in the antennae following starvation (Figure 2A). Do the authors know whether they change in the central nervous system under these conditions? Have the authors (or has anyone else) checked the expression pattern of sulfakinin in the antennae? This information would help determine whether the sulfakinin signal that acts on SkR1 is released from neurons in the central nervous system (Figure S4C) or whether it is also released from the neurons in the olfactory organs. Based on the immunochemistry results shown in Figure S4C, it would also be interesting to determine whether the intensity of anti-sulfakinin immunoreactivity changes before versus after starvation. This could help establish whether sulfakinin is released during starvation.

      We added the expression data showing the the mRNA level of Sk in the head that is higher after refeeding in Fig. S3. The change in the expression of Sk is also added in the text (lines 107-110). We were unable to identify the Sk neurons in the antennae suggesting possibility of the direct action of humoral Sk on the antennae.

      (8) In Figure 2A, the authors show that the expression levels of some neuropeptides system components change during starvation. However, it would be helpful if the authors could include more detailed information on how the results are shown in the figure legends (e.g., the expression level of each candidate in fed flies was set as 1, etc).

      We revised the figure caption to explain the Figure 2 with the expression values in the figure legend.

      (9) In Figure 2D, null mutant males of sulfakinin and SkR1 consume more food at all times compared to the wild type. However, the corresponding mutant females consume more food only at night. Is this because the wild-type female flies eat more food during the day? In a related issue, Figure 2D shows differences in food consumption measured at different times of day, however, this is not directly addressed in the text, which instead mentions that "the amount of excess food consumed by the mutants was dependent on the duration of the starvation period in both sexes".

      Thank you for the important suggestions. We speculate that the difference of feeding amounts of females occurring only at night is due to the high basal feeding rate of females during the daytime, masking the increase in feeding in the knockout of Sk signaling. As suggested, we have added a relevant description of the difference in food consumption. In addition, we changed the Y-axis scale in the figure for a justified comparison between males and females. See line 123-128.

      (10) It isn't clear how the time of day relates to the duration of starvation. This suggests that mutant females only consume more at 21:00 (presumably at night) whereas males consume more throughout the day. Does this suggest an interaction with the circadian system? What is the duration of starvation in Figure 3A? In a related issue, in Figure 4 it would be useful to know what time of day the EAG analysis was done because the data shown in Figure 2D suggests that the time of day significantly impacts behavioral responses. And does the red versus blue color scheme of the OR subunits represent up/downregulated levels in wild-type animals? Please define this for the reader.

      In addition to the response to the point 9, responding to the issue of feeding amount in females. As the reviewer noted, there was indeed a diurnal difference in food amount consumed by B. dorsalis. However, whether this is related to circadian rhythms is something we haven't studied for further in-depth. Measuring food intake at these 3 times of day, we all ensured that the duration of starvation was the same 12 h. The duration of starvation in Figure 3A is 12h. We have mentioned this in the manuscript. See line 267-268.

      The EAG for sex pheromones and body surface extracts were measured form 21:00-23:00, and food odor was measured from 9:00-11:00. The times of the experiments are described in the revision. See line 309-311.

      Accordingly, we made a revision of the figure caption for explaining the colored fonts. Red color represents a set of ORs related with foraging and blue color is for a set of ORs related with mating. Therefore, the ORs with red color were upregulated in starved wild-type animals and the ORs with blue color were downregulated in starved wild-type flies. We have defined this in the revised manuscript. See line 672-673.

      (11) The authors convincingly show that SKR1 is present in the antennae and is co-expressed with orco. It would be useful to discuss whether this receptor is also expressed in other tissues where there may be additional sites of action of this pathway.

      Indeed, SkR1 is also expressed in the Drosophila brain. We added the discussion on the expression and additional sites of action of SKR1 within the central nervous system. See line 200-205.

      (12) It isn't clear what the dotted arrows in the model shown in Figure 5 represent.

      Dashed arrows represent the additional possible pathways that have not been tested in this study, but not excluded in the model. Please see the discussion for details of additional possible factors modulating odorant sensitivity relevant to satiety. See line 210-229.

      (13) In Figure 5, the authors indicate that satiation suppresses SkR1 expression. It would be helpful if the authors tested the expression level of SkR1 in re-fed flies (by feeding the flies after 12h starvation) to see whether levels of expression are rapidly restored to the levels seen in satiated animals. Such a result could further support the claims made by the authors.

      Thank for your suggestions. Indeed, refeeding after 12h starvation significantly decreased SkR1. We added the result in supporting information (Fig. S3). See line 713. Results see line 107-110.

      (14) The authors show that locomotor activity is unaffected in the mutants but body size comparison would be more useful here since this could also contribute to baseline differences in meal size.

      In the revision, we provided a comparison between WT and Sk-/- in the supplementary data. Results showed that mutant flies have the same body size as the WT flies. (Fig. S7) See line 742. Results see line 120-121.

      (15) Have the authors tested the behavioral phenotypes of heterozygotes mutant of both Sk and SkR1 flies? This may reveal whether a reduced expression of Sk-SkR1 will also cause significant changes in the foraging and mating behaviors seen during starvation.

      We tested the behavioral phenotypes of heterozygous mutant of Sk knockout flies. The results showed that foraging and mating behaviors of Sk heterozygous mutants were unaffected during starvation, suggesting the mutants are completely recessive. We have added the results in supporting information (Fig. S8). See line 746. Results see line 132-135.

      (16) It would be useful to provide information about which SK peptide is detected by the antibody used in Figure S4C. In Figures S4C and S5D, it would be useful to include a counterstain to show that the general morphology is unaffected in the mutants.

      As suggested, we added a detailed description for rabbit anti-BdSk antibody. See line 362-363. We have improved the background image to be available to show the general structure. So counter staining would not be essential.

      (17) The figure legends for supporting figures need to be improved as they are currently difficult to understand. For example, in S2: what is the meaning of "different removal of antennae"? In S3: it isn't clear how the authors evaluated the responses in EAG experiments; in S4A: there are several DNA sequences that do not appear in the main text of the manuscript; in S4C: the meaning of the boxes and the dots is unclear, as is the figure to the left; in S5D, the authors explain only the suppression of SKR1, yet the figure indicates some images for SKR IHC. These are only a few examples; we ask that the authors revise and improve the legends for supporting figures.

      For S2, we removed the data as suggested. For S3, we added a sentence describing the method for measuring in detail. See line 707-709. For S4, the figure in the revision is significantly changed and added a detailed description in the legend (lines 717-724 in the revision). For S5, we have improved our description. See line 731-734. In addition, we have checked all the figure legends of our manuscript and changes were displayed in track version.

      Minor issues

      (1) It isn't clear what the meaning of "the complexity of sulfakinin pathways" is. Please explain.

      We have rewritten the sentence in the revised manuscript by adding the description as “…complexity of Sk pathways, special and temporal dynamics and multiple ligands and receptors, is…”. See line 61-65.

      (2) Please double-check the calls to the various figures in the text.

      We have double-checked the calls to all the figures in the text to make sure they were correct.

      (3) L125: What is the meaning of "olfactory reprogramming"? Please explain.

      We rephrased it to “alteration of olfactory sensitivities”. See line 145.

      (4) L135: After mentioning qRT-PCR the authors should include a call to a figure that shows these results.

      Thank you for your suggestion, the qRT-PCR results are shown in Figure 4B, and we have added it as suggested. See line 154.

      (5) L270: Details are provided for the extraction of the pheromone. However, more details are needed on how the EAG and other functional assays were done.

      We have described the assay procedures in detail in the materials and method part. See line 298-311.

      (6) Figure 2B. Please remove the period(".") at the C-terminal end of WT sk.

      We are sorry for our mistake. We have corrected it.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      […] Weaknesses:

      While there are no glaring weaknesses in this study, it should be noted that a great deal of literature has pinpointed the MPOA (and specifically inhibitory cells in this area) as being critical to sexual behavior, including female mating. However, no study to my knowledge has explored self-paced female mating with such fine control over manipulating and monitoring cellular activity in this region. In addition, this study may act to inspire others to further explore the additional brain regions found to show upregulation of neural activity (Fos) during mating completion in the female using the data sets generated here.

      Reviewer #2 (Public Review):

      […] Weaknesses:

      The authors include an elegant manipulation of ejaculation-activated neurons in the MPOA using DREADD. However, this study was limited to show that activation of previously activated cells was sufficient to reduce approach behavior in a paced mating paradigm and receiving intromissions in a home cage mating paradigm. An inhibition approach using DREADD would have been a great complement to this study as it would have examined if activation of the cells was required. Moreover, additional tests for sexual motivation would have greatly strengthened the overall conclusions.

      Reviewer #3 (Public Review):

      […] Weaknesses:

      (1) Their activity-dependent labeling strategy is not exclusive to mating completion but instead includes all neurons active before, during, and after the social encounter. In the manuscript, the authors did not discuss the time course of Fos activation or the timeframe of the FosTRAP labeling strategy. Fos continues to be expressed and is detectable for hours following neural activation. Therefore, the FosTRAP strategy also labels neurons that were activated 3 hours before the injection of 4-OHT. The original FosTRAP2 paper which is cited in this manuscript (DeNardo et al, 2019) performed a detailed analysis of the labeling window in Supplementary Figure 2 of that paper. Here is quoted text from that paper: "Resultant patterns of tdTomato expression revealed that the majority of TRAPing occurred within a 6-hour window centered around the 4-OHT injection." Thus, the FosTRAP "mating completion" groups throughout this manuscript also include neurons activated 3 hours before mating completion, which includes neurons activated during appetitive and consummatory mating behaviors.

      This makes all of the FosTRAP data very difficult to interpret. Compounding this is the issue that the two groups the authors compare in their experiments are females administered 4-OHT following appetitive investigation behaviors (with the male removed before mating behaviors occurred) and females administered 4-OHT following mating completion. The "appetitive" group labeled neurons activated only during appetitive investigation, but the "completion" group labeled neurons activated during appetitive investigations, consummatory mating bouts, and mating completion. Therefore, in the brain-wide analysis of Figure 2, it is impossible to identify brain regions that were activated exclusively by mating completion and not by consummatory mating behaviors. This could have been achieved if the "completion" group was compared to a group of females that had commenced consummatory mating behaviors but were separated from the male before mating was completed. Then, any neurons labeled by the "completion" FosTRAP but not the "consummatory" FosTRAP would be neurons specifically activated by mating completion. In the current brain-wide analysis experiments, neurons activated by consummatory behaviors and mating completion can not be disassociated.

      This same issue is present in the interpretation of the chemogenetic activation data in Figure 6. In the experiments of Figure 6, the authors are activating neurons naturally activated during consummatory mating behaviors as well as those activated during mating completion.

      We appreciate the reviewers comments and concerns about the TRAP method.

      First, we agree that the FosTRAP method does not have the sensitivity to separate ensembles that happen within a short time window. From our preliminary results, we have observed that the cells that inject 4-OHT after mating completion induce more tdTomato cells in the MPN than injection after appetitive behavior or consummatory behavior (Author response image 1).

      To further compare the difference between the “consummatory” and “completion” ensemble, we included an additional cohort where we TRAP cells responding to consummatory behavior. This cohort is added to Figure 2, 6, S3, S4, S9, S10 and S11. From the whole brain mapping of TRAP cells, we found that many hypothalamic and extended amygdala areas including the medial preoptic area, and the bed nucleus of stria terminalis were shown to have significantly larger tdTomato+ cell density in the completion group than in the appetitive group while there was a tendency that the consummatory group also had larger cell density than the appetitive group. In the Gq-DREADD experiment, we found that the Completion-hM3Dq group but not the Consummatory-hM3Dq group showed the reduction of sexual motivation of the female mouse in the self-paced mating assay (Figure 6). The Completion-hM3Dq group but not the Consummatory-hM3Dq group also showed significantly low intromission events and tended to show lower receptivity in the home cage mating assay (Figure S10). Furthermore, post-hoc histological analysis also showed that the c-Fos+ and TRAP labeled cells in the MPN tended to be the larger in the Completion-hM3Dq group than in the Consummatory-hM3Dq group (Figure S9). These results, together with the in vivo Calcium imaging experiments in Figure 3, 4 and 5, suggests that the MPN contains male-ejaculation responsive cells that are distinct with the male-mounting responsive cells and that they are sufficient to suppress female sexual motivation.

      However, it is true that with the current state of mouse genetic tools, we do not have any methods with higher time accuracy. We have discussed the limitations of FosTRAP method regarding its low time sensitivity in the Discussion section.

      Author response image 1.

      Representative image showing TRAP labeling in the MPN after mating completion and intromission

      (2) This study does not definitively show that the female mice used in this study display decreased sexual motivation after the completion of mating. The females exhibit reduced interaction with males that had also just completed mating, but it is unclear if the females would continue to show reduced interaction time if given the choice to interact with a male that was not in the post-ejaculatory refractory period. Perhaps, these females have a natural preference to interact more with sexually motivated males compared to recently mated (not sexually motivated) males. To definitively show that these females exhibit decreased sexual motivation the authors should perform two control experiments: 1) provide the females with access to a fully sexually motivated male after the females have completed mating with a different male to see if interaction time changes, and 2) compare interaction time toward mated and non-mated males using the self-paced mating assay. These controls would show that the reduction in the interaction time is because the females have reduced sexual motivation and not because these females just naturally interact with sexually motivated males more than males in the post-ejaculatory refractory period.

      We highly appreciate the reviewers comments regarding the interpretation of the self-paced mating assay. To address the concerns, we added an experiment where the female subjects were introduced to a novel sexually motivated male mice in the self-paced mating assay immediately after receiving ejaculation (Figure S2). As result, we found that similar to the self-paced mating assay using the same male animal, the female subject spends significantly more time in the isolation zone on the post-ejaculation day when compared to the pre-ejaculation day.

      (3) It is unclear how the transient 90-second response of these MPOA neurons following the completion of mating causes the prolonged reduction in female sexual motivation that is at the minutes to hours timeframe. No molecular or cellular mechanism is discussed.

      (4) The authors discuss potential cell types and neural population markers within the MPOA and go into some detail in Figure S3. However, their experiments are performed with only the larger excitatory and inhibitory MPOA neural populations.

      While the molecular or cellular mechanism of prolonged activity of MPOA neurons is  critical to understand the neural mechanism of how sustained neural activity in the MPOA suppress female sexual motivation, it is out of the reach of the current manuscript and a subject of future studies. We have added a section in the discussion part to further discuss the potential molecular mechanisms.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      If the authors haven't already, it would be useful if the authors could make the brain-wide analysis of Fos activity publicly available.

      We have distributed the data to https://dandiarchive.org/

      I would also make sure the n's are included in each Figure Legend for each panel (some are missing in the Supplementals).

      We appreciate the comment, we have added the number of subjects to Figure 3, 4, 5.

      It would also be best to provide clearer labels to some of the Figures, for example, Figure 5D, the Types should also be labeled with what behaviors they correspond to.

      We appreciate the comment. Figure 5 is focused on post-ejaculation neural activity. The cell types are categorized based neural activity after experiencing male ejaculation, it does not correspond to any behaviors.

      Reviewer #2 (Recommendations For The Authors):

      (1) A first recommendation is to replace the use of the term "mating completion" with "ejaculation". Male and female rodents display a period of reduced approach behavior following display or experiencing ejaculation, which is referred to as the post-ejaculatory interval. The current studies investigate the neural ensemble that contributes to this post-ejaculatory interval in female mice. In addition, male and female rodents will display a prolonged period of sexual inactivity referred to as satiety, which is typically observed after repeated display or experience of ejaculations. The current studies do not investigate satiety. Moreover, in the current studies, female mice appeared to display approach behavior (time in the interaction zone) even within the 10 minutes following experiencing ejaculation (Fig 1F). Hence, the term "completion" is not accurate and should be replaced by "ejaculation" in all figures and throughout the manuscript. Replacing completion with ejaculation will also clarify what defines "onset of completion", which this reviewer assumes refers to the onset of ejaculatory behavior observed in the male.

      Thank you for the comment. We agree that the mating completion was inappropriate. We have changed the wording to ejaculation or post-ejaculatory period.

      (2) Likewise, a variety of other terms and descriptions need to be adjusted for consistency and accuracy. For example, "room" when referring to the interaction or isolation zones; "onset of mating completion" when referring to ejaculation; "male intruder" to refer to the introduction of the male mating partner, but using a term typically used for an intruder-resident aggression test. Replacing these terms will aid in reducing confusion for the reader and more accurately describe the behavioral parameters.

      We appreciate the comment. We have updated the terms “male intruder” to “partner”, “room” to “area” or “zone”.

      (3) The use of the paced mating paradigm is a strength of these studies. This paradigm has been widely used and validated to study female sexual behavior in rodents. Please refer to recent reviews and landmark papers using this paradigm in addition to the current cited papers to better reflect the vast wealth of studies that previously reported the behavioral data that were replicated in this study.

      We have added a section discussing the self-paced mating assay, its merits and caveats P8.

      (4) In the paced mating test, females can pace the receipt of sexual stimulation, and latencies to withdraw and return to the male-containing chamber are considered indicators of sexual motivation. Female withdrawal will increase with the intensity of the sexual stimulation and latency to return is longer following ejaculation. Paced mating is thus a balance of approach and withdrawal behaviors that increases reward and likelihood of pregnancy for females. Moreover, ejaculation-induced withdrawal and longer latencies to return and approach are altered by hormonal status and by the introduction of a novel male partner. Thus, female sexual behavior is complex and withdrawal behavior (in this paper measured as time spent in an isolation zone) needs to be interpreted with caution and not simply referred to as sexual motivation. I recommend expanding the description of the paradigm to highlight the strengths and limitations of this paradigm and use caution to interpret time spent in the isolation zone as a lack of sexual motivation. I also recommend referring to the period after ejaculation as the post-ejaculatory interval (instead of completion).

      Thank you for the comment. We have changed the wording in the manuscript to adjust the way it refers to sexual motivation.

      (5) In the current paper, time in the isolation zone and the number of transitions are used as the behavioral measures. Latencies, which are typically included in paced mating studies, were missing from the data. If data are available for latencies to withdraw and return to the interaction zone after mount, intromission, and ejaculation, please add these data. If such data were not collected or are not available, please recognize this caveat.

      Thank you for the comment. In figure 1, which all animals did experience male ejaculation, we added latency analysis (Figure 1I and 1P). The result indicates as suggested in the literature, female mice took significantly longer to return the interaction zone after male-ejaculation.

      (6) The brain-wide mapping study of cFos expression after ejaculation confirms and extends prior findings, mostly in rats. Please reference prior papers in female rodents showing cFos after ejaculation and discuss how the current data replicate or differ from prior data.

      In the manuscript P8 L351, we have referred to Pfaus et al., 1993 to discuss the similarity in the c-Fos expression pattern studied in rats. We have further added descriptions to emphasize the similarity between the two datasets.

      (7) A paragraph describing the specific cell types that are activated in the MPOA is an essential part of the study and is described in detail, but only shown in supplementary figures. Given the emphasis on this particular part of the study, a recommendation is to incorporate these data as a regular figure instead of supplementary material.

      While we greatly appreciate the comment, we consider that the molecular characterization of MPOA neurons are not the main focus  of the paper and decided to keep it in the supplementary figure.

      (8) Calcium imaging studies were performed in the home cage for obvious practical reasons. However, in the home cage testing, the females withdraw from the males using a different approach and do not exit an interaction zone through a division. There may also be differences in the male sexual behavior patterns and thus the stimulation that females receive from the male. Yet, it appears that ejaculation induces similar patterns of neural activation in this paradigm. Thus, it is likely that neuron activation is a result of receiving ejaculation, rather than withdraw behavior. Please briefly discuss the comparisons between the cFos and calcium imaging conclusions in these two different paradigms.

      We have added a section discussing the self-paced mating assay, its merits and caveats P8. Withdrawal and latency and its interpretation is discussed in this section.

      (9) The final study includes the manipulation of ejaculation-activated neurons in the MPOA using DREADD. This study was limited to show that activation of previously activated cells was sufficient to reduce approach behavior in a paced mating paradigm and receiving intromissions in a home cage mating paradigm. An inhibition approach using DREADD would have been a great complement to this study as it would have shown if activation of the cells was required. Moreover, additional tests for sexual motivation, such as partner preference tests would have greatly strengthened the results since a lack of entering an interaction zone can also be explained by impaired sensory processing or locomotor behavior. Finally, CNO also appeared to impact time in the isolation zone for a subset of animals in the ejaculation (completion) control group and the appetitive group. These effects didn't reach statistical significance, but groups also had low sample sizes (n=6-7) and may thus have been underpowered. The recommendation is to include these caveats and shortcomings in the discussion of these results.

      We appreciate the comments. We first added an inhibitory approach to show the necessity of MPOA neurons. As result, we found that the inhibition of these neurons did not affect the behavior in the self-paced mating assay but increased the subjects sexual receptivity (Figure S11). For the low sample size, we have added a power analysis in the statistical section.

      (10) The studies utilized ovariectomized females with hormone priming. Since sexual receptivity in females is highly dependent on the hormonal milieu, the authors are encouraged to add an explanation of why ovariectomized females were used and if the results may have differed in cycling females.

      We appreciate the comments. The female subjects used in the TRAP experiment will be needing to experience ejaculation from the male mice twice, once to label the cells, and second during the reactivation. In order to avoid pregnancy during the first experience, we ovariectomized the female and controlled their hormonal conditions. This method has been used successfully in other sexual behavior studies (Yang et al., 2013, Ring., 1944.).  This was described in P11. We have further demonstrated in Figure 1N-T that female mice were not ovariectomized and were under the natural estrus cycle showed similar suppression of sexual interaction after the completion of mating. The manuscript was updated to discuss that the behavior change after mating completion is not dependent on the ovary.

      (11) Overall, the paper lacks references to relevant prior studies. For example, many studies have been reported over the past 2-3 decades about the effects of female rodent sexual behavior on activation in the brain and the effects of different vaginocervical stimulation on pregnancy and fertility. It is absolutely the case that much remains unknown about the complex neural circuitries that control behavior during the post-ejaculatory interval and sexual satiety in both male and female rodents, but studies have indicated roles for hypothalamic areas, bed nucleus of the stria terminals, ventral tegmental area, posterior thalamus, and prefrontal cortex. Hence, the current introduction and discussion do not adequately summarize or acknowledge these prior investigations and therefore place these new findings in the context of what was previously known.

      We appreciate the comment and added references to P2 L65, P8 L355-357 to discuss existing literature about c-Fos mapping analysis after ejaculation or genital stimulation in female rats.

      (12) Finally, sample sizes appear to be modest, ranging n=4-8 (except n=14 in the completion group in Figure S7) and vary between groups within and between studies. Please explain in the methods section how sample sizes were pre-determined and acknowledge if studies may have potentially been underpowered.

      The sample size for behavior experiments in this study were n = 6-9. This was predetermined based on previous studies examining female sexual behavior (Ishii et al. 2017, Liu et al. 2022, Yin et al. 2022). To further examine the number of animals required for our behavioral experiments, we pooled data used in this study and conducted a power analysis (n = 111 pooled data, control n = 94, stim n = 17). We conducted a power analysis using the variance calculated from pooled average time in isolation zone. These data were pooled from control animals in each experiment (eg. animals with GFP control virus injected, saline injected, etc.). The average time in isolation zone in the after ejaculation or after reactivating the completion cells was 420 ± 210 seconds, and 49 ± 91 seconds in the control group (mean ± s.d.). Within this population, we found that 5 animals were sufficient to detect the difference (p < 0.05, power = 0.8) in Students t-test. We have added this explanation in the supplemental experimental procedure, page P18, line 817-827.

      Reviewer #3 (Recommendations For The Authors):

      The authors should discuss the fact that the FosTRAP2 strategy labels neurons activated 3 hours before the 4-OHT injection. As the manuscript is written, it seems to suggest that the 4-OHT injection given following mating completion only labeled neurons activated during mating completion. This is very misleading. I respect the amount of work and rigor that went into these experiments. The single-cell imaging, implementation of the FosTRAP strategy, and behavioral analysis are all well executed. Novel insights into the neural regulation of female sexual drive can be gleaned from the neural imaging experiments. Unfortunately, the limitations of the FosTRAP strategy make those studies very difficult to interpret, and therefore, a more candid discussion and re-interpretation of the data from the FosTRAP experiments is needed.

      We appreciate the reviewers comments and concerns about the TRAP method.

      First, we agree that the FosTRAP method does not have the sensitivity to separate ensembles that happen within a short time window. From our preliminary results, we have observed that the cells that inject 4-OHT after mating completion induce more tdTomato cells in the MPN than injection after appetitive behavior or consummatory behavior (Author response image 1).

      To further compare the difference between the “consummatory” and “completion” ensemble, we included an additional cohort where we TRAP cells responding to consummatory behavior. This cohort is added to Figure 2, 6, S3, S4, S9, S10 and S11. From the whole brain mapping of TRAP cells, we found that many hypothalamic and extended amygdala areas including the medial preoptic area, and the bed nucleus of stria terminalis were shown to have significantly larger tdTomato+ cell density in the completion group than in the appetitive group while there was a tendency that the consummatory group also had larger cell density than the appetitive group. In the Gq-DREADD experiment, we found that the Completion-hM3Dq group but not the Consummatory-hM3Dq group showed the reduction of sexual motivation of the female mouse in the self-paced mating assay (Figure 6). The Completion-hM3Dq group but not the Consummatory-hM3Dq group also showed significantly low intromission events and tended to show lower receptivity in the home cage mating assay (Figure S10). Furthermore, post-hoc histological analysis also showed that the c-Fos+ and TRAP labeled cells in the MPN tended to be the larger in the Completion-hM3Dq group than in the Consummatory-hM3Dq group (Figure S9). These results, together with the in vivo Calcium imaging experiments in Figure 3, 4 and 5, suggests that the MPN contains male-ejaculation responsive cells that are distinct with the male-mounting responsive cells and that they are sufficient to suppress female sexual motivation.

      However, it is true that with the current state of mouse genetic tools, we do not have any methods with higher time accuracy. We have discussed the limitations of FosTRAP method regarding its low time sensitivity in the Discussion section.

      Editor notes:

      Should you choose to revise your manuscript, please include full statistical reporting in the main text including test statistic, degrees of freedom, an exact P value.

      Thank you for the comment. The statistical values were added to the manuscript.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Reviewer #3 (Public review):

      Human and simian immunodeficiency viruses (HIV and SIV, respectively) evolved numerous mechanisms to compromise effective immune responses but the underlying mechanisms remain incompletely understood. Here, Yamamoto and Matano examined the humoral immune response in a large number of rhesus macaques infected with the difficult-to-neutralize SIVmac239 strain and identified a subgroup of animals showing significant neutralizing Ab responses. Sequence analyses revealed that in most of these animals (7/9) but only a minority in the control group (2/19) SIVmac variants containing a CD8+ T-cell escape mutation of G63E/R in the viral Nef gene emerged. Functional analyses revealed that this change attenuates the ability of Nef to stimulate PI3K/Akt/mTORC2 signalling. The authors propose that this improved induction of SIVmac239 nAb is reciprocal to antibody dysregulation caused by a previously identified human PI3K gain-of-function mutation associated with impaired anti-viral B-cell responses. Altogether, the results suggest that PI3K signalling plays a role in B-cell maturation and generation of effective nAb responses. Preliminary data indicate that Nef might be transferred from infected T cells to B cells by direct contact. However, the exact mechanism and the relevance for vaccine development requires further studies

      Strengths of the study are that the authors analyzed a large number of SIVmac-infected macaques to unravel the biological significance of the known effect of the interaction of Nef with PI3K/Akt/mTORC2 signaling. This is interesting and may provide a novel means to improve humoral immune responses to HIV. In the revised version the authors made an effort to address previous concerns. Especially, they provide data supporting that Nef might be transferred to B cells by direct cell-cell contact. In addition, the provide some evidence that G63R that also emerged in most animals does not share the disruptive effect of G63G although experimental examination and discussion why G63R might emerge remains poor. Another weakness that remains is that some effects of the G63E mutation are modest and effects were not compared to SIVmac constructs lacking Nef entirely. The evidence for a role of Nef G63E mutation on PI3K and the association with improved nAb responses was largely convincing and it is appreciated that the authors provide additional evidence for a potential impact of "soluble" Nef on neighboring B cells. However, the experimental set-up and the results are difficult to comprehend. It seems that direct cell-cell contact is required and membranes are exchanged. Since Nef is associated with cellular membranes this might lead to some transfer of Nef to B cells. However, the immunological and functional consequences of this remain largely elusive. Alternatively, Nef-mediated manipulation of helper CD4 T cells might also impact B cell function and effective humoral immune responses. As previously noted, the presentation of the results and conclusions was in part very convoluted and difficult to comprehend. While the authors made attempts to improve the writing parts of the manuscript are still challenging to follow. This applies even more to the rebuttal (complex words combined with poor grammar), which made it difficult to assess which concerns have been satisfactory addressed.

      We are grateful for the visionary comments. Based on suggestion, we have edited the writing throughout and appended remarks on certain points raised in the Discussion section. For points that need experimentation, we would like to address them in a follow-up study now under preparation.

      Reviewer #3 (Recommendations for the authors):

      Additional editing of the manuscript is highly recommended to make the results accessible for a broad readership.

      We are grateful for the important suggestion. Accordingly, we have made editing of the manuscript aimed for a broad readership.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Wang et al. generate XAP5 and XAP5L knockout mice and find that they are male infertile due to meiotic arrest and reduced sperm motility, respectively. RNA-Seq was subsequently performed and the authors concluded that XAP5 and XAP5L are antagonistic transcription factors of cilliogenesis (in XAP5-KO P16 testis: 554 genes were unregulated and 1587 genes were downregulated; in XAP5L-KO sperm: 2093 genes were unregulated and 267 genes were downregulated).

      We are grateful for the comprehensive summary.

      Strengths:

      Knockout mouse models provided strong evidence to indicate that XAP5 and XAP5L are critical for spermatogenesis and male fertility.

      Thank you for your positive comment.

      Weaknesses:

      The key conclusions are not supported by evidence. First, the authors claim that XAP5 and XAP5L transcriptionally regulate sperm flagella development; however, detailed molecular experiments related to transcription regulation are lacking. How do XAP5 and XAP5L regulate their targets? Only RNA-Seq is not enough. Second, the authors declare that XAP5 and XAP5L are antagonistic transcription factors; however, how do XAP5 and XAP5L regulate sperm flagella development antagonistically? Only RNA-Seq is not enough. Third, I am concerned about whether XAP5 really regulates sperm flagella development. XAP5 is specifically expressed in spermatogonia and XAP5-cKO mice are in meiotic arrest, indicating that XAP5 regulates meiosis rather than sperm flagella development.

      Thank you for the critical comments. To strengthen our conclusions, we have included XAP5/XAP5L CUT&Tag data in our revised manuscript. This highly sensitive method has allowed us to identify direct target genes of XAP5 and XAP5L (Table S1, Figure S6). Notably, our results demonstrate that both FOXJ1 and RFX2 are occupied by XAP5 (Figure 4G). Additionally, real-time PCR validation confirmed that RFX2 is also associated with XAP5L, even though enriched peaks for the RFX2 gene were not detected in the initial CUT&Tag data (Figure 4G). These findings indicate that XAP5 and XAP5L regulate the expression of FOXJ1 and RFX2 by directly binding to these genes. De novo motif analyses revealed that XAP5 and XAP5L shared a conserved binding sequence (CCCCGCCC/GGGCGGGG) (Figure S6C), and the bound regions of FOXJ1 and RFX2 contain this sequence. Further analysis shows that many XAP5L target genes are also targets of XAP5 (Figure S6G), despite the limited number of identified XAP5L target genes. This differential binding and regulation of shared target genes underscore the antagonistic relationship between XAP5 and XAP5L. Collectively, these findings provide additional support for the idea that XAP5 and XAP5L function as antagonistic transcription factors, acting upstream of transcription factor families, including FOXJ1 and RFX factors, to coordinate ciliogenesis during spermatogenesis.

      While we agree that XAP5 primarily regulates meiosis during spermatogenesis, our data also indicate that many cilia-related genes, including key transcription regulators of spermiogenesis such as RFX2 and SOX30, are downregulated in XAP5-cKO mice and are bound by XAP5 (Figure 4, Figures S4 and S6). It is important to note that genes coding for flagella components are expressed sequentially and in a germ cell-specific manner during development. When we refer to "regulating sperm flagella development", we mean the spatiotemporal regulation. We have revised the manuscript to clarify this point.

      Reviewer #2 (Public Review):

      In this study, Wang et al., report the significance of XAP5L and XAP5 in spermatogenesis, involved in transcriptional regulation of the ciliary gene in testes. In previous studies, the authors demonstrate that XAP5 is a transcription factor required for flagellar assembly in Chlamydomonas. Continuing from their previous study, the authors examine the conserved role of the XAP5 and XAP5L, which are the orthologue pair in mammals.

      XAP5 and XAP5L express ubiquitously and testis specifically, respectively, and their absence in the testes causes male infertility with defective spermatogenesis. Interestingly, XAP5 deficiency arrests germ cell development at the pachytene stage, whereas XAP5L absence causes impaired flagellar formation. RNA-seq analyses demonstrated that XAP5 deficiency suppresses ciliary gene expression including Foxj1 and Rfx family genes in early testis. By contrast, XAP5L deficiency abnormally remains Foxj1 and Rfx genes in mature sperm. From the results, the authors conclude that XAP5 and XAP5L are the antagonistic transcription factors that function upstream of Foxj1 and Rfx family genes.

      This reviewer thinks the overall experiments are performed well and that the manuscript is clear. However, the current results do not directly support the authors' conclusion. For example, the transcriptional function of XAP5 and XAP5L requires more evidence. In addition, this reviewer wonders about the conserved XAP5 function of ciliary/flagellar gene transcription in mammals - the gene is ubiquitously expressed despite its functional importance in flagellar assembly in Chlamydomonas. Thus, this reviewer thinks authors are required to show more direct evidence to clearly support their conclusion with more descriptions of its role in ciliary/flagellar assembly.

      Thank you for your thoughtful review of our work. We appreciate your positive feedback on the overall quality of the experiments and the clarity of the manuscript. In response to your concerns, we have included new experimental data and made revisions to the manuscript (lines 193-217) to better support our conclusions, particularly regarding the transcriptional function of XAP5 and XAP5L. Additionally, we have expanded on the role of XAP5 in ciliary and flagellar assembly to provide more direct evidence for its functional importance. Thank you for your insights.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      The title (Control of ciliary transcriptional programs during spermatogenesis by antagonistic transcription factors) is not specific and does tend to exaggerate.

      Thank you for the comment, and we appreciate the opportunity to clarify the appropriateness of the title. Our paper extensively investigates the transcriptional regulation of ciliary genes during spermatogenesis. It demonstrates that XAP5/XAP5L are key transcription factors involved in this process. The title reflects our primary focus on the transcriptional programs that govern ciliary gene expression. Moreover, our paper shows that XAP5 positively regulates the expression of ciliary genes, particularly during the early stages of spermatogenesis, while XAP5L negatively regulates these genes. This antagonistic relationship is a crucial aspect of the study and is effectively conveyed in the title. In addition, our revised paper provides detailed insights into how XAP5/XAP5L control ciliary gene expression during spermatogenesis.

      Figure 4C: FOXJ1 and RFX2 are absent in sperm from WT mice. Are you sure? They are highly expressed in WT testes.

      Thank you for your careful review. While FOXJ1 and RFX2 are indeed highly expressed in the testes of wild-type (WT) mice, our data show that they are not detectable in mature sperm. This observation is consistent with published single-cell RNA-seq data(Jung et al., 2019), which indicate that FOXJ1 and RFX2 are primarily expressed in spermatocytes but not in spermatids (Figure S7). This expression pattern aligns with that that of IFT-particle proteins, which are essential for the formation but not the maintenance of mammalian sperm flagella(San Agustin, Pazour, & Witman, 2015).

      XAP5 is specifically expressed in spermatogonia and XAP5-cKO mice are in meiotic arrest, indicating that XAP5 regulates meiosis rather than sperm flagella development.

      We appreciate your insightful comments. As mentioned above, we agree that XAP5 primarily regulates meiosis during spermatogenesis. When we mentioned "regulating sperm flagella development," we were referring to the spatiotemporal regulation of these processes. We have revised the manuscript to clarify this distinction. Thank you for your understanding.

      The title of Figure 2 (XAP5L is required for normal sperm formation) is not accurate because the progress of spermatogenesis and sperm count is normal in XAP5L-KO mice (only sperm motility is reduced).

      We apologize for any confusion caused by the previous figure. It did not accurately convey the changes in sperm count. In the revised Figure 2B, we clearly demonstrate that the sperm count in XAP5L-KO mice is indeed lower than that in WT mice. This revision aims to provide a more accurate representation of the effects of XAP5L deficiency on spermatogenesis. Thank you for bringing this to our attention.

      Reviewer #2 (Recommendations For The Authors):

      (1) Although XAP5 and XAP5L deficiency alters the transcription of Foxj1 and Rfx family genes, which are the essential transcription factors for the ciliogenesis, current data do not directly support that XAP5 and XAP5L are the upstream transcription factors. The authors need to show more direct evidence such as CHIP-Seq data.

      Thank you for your valuable feedback! In this revised manuscript, we have included data identifying candidate direct targets of XAP5 and XAP5L using the highly sensitive CUT&Tag method (Kaya-Okur et al., 2019). Our results show that XAP5 occupies both FOXJ1 and RFX2 (Figure 4G). Furthermore, real-time PCR validation of the CUT&Tag experiments confirmed that RFX2 is also occupied by XAP5L (Figure 4G), despite the initial CUT&Tag data not revealing enriched peaks for the RFX2 gene (Table S1). Unfortunately, the limited number of enriched peaks identified for XAP5L (Table S1) suggests that the XAP5L antibody used in the CUT&Tag experiment might have suboptimal performance, which prevented us from detecting occupancy on the FOXJ1 promoter. Nevertheless, these additional data provide strong evidence that XAP5 and XAP5L function as upstream transcription factors for FOXJ1 and RFX family genes, supporting their essential roles in ciliogenesis.

      (2) Shared transcripts that are altered by the absence of either XAP5 or XAP5L do not clearly support they are antagonistic transcription factors.

      Thank you for your insightful comment. In our revised manuscript, we performed CUT&Tag analysis to identify target genes of XAP5 and XAP5L. Motif enrichment analysis revealed conserved binding sequences for both factors (Figures S6C), indicating a subset of shared downstream genes between XAP5 and XAP5L. Among the downregulated genes in XAP5 cKO germ cells, 891 genes were bound by XAP5 (Figure S6D). Although the number of enriched peaks identified for XAP5L was limited, 75 of the upregulated genes in XAP5L KO sperm were bound by XAP5L (Figure S6E). Importantly, of these 75 XAP5L target genes, approximately 30% (22 genes) were also identified as targets of XAP5 (Figure S6G), further support the idea that XAP5 and XAP5L function as antagonistic transcription factors.

      (3) XAP5 seems to be an ancient transcription factor for cilia and flagellar assembly. However, XAP5 expresses ubiquitously in mice. How can this discrepancy be explained? Is it also required for primary cilia assembly? Are their expression also directly linked to ciliogenesis in other types of cells?

      Thank you for the thoughtful questions. The ubiquitous expression of XAP5 in mice can be understood in light of its role as an ancient transcription factor for cilia and flagellar assembly. Given that cilia are present on nearly every cell type in the mammalian body (O'Connor et al., 2013), this broad expression pattern makes sense. In fact, XAP5 serves not only as a master regulator of ciliogenesis but also as a critical regulator of various developmental processes (Kim et al., 2018; Lee et al., 2020; Xie et al., 2023).

      Our current unpublished work demonstrates that XAP5 is essential for primary cilia assembly in different cell lines. The loss of XAP5 protein results in abnormal ciliogenesis, further supporting its vital role in ciliary formation across different cell types.

      We believe that the widespread expression of XAP5 reflects its fundamental importance in multiple cellular processes, including ciliogenesis, development, and potentially other cellular functions yet to be discovered.

      (4) XAP5L causes impairs flagellar assembly. Have the authors observed any other physiological defects in the absence of XAP5L in mouse models? Such as hydrocephalus and/or tracheal defects?

      Thank you for the questions. We have carefully examined XAP5L KO mice for other physiological defects. To date, we have not observed any additional physiological abnormalities. Specifically, we assessed the condition of tracheal cilia in XAP5L KO mice and found no significant differences compared to wild-type (WT) mice, as illustrated in Author response image 1 below.

      Author response image 1.

      References

      Jung, M., Wells, D., Rusch, J., Ahmad, S., Marchini, J., Myers, S. R., & Conrad, D. F. (2019). Unified single-cell analysis of testis gene regulation and pathology in five mouse strains. Elife, 8. doi:10.7554/eLife.43966

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      Kim, Y., Hur, S. W., Jeong, B. C., Oh, S. H., Hwang, Y. C., Kim, S. H., & Koh, J. T. (2018). The Fam50a positively regulates ameloblast differentiation via interacting with Runx2. J Cell Physiol, 233(2), 1512-1522. doi:10.1002/jcp.26038

      Lee, Y.-R., Khan, K., Armfield-Uhas, K., Srikanth, S., Thompson, N. A., Pardo, M., . . . Schwartz, C. E. (2020). Mutations in FAM50A suggest that Armfield XLID syndrome is a spliceosomopathy. Nature Communications, 11(1). doi:10.1038/s41467-020-17452-6

      O'Connor, A. K., Malarkey, E. B., Berbari, N. F., Croyle, M. J., Haycraft, C. J., Bell, P. D., . . . Yoder, B. K. (2013). An inducible CiliaGFP mouse model for in vivo visualization and analysis of cilia in live tissue. Cilia, 2(1), 8. doi:10.1186/2046-2530-2-8

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    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this manuscript, Corso-Diaz et al, focus on the NRL transcription factor (TF), which is critical for retinal rod photoreceptor development and function. The authors profile NRL's protein interactome, revealing several RNA-binding proteins (RBPs) among its components. Notably, many of these RBPs are associated with R-loop biology, including DHX9 helicase, which is the primary focus of this study. R-loops are three-stranded nucleic acid structures that frequently form during transcription. The authors demonstrate that R-loop levels increase during photoreceptor maturation and establish an interaction between NRL TF and DHX9 helicase. The association between NRL and RBPs like DHX9 suggests a cooperative regulation of gene expression in a cell-type-specific manner, an intriguing discovery relevant to photoreceptor health. Since DHX9 is a key regulator of R-loop homeostasis, the study proposes a potential mechanism where a cell-type-specific TF controls the expression of certain genes by modulating R-loop homeostasis. This study also presents the first data on R-loop mapping in mammalian retinas and shows the enrichment of R-loops over intergenic regions as well as genes encoding neuronal function factors. While the research topic is very important, there is some concern regarding the data presented: there are substantial data supporting the interaction between NRL and DHX9, including pull-down experiments and proximity labeling assay (PLA), however, the data showing an interaction between NRL and DDX5, another R-loop-associated helicase, are inadequate. Importantly, the data supporting the claim that NRL interacts with R-loops are absolutely insufficient and at best, correlative. The next concerns are regarding the R-loop mapping data analysis and visualization.

      Strengths:

      There is compelling evidence that the NRL transcription factor interacts with several RNA binding proteins, and specifically, sufficient data supporting the interaction of NRL with DHX9 helicase.

      A major strength is the use of the single-stranded R-loop mapping method in the mouse retina.

      Weaknesses:

      (1) Figure S1A: There is a strong band in GST-IP (control IP) for either HNRNPUI1 or HNRNPU, although the authors state in their results that there is a strong interaction of these two RBPs with NRL.

      Under our experimental conditions, most RNA-binding proteins displayed higher binding to glutathione beads (Fig. S1A). However, GST-NRL purifications showed much stronger signals for respective RBPs. In the case of HNRNPU and HNRNPUl1, white bands that are indicative of substrate depletion due to higher protein levels are observed in GST-NRL lanes. Additionally, in Figures 1B and 1C, there is a clear enrichment of HNRNPU and HNRNPUl1 above the background signal. We added this to the text. See page 5.

      Both DHX9 and DDX5 samples have a faint band in the GST-IP.

      RNA-binding proteins may display some background as observed in other studies (e.g. PMID: 32704541). We think that showing the raw data without decreasing the exposure time is useful and that there is a clear enrichment compared to controls.  In addition, we tested the interaction in multiple systems.

      There is an extremely faint band for HNRNPA2B1 in the GST-NRL IP lane. Given this is a pull-down with added benzonase treatment to remove all nucleic acids, these data suggest, that previously observed NRL interactions with these particular RBPs are mediated via nucleic acids. Similarly, there is a loss of band signal for HNRNM in this assay, although it was identified as an NRL-interacting protein in three assays, which again suggests that nucleic acids mediate the interaction.

      Thank you for highlighting this point. We mention in the manuscript that the interaction between HNRNPM and A1 depends on nucleic acids, as noted by the reviewer, since there is no obvious band after the pull-down. We have now added that the interaction of NRL with HNRNPA1B1 is likely dependent on nucleic acids as well, given its weak signal. See page 5.

      (2) The data supporting NRL-DDX5 interaction in rod photoreceptor nuclei is very weak. In Figure 2D, the PLA signal for DDX5-NRL is very weak in the adult mouse retina and is absent in the human retina, as shown in Figure 2H.

      We agree with the reviewer. We think that the signal for DDX5 is weak, and we addressed this in the text. We noted on page 7: “Taken together, these findings suggest a strong interaction between NRL and DHX9 throughout the nuclear compartment in the retina and that a transient and/or more regulated interaction of NRL with DDX5 may require additional protein partners.”  We have modified this sentence to add that the data also suggest transient interaction or the requirement of additional protein partners for stable interaction. See page 7.

      Given that there is no NRL-KO available for the human PLA assay, the control experiments using single-protein antibodies should be included in the assay. Similarly, the single-protein antibody control PLA experiments should be included in the experimental data presented in Figure 2J.

      Thank you for the suggestion. We performed PLAs using both DHX9 and IgG in the human retina and observed no specific amplification signal. Some background is observed outside the nucleus and in the extracellular space. We added these results to the text and to the supplementary information. See page 7 and Fig.S2B.

      (3) The EMSA experiment using a probe containing NRL binding motif within the DHX9 promoter should include incubation with retina nuclear extracts depleted for NRL as a control.

      In EMSA experiments, we used bovine retina to obtain enough protein quantities. As suggested by the reviewer, using NRL depleted extract would increase the specificity of observed gel shift and complement our pre-immune serum as a negative control. However, removal of all the NRL protein using the antibodies available was not feasible. In the future, we will use enough mice to obtain large quantities of protein for this experiment and will collect retinas from Nrl knockout as negative control.

      (4) There is a reduced amount of DHX9 pulled down in NRL-IP in HEK293 cells, but there is no statistically significant difference in the reciprocal IP (DHX9-IP and blotting for NRL) (Figure 4C).

      We believe the reviewer is referring to the data in Figure 4C showing that RNase H treatment led to significantly reduced pulldown of DHX9 as compared to control, but the reciprocal IP in Figure 4D showed no statistical significance between control and RNase H treatment. In Figure 4D, we hypothesize that NRL may account for only a small proportion of DHX9’s interactome, so the change in NRL levels could not be detected due to the sensitivity of our assay. DHX9 likely constitutes a large proportion of NRL’s interactome in HEK293 cells, hence the change in DHX9 level was more obvious when pulling down with NRL. We added this information to the results. See page 8.

      (5) The only data supporting the claim that NRL interacts with R-loops are presented in Figure 5A.

      Additional evidence that NRL interacts with R-loops comes from DRIP-Seq experiments where signals from R-loops overlap with NRL ChIP-Seq signals (Figure 7A). This shows that R-loops and NRL co-occur on multiple genomic regions. In addition, indirect evidence of NRL and R-loops’ interaction is shown in pull down experiments and PLA assays where R-loops influence DHX9 and NRL binding. We clarified this in the discussion. See page 14.

      This is a co-IP of R-loops and then blotting for NRL, DHX9, and DDX5. Here, there is no signal for DDX5, quantification of DHX9 signal shows no statistically significant difference between RNase H treated and untreated samples, while NRL shows a signal in RNase H treated sample. These data are not sufficient to make the statement regarding the interaction of NRL with R-loops.

      Thank you for this comment. We respectfully disagree as we observe statistically significant enrichment for both NRL and DHX9 in these experiments (See Fig5A). Some NRL continues to bind to DNA that is pulled down nonspecifically, which may be expected since NRL is a transcription factor. See for example R-loop binding by the transcription factor Sox2 (PMID: 32704541). However, binding to R-loops is evidenced by an enrichment compared to RNase H-treated sample. We clarified this in Results section (See page 9).

      (6) Regarding R-loop mapping, the data analysis is quite confusing. The authors perform two different types of analyses: either overall narrow and broad peak analysis or strand-specific analysis. Given that the authors used ssDRIP-seq, which is a method designed to map R-loops strand specifically, it is confusing to perform different types of analyses.

      Thank you for highlighting this point. This has enhanced the clarity of the methods and enriched the discussion. We aimed to identify R-loops as accurately as possible. We conducted two types of analyses to capture different aspects of R-loops: one that looks at overall patterns (narrow and broad peaks) and another that focuses on specific strands of DNA.

      Using ssDRIP-seq, which is designed to map R-loops on specific strands, allowed us to examine R-loops formed in only one strand and those formed on both strands. To identify strand-specific R-loops, we filtered our RNase-H enriched peaks for those enriched on one strand compared to the opposite strand. We clarified the analysis in the results section, and Figure 6B. See page 10 and methods section page 25.

      Next, the peak analysis is usually performed based on the RNase H treated R-loop mapping; what does it mean then to have a pool of "Not R-loops", see Figure 6B?

      The “Not R-loop” group refers to peaks called using the opposite strand that are not observed when calling peaks using RNase H as control. We modified this figure for clarity (Figure 6B).

      In that regard, what does the term "unstranded" R-loops mean? Based on the authors' definition, these are R-loops that do not fall within the group of strand-specific R-loops. The authors should explain the reasons behind these types of analyses and explain, what the biological relevance of these different types of R-loops is.

      Thank you for helping us clarify this point. Unstranded R-loops are DNA regions containing DNA:RNA hybrids on both plus and minus strands and possibly representing bidirectional transcription by Pol II. We observed that unstranded R-loops are enriched only in intergenic regions, H3K9me3 regions, and downstream of the transcriptional termination site (TTS). We added to the discussion the possible implications of these enrichments, including regulation of Pol II termination and transcription of long genes.  See Page 13.

      (7) It would be more useful to show the percent distribution of R-loops over the different genomic regions, instead of showing p-value enrichment, see Figure 6C.

      Since most of the genome is non-coding, plotting the distribution as a proportion was not informative since the vast proportion of the data falls in intergenic regions. However, we created a new figure showing observed vs. expected ratio that seems to be more informative and moved the current p-value figure to the supplement in revised version. See Figure 6C and S6D.

      (8) Based on the model presented, NRL regulates R-loop biology via interaction with RBPs, such as DHX9, a known R-loop resolution helicase. Given that the gene targets of NRL TF are known, it would be useful to then analyze the R-loop mapping data across this gene set.

      Thank you for this suggestion. We performed an analysis of R-loops on NRL-regulated genes. Interestingly, NRL target genes have an enrichment of stranded R-loops at the promoter/TSS and unstranded R-loops on the gene body compared to all Ensembl genes (Figure S7B). We added a table containing all NRL-regulated genes we used for this analysis (table S5) and a figure showing this result (Fig. S7B).

      Reviewer #2 (Public review):

      Summary:

      The authors utilize biochemical approaches to determine and validate NRL protein-protein interactions to further understand the mechanisms by which the NRL transcription factor controls rod photoreceptor gene regulatory networks. Observations that NRL displays numerous protein-protein interactions with RNA-binding proteins, many of which are involved in R-loop biology, led the authors to investigate the role of RNA and R-loops in mediating protein-protein interactions and profile the co-localization of R-loops with NRL genomic occupancy.

      Strengths:

      Overall, the manuscript is very well written, providing succinct explanations of the observed results and potential implications. Additionally, the authors use multiple orthogonal techniques and tissue samples to reproduce and validate that NRL interacts with DHX9 and DDX5. Experiments also utilize specific assays to understand the influence of RNA and R-loops on protein-protein interactions. The authors also use state-of-the-art techniques to profile R-loop localization within the retina and integrate multiple previously established datasets to correlate R-loop presence with transcription factor binding and chromatin marks in an attempt to understand the significance of R-loops in the retina.

      Weaknesses:

      In general, the authors provide superficial interpretations of the data that fit a narrative but fail to provide alternative explanations or address caveats of the results. Specifically, many bands are present in interaction studies either in control lanes (GST controls) of Westerns or large amounts of background in PLA experiments.

      We have added additional information to the text regarding the presence of background signals in pull downs. We wish to note that experimental samples always exceeded background signals.  We believe that reporting these raw findings (rather than showing shorter exposures) is valuable for the scientific community. We did not observe any background in the proximity ligation assay (PLA) that exceeded what is typically expected, and the signals were clearly discernible. Cases where signals are weaker, such as with DDX5, have been highlighted. In addition, we added a DHX9-IgG negative control for the human PLA experiment. See page 5 and Fig. S2B.

      Additionally, the lack of experiments testing the functional significance of Nrl interactions or R-loops within the developing retina fails to provide novel biological insights into the regulation of gene regulatory networks other than, 'This could be a potentially important new mechanism'.

      We agree that functional experiments are necessary to understand the molecular mechanisms behind R-loop regulation in the retina; however, we believe it goes beyond the scope of this initial characterization (as this is the first report on R-loops in the retina). We are currently pursuing these studies.

      We performed new analysis on NRL-regulated genes as suggested by reviewer 1. We show that NRL target genes have an enrichment of stranded R-loops at the promoter/TSS and unstranded R-loops on the gene body compared to all Ensembl genes (Figure S7B), providing further evidence of the functional  interaction between NRL and R-loops. See table S5 and Fig. S7B, and discussion.

      Additionally, the authors test the necessity of RNA for NRL/DHX9 interactions but don't show RNA binding of NRL or DHX9 or the sufficiency of RNA to interfere/mediate protein-protein interactions. Recent work has highlighted the prevalence of RNA binding by transcription factors through Arginine Rich Motifs that are located near the DNA binding domains of transcription factors.

      We agree that the role of RNA in these complexes is very exciting, and we are currently pursuing these studies. However, we believe that they fall outside the scope of this initial report on R-loops in the retina.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      There are a couple of minor comments:

      (1) Unfinished sentence; page 11, the end of the first paragraph.

      Thank you for catching this error. We removed the unfinished text.

      (2) Page 6: Figure S2A should be Figure S2.

      In general, the manuscript would benefit from a deeper explanation of the biological relevance of R-loop formation and the connection to NRL TF and the expression of genes regulated by NRL. In this regard, a more substantial description of the model would be useful.

      We have modified the discussion for clarity and included new ideas on possible roles of R-loops in gene regulation of photoreceptors.

      Reviewer #2 (Recommendations for the authors):

      (1) The specificity of interactions needs to be addressed:

      - Figure 1B - HNRNPUI1 bands present in GST control.

      - Figure 1C - Bands present in the Empty Vector control IP for HNRNPU and DHX9.

      - Supplemental Figure 1A - most proteins are present in GST control suggesting prevalent binding to GST and lack of specificity for other interactions.

      Thank you for your comment. RNA-binding proteins can have more background as observed in other studies (e.g. PMID: 32704541) but there is always a higher signal in experimental samples compared to controls. While we agree that we can enhance the conditions for immunoprecipitation (IP) by optimizing washing buffers, exposure and other parameters, we believe the current methods tell the story. We have added additional text explaining this. See page 5.

      (2) Use of the term 'Strongest' interaction - IPs don't directly address the strength of interaction, but depend on levels of expression AND affinity. The strength of interaction should be tested using techniques like an OCTET or SPR assay. One can also quantify the effect that RNA would have in such an assay.

      Thank you for your suggestion. We replaced the term 'stronger' with “higher signal” and “robust” at most places. The source of protein lysates is the same for experiments and controls, thus the amount of protein is consistent in both conditions, and not dependent on level of gene expression.

      (3) In supplemental tables, please use the proper gene names, not the UniProt peptide name. For example, there are no genes named ELAV1-ELAV4. These should be ELAVL1-ELAVL4. A short glance identifies >10 gene name errors.

      Thank you for the suggestion. We updated current gene names in all tables.

      (4) Please provide the rationale for the choice of DNA sequence for the DHX9 nucleotide sequence used for EMSA assays. In the human DHX9 locus, the NRL ChIP-seq peak looks to be contained in Intron1 whereas the NRL ChIP-seq peak in mouse DHX9 looks to be in the proximal upstream promoter. Did the authors choose an evolutionarily conserved sequence in the promoter region that contained the NRL motif or does the probe sequence arise from the sequence that has known NRL binding as assayed by NRL ChIP-seq? A zoomed-in image of the NRL ChIP-seq pile-ups in the DHX9 locus in each species would be beneficial.

      Thank you for this suggestion. The probe was chosen by scanning for NRL binding motifs on the Chip-Seq peak at the human DHX9 promoter. We added a Zoom-in image of the ChIP-Seq or CUT&RUN reads for NRL on both human and mouse retinas. Figure 3D shows NRL binding in both species in regions containing the homologous motif. The sequence is partially conserved and shown in the figure.

      (5) Normalization in RNaseH/RNaseA Co-IP experiments. Why does RNAseH treatment result in increased NRL IP (increased NRL expression?) or does RNaseA treatment cause reduced IP of DHX9? These differences seem to cause a 'denominator' effect, leading the Authors to conclude decreased co-IP of DHX9 with NRL when R-loops are inhibited or increased co-IP of NRL with DHX9 when RNA is degraded. An alternate interpretation would be that inhibiting the R-loop binding of NRL unmasks the epitope for antibody recognition. The authors should test NRL binding to RNA and determine if RNA binding affects the co-IP of NRL with DHX9.

      We agree that removing total RNA by RNase A or R-loops by RNase H may alter the accessibility of our antibodies to the epitopes, resulting in the differences in the level of total protein pulled down. However, we quantified the relative level of the associating protein to the total protein and confirmed, in reciprocal assays, that RNase A treatment led to increased interaction between NRL and DHX9. However, the quantification was not consistent between the reciprocal IPs upon RNase H treatment. We reason that in Figure 4D, as NRL may account for only a small proportion of DHX9’s interactome, the change in NRL level could not be detected due to the sensitivity of our assay. However reciprocally, DHX9 can constitute a larger proportion of NRL’s interactome in HEK293 cells, hence the change in DHX9 level was more obvious. We added this information to the text. See page 8.

      (6) Figure 7 - Malat1 - there doesn't seem to be an overlap of NRL with Stranded R-loop peaks in this image. Nrl seems to flank the region of R-loops.

      We changed Malat1 for Mplkip that shows a direct overlap of Nrl binding and R-loops. See Figure 7C.

      (7) Results end with 'A Model'. Seems like some concluding remarks and references to Figure 8 were mistakenly left out.

      Thank you for catching this typo. We removed the misplaced text.

      (8) Model and Discussion - authors should show raw data for RHO with respect to NRL binding and R-loops. No evidence was provided regarding R-loops (or lack thereof) in the Rhodopsin locus. Additionally, conclusions stating that "R-loops... are specifically depleted from genes, such as Rhodopsin, with high expression levels" go against Figures 7B and 7C. Malat1 is one of the highest expressed genes in the retina and contains R-loops.

      Thank you for helping us clarify our hypothesis. We added a genome browser view of Rhodopsin showing the absence of R-loops (Fig. S8). We hypothesize that R-loops could interfere with achieving higher rates of transcription, however we did not mean to say that all high expressed genes lack R-loops. We have rephrased the discussion to clarify this point.

      (9) Neuronal genes, particularly those involved in synaptic transmission are known to be, on average, longer than most genes (Gabel, 2015; PMID: 25762136). Is it possible that R-loops are detected at genes involved in synaptic function/structure solely because of transcript length, as it takes longer for transcription termination to resolve in genes that are longer? A plot showing R-loop enrichment and transcript length would address this.

      We added a plot showing gene length in relation to R-loops and expression levels. We observed that R-loops are more common over long genes regardless of their expression levels. We also observed that the concomitant presence of stranded and unstranded R-loops is restricted to the longest genes in most cases. We added this to Figure 7D.

    1. Author response:

      The following is the authors’ response to the original reviews.

      We thank the reviewers for valuable feedback and comments. Based on the feedback we revised the manuscript and believe that we addressed most of the reviewers' raised points. Below we include a summary of key revisions and point-by-point responses to reviewers comments.

      Abstract/Introduction

      We further emphasized EP-GAN strength in parameter inference of detailed neuron parameters vs specialized models with reduced parameters.

      Results

      We further elaborated on the method of training EP-GAN on synthetic neurons and validating on both synthetic and experimental neurons.

      We added a new section Statistical Analysis and Loss Extension which includes:

      - Statistical evaluation of baseline EP-GAN and other methods on neurons with multi recording membrane potential responses/steady-state currents data: AWB, URX, HSN

      - Evaluation of EP-GAN with added resting potential loss + longer simulations to ensure stability of membrane potential (EP-GAN-E)

      Methods

      We added a detailed explanation on "inverse gradient process"

      We added detailed current/voltage-clamp protocols for both synthetic and experimental validation and prediction scenarios (table 6)

      Supplementary

      We added error distribution and representative samples for synthetic neuron validations (Fig S1)

      We added membrane potential response statistical analysis plots for existing methods for AWB, URX, HSN (Fig S6)

      We added steady-state currents statistical analysis plots on EP-GAN + existing methods for AWB, URX, HSN (Fig S7)

      We added mean membrane potential errors for AWB, URX, HSN normalized by empirical standard deviations for all methods (Table S4)

      Please see our point-by-point responses to specific feedback and comment below.

      Reviewer 1:

      First, at the methodological level, the authors should explain the inverse gradient operation in more detail, as the reconstructed voltage will not only depend on the evaluation of the right-hand side of the HH-equations, as they write but also on the initial state of the system. Why did the authors not simply simulate the responses?

      We thank the reviewer for the feedback regarding the need for further explanation. We have revised the Methods section to provide a more detailed description of the inverse gradient process. The process uses a discrete integration method, similar to Euler’s formula, which takes systems’ initial conditions into account. For the EP-GAN baseline, the initial states were picked soon after the start of the stimulus to reconstruct the voltage during the stimulation period. For EP-GAN with extended loss (EP-GAN-E), introduced in this revision in sub-section Statistical Analysis and Loss Extension, initial states before/after stimulations were also taken into account to incorporate resting voltage states into target loss.

      Since EP-GAN is a neural network and we want the inverse gradient process to be part of the training process (i.e., making EP-GAN a “model informed network”), the process is expected to be implemented as a differentiable function of generated parameter p. This enables the derivatives from reconstructed voltages to be traced back to all network components via back-propagation algorithm.

      Computationally, this requires the implementation of the process as a combination of discrete array operations with “auto-differentiation”, which allows automatic computation of derivatives for each operation. While explicit simulation of the responses using ODE solvers provides more accurate solutions, the algorithms used by these solvers typically do not support such specialized arrays nor are they compatible with neural network training. We thus utilized PyTorch tensors [54], which support both auto-differentiation and vectorization to implement the process.

      The authors did not allow the models time to equilibrate before starting their reconstruction simulations, as testified by the large transients observed before stimulation onset in their plots. To get a sense of whether the models reproduce the equilibria of the measured responses to a reasonable degree, the authors should allow sufficient time for the models to equilibrate before starting their stimulation protocol.

      In the added Statistical Analysis and Loss Extension under the Results section, we added results for EP-GAN-E where we simulate the voltage responses with 5 seconds of added stabilization period in the beginning of simulations. The added period mitigates voltage fluctuations observed during the initial simulation phase and we observe that simulated voltage responses indeed reach stable equilibrium for both prior stimulations and for the zero stimulus current-clamp protocol (Figure 5 bottom, Column 3).

      In fact, why did the authors not explicitly include the equilibrium voltage as a target loss in their set of loss functions? This would be an important quantity that determines the opening level of all the ion channels and therefore would influence the associated parameter values.

      EP-GAN baseline does include equilibrium voltage as a target loss since all current-clamp protocols used in the study (both synthetic and experimental) include a membrane potential trace where the stimulus amplitude is zero throughout the entire recording duration (see added Table 6 for current clamp protocols), thus enforcing EP-GAN to optimize resting membrane potential alongside with other non-zero stimulus current-clamp scenarios.

      To further study EP-GAN’s accuracy in resting potential, we evaluated EP-GAN with supplemental resting potential target loss and evaluated its performance in the sub-section Statistical Analysis and Loss Extension. The added loss, combined with 5 seconds of additional stabilization period, improved accuracy in predicting resting potentials by mitigating voltage fluctuations during the early simulation phase and made significant improvements to predicting AWB membrane potential responses where EP-GAN baseline resulted in overshoot of the resting potential.

      The authors should provide a more detailed evaluation of the models. They should explicitly provide the IV curves (this should be easy enough, as they compute them anyway), and clearly describe the time-point at which they compute them, as their current figures suggest there might be strong transient changes in them.

      We included predicted IV-curve vs ground truth plots in addition to the voltages in the supplementary materials (Figure S2, S5) in the original submitted version of the manuscript. In this revision, we added additional IV-curve plots with statistical analysis for the neurons with multi-recording data (AWB, URX, HSN) in the supplementary materials (Figure S7).

      For the evaluation of predicted membrane potential responses, we added further details in Validation Scenarios (Synthetic) under Results section such that it clearly explains on the current-clamp protocols used for both synthetic and experimental neurons and which time interval the RMSE evaluations were performed.

      In the sub-section Statistical Analysis and Loss Extension, we introduced a new statistical metric in addition to RMSE, applied for neurons AWB, URX, HSN which evaluates the percentage of predicted voltages that fall within the empirical range (i.e., mean +- 2 std) and voltage error normalized by empirical standard deviations (Table S4).

      The authors should assess the stability of the models. Some of the models exhibit responses that look as if they might be unstable if simulated for sufficiently long periods of time. Therefore, the authors should investigate whether all obtained parameter sets lead to stable models.

      In the sub-section Statistical Analysis and Loss Extension, we included individual voltage traces generated by both EP-GAN baseline and EP-GAN-E (extended) with longer simulation (+5 seconds) to ensure stability. EP-GAN-E is able to produce equilibrium voltages that are indeed stable and within empirical bounds throughout the simulations for the zero-stimulus current-clamp scenario (column 3) for the 3 tested neurons (AWB, URX, HSN).

      Minor:

      The authors should provide a description of the model, and it's trainable parameters. At the moment, it is unclear which parameter of the ion channels are actually trained by the methodology.

      The detailed description of the model and its ion channels can be found in [7]. Supplementary materials also include an excel table predicted parameters which lists all EP-GAN fitted parameters for 9 neurons (+3 new parameter sets for AWB, URX, HSN using EP-GAN-E) included in the study, the labels for trainability, and their respective lower/upper bounds used during training data generation. In the revised manuscript, we further elaborated on the above information in the second paragraph of the Results section.

      Reviewer 2:

      Major 1: While the models generated with EP-GAN reproduce the average voltage during current injections reasonably well, the dynamics of the response are not well captured. For example, for the neuron labeled RIM (Figure 2), the most depolarized voltage traces show an initial 'overshoot' of depolarization, i.e. they depolarize strongly within the first few hundred milliseconds but then fall back to a less depolarized membrane potential. In contrast, the empirical recording shows no such overshoot. Similarly, for the neuron labeled AFD, all empirically recorded traces slowly ramp up over time. In contrast, the simulated traces are mostly flat. Furthermore, all empirical traces return to the pre-stimulus membrane potential, but many of the simulated voltage traces remain significantly depolarized, far outside of the ranges of empirically observed membrane potentials. While these deviations may appear small in the Root mean Square Error (RMSE), the only metric used in the study to assess the quality of the models, they likely indicate a large mismatch between the model and the electrophysiological properties of the biological neuron.

      EP-GAN main contribution is targeted towards parameter inference of detailed neuron model parameters, in a compute efficient manner. This is a difficult problem to address even with current state-of-the-art fitting algorithms. While EP-GAN is not perfect in capturing the dynamics of the responses and RMSE does not fully reflect the quality of predicted electrophysiological properties, it’s a generic error metric for time series that is easily interpretable and applicable for all methods. Using such a metric, our studies show that EP-GAN overall prediction quality exceeds those of existing methods when given identical optimization goals in a compute normalized setup.

      In our revised manuscript, we included a new section Statistical Analysis and Loss Extension under Results section where we performed additional statistical evaluations (e.g., % of predicted responses within empirical range) of EP-GAN’s predictions for neurons with multi recording data. The results show that predicted voltage responses from EP-GAN baseline (introduced in original manuscript) are in general, within the empirical range with ~80% of its responses falling within +- 2 empirical standard deviations, which were higher than existing methods: DEMO (57.9%), GDE3 (37.9%), NSDE (38%), NSGA2 (60.2%).

      Major 2: Other metrics than the RMSE should be incorporated to validate simulated responses against electrophysiological data. A common approach is to extract multiple biologically meaningful features from the voltage traces before, during and after the stimulus, and compare the simulated responses to the experimentally observed distribution of these features. Typically, a model is only accepted if all features fall within the empirically observed ranges (see e.g. https://doi.org/10.1371/journal.pcbi.1002107). However, based on the deviations in resting membrane potential and the return to the resting membrane potential alone, most if not all the models shown in this study would not be accepted.

      In our original manuscript, due to all of our neurons’ recordings having a single set of recording data, RMSE was chosen to be the most generic and interpretable error metric. We conducted additional electrophysiological recordings for 3 neurons in prediction scenarios (AWB, URX, HSN) and performed statistical analysis of generated models in the sub-section Statistical Analysis and Loss Extension. Specifically, we evaluated the percentage of predicted voltage responses that fall within the empirical range (empirical mean +- 2 std, p ~ 0.05) that encompass the responses before, during and after stimulus (Figure 5, Table 5) and mean membrane potential error normalized by empirical standard deviations (Table S4).

      The results show that EP-GAN baseline achieves average of ~80% of its predicted responses falling within the empirical range, which is higher than the other methods: DEMO (57.9%), GDE3 (37.9%), NSDE (38%), NSGA2 (60.2%). Supplementing EP-GAN with additional resting potential loss (EPGAN-E) increased the percentage to ~85% with noticeable improvements in reproducing dynamical features for AWB (Figure 5). Evaluations of membrane potential errors normalized by empirical standard deviations also showed similar results where EP-GAN baseline and EP-GAN-E have average error of 1.0 std and 0.7 std respectively, outperforming DEMO (1.7 std), GDE3 (2.0 std), NSDE (3.0 std) and NSGA (1.5 std) (Table S4).

      Major 3: Abstract and introduction imply that the 'ElectroPhysiome' refers to models that incorporate both the connectome and individual neuron physiology. However, the work presented in this study does not make use of any connectomics data. To make the claim that ElectroPhysiomeGAN can jointly capture both 'network interaction and cellular dynamics', the generated models would need to be evaluated for network inputs, for example by exposing them to naturalistic stimuli of synaptic inputs. It seems likely that dynamics that are currently poorly captured, like slow ramps, or the ability of the neuron to return to its resting membrane potential, will critically affect network computations.

      In the paper, EP-GAN is introduced as a parameter estimation method that can aid the development of ElectroPhysiome, which is a network model - these are two different method types and we do not claim EP-GAN is a model that can capture network dynamics. To avoid possible confusion, we made further clarifications in the abstract/introduction that EP-GAN is a machine learning approach for neuron HH-parameter estimation.

      I find it hard to believe that the methods EP-GAN is compared to could not perform any better. For example, multi-objective optimization algorithms are often successful in generating models that match empirical observations very well, but features used as target of the optimization need to be carefully selected for the optimization to succeed. Likely, each method requires extensive trial and error to achieve the best performance for a given problem. It is therefore hard to do a fair comparison. Given these complications, I would like to encourage the authors to rethink the framing of the story as a benchmark of EP-GAN vs. other methods. Also, the number of parameters does not seem that relevant to me, as long as the resulting models faithfully reproduce empirical data. What I find most interesting is that EP-GAN learns general relationships between electrophysiological responses and biophysical parameters, and likely could also be used to inspect the distribution of parameters that are consistent with a given empirical observation.

      We thank the reviewer for providing this perspective. While it is indeed difficult to have a completely fair comparison between existing optimization methods vs EP-GAN due to the fundamental differences in their algorithms, we believe that the current comparisons with other methods are justified as they provide baseline performance metrics to test EP-GAN for its intended use cases.

      The main strength of EP-GAN, as previously mentioned, is in its ability to efficiently navigate large detailed HH-models with many parameters so that it can aid in the development of nervous system models such as ElectroPhysiome, potentially fitting hundreds of neurons in a time efficient manner.

      While EP-GAN’s ability to learn the general relationship between electrophysiological responses and parameter distribution are indeed interesting and warrant a more careful examination, this is not the main focus of the paper since in this work we focus on introducing EP-GAN as a methodology for parameter inference.

      In this context, we believe the comparisons with other methods conducted in a compute normalized manner (i.e., each method is given the same # of simulations) and identical optimization targets provides an adequate framework for evaluating the aforementioned EP-GAN aim. Indeed, while EPGAN excels with larger HH-models, it performs slightly worse than DE for smaller models such as the one used by [16] despite it being more compute efficient (Table S2).

      To emphasize the EP-GAN aim, we revised the main manuscript description to focus on its intended use in parameter inference of detailed neuron parameters vs specialized models with reduced parameters.

      I could not find important aspects of the methods. What are the 176 parameters that were targeted as trainable parameters? What are the parameter bounds? What are the remaining parameters that have been excluded? What are the Hodgkin-Huxley models used? Which channels do they represent? What are the stimulus protocols?

      The detailed description and development of the HH-model that we use and its ion channel list can be found in [7]. Supplementary materials also include an excel table predicted parameters which lists all EP-GAN fitted parameters for 9 neurons (+3 new parameter sets for AWB, URX, HSN using EPGAN-E), the labels for trainability, and parameter bounds used for parameters during the generation of training data.

      We also added a new Table which details the current/voltage clamp protocols used for 9 neurons including the ones used for evaluating EP-GAN-E, which was supplemented with longer simulation time to ensure voltage stability (please see Table 6).

      I could not assess the validation of the EP-GAN by modeling 200 synthetic neurons based on the data presented in the manuscript since the only reported metric is the RMSE (5.84mV and 5.81mV for neurons sampled from training data and testing data respectively) averaged over all 200 synthetic neurons. Please report the distribution of RMSEs, include other biologically more relevant metrics, and show representative examples. The responses should be carefully investigated for the types of mismatches that occur, and their biological relevance should be discussed. For example, is the EP-GAN biased to generate responses with certain characteristics, like the 'overshoot' discussed in Major 1? Is it generally poor at fitting the resting potential?

      We thank the reviewer for the feedback regarding the need for additional supporting data for synthetic neuron validations. In the revised supplementary materials Figure S1, we included the distribution of RMSE errors for both groups of synthetic neuron validations (validation/test set) and representative samples for both EP-GAN baseline and EP-GAN-E. Notably, the inaccuracies observed during the experimental neuron predictions (e.g., resting potential, voltage overshoot) do not necessarily generalize to synthetic neurons, indicating that such mismatches could stem from the differences between synthetic neurons used for training and experimental neurons for predictions. While synthetic neurons are generated according to empirically determined parameter bounds, some experimental neuron types are rarer than the others and may also involve other channels that have not been recorded or modeled in [7], which can affect the quality of predicted parameters (see 2nd and 4th paragraphs of Discussions section for more detail). Also, properties such as recording error/noise that are often present in experimental neurons are not fully accounted for in synthetic neurons.

      To further study how these mismatches can be mitigated, in the revision we added an extended version of EP-GAN where target loss was supplemented with additional resting potential and 5 seconds of stabilization period during simulations (EP-GAN-E described in Statistical Analysis and Loss Extension). With such extensions, EP-GAN-E was able to improve its accuracies on both resting potentials and dynamical features with the most notable improvements on AWB where predicted voltage responses closely match slowly rising voltage response during stimulation. EPGAN-E is an example of further extensions to loss function that account for additional experimental features.

      Furthermore, the conclusion of the ablation study ('EP-GAN preserves reasonable accuracy up to a 25% reduction in membrane potential responses') does not seem to be justified given the voltage traces shown in Figure 3. For example, for RIM, the resting membrane potential stays around 0 mV, but all empirical traces are around -40mV. For AFD, all simulated traces have a negative slope during the depolarizing stimuli, but a positive slope in all empirically observed traces. For AIY, the shape of hyperpolarized traces is off.

      Since EP-GAN baseline optimizes voltage responses during the stimulation period, RMSE was also evaluated with respect to this period. From these errors, we evaluated whether the predicted voltage error for each ablation scenario fell within the 2 standard deviations from the mean error obtained from synthetic neuron test data (i.e. the baseline performance). We found that for input ablation for voltage responses, the error was within such range up to 25% reduction whereas for steady-state current input ablation, all 25%, 50% and 75% reductions resulted in errors within the range.

      We extended the “Ablation Studies” sub-section so that the above reasoning is better communicated to the readers.

      Additionally, I found a number of minor issues:

      Minor 1: Table 1 lists the number of HH simulations as '32k (11k · 3)'. Should it be 33k, since 11.000 times 3 is 33.000? Please specify the exact number of samples.

      Minor 2: x- and y-ticks are missing in Fig 2, Fig 3, Fig S1, Fig S2, Fig S3 and Fig S4.

      Minor 3: All files in the supplementary zip file should be listed and described.

      Minor 4: Code for training the GAN, generation of training datasets and for reproducing the figures should be provided.

      Minor 5: In the reference (Figure 3A, Table 1 Row 2): should this refer to Table 2?

      Minor 6: 'the ablation is done on stimulus space where a 50% reduction corresponds to removing half of the membrane potential responses traces each associated with a stimulus.' - which half is removed?

      We thank the reviewer for pointing out these errors in the original manuscript. The revised manuscript includes corrections for these items. We will publish the python code reproducing the results in the public repository in the near future.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Since multiple Reviewers requested that the results describing effects of TTX treatment on GluA2 receptor levels detected by immunofluorescence and confocal imaging be revised, we have made substantial changes, which are described below. We believe the changes have greatly improved the manuscript and thank the reviewers for their comments.

      Lack of significant increase in GluA2 receptor data is due to too few cultures sampled; anything could have happened [in one] particular dissociation. A concern that the TTX effect might vary greatly from culture to culture was why we felt it was important to match the receptor measurements on the same cultures that we recorded mEPSCs. We now present the culture means in Figure 5A (mEPSCs) and 5B (GluA2 receptor cluster size). These plots make it clear that the variability in the GluA2 receptor cluster size effect is not attributable to a failure of that culture to show a homeostatic effect. That is, the variability in GluA2 receptor effect is independent of the variability in mEPSC effect. To increase sample size, we examined 2 additional cultures for synaptic GluA2 receptor levels in control vs. TTX treatment. These cultures showed very modest increases (Figure 5C). When cell means from these experiments were pooled with those from the 3 matched cultures, the TTX effect was still not statistically significant (Figure 5G).

      Lack of significant increase in GluA2 receptor data is due to the choice to restrict our analysis to the primary dendrite, close to the cell body. We restricted our analysis to the primary dendrite because Figure 3 in Turrigiano et al, 1998, shows the increased response to exogenously applied glutamate after TTX treatment is greatest close to the cell body and wanes as the glutamate is applied further away (added to Results, new lines 388-389).

      Variability in GluA2 receptor data is due to the much smaller number of synapses sampled, compared to mEPSCs. We matched the sampling for mEPSC amplitude data to that of imaging data by taking only 20 samples from each electrophysiological recording. Each mEPSC represents one synapse; in a set of 20 mEPSCs some might come from the same synapse, so that we are sampling from £ 20 synapses. The effect of TTX on mEPSC amplitudes remained significant despite the reduced samples per cell (Figure 5A).

      Why do we fail to show a significant increase in receptors when this has been shown in many studies?

      We have added to our discussion the point that several studies, including Wang et al. 2019, use the number of puncta, rather than the number of cells, as the sample number. We ran an analysis of GluA2 receptor cluster size where we sampled multiple synapses per cell, and used the number of clusters as the sample n. We found that even with as few as 6 synapses randomly selected from each cell, the effect of TTX on GluA2 receptor cluster size became highly significant (p = 0.001 for data from 3 cultures and p = 0.005 for data from 5 cultures) (see new lines 400-406 in Discussion). In sum, our data are not very different from that of some previous studies. We are not arguing that receptors do not increase. Instead our point is that the increase is more variable than the increase in MESPC amplitude and thus takes a much bigger sample size to detect. In sum, the difference between the mEPSC data and the receptor data is that the mEPSC data consistently show a ~20-25% increase, whereas the receptor data do not always show an increase and sometimes the increase is only ~10%. Finally, we added two matched culture experiments examining synaptic GluA1 receptor cluster characteristics. GluA1 receptor cluster size decreased in one culture, and increased very modestly in the other (Supplemental Figure 1B), whereas mEPSC amplitude robustly increased (Supplemental Figure 1A; Results, new lines 265-268).

      We conclude that these data support the idea that there is another contributor to the TTXinduced increase in quantal size.

      Other changes in presentation of GluA2 receptor results: Since the effects on intensity and integral are of lesser magnitude than that on cluster size, we have removed these results from the graphs, although they are presented in Table 1. We have removed Figure 6, the presentation of individual culture results, since these results are now conveyed in Figure 5A-C. We have removed graphs depicting GluA2 receptor cluster size in response to TTX in Rab3A-/- cultures, but these data are still presented in Table 1.

      We address other detailed comments below.

      Public Reviews:

      Reviewer #1 (Public review):

      (2) The effects of Rab3A on TTX-induced mini frequency modulation remains unclear, because TTX does not induce a change in mini frequency in the Rab3A+/Ebd control (Fig. 2). The respective conclusions should be revised accordingly (l. 427).

      The effects on mini frequency were added for completeness, but given the lack of consistently significant changes with TTX treatment or changes in the KO or Rab3A<sup>Ebd/Ebd</sup> cultures, we have removed comment on these results from the Discussion.

      (3) The model is still not supported by the data. In particular, data supporting a negative regulation of Rab3A by APs, Rab3A-dependent release of a tropic factor, or a Rab3Adependent increase in GluA2 abundance are not presented.

      We have removed the model from the manuscript.

      (4) Data points are not overlapping and appear "quantal" in most box plots. How were the data rounded?

      The appearance of quantal variation in cell amplitude means is due to the binning that is part of the creation of the box plot. We have not remade the figures without binning, because the binning provides a visual depiction of the distribution of the data points. We have added the bin sizes to the appropriate figure legends.

      Reviewer #2 (Public review):

      However, the authors still have not provided further investigation of the mechanisms behind the role of Rab3A in this form of plasticity, and the revision therefore has added little to the significance of the study. Moreover, the experimental design for the investigation of the mismatch between mEPSC amplitude and GluA2 cluster fluorescence remains questionable, making it difficult to draw any credible conclusions from groups of data that not only look similar to the eye but also show no significance statistically.

      To our knowledge, no other study has matched measurements of mEPSC amplitude in the same cultures where synaptic receptor levels were assessed. As stated above, we have revised the presentation of GluA2 receptor results, concluding from the lack of significant effects on receptor levels that the mEPSC amplitude increase cannot be fully explained by the receptor data (which is strengthened by addition of two more cultures analyzed for GluA2 immunofluorescence). This is an important addition to the significance of the study.

      In summary, this study establishes that neuronal Rab3A plays a role in homeostatic synaptic plasticity, but so do a number of other molecules that have been implicated in homeostatic synaptic plasticity in the past two decades (only will grow with the new techniques such as RNAseq). Without going beyond this finding and demonstrating how exactly Rab3A participates in the induction and/or expression of this form of plasticity, or maybe the potential Rab3A-mediated functional and behavioral defects in vivo, the contribution of the current study to the field is limited. However, given the presynaptic location of Rab3A, this finding could serve as a starting point for researchers interested in pre-postsynaptic cross-talk during homeostatic plasticity in general.

      We previously published a review in which we list 19 molecules known at that time to be important for homeostatic synaptic plasticity (see Table 2, Koesters et al., 2024), and they fall into two categories: molecules involved in glutamate receptor expression or trafficking, and signaling molecules. Rab3A is the first synaptic vesicle protein to be implicated in homeostatic plasticity of quantal size. We have added this point to the Discussion, new lines 473-476. By demonstrating that Rab3A is not acting in glia (which release TNF, which regulates receptor expression), and that GluA2 receptor levels do not explain the homeostatic mEPSC increase in our experimental conditions, we have ruled out two major mechanisms.

      Reviewer #3 (Public review):

      Other questions arise from the NASPM experiments, used to justify looking at GluA2 (and not GluA1) in the immunostaining. First, there is a frequency effect that is unclear in origin. One would expect NASPM to merely block some fraction of the post-synaptic current, and not affect pre-synaptic release or block whole synapses. However the change in frequency seems to argue (as the authors do) that some synapses only have CP-AMPARs, while the rest of the synapses have few or none. Another possibility is that there are pre-synaptic NASPM-sensitive receptors that influence release probability. Further, the amplitude data show a strong trend towards smaller amplitude following NASPM treatment (Fig 3B). The p value for both control and TTX neurons was 0.08 - it is very difficult to argue that there is no effect. The decrease on average is larger in the TTX neurons, and some cells show a strong effect. It is possible there is some heterogeneity between neurons on whether GluA1/A2 heteromers or GluA1 homomers are added during HSP. This would impact the weakly supported conclusions about the GluA2 imaging vs mEPSC amplitude data.

      We cannot rule out that the NAPSM-induced decrease in mEPSC frequency is due to a loss of presynaptic glutamate receptor enhancement of release probability, and have added this statement to the Results, new lines 202-204. Regarding the p value of 0.08—we are not arguing that NASPM has no effect on mEPSC amplitude, only that it has no effect on the homeostatic increase in amplitude after TTX treatment. An increase in GluA1/A2 heteromers should have been detected in our imaging studies.

      Unaddressed issues that would greatly increase the impact of the paper:

      (1) Is Rab3A acting pre-synaptically, post-synaptically or both? The authors provide good evidence that Rab3A is acting within neurons and not astrocytes. But where it is acting (pre or post) would aid substantially in understanding its role. They could use sparse knockdown of Rab3A, or simply mix cultures from KO and WT mice (with appropriate tags/labels). The general view in the field has been that HSP is regulated post-synaptically via regulation of AMPAR trafficking, and considerable evidence supports this view. The more support for their suggestion of a pre-synaptic site of control, the better.

      We agree that doing co-cultures of Rab3A-/- and Rab3A+/+ neurons is the definitive experiment to determine the locus of action of Rab3A in homeostatic synaptic plasticity. We hope to examine this question in a future manuscript.

      (2) Rab3A is also found at inhibitory synapses. It would be very informative to know if HSP at inhibitory synapses is similarly affected. This is particularly relevant as at inhibitory synapses, one expects a removal of GABARs (ie the opposite of whatever is happening at excitatory synapses). If both processes are regulated by Rab3A, this might suggest a role for this protein more upstream in the signaling; an effect only at excitatory synapses would argue for a more specific role just at these synapses.

      We agree that it would be very interesting to determine if the homeostatic decrease in mIPSCs after activity blockade depends on Rab3A. We hope to address this question in the future.

      Recommendations for the authors:

      Reviewer #3 (Recommendations for the authors):

      Minor points:

      The abstract is a bit repetitive in places. Some editing would be advised.

      We did not identify anything repetitive in the abstract except the parallel construction referring to the previous findings at the NMJ and current findings in cortical neurons. However, we have eliminated a section in the introduction which went into detail about the receptor imaging results (previous lines 103-110).

      Line 77: 'shift toward early awakening' is unclear; do you mean shorter sleep/wake cycle? Other circadian issues? A more complete description is needed.

      We have moved the additional detail about the Earlybird mutation’s effect on circadian period from the Results to the Introduction, new lines 77 to 79.

      The results section has many passages that seem more like discussion, offering various interpretation and alternatives for the data. While some commentary is appropriate, to justify the next series of experiments and maintain a logical flow, this manuscript has rather a high amount of this. Some editing and shifting material to the discussion might be warranted.

      We have reduced the commentary in the Results section.

      Line 245: GluA2 homomers are really unlikely, as they won't pass current (unless unedited) and don't often if ever form. But GluA2/A3 heteromers are likely (and detected by their methods).

      GluA2 homomers do conduct current, albeit less than heteromers (Swanson et al., 1997; Oh and Derkach, 2005; Coombs et al., 2019). [The Oh and Derkach paper shows a GluA2 homomer current in Supplementary Figure 3]. We have modified the text to acknowledge that the GluA2 receptor imaging will detect heteromers and homomers (Results, new lines 214 to 215).

      Line 258: If the number of synaptic pairs analyzed was usually <20, what was the average and range of pairs? This gets into the sampling issue.

      We have added the average number of synaptic sites (20.4 ± 6.5) and range (11-38) to the text, Results, new line 229.

      Are the stats of the baseline mEPSC amplitude and frequency shifts (WT vs KO on WT feeder layer) given somewhere (lines 398-402)? If not, please add them.

      These stats have been added to the text, mEPSC amplitude, (CON, WT on WT, 13.3 ± 0.5 pA; CON, KO on WT, 15.2 ± 1.1 pA, p = 0.23, Kruskal-Wallis test), new lines 325-326 and frequency, (CON, WT on WT, 2.54 ± 0.57 sec<sup>-1</sup>; CON, KO on WT, 4.46 ± 1.21 sec<sup>-1</sup>, p = 0.23, Kruskal-Wallis test), new lines, 329-330.

      25mM K+ is going to be much more than 'mildly' depolarizing (line 697). Should just skip that word.

      ‘mildly’ has been removed.

      The section on MiniAnalysis seems overly argumentative, and there is no need to discuss flaws in the Wu paper. The important thing (a bit buried at the end of this section) is that the manual mini selection was done blind to condition, which is the normal way of dealing with potential bias. It would be better to limit the methods to describing what was done.

      The bulk of the justification of manual analysis has been removed from the text.

      The discussion of potential conductance changes (lines 534-6) seems somewhat unwarranted.

      Modification of GluA1 phosphorylation in the GluA1/A2 heteromer would not be detected by NASPM (and the NASPM data being a bit inconclusive anyway). Further, auxiliary subunits (like TARPs) can alter conductance of any of the AMPARs. So I don't think they have enough data to exclude such a possibility.

      The discussion of contributions of conductance have been removed from the text.

      Coombs ID, Soto D, McGee TP, Gold MG, Farrant M, Cull-Candy SG (2019) Homomeric GluA2(R) AMPA receptors can conduct when desensitized. Nat Commun 10:4312.

      Oh MC, Derkach VA (2005) Dominant role of the GluR2 subunit in regulation of AMPA receptors by CaMKII. Nat Neurosci 8:853-854.

      Swanson GT, Kamboj SK, Cull-Candy SG (1997) Single-channel properties of recombinant AMPA receptors depend on RNA editing, splice variation, and subunit composition. J Neurosci 17:5869.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary:

      In this manuscript, the authors investigate the role of BEND2, a novel regulator of meiosis, in both male and female fertility. Huang et al have created a mouse model where the fulllength BEND2 transcript is depleted but the truncated BEND2 version remains. This mouse model is fertile, and the authors used it to study the role of BEND2 on both male and female meiosis. Overall, the full-length BEND2 appears dispensable for male meiosis. The more interesting phenotype was observed in females. Females exhibit a lower ovarian reserve suggesting that full-length BEND2 is involved in the establishment of the primordial follicle pool.

      Strengths:

      The authors generated a mouse model that enabled them to study the role of BEND2 in meiosis. The role of BEND2 in female fertility is novel and enhances our knowledge of genes involved in the establishment of the primordial follicle pool.

      Weaknesses:

      The manuscript extensively explores the role of BEND2 in male meiosis; however, a more interesting result was obtained from the study of female mice. Only a few experiments were performed using female mice, therefore, more experiments should be performed to complete the story of the role of BEND2 on female fertility. In addition, the title and abstract of the manuscript do not align with the story, as female fertility is only a small portion of the data compared to the male fertility section.

      We appreciate the reviewer’s thoughtful summary, recognition of the strengths of our study, and constructive feedback. In the revised manuscript, we have performed additional experiments to enhance our understanding of the role of BEND2 in female gametogenesis. These new experiments provide further insights into the establishment of the ovarian reserve and the role of BEND2 in female fertility.

      Additionally, we have rewritten the title, abstract, and introduction to better align with the content of the manuscript and to reflect the balance between the male and female fertility results. We believe these changes address the reviewer’s concerns and improve the overall clarity and focus of the manuscript.

      Reviewer #1 (Recommendations For The Authors):

      • I recommend that the authors re-organize their abstract and introduction to accurately reflect the manuscript's primary focus on male fertility. Right now, the title of the manuscript is misleading. The manuscript does not investigate reproductive aging; rather, it primarily describes the depletion of primordial follicle number. The mechanism behind this depletion and whether this phenotype accelerates reproductive aging, are not explored. Clarifying these points will help align the title and content of the manuscript more accurately.

      We thank the reviewer for this suggestion. We agree that the original title and abstract did not fully capture the focus of the study. In response, we have rewritten the title, abstract, and introduction to better align with the results presented, focusing more clearly on the implications of the effects of the full-length BEND2 depletion for spermatogenesis and oogenesis. These revisions ensure that the title, the abstract, and the manuscript's introduction are now more accurately reflective of the work performed.

      • Figure 1: I couldn't find the validation of the polyclonal antibody against BEND2 that the authors generated.

      Regarding this query about the validation of the polyclonal antibody against BEND2, we apologize for any confusion. We would like to clarify that this validation is indeed presented in Figure 2 of our manuscript. To ensure this information is easily accessible, we have revised the text to explicitly mention the validation in Figure 2.

      • Figure 2A: Could you provide the actual numbers for the weight of the mice testis?

      In response to this question regarding Figure 2A and the weights of the mice testis, we have now included this data in a graph in Fig 2A and Table S1 and added this information in the results section.

      • Figure 2C and D: I am confused by the fact that in the WB we can appreciate a high expression of the p75 protein, but the signal is very low in the IF (Figure 2D).

      We thank the reviewer for raising this point. We acknowledge the apparent discrepancy between the strong p75 signal observed in the Western blot (Fig. 2C) and the weaker signal seen in the immunofluorescence (Fig. 2D). We think several factors could contribute to this difference, such as differences in sensitivity and detection methods, epitope accessibility, protein localization or differences in sample preparation, antibody affinity, and experimental conditions between Western blot and IF.

      • In the same figure, the authors also mention that the p75 protein is functional. On what basis do they rely on reaching this conclusion?

      We acknowledge that we cannot definitively confirm the functionality of the p75 protein. Our assumption was based on the observed fertility of the male mice and existing literature indicating that BEND2 is essential for completing meiosis (Ma et al., 2022). However, we understand the importance of clarity in our claims. To avoid any potential confusion, we have revised the sentence to read: "The p75 BEND2 protein—likely corresponding to an exon 11-skipped transcript—is present and might be functional in our mutant testis, based on the observed phenotype (see below)."

      • The phenotype in females is very interesting. The authors conclude that BEND2 influences primordial follicle formation, oocyte quality, fertility, and reproductive aging by (1) performing follicle counts, (2) analyzing the litter size, and (3) analyzing meiotic progression. Given that the authors build their story around these experiments, I strongly encourage them to expand the section on female fertility, or reorganize the manuscript, or be more cautious with some of their conclusions. They might consider performing additional experiments such as:

      - Oocyte quality: To determine whether BEND2 impacts oocyte quality, mice should be stimulated with hormones and oocyte quality should be analyzed (GV, MI, MII progression, spindle morphology and/or fertilization, and embryo development). Does the decrease in primordial follicles correlate with the number of ovulated oocytes, or is the impact only on oocyte quality?

      We appreciate the reviewer's suggestion to assess the impact of BEND2 on oocyte quality. Following the reviewer’s recommendation, we stimulated three control and three mutant mice. We analyzed the number of ovulated oocytes, their fertilization rate, and the percentage of embryos that developed to the blastocyst stage. These new results are included in the revised manuscript (see Results section and new Table 1). Our analyses indicate that for all parameters assessed, control and mutant oocytes behaved similarly. Specifically, there were no significant differences in the number of ovulated oocytes, fertilization rates, or the ability of embryos to progress to the blastocyst stage between the control and mutant groups. These findings suggest that mutant oocyte quality is comparable to control mice of a similar age. We have incorporated these new results into the manuscript.

      - Reproductive aging: A fertility trial would provide more information on whether BEND2 depletion triggers an acceleration of reproductive aging. In addition, the oldest mice used by the authors are 9 months old, and at this point, fertility has not declined yet.

      We appreciate the reviewer's suggestion regarding the assessment of reproductive aging. However, we respectfully disagree with the assertion that fertility has not declined by 9 months of age. In our colony, we have observed a significant decline in fertility around 10 months of age. Specifically, out of 18 10-month-old female mice placed in breeding cages, we observed only three pregnancies within the first 30 days (N.N. and I.R., data not published). Based on these observations, we determined that fertility begins to decline around this age in our colony, which informed our decision to use 9-month-old mice as the oldest age group for our analysis. Thus, this age is appropriate for evaluating the potential effects of BEND2 depletion on reproductive aging in our specific mouse population.

      - The observation that the primordial follicle pool is already diminished in mice that are 1 week old is very interesting. Some experiments that the authors could perform to figure out the mechanism are: (1) Analyzing apoptosis. Are the primordial follicles dying during the pool's establishment, or is this an ongoing apoptotic process throughout the mice's lifespan? (2) If the authors still have ovaries from mice younger than 1 week of age (when the primordial pool is forming), they could perform DDX4 staining and quantify the number of oocytes in follicles and the total number of oocytes. These experiments would provide mechanistic insights into whether BEND2 impacts the formation of the primordial follicle pool or if the pool forms but is then depleted.

      We appreciate the reviewer's suggestion to further explore the mechanism behind the reduced primordial follicle pool. In response, we have analyzed the number of DDX4positive cells (DDX4 labels oocytes) in newborn mutant and wild-type animals. Our results show that mutant ovaries contain significantly fewer oocytes compared to controls (see new Fig. 5). This finding supports the hypothesis that BEND2 is critical for the establishment of a normal ovarian reserve. We are grateful for this suggestion, as these additional data reinforce our conclusion that BEND2 is required to determine a normal ovarian reserve in mice.

      • What is the red signal in Supplementary Figure 1C?

      This image depicts the BEND2 staining pattern in 16 days post-coitum (dpc) wild-type mouse ovaries. To clarify this and prevent any confusion, we have updated the figure legend to explicitly state that the sample shown is from a wild-type mouse.

      • Please spell out the full term of all the acronyms.

      We apologize for the oversight in not fully spelling out some acronyms in the original manuscript. We have carefully reviewed the entire manuscript and have ensured that all acronyms are now spelled out in full upon their first use in the revised version. We want to thank the reviewer for bringing this to our attention.

      • Is Line-1 also dysregulated in the ovary? This was one of the main findings from the male part. It would be interesting to perform the same analysis in the ovary since Line1 has a role in establishing the ovarian reserve (PDMI: 31949138).

      We thank the reviewer for this insightful suggestion. We have analyzed the number of LINE1 and SYCP3-positive cells in wild-type and mutant newborn ovaries (new Fig. S4). Our results show no significant difference between the two genotypes, suggesting that LINE-1 is not dysregulated in newborn Bend2 mutant oocytes. These findings indicate that, at least in the context of the newborn ovary, LINE-1 does not appear to be affected by BEND2 depletion.

      Reviewer #2 (Public Review):

      In their manuscript entitled "BEND2 is a crucial player in oogenesis and reproductive aging", the authors present their findings that full-length BEND2 is important for repair of meiotic double strand break repair in spermatocytes, regulation of LINE-1 elements in spermatocytes, and proper oocyte meiosis and folliculogenesis in females. The manuscript utilizes an elegant system to specifically ablate the full-length form of BEND2 which has been historically difficult to study due to its location on the X chromosome and male sterility of global knockout animals.

      While the manuscript is an overall excellent addition to the field, it would significantly benefit from a few additional experiments, as well as some additional clarification/elaboration.

      The claim that BEND2 is required for ovarian reserve establishment is not supported, as the authors only look at folliculogenesis and oocyte abundance starting at one week of age, after the reserve is formed. Analysis of earlier time points would be much more convincing and would parse the role of BEND2 in the establishment vs. maintenance of this cell population. In spermatocytes, the authors demonstrate a loss of nuclear BEND2 in their mutant but do not comment on the change in localization (which is now cytoplasmic) of the remaining protein in these animals. This may have true biological significance and a discussion of this should be more thoroughly explored.

      We thank the reviewer for their thoughtful feedback and constructive suggestions to improve our manuscript.

      In response to the comment regarding the establishment of the ovarian reserve, we have now analyzed Bend2 mutant and control newborn ovaries. Our results show a significant reduction in the number of DDX4-positive cells in mutant ovaries compared to controls. These findings demonstrate that BEND2 is required for the establishment of the ovarian reserve, as the reduction is evident at birth.

      Regarding the cytoplasmic staining of BEND2 in mutant spermatocytes, we did perform secondary-antibody-only controls using goat anti-rabbit Cy3 to address the specificity of the signal. The staining observed in the Bend2 mutants closely resembles background staining, suggesting that the cytoplasmic signal is nonspecific. Therefore, we do not believe this represents a meaningful change in the localization of BEND2 protein in the mutants. We have clarified this in the revised manuscript to address this point.

      We hope these additional experiments and clarifications strengthen the manuscript and address the reviewer’s concerns.

      Reviewer #2 (Recommendations For The Authors):

      Major points:

      (1) The title of the manuscript does not accurately capture the content of the work. The vast majority of the data presented here is from the male, which is not reflected at all in the title - perhaps considering revising it?

      Thank you for your valuable suggestion. We agree that the original title did not fully reflect the focus of the manuscript. In response, we have revised the title, along with the abstract and introduction, to more accurately capture the content of the study and the emphasis on the male data. These changes ensure that the manuscript more clearly aligns with the results presented.

      (2) In Figure 2D, the authors demonstrate that WT BEND2 expression and localization are lost in the mutant, but staining is still apparent, just in the cytoplasm. Did the authors perform secondary-antibody-only controls to determine if this was background staining or real staining? If real, can they comment on the change in localization of the protein?

      We thank the reviewer for this insightful question. We have indeed performed secondary antibody-only controls using goat anti-rabbit Cy3. The staining observed in the Bend2 mutants closely resembles background staining, suggesting that the signal in the cytoplasm is not specific. Therefore, we do not believe this staining represents any real or meaningful expression of the BEND2 protein in the mutants.

      (3) In Figure S2A, the authors show Ku70 staining and describe that it is similar between the genotypes, but - to my eye - it looks quite distinctly different. It appears to stain in patches in WT SYCP3+ spermatocytes, versus staining in patches in the more mature, SYCP3- germ cells closer to the lumen in the mutant. Can the authors please clarify, or provide arrows to point which foci they are referring to?

      We apologize for the confusion caused by the image provided in the original submission. Upon review, we realized that the mutant image was not fully representative of the staining pattern observed in the majority of mutant samples. We have replaced this image with a new one in the revised manuscript, which more accurately reflects the similarity in Ku70 staining between wild-type and mutant testis. In this updated Figure S2, we have also included arrowheads to indicate the relevant foci, making it clearer to the reader. We have updated the figure legend to correspond with these changes as well.

      (4) The authors state that BEND2 is "required to establish the ovarian reserve during oogenesis" but this has not been demonstrated. The authors do show a reduced density of primordial follicles at one week of age. While this is compelling data, the ovarian reserve is established earlier in the mouse, around postnatal days 0-1, so it is not clear from this manuscript whether BEND2 is required for the maintenance of this population after PND1, leading to reduced numbers by 1 week of age, OR if it is required for the establishment of this population, which would result in reduced numbers of oocytes around the time of birth. This is a critical experiment that should be performed in order to determine which of these possibilities is likely the case. Ideally, looking at embryonic through early postnatal time points during ovarian development would be very helpful.

      We thank the reviewer for raising this important point. As mentioned earlier in response to Reviewer 1, we have performed the experiment suggested by Reviewer 2 and analyzed the number of DDX4-positive cells in newborn ovaries. Our results show that Bend2 mutant ovaries have fewer oocytes at birth than wild-type controls (Fig. 5H). This finding reinforces our conclusion that BEND2 is indeed required to establish the ovarian reserve, as the reduction in oocyte number is evident at the time of birth. We agree that this additional data strengthens our original claim, so we have included these results in the revised manuscript.

      Reviewer #3 (Public Review):

      Summary:

      Huang et al. investigated the phenotype of Bend2 mutant mice which expressed a truncated isoform. This mutant male showed increasing apoptosis due to unrepaired double-strand breaks. However, this mutant male has fertility, and this enabled them to analyze Bend2 function in females. They revealed that Bend2 mutation in females showed decreasing follicle numbers which leads to loss of ovarian reserve.

      Strengths:

      Since their Bend2 mutant males were fertile, they were able to analyze the function of Bend2 in females and they revealed that loss of Bend2 causes less follicle formation.

      Weaknesses:

      Why the phenotype of their mutant male is different from previous work (Ma et al.) is not clear enough although they discuss it.

      We appreciate the reviewer’s comment regarding the differences between our Bend2 mutant male phenotype and the previously reported phenotype by Ma et al., 2022. We believe this discrepancy is due to the fact that the Bend2 locus encodes two BEND2 isoforms: p140 and p80. In contrast to the previous study, where both proteins were ablated by mutation employed (the deletion of exons 12 and 13), our exon 11 deletion specifically ablates p140 expression while allowing the expression of p80 in the testis.

      Based on the distinct phenotypes observed in the two Bend2 mutant mouse models, we hypothesize that p80 is sufficient to fulfill BEND2’s roles in meiosis, which could explain why our Bend2 mutant males remain fertile. We have rewritten the relevant sections in the results and discussion to better articulate this hypothesis and clarify the potential mechanisms behind the observed phenotypic differences.

      We hope these clarifications and additional details adequately address the reviewer’s concerns.

      Reviewer #3 (Recommendations For The Authors):

      (1) The authors showed that Bend2 mutant females had decreased fertility. This may be due to decreased ovarian reserve. Did the authors check if the mutant mice decreased or lost fertility faster than WT? If the authors have the data, please refer to it in the manuscript.

      We followed the breeding performance of a small number of control and Bend2 mutant females, and preliminary observations suggested no clear differences between the two groups. However, due to the limited sample size, we felt that these data were not conclusive enough to be included in the manuscript. We agree that a more thorough analysis of fertility decline over time would be valuable, and we plan to address this question in a future study.

      (2) In Figure 1 A, there is no exon1 in the upper figure.

      We thank the reviewer for pointing this out. We have revised Figure 1A to include exon 1 and ensure the schematic is accurate. The updated figure is included in the revised version of the manuscript.

      (3) Figure 3A, it would be nice to show several tubules of the testis section as well as an enlarged one.

      Following the reviewer's advice, we have revised Figure 3A to include new images showing several tubules and an enlarged view of one section of a tubule. These updates are included in the revised manuscript to better represent the testis sections.

      (4) Please be consistent with the format of the graph, especially Supplemental figures 2C and 4D.

      We have revised the figures, including Supplemental Figures 2C and 4D, to ensure consistency in the format throughout the manuscript. We have made modifications to the figures to align them more closely and improve the overall presentation.

    1. Author response:

      We are grateful to the reviewers and editors for their time and positive assessment of our manuscript. We will incorporate all their comments to further improve our work. In the revised version of the manuscript, we will provide a more detailed description of the quantification of the wrapping index and further explain the differential roles of Htl and Uif during cell growth versus the role of Notch during axon wrapping. In addition, we will perform further experiments using combinations of reporters and antibodies to further explore the relationship between Htl, Uif and Notch. The discussion will be expanded and possible mechanisms by which Uif 'stabilises' a specific membrane domain will be included.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      This work seeks to provide genetic evidence for a role for beta-adrenergic receptors that regulate heart rate and blood flow on cavernous malformation development using a zebrafish model, and to extend information regarding beta-adrenergic drug blockade in cavernous malformation development, with the idea that these drugs may be useful therapeutically.

      Strengths:

      The work shows that genetic loss of a specific beta-adrenergic receptor in zebrafish, adrb1, prevents embryonic venous malformations and CCM in adult zebrafish brains. Two drugs, propranolol and metoprolol, also blunt CCM in the adult fish brain. These findings are predicted to potentially impact the treatment of human CCM, and they increase understanding of the factors leading to CCM.

      Response 1: We are grateful for the reviewer’s acknowledgment of this study’s potential translational significance.

      Weaknesses:

      There are minor weaknesses that detract slightly from enthusiasm, including poor annotation of the Figure panels and lack of a baseline control for the study of Klf2 expression (Figure 4).

      Response 2: We agree. Annotation of the Figure panels were added, and a baseline control for the study of klf2a expression (Figure 4) was added. Details were described in the response to “recommendations for the authors”.

      Reviewer #2 (Public review):

      Summary:

      Previously, the authors developed a zebrafish model for cerebral cavernous malformations (CCMs) via CRISPR/Cas9-based mosaic inactivation of the ccm2 gene. This model yields CCM-like lesions in the caudal venous plexus of 2 days post-fertilization embryos and classical CNS cavernomas in 8-week fish that depend, like the mouse model, on the upregulation of the KLF2 transcription factor. Remarkably, the morpholino-based knockdown of the gene encoding the Beta1 adrenergic receptor or B1AR (adrb1; a hemodynamic regulator) in fish and treatment with the anti-adrenergic S enantiomer of propranolol in both fish and mice reduce the frequency and size of CMM lesions.

      In the present study, the authors aim to test the model that adrb1 is required for CCM lesion development using adrb1 mutant fish (rather than morpholino-mediated knockdown and pharmacological treatments with the anti-adrenergic S enantiomer of propranolol or a racemic mix of metoprolol (a selective B1AR antagonist).

      Strengths:

      The goal of the work is important, and the findings are potentially highly relevant to cardiovascular medicine.

      Response 3: We are grateful for the reviewer’s acknowledgment of this study’s scientific importance and clinical relevance.

      Weaknesses:

      (1) The following figures do not report sample sizes, making it difficult to assess the validity of the findings: Figures 1B and D (the number of scored embryos is missing), Figures 2G and 3B (should report both the number of fish and lesions scored, with color-coding to label the lesions corresponding to individual fish in which they were found).

      Response 4: We agree. Sample sizes of Figures 1B and D were added in the figures and figure legends. Sample sizes of Figures 2G and 3B were added in their figure legend respectively. The lesion volume in Figures 2G and 3B is the total lesion volume in each brain.

      (2) Figure 4 has a few caveats. First, the use of adrb1 morphants (rather than morphants) is at odds with the authors' goal of using genetic validation to test the involvement of adrb1 in CCM2-induced lesion development.

      Second, the authors should clarify if they have validated that the tnnt (tnnt2a) morpholino phenocopies tnnt2a mutants in the context in which they are using it (this reviewer found that the tnnt2a morpholino blocks the heartbeat just like the mutant, but induces additional phenotypes not observed in the mutants).

      Response 5: We appreciate the reviewer’s comments; however, generating adrb1<sup>-/-</sup> and tnnt2a<sup>-/-</sup> klf2a reporter fish, while also ensuring the presence of only one EGFP transgene allele for intensity measurement, would require prohibitively time-consuming breeding efforts.

      The use of morpholinos for tnnt2a and adrb1, as well as their effects on the heart, have been well-documented in previous studies (Sehnert AJ et al., Nat Genet. 2002;31:106-10; Steele SL et al., J Exp Biol. 2011;214:1445-57).

      Third, the data in Figure 4E is from just two embryos per treatment, a tiny sample size. Furthermore, judging from the number of points in the graph, only a few endothelial PCV cells appear to have been sampled per embryo. Also, judging from the photos and white arrowheads and arrows (Figure 4A-D), only the cells at the ventral side of the vessel were scored (if so, the rationale behind this choice requires clarification).

      Response 6: We have increased the sample size, as described in the Figure 4 legend. Regarding the scoring of endothelial nuclei, we focused on the ventral side of the vessel because nuclei on the dorsal side often reside at branching points of the venous plexus. This positional variance could influence klf2a expression levels; thus, we focused on the ventral surface to limit this potential confounding variable.

      Fourth, it is unclear whether and how the Tg(kdrl:mcherry)is5 endothelial reporter was used to mask the signals from the klf2a reporter. The reviewer knows by experience that accuracy suffers if a cytosolic or cell membrane signal is used to mask a nuclear green signal.

      Response 7: We agree that it is theoretically possible for Förster resonance energy transfer (FRET) to occur, as the emission spectrum of EGFP (495-550 nm in our filter setup) overlaps with the absorption spectrum of mCherry. However, several factors reduce the likelihood of FRET in our experimental setup:

      (1)  Without a nuclear localization signal, the majority of mCherry is localized in the cytoplasm, although small amounts may passively diffuse into the nucleus.

      (2)  EGFP, on the other hand, is predominantly localized in the nucleus due to the presence of a nuclear localization signal.

      (3)  FRET requires two fluorophores to be within a proximity of 8-10 nanometers or less for efficient energy transfer. The nuclear envelope, with a typical thickness of 30-50 nanometers, separates nuclear EGFP from cytoplasmic mCherry and FRET efficiency is inversely proportional to the sixth power of the distance between donor and acceptor. Thus, the theoretical likelihood of significant energy transfer under these conditions is low.

      To empirically examine potential FRET between nuclear EGFP and mcherry in our experiment setup, we scanned and scored the Tg(klf2a:H2b-EGFP; kdrl:mcherry) double transgenic embryos and Tg(klf2a:H2b-EGFP) embryos for EGFP intensity. The result is attached here:

      Author response image 1.

      42 endothelial nuclei from 7 embryos were scored as described in the Experimental Procedures of the manuscript. Two tailed t test were performed. P=0.4529

      Finally, the text and legend related to Figure 4 could be more explicit. What do the authors mean by a mosaic pattern of endothelial nuclear EGFP intensity, and how is that observation reflected in graph 4E? When I look at the graph, I understand that klf2a is decreased in C-D compared to A-B. Are some controls missing? Suppose the point is to show mosaicism of Klf2a levels upon ccm2 CRISPR. Don't you need embryos without ccm2 CRISPR to show that Klf2a levels in those backgrounds have average levels that vary within a defined range and that in the presence of ccm2 mosaicism, some cells have values significantly outside that range? Also, in 4A-D, what are the white arrowheads and arrows? The legend does not mention them.

      Response 8: We have revised our description of Figure 4 to better convey that mosaic expression of KLF2a is evidenced by the wide variability of klf2a reporter intensity in endothelial cells in ccm2 CRISPR embryos. A baseline control for the study of klf2a expression was added to Figure 4. The arrowheads and arrows in Figure 4A-D are explained in Figure 4 legends.

      Given the practical relevance of the findings to cardiovascular medicine, increasing the strength of the evidence would greatly enhance the value of this work.

      Recommendations for the authors:

      Reviewing Editor:

      Concerns about the labeling of figures and sample sizes should both be addressed, as detailed in the reviews, as this will be important to ensure the robustness of the claims.

      Reviewer #1 (Recommendations for the authors):

      Overall a strong research advance that provides rigorous genetic analysis and further drug testing in the zebrafish CCM model. There are some minor issues that, if addressed, would strengthen the work.

      Minor issues:

      (1) Figures in general are very poorly annotated and labeled. None of the images in Figures 1-3 show the reporter used to visualize vessels/CM, and the scale bars are not sized in the Figures or legends. Figure 1B is an experiment where the effects of a drug that increases heart rate are evaluated in mutants and controls, but the drug is not mentioned in the figure panel. Figure 1D shows the percentage of embryos with CVP dilation, but the graph and accompanying description does not define whether the percent is relative to the total embryos from the intercross or the percent of that category having the CVP dilation.

      Response 9: Changes were made in Figures and Figure legends. The transgenic reporter line Tg(fli1:EGFP) was annotated in Figures 1-3. Scale bars were sized in the Figures and Figure legends. The chemical used for Figure 1B was annotated in the Figure. The percentage of CVP dilation in the graph was explained in the Figure legend.

      (2) Figure 4 does not include baseline data in unmanipulated embryos scored at the same time to show the increase in Klf2 expression with mosaic ccm2 deletion. This is important as the result in E is interpreted as a lack of change in the increase.

      Response 10: A baseline control for the study of klf2a expression in Figure 4 was added.

      Reviewer #2 (Recommendations for the authors):

      SUGGESTIONS FOR EXPERIMENTS, DATA, OR ANALYSES

      (1) For maximum rigor, in the Figure 4 experiment, use adrb1 mutants and tnnt2a (silent heart) mutants (or verify that the adrb1 and tnnt2a morpholinos faithfully copy the phenotype of interest). See: Guidelines for morpholino use in zebrafish (PMID: 29049395; PMCID: PMC5648102).

      Response 11: See Response 5.

      (2) Increase sample sizes if appropriate.

      Response 12: In the revised version of the manuscript, we have increased the sample size, as described in the Figure 4 legend.

      (3) The imaging and fluorescence intensity analysis methods require more detail for reproducibility's sake. Please provide this information. See as a guideline: Guillermo MarquésThomas PengoMark A Sanders (2020) Science Forum: Imaging methods are vastly underreported in biomedical research eLife 9:e55133.

      Response 13: We added detailed procedures for the “Airyscan imaging and fluorescence intensity analysis” in the “Experimental Procedures”.

      (4) I suggest further clarifying how inhibition of B1AR prevents cavernoma formation. Given that lesion formation is suppressed in adrb1 mutants (which have slow blood flow) and 2,3-BDM treatment (which also slows blood flow) has a similar effect, the beneficial effects of propranolol and metoprolol might be due to the slowing of blood flow via B1AR targeting rather than reflecting that B1AR is a critical component of the genetic circuit for cavernoma formation. Indeed, in prior work by the same first author and collaborators (Elife 2021 May 20:10:e62155), the investigators observed reduced cavernoma formation in embryos devoid of cardiac contractility and thus lacking blood flow (tnnt2a morphants). Such a scenario does not take away the value of a pharmacological treatment. Still, it implies a different mechanism and allows potentially many other drugs with similar effects on blood flow to be effective.

      Discussing how B1AR activity is regulated and outlining future experiments would be helpful. Suggestions for the latter include testing the effect of normalizing blood flow in adrb1 mutants with a drug or providing exogenous B1AR in the myocardium or the endothelium to test the model further.

      Response 14: We are grateful for the reviewer’s suggestions and added the statement for future experiments.

      MINOR CORRECTIONS TO TEXT AND FIGURES

      (1) Figure 4E: Label the four genotypes explicitly, rather than A-D for the reader's ease.

      (2) Legend of Figure 4: "(F) EGFP intensity...". It should be (E).

      CITATIONS TO CORRECT

      (1) The citation for the Tg(kdrl:mcherry)is5 transgene needs to be corrected (reference 29 is from the Stainier lab). However, the "is" designation is for the Essner lab (https://zfin.org/action/feature/view/ZDB-ALT-110127-25)

      Response 15: Corrections were made as instructed.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewing Editor Comment:<br /> Please note that all three reviewers suggested this manuscript would best fit as a resource paper at eLife.

      Reviewer #1 (Public review):

      Summary:

      This impressive study presents a comprehensive scRNAseq atlas of the cranial region during neural induction, patterning, and morphogenesis. The authors collected a robust scRNAseq dataset covering six distinct developmental stages. The analysis focused on the neural tissue, resulting in a highly detailed temporal map of neural plate development. The findings demonstrate how different cell fates are organized in specific spatial patterns along the anterior-posterior and medial-lateral axes within the developing neural tissue. Additionally, the research utilized high-density single-cell RNA sequencing (scRNAseq) to reveal intricate spatial and temporal patterns independent of traditional spatial techniques.

      The investigation utilized diffusion component analysis to spatially order cells based on their positioning along the anterior-posterior axis, corresponding to the forebrain, midbrain, hindbrain, and medial-lateral axis. By cross-referencing with MGI expression data, the identification of cell types was validated, affirming the expression patterns of numerous known genes and implicating others as differentially expressed along these axes. These findings significantly advance our understanding of the spatially regulated genes in neural tissues during early developmental stages. The emphasis on transcription factors, cell surface, and secreted proteins provides valuable insights into the intricate gene regulatory networks underpinning neural tissue patterning. Analysis of a second scRNAseq dataset where Shh signaling was inhibited by culturing embryos in SAG identified known and previously unknown transcripts regulated by Shh, including the Wnt pathway.

      The data includes the neural plate and captures all major cell types in the head, including the mesoderm, endoderm, non-neural ectoderm, neural crest, notochord, and blood. With further analyses, this high-quality data promises to significantly advance our understanding of how these tissues develop in conjunction with the neural tissue, paving the way for future breakthroughs in developmental biology and genomics.

      Strengths:

      The data is well presented in the figures and thoroughly described in the text. The quality of the scRNAseq data and bioinformatic analysis is exceptional.

      Weaknesses:

      No weaknesses were identified by this reviewer.

      Reviewer #2 (Public review):

      Summary:

      Brooks et al. generate a gene expression atlas of the early embryonic cranial neural plate. They generate single-cell transcriptome data from early cranial neural plate cells at 6 consecutive stages between E7.5 to E9. Utilizing computational analysis they infer temporal gene expression dynamics and spatial gene expression patterns along the anterior-posterior and mediolateral axis of the neural plate. Subsequent comparison with known gene expression patterns revealed a good agreement with their inferred patterns, thus validating their approach. They then focus on Sonic Hedgehog (Shh) signalling, a key morphogen signal, whose activities partition the neural plate into distinct gene expression domains along the mediolateral axis. Single-cell transcriptome analysis of embryos in which the Shh pathway was pharmacologically activated throughout the neural plate revealed characteristic changes in gene expression along the mediolateral axis and the induction of distinct Shh-regulated gene expression programs in the developing fore-, mid-, and hindbrain.

      Strengths:

      This manuscript provides a comprehensive transcriptomic characterisation of the developing cranial neural plate, a part of the embryo that to my knowledge has not been extensively analysed by single-cell transcriptomic approaches. The single-cell sequencing data appears to be of high quality and will be a great resource for the wider scientific community. Moreover, the computational analysis is well executed and the validation of the sequencing data using published gene expression patterns is convincing. Taken together, this is a well-executed study that describes a relevant scientific resource for the wider scientific community.

      Weaknesses:

      Conceptually, the findings that gene expression patterns differ along the rostrocaudal, mediolateral, and temporal axes of the neural plate and that Shh signalling induces distinct target genes along the anterior-posterior axis of the nervous system are more expected than surprising. However, the strength of this manuscript is again the comprehensive characterization of the spatiotemporal gene expression patterns and how they change upon ectopic activation of the Shh pathway.

      Reviewer #3 (Public review):

      Summary:

      The authors performed a detailed single-cell analysis of the early embryonic cranial neural plate with unprecedented temporal resolution between embryonic days 7.5 and 8.75. They employed diffusion analysis to identify genes that correspond to different temporal and spatial locations within the embryo. Finally, they also examined the global response of cranial tissue to a Smoothened agonist.

      Strengths:

      Overall, this is an impressive resource, well-validated against sets of genes with known temporal and spatial patterns of expression. It will be of great value to investigators examining the early stages of neural plate patterning, neural progenitor diversity, and the roles of signaling molecules and gene regulatory networks controlling the regionalization and diversification of the neural plate.

      Weaknesses:

      The manuscript should be considered a resource. Experimental manipulation is limited to the analysis of neural plate cells that were cultured in vitro for 12 hours with SAG. Besides the identification of a significant set of previously unreported genes that are differentially expressed in the cranial neural plate, there is little new biological insight emerging from this study. Some additional analyses might help to highlight novel hypotheses arising from this remarkable resource.

      We thank all three reviewers for their thoughtful and constructive public reviews and believe they nicely capture the contributions of our study. We agree that this article represents a valuable resource for the community and agree with its designation as a Tools and Resources article.

      We also thank the reviewers for their useful suggestions for improving the manuscript. In addition to addressing most of their comments, described below, we note that we have changed midbrain-hindbrain boundary (MHB) to rhombomere 1 (r1) throughout the paper and in Tables S4, S7, S10, and S11, as this designation is more closely aligned with the literature on this region. In addition, we added the anterior-posterior and mediolateral cluster identities from our wild-type analysis for the genes that were differentially expressed in SAG-treated embryos in Table S11. Lastly, we have added a new figure (Figure 5—figure supplement 2), as suggested by Reviewer 2, in which we compare our results with the published expression of genes in neural progenitor domains along the dorsal-ventral axis of the spinal cord.

      Reviewer #1 (Recommendations for the authors):

      I have a few small suggestions for improving the presentation of the data.

      (1) It would be helpful to show illustrations and embryo images of all the stages utilized in the analysis in Figures 1A and B.

      (2) It was difficult to distinguish all the different colors in Figures 3B and 4B. Could you label, as in Figure 4, supplements 1D, F?

      (3) I was confused by the position of the color code key for Figure 7D-J, thinking it belonged to panels B and C. Could you put it under the figure/heatmap key so that it is clearly linked to panels D-J?

      Thank you for these suggestions. We have incorporated the third suggestion to improve readability, but were not able to make the first two changes due to space limitations.

      Reviewer #2 (Recommendations for the authors):

      I only have a couple of minor additional suggestions/questions for the authors:

      (1) The authors state that nearly half of the transcripts they found as differentially regulated in SAG-treated embryos were also characterized as spatially regulated in the wild-type embryos. It would be great if the authors could provide more detail here. How many of the transcripts that are differentially regulated along the mediolateral axis of the wild-type are characterized as differentially regulated in the SAG-treated embryos? How does this further break down into where these genes are expressed along the mediolateral and the anterior-posterior axes? I am aware that the authors answer some of these questions already by providing examples, but a more systematic characterisation would be appreciated here.

      We have updated Table S11 to include the anterior-posterior and mediolateral cluster identities of differentially expressed genes in SAG-treated embryos, where applicable. In addition, we have added more discussion of the genes from our SAG analysis that were also found to be spatially patterned in wild-type embryos to the fourth paragraph of the last results section.

      (2) Related to the previous question, the authors nicely demonstrate that SAG treatment of embryos causes many transcriptional changes, including the expression/repression of several transcription factors well-known to mediate spatial patterning, raising the question of which of these effects are directly due to gene regulation by the Shh pathway and which effects are secondary consequences of transcriptional changes of other transcription factors. Similarly, the authors' results also suggest that some genes are only induced in specific parts along the neuraxis, raising the question of why. The authors could attempt some type of regulon-interference approaches to identify further candidates that may mediate these effects.

      This is an excellent suggestion for a future extension of this work, as we agree that validation of the predicted SHH targets, including which targets are direct, indirect, or region-specific, would be required to evaluate the predictions of this scRNA-seq analysis.

      (3) The authors report that they observed 'a previously unreported inhibition of Scube2' upon SAG treatment of the embryos. At least in the spinal cord Scube2 is well-known to be expressed at a distance from the source of Shh secretion (e.g. Kawakami et al. Curr. Biol. 2005), thus the direct or indirect repression by Shh signalling is strongly expected. Moreover, a recent preprint (Collins et al. bioRxiv, https://doi.org/10.1101/469239 ) suggests that the interaction between Shh and Scube2 can mediate the scale-invariance of Shh patterning. Of note, the authors of this preprint also state that 'upregulation of Shh represses scube2 expression while Shh downregulation increases scube2 expression thus establishing a negative feedback loop.'

      Thank you for this suggestion. We have added these references.

      (4) The authors partition genes based on different diffusion components as being differentially expressed along the mediolateral axis. However, starting from ~e8.5, neural progenitors in the neural tube can be partitioned based on the expression of well-characterised combinatorial sets of transcription factors into molecularly defined progenitor domains that subsequently give rise to functionally distinct types of neurons. How much of this patterning process can the authors capture with their diffusion component analysis and does their data also allow them to capture these finer-grained differences in gene expression along the mediolateral and prospective dorsal-ventral axis of the neural tube that are known to exist?

      This is a very interesting point. We have added a new figure showing UMAPs of the E8.5-9.0 cranial neural plate for a subset of 29 genes (described in Delile et al., 2019) that define distinct neural progenitor domains along the dorsal-ventral axis of the spinal cord (Figure 5—figure supplement 2). We observed that 18 of 20 genes that were detected in the midbrain/r1 region in our dataset were expressed in broad domains along the mediolateral axis of the cranial neural plate that were roughly consistent with their expression domains along the dorsal-ventral axis of the spinal cord. Of these 18 genes, 14 were patterned along both anterior-posterior and mediolateral axes, 2 were patterned only along the mediolateral axis, and 2 were patterned only along the anterior-posterior axis. These results suggest a general correspondence between mediolateral patterning in the cranial neural plate and dorsal-ventral patterning in the spinal cord. However, less refinement of these domains along the mediolateral axis was observed in the cranial neural plate, possibly because the relatively early, pre-closure stages captured by our dataset may be before the establishment of secondary feedback systems that lead to fine-scale patterning of mutually exclusive neural precursor domains. These results are described in the last paragraph of the results section titled “An integrated framework for analyzing cell identity in multiscale space.”

      (5) The authors state that they will not only make the raw sequencing data but also the processed intermediate data files available. This is greatly appreciated as it strongly facilitates the re-use of the data. However, it would be also appreciated if the authors made the computational code publicly available that was used to analyze the data and generate the figure panels in the manuscript.

      We have deposited the processed h5ad files in the GEO database, accession number GSE273804. Additionally, we have made interactive python notebooks available with the code used to analyze gene expression and generate the figures in this study, as well as code used to automatically generate customizable links to gene expression images in the Mouse Genome Informatics Gene Expression database, on our lab GitHub page (https://github.com/ZallenLab). We have updated the Data availability section to reflect these changes.

      Reviewer #3 (Recommendations for the authors):

      (1) Considering that individual progenitor domains in the developing neural tube are typically sharply delineated with few cells exhibiting mixed identities, it is interesting that clustering of single-cell data results in a largely continuous “cloud” of cells. Is this because the early neural plate cells have not yet crystallized their identity, or would clustering based on a smaller set of genes that exhibit high variance across only neural plate cells result in improved granularity, allowing for better characterization and quantification of distinct progenitor subtypes?

      Thank you for raising this interesting point. The apparent continuity of gene expression in the cranial neural plate could reflect a gene signature shared by cranial neural plate cells and that cells may not be extensively regionalized into unique populations at these early stages. We now discuss these possibilities in the third paragraph of the discussion.

      (2) Can the authors clarify how neural plate cells were identified and how they were distinguished from the anterior epiblast?

      Cell typing was performed by supervised clustering based on known markers of fate. Cranial neural plate cells were identified by their expression of pan-neural factors (Sox2 and Sox3), early or late neural plate markers (Cdh1 or Cdh2), and the lack of markers associated with non-neural ectodermal cell fates (Grhl2, Krt18, Tfap2a) or other cell types (Ets1, T, Tbx6). Full gene sets used to identify all cell types in our analysis are provided in Supplementary Table 13.

      (3) Did the study identify cells with cranial placode identity? Cranial placodes emerge during the same period, and it would be useful to highlight them in Figure 1.

      Thank you for highlighting this point. Examination of the early placode markers Six1 and Eya1 indicates that cranial placode cells are a subset of the cells in PhenoGraph cluster 17 in our full dataset Figure 1—figure supplement 1). We now mention this along with other cell types of interest in the last paragraph of the discussion.

      (4) It could be interesting to provide more information about the novel genes identified as differentially expressed along the AP or mediolateral axes. Do they belong to gene families that were not previously implicated in neural patterning, or do they point to novel biological mechanisms controlling neural patterning?

      Diverse gene families are represented by the genes that are patterned along the anterior-posterior and mediolateral axes of the cranial neural plate at these stages, likely due to the large number of genes that are spatially patterned in this tissue. Further investigation of the biological mechanisms suggested by these patterns is an important direction for future work, both in terms of molecularly classifying the genes identified as well as directly investigating their roles in neural patterning using genetic analysis.

      (5) It would be helpful to discuss how the data presented here compare to other relevant single-cell analyses, such as PMC10901739. This would help to highlight aspects that are unique to this study.

      We have added this reference as well as an earlier study from these authors and we discuss how our study complements this work in the introduction.

      (6) The inclusion of single-cell data from control embryos that were cultured for 12 hours is of great interest. The authors should identify the set of genes that are deregulated in cultured cells and, taking advantage of their detailed temporal series, examine whether the maturation of cultured embryos progresses normally or whether there are genes that fail to mature correctly in vitro.

      We agree that an analysis of the impact of ex vivo culture on gene expression would be useful. However, the large difference in the number of cells in our wild-type and cultured embryo datasets, as well as the lack of time-course data for the cultured embryos, could make a comparison between our current cultured and non-cultured embryo datasets difficult to interpret.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      The authors studied how hippocampal connectivity gradients across the lifespan, and how these relate to memory function and neurotransmitter distributions. They observed older age with less distinct transitions and observed an association between gradient de-differentiation and cognitive decline.

      This is overall an innovative and interesting study to assess gradient alterations across the lifespan and its associations to cognition.

      The paper is well-written, and the methods appear sound and thoughtful. There are several strengths, including the inclusion of two independent cohorts, the use of gradient mapping and alignment techniques, and an overall sound statistical and analysis framework. There are several areas for potential improvements in the paper, and these are listed below:

      We thank the Reviewer for their positive assessment and summary of our work. We address each of the Reviewer’s comments below, and outline the revisions we have made to the manuscript based on the Reviewer’s suggestions.

      (1) The reported D1 associations appear a bit post-hoc in the current work and I was unclear why the authors specifically focussed on dopamine here, as other transmitter systems are similar present at the level of the hippocampus and implicated in aging.

      Other neurotransmitter systems may indeed be relevant in the context of hippocampal function in aging. In this study, however, we included a specific research question about the DA D1 receptor (D1DR) based on previous research 1) emphasizing the role of DA neuromodulation in maintaining functional network segregation in aging to support cognition (Pedersen et al., 2023), 2) reporting heterogeneous distribution of DA markers across the hippocampus, supporting efficient modulation of distinct behaviors (Dubovyk & ManahanVaughan, 2019; Edelmann & Lessmann, 2018; Gasbarri et al., 1994; Kempadoo et al., 2016), and 3) demonstrating the spatial distribution of D1DRs as varying across neocortex along a unimodal-transmodal gradient (Pedersen et al., 2024). To which degree this variation might be reflected in cortico-hippocampal connectivity, however, remained to be investigated. As such, one of the study’s specific aims was to evaluate the spatial distribution of D1DRs as a molecular correlate of the hippocampus’ functional organization. Importantly, we were interested in mapping associations between individual differences in the organization of connectivity and D1DRs. This was uniquely enabled by utilizing the DyNAMiC sample, as it includes structural and functional MRI data in combination with D1DR PET in the same individuals across the adult lifespan (n=180). However, after observing significant spatial correspondence between functional organization and D1DR expressed by the second hippocampal gradient (G2), we did indeed perform complimentary analyses with group-averaged data of additional dopamine markers (D2DR from a subsample of our participants, as well as DAT and FDOPA from open sources) to test the generalizability of the original finding. Taken together, the original analyses based on subject-level data and complimentary group-level analyses provided support for the interpretation of G2 as a dopaminergic mode.

      We have updated the manuscript to clarify the focus on the D1 receptor and the contribution of including additional DA markers.

      Updated paragraph in the Introduction, pages 5-6:

      “Dopamine (DA) is one of the most important modulators of hippocampus-dependent function(47,48), and influences the brain’s functional architecture through enhancing specificity of neuronal signaling(49). Consistently, there is a DA-dependent aspect of maintained functional network segregation in aging which supports cognition(50). Animal models suggest heterogeneous patterns of DA innervation(51,52) and postsynaptic DA receptors(53), across both transverse and longitudinal hippocampal axes, likely allowing for separation between DA modulation of distinct hippocampus-dependent behaviors(47). Moreover, the human hippocampus has been linked to distinct DA circuits on the basis of long-axis variation in functional connectivity with midbrain and striatal regions(54,55). Taken together with recent findings revealing a unimodal-transmodal organization of the most abundantly expressed DA receptor subtype, D1 (D1DR), across cortex(56), we tested the hypothesis that the organization of hippocampal-neocortical connectivity partly reflects the underlying distribution of hippocampal DA receptors, predicting predominant spatial correspondence for any hippocampal gradient conveying a unimodal-transmodal pattern across cortex.”

      Updated sections in the Results, page 13-14:

      “Our next aim was to investigate to which extent the distribution of hippocampal DA D1 receptors (D1DRs), measured by [<sup>11</sup>C]SCH23390 PET in the DyNAMiC(58) sample, may serve as a molecular correlate of the hippocampus’ functional organization.”

      “Complimentary analyses were then conducted to further evaluate G2 as a dopaminergic hippocampal mode by utilizing additional DA markers at group-level.”

      Moreover, the authors may be aware that multiple PET tracers are somewhat challenged in the mesiotemporal region. Is this the case for the D1 receptor as well? The hippocampus is a small and complex structure, and PET more of a low res technique so one would want to highlight and discuss the limitations of the correlations with PET maps here and/or evaluate whether the analysis adds necessary findings to the study.

      We thank the Reviewer for raising this point. The lower resolution of PET is indeed a relevant aspect to consider when quantifying D1DR availability in the hippocampus, even though previous research indicate high test-retest reliability of [<sup>11</sup>C]SCH23390 PET measurement in this region (Kaller et al., 2017). We have now elaborated on PET limitations in the Discussion of the revised manuscript.

      In our study, we made efforts to reduce potential partial volume effects (PVE) by correcting our PET data, and tested spatial associations between our functional gradients and D1DR maps using trend-surface modelling (TSM), rather than through voxel-wise comparisons. This allowed us to evaluate the spatial correspondence between functional connectivity and D1DRs at a level of spatial trends, estimated using TSM models computed at increasing levels of complexity. The results showed consistent spatial overlap between G2 and D1DRs across these models, that is, across spatial trends described at coarser-to-finer scales. Furthermore, this was replicated across several DA markers with PET and SPECT data from independent samples.

      Taken together, we agree with the Reviewer that the spatial correspondence observed between G2 and hippocampal D1DRs should be interpreted in the context of resolution-related limitations inherent to PET imaging. However, we strongly believe that our DA analyses offer valuable insight to the molecular underpinnings of hippocampal functional organization.

      Updated paragraph in the Discussion, pages 25-26:

      “We discovered that G2, specifically, manifested organizational principles shared among function, behavior, and neuromodulation. Meta-analytical decoding reproduced a unimodalassociative axis across G2 (Figure 3B), and analyses in relation to the distribution of D1DRs – which vary across cortex along a unimodal-transmodal axis(76,77) – demonstrated topographic correspondence both at the level of individual differences and across the group. It should, however, be acknowledged that PET imaging in the hippocampus is associated with resolutionrelated limitations, although previous research indicate high test-retest reliability of [<sup>11</sup>C]SCH23390 PET to quantify D1DR availability in this region(78). As such, mapping the distribution of hippocampal D1DRs at a fine spatial scale remains challenging, and replication of our results in terms of overlap with G2 is needed in independent samples. Here, we evaluated the observed spatial overlap between G2 topography and D1DRs across multiple TSM model orders, showing correspondence between modalities from simple to more complex parameterizations of their spatial properties. Topographic correspondence was additionally observed between G2 and other DA markers from independent datasets (Figure 3B), suggesting that G2 may constitute a mode reflecting a dopaminergic phenotype, which contributes to the currently limited understanding of its biological underpinnings.”

      From my (perhaps somewhat biased) perspective, it might be valuable to instead or in addition look at measures of hippocampal microstructure and how these relate to the functional aging effects. This could be done, if available, using data from the same subjects (eg based on quantitative MRI contrasts and/or structural MRI) and/or using contextualization findings as implemented in eg hippomaps.readthedocs.io

      We thank the Reviewer for this suggestion. We performed additional analyses investigating the spatial overlap between our connectivity gradients and estimates of hippocampal microstructure, computed as the ratio of T1- over T2-weighted (T1w/T2w) images (Glasser & Von Essen, 2011; vos de Wael et al., 2018). Analyses of spatial correspondence then followed the TSM-based method used to test the spatial overlap between functional connectivity gradients and D1DR distribution. Applying TSM to the T1w/T2w image computed for each participant yielded subject-level model parameters describing microstructure topography, which were then entered as predictors of connectivity topography in multivariate GLMs (separate models for each gradient and hemisphere, 6 models in total).

      Analyses revealed that microstructure of the right hippocampus significantly predicted gradient topography of right-hemisphere G1 (F = 1.325, p \= 0.034), while no other links between connectivity gradients and microstructure emerged as significant (F 0.930-1.184, ps 0.7060.079).

      These results, suggesting an association along the anteroposterior axis, deviate from previous findings linking hippocampal microstructure to G3-like, medial-lateral, connectivity organization (vos de Wael et al., 2018). As we believe that comprehensive analyses of our gradients in relation to microstructure across the lifespan would be best addressed in future work, we have not included these analyses of microstructure in the revised manuscript.

      (2) Can the authors clarify why they did not replicate based on cohorts that are more widely used in the community and open access, such as CamCAN and/or HCP-Aging? It might connect their results with other studies if an attempt was made to also show that findings persist in either of these repositories.

      We agree with the Reviewer that replication in samples such as CamCAN and/or HCP-Aging would provide valuable opportunities to connect our findings with those of other studies using those datasets. Here, we included the Betula dataset (Nilsson et al., 2004) as our replication sample, as it was immediately available to us, included a large sample of adults in a comparable age, and a word recall episodic memory task closely aligned with the one included in DyNAMiC. Importantly, leveraging the Betula dataset as our replication sample allows us to link our findings to a wide range of previous studies central to the understanding of neurocognitive aging in general, and hippocampal aging in particular (Nyberg, 2017; Nyberg et al., 2020). Betula is a large longitudinal project that has been tracking individuals since 1988, and is part of the National E-infrastructure for Aging Research (NEAR: www.near-aging.se), through which data from several Swedish studies are made available to both national and international researchers. While we acknowledge the value of extending replication efforts to datasets like CamCAN and HCP-Aging, we emphasize the significant contribution of having replicated our connectivity gradients in the Betula dataset.

      (3) The authors applied TSM and related these parameters to topographic changes in the gradients. I was wondering whether and how such an approach controls for autocorrelation present in both the PET map and gradients. Could the authors clarify?

      The Reviewer raises an important topic in spatial autocorrelation. The TSM approach used to parameterize the topography of the functional gradients and D1DR distribution, and to test the spatial correspondence between modalities, did not include any specific method to control for autocorrelation. Here, we highlight two aspects of our study in relation to this point. First, we demonstrated in the Supplementary information (S. Figure 4) that autocorrelation induced by spatial smoothing likely has limited effects on overall gradient topography and the ability of TSM parameters to capture meaningful inter-individual differences in terms of age. Second, in the case of spatial overlap effects being significantly impacted by autocorrelation, we would expect the association between right-hemisphere G2 and D1DR topography to similarly emerge for G2 in the left hemisphere. The absence of such an association may speak to a limited effect of spatial autocorrelation.

      (4) The TSM approach quantifies the gradients in terms of x/y/z direction in a cartesian coordinate system. Wouldn't a shape intrinsic coordinate system in the hippocampus also be interesting, and perhaps even be more efficient to look at here (see eg DeKraker 2022 eLife or Paquola et al 2020 eLife)?

      This is a very relevant question and we appreciate the Reviewer’s suggestion. We recognize that there may be several benefits associated with adopting a shape-intrinsic coordinate system when characterizing effects in the hippocampus, given its curved/folded anatomy. Approaches like the ones adopted in DeKraker et al., 2022 and Paquola et al., 2020, utilizes geodesic coordinate frameworks to represent the hippocampus in surface space, enabling mapping of connectivity onto the hippocampal surface while respecting its inherent curvature and topology. We anticipate that quantifying gradients within such a framework would especially benefit identification of connectivity change across the hippocampal surface relative to reference points such as subfield boundaries, while minimizing effects of interindividual differences in hippocampal shape and folding. In our study, hippocampal gradients and their associated cortical patterns were computed in volumetric space, with TSM subsequently used to parameterize the change in connectivity along these gradients. This indeed yields a description of connectivity change within a coordinate system less specific to hippocampal anatomy, but may favor generalizability and integration with previous gradient findings within and beyond the hippocampus (e.g., Przeździk et al., 2019; Tian et al., 2020; Katsumi et al., 2023; Navarro-Schröder et al., 2015), as well as connections with broader neuroimaging frameworks through techniques such as meta-analytical decoding. In our view, the different coordinate frameworks offer complimentary insight to hippocampal organization, and while we have opted to not undertake novel analyses to explore our gradients within a geodesic coordinate system for the purposes of this paper, we recognize the importance of such evaluation of our gradients in future analyses. We have made updates to the Discussion in the revised manuscript on this topic (pages 23-24):

      “Greater anatomical specificity, with more precise characterization of connectivity in relation to subfield boundaries while minimizing effects of inter-individual differences in hippocampal shape and folding, might be achieved by adopting techniques implementing a geodesic coordinate system to represent effects within the hippocampus(68,69).”

      Reviewer #2 (Public Review):

      Summary:

      This paper derives the first three functional gradients in the left and right hippocampus across two datasets. These gradient maps are then compared to dopamine receptor maps obtained with PET, associated with age, and linked to memory. Results reveal links between dopamine maps and gradient 2, age with gradients 1 and 2, and memory performance.

      Strengths:

      This paper investigates how hippocampal gradients relate to aging, memory, and dopamine receptors, which are interesting and important questions. A strength of the paper is that some of the findings were replicated in a separate sample.

      Weaknesses:

      The paper would benefit from added clarification on the number of models/comparisons for each test. Furthermore, it would be helpful to clarify whether or not multiple comparison correction was performed and - if so - what type or - if not - to provide a justification. The manuscript would furthermore benefit from code sharing and clarifying which results did/did not replicate.

      We thank the Reviewer for their positive assessment and suggestions regarding further clarifications. We have addressed the Reviewer’s comments in a point-by-point manner under the “Recommendations for the authors” section.

      Reviewer #3 (Public Review):

      Summary:

      In this study, the authors analyzed the complex functional organization of the hippocampus using two separate adult lifespan datasets. They investigated how individual variations in the detailed connectivity patterns within the hippocampus relate to behavioral and molecular traits. The findings confirm three overlapping hippocampal gradients and reveal that each is linked to established functional patterns in the cortex, the arrangement of dopamine receptors within the hippocampus, and differences in memory abilities among individuals. By employing multivariate data analysis techniques, they identified older adults who display a hippocampal gradient pattern resembling that of younger individuals and exhibit better memory performance compared to their age-matched peers. This underscores the behavioral importance of maintaining a specific functional organization within the hippocampus as people age.

      Strengths:

      The evidence supporting the conclusions is overall compelling, based on a unique dataset, rich set of carefully unpacked results, and an in-depth data analysis. Possible confounds are carefully considered and ruled out.

      Weaknesses:

      No major weaknesses. The transparency of the statistical analyses could be improved by explicitly (1) stating what tests and corrections (if any) were performed, and (2) justifying the elected statistical approaches. Further, some of the findings related to the DA markers are borderline statistically significant and therefore perhaps less compelling but they line up nicely with results obtained using experimental animals and I expect the small effect sizes to be largely related to the quality and specificity of the PET data rather than the derived functional connectivity gradients.

      We thank the Reviewer for the thoughtful summary and positive assessment of our work. To increase transparency of the statistical analyses, we have in the revised manuscript added information regarding statistical tests and corrections for multiple comparisons. In the Results, p-values were reported at an uncorrected statistical threshold, and we have in the revised manuscript included the corresponding p-values adjusted for multiple comparisons using the Benjamini-Hochberg method to control the false discovery rate (FDR). Finally, in the revised manuscript, we have now elaborated on the potential limitations of our PET analyses and we include the updated paragraph below.

      Addition made to the Results section, page 13:

      “Individual maps of D1DR binding potential (BP) were also submitted to TSM, yielding a set of spatial model parameters describing the topographic characteristics of hippocampal D1DR distribution for each participant. D1DR parameters were subsequently used as predictors of gradient parameters in one multivariate GLM per gradient (in total 6 GLMs, controlled for age, sex, and mean FD). Results are reported with p-values at an uncorrected statistical threshold and p-values after adjustment for multiple comparisons using the Benjamini-Hochberg method to control the false discovery rate (FDR).”

      Addition made to the Results section, page 15:

      “Effects of age on gradient topography were assessed using multivariate GLMs including age as the predictor and gradient TSM parameters as dependent variables (controlling for sex and mean frame-wise displacement; FD). One model was fitted per gradient and hemisphere, each model including all TSM parameters belonging to a gradient (in total, 6 GLMs).”

      Addition made to the Results section, page 17:

      “Models were assessed separately for left and right hemispheres, across the full sample and within age groups, yielding eight hierarchical models in total. Results are reported with p-values at an uncorrected statistical threshold and p-values after FDR adjustment.”

      Updated paragraph in the Discussion, pages 25-26:

      “We discovered that G2, specifically, manifested organizational principles shared among function, behavior, and neuromodulation. Meta-analytical decoding reproduced a unimodalassociative axis across G2 (Figure 3B), and analyses in relation to the distribution of D1DRs – which vary across cortex along a unimodal-transmodal axis(76,77) – demonstrated topographic correspondence both at the level of individual differences and across the group. It should, however, be acknowledged that PET imaging in the hippocampus is associated with resolutionrelated limitations, although previous research indicate high test-retest reliability of [<sup>11</sup>C]SCH23390 PET to quantify D1DR availability in this region(78). As such, mapping the distribution of hippocampal D1DRs at a fine spatial scale remains challenging, and replication of our results in terms of overlap with G2 is needed in independent samples. Here, we evaluated the observed spatial overlap between G2 topography and D1DRs across multiple TSM model orders, showing correspondence between modalities from simple to more complex parameterizations of their spatial properties. Topographic correspondence was additionally observed between G2 and other DA markers from independent datasets (Figure 3B), suggesting that G2 may constitute a mode reflecting a dopaminergic phenotype, which contributes to the currently limited understanding of its biological underpinnings.”

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Please see the comments in the public review.

      We thank the Reviewer for their comments and recommendations, and have addressed them in the “Public review” section.

      Reviewer #2 (Recommendations For The Authors):

      (1) All statistical analyses are based on linear regressions using trend surface modeling (TSM) parameters that parameterize gradients at the subject level. These models resulted in 9 parameters for gradient 1 and 12 parameters each for gradients 2 and 3. The text states that 'Effects of age on gradient topography was assessed using multivariate GLMs including age as the predictor and gradient TSM parameters as dependent variables (controlling for sex and mean frame-wise displacement; FD)'. Please clarify whether these GLMs were fitted separately for each TSM parameter (i.e., 9+12+12=33 models for both left and right = 66 total models) or on the overall model?

      We appreciate the Reviewer’s request for clarification on this matter. These GLMs were fitted on the overall TSM model, that is, through one GLM per gradient (3) and hemisphere (2), each one including all TSM parameters belonging to a gradient (in total, 6 GLMs).

      In the revised manuscript, we have added more details to the Results section, page 15: “Effects of age on gradient topography were assessed using multivariate GLMs including age as the predictor and gradient TSM parameters as dependent variables (controlling for sex and mean frame-wise displacement; FD). One model was fitted per gradient and hemisphere, each model including all TSM parameters belonging to a gradient (in total, 6 GLMs).”

      (2) Similarly, for memory it appears that multiple models were performed (left and right, young, middle-aged, old, whole groups). Please clarify whether and how multiple comparison correction was performed in this case.

      In the revised manuscript, we have now specified the number of analyses conducted in relation to memory performance. We have also clarified that p-values were reported at an uncorrected statistical threshold, and we have in the revised manuscript included the corresponding p-values adjusted for multiple comparisons using the Benjamini-Hochberg method to control the FDR.

      Updated section in the Results, page 17:

      “Models were assessed separately for left and right hemispheres, across the full sample and within age groups, yielding eight hierarchical models in total. Results are reported with p-values at an uncorrected statistical threshold and p-values after FDR adjustment.”

      (3) Although I applaud the authors for their replication efforts, the results do not appear to replicate well. For example, memory was linked to gradient 2 in the whole group but to gradient 1 in the young group. Furthermore, dopamine was linked to gradient 2 in the right but not the left hemisphere. Although the overall group-level gradients were very stable between the two datasets, it is not clear whether the age findings replicated and the memory subgroup findings only replicated at trend level for memory and only partially replicated at the TSM parameter level.

      We thank the Reviewer for highlighting the inclusion of a replication dataset as a strength of our study, and we appreciate the recommendation to clarify to which extent results replicated. We provide a response to the Reviewer’s points below, and specify the revisions made to the manuscript in relation to this topic.

      The main aim of our study was to characterize the topographic organization of functional hippocampal-neocortical connectivity within the hippocampus across the adult lifespan, as previous studies have limited their focus to younger adults. Given the lack of previous studies for comparison, together with our identification of a novel secondary long-axis connectivity gradient (G2) taking precedence over the previously established medial-lateral G3, we included the Betula sample (Nilsson et al., 2004) for the purpose of replication. There was a high level of consistency between our main dataset and our replication dataset, with gradients 1-3 in left and right hemispheres identified in both samples.

      Further use of the replication dataset, beyond the identification of the connectivity gradients, was originally not planned. As such, not all subsequent analyses in the main dataset were conducted in the replication dataset. However, we found it critical to evaluate the observation that older individuals who maintained a youth-like gradient topography also exhibited higher levels of memory performance in an independent sample. This was possible given that the replication dataset included a comparable number of participants in similar ages and a word recall episodic memory task corresponding well to the one used in DyNAMiC. Overall, we conclude that these analyses replicated well across samples. Firstly, topography of lefthemisphere G1 informed the classification of older adults into youth-like and aged subgroups in both samples. Furthermore, in both samples, we observed that the older subgroups identified based on G1 topography also exhibited the youth-like vs. aged pattern in G2 topography. This pattern was, however, evident also in G3 only in the main sample, possibly suggesting a limited contribution of G3 topography in determining overall functional profiles in older age. In terms of the behavioral relevance of maintaining youth-like gradient topography in older age, we observed effects on word recall performance in both samples; although the Reviewer correctly points out that, the difference between subgroups was significant at trend-level (p = 0.058) in the replication dataset. While this indeed underscores the importance of replication efforts in additional samples, we argue that the pattern observed in our replication dataset is overall consistent with, and conveys effects in the expected direction based on, the original observations in our main dataset.

      In revising the manuscript, we have performed additional analyses for replication purposes in terms of memory. Originally, we observed a significant association between G2 topography and episodic memory across the main sample. However, this effect did not remain significant after FDR adjustment for multiple comparisons. To evaluate this association further, we conducted a corresponding hierarchical multiple regression analysis in the replication dataset, which supported a role of G2 in memory (Adj. R<sup>2</sup> = 0.368, ΔR<sup>2</sup> = 0.081, F= 1.992, p = 0.028). Together, these analyses suggest that inter-individual differences in episodic memory performance may in part be explained by the spatial characteristics of G2 across the adult lifespan, although increased statistical power in relation to the large number of TSM parameters included in the hierarchical regression models may be needed to explore this association in smaller, age-stratified, groups. Relatedly, it is worth mentioning that higher levels of memory performance in older age were linked to the maintenance of youth-like G2 topography in both our main and replication datasets.

      In parallel, topographic parameters of G1 predicted memory performance in the younger adults, which successfully replicates TSM-based results previously reported in Przeździk et al., 2019. Although similar associations were not evident within the other age groups, a link between G1 topography and memory was demonstrated in older age based on a) the identification of individuals maintaining a youth-like G1 profile and higher levels of memory, within which b) memory performance was, as in young adults, significantly predicted by G1 topography.

      The spatial correspondence between G2 topography and distribution of hippocampal D1DRs was lateralized to the right, and as the Reviewer points out, as such did not replicate across hemispheres. To which extent replication across hemispheres should be expected in this case is, however, difficult to determine. Lateralization and/or hemispheric asymmetry is commonly observed in numerous hippocampal features, from the molecular level to its functional involvement in behavior (Nematis et al., 2023; Persson & Söderlund, 2015), including various dopaminergic markers tested in the animal literature (Afonso et al., 1993; Sadeghi et al., 2017). Yet, potential differences between hemispheres in D1DR availability and the spatial distribution of receptors along hippocampal axes remain less studied in humans. More data is therefore needed to determine the nature of this right-hemisphere lateralization.

      In sum, we argue that our results show a good level of replication across independent datasets and across analyses in our main dataset. Whereas this study did not attempt replication of all analyses conducted in the main dataset, it has through replication across independent samples provided support for its main findings – the organization of hippocampal-neocortical connectivity along three main hippocampal gradients across the adult lifespan, and the gradient topography-based identification of older individuals maintaining a youth-like hippocampal organization in older age.

      The revised manuscript includes edits made to incorporate the new analyses and clarifications of observations in relation to memory.

      In the Results, page 17:

      “Observing that the association between G2 and memory did not remain significant after FDR adjustment, we performed the same analysis in our replication dataset, which also included episodic memory testing. Consistent with the observation in our main dataset, G2 significantly predicted memory performance (Adj. R<sup>2</sup> = 0.368, ΔR<sup>2</sup> = 0.081, F= 1.992, p = 0.028) over and above covariates and topography of G1. Here, the analysis also showed that G1 topography predicted performance across the sample (Adj. R<sup>2</sup> = 0.325, ΔR<sup>2</sup> = 0.112, F= 3.431, p < 0.001).”

      In the Discussion, page 26:

      “Results linked both G1 and G2 to episodic memory, suggesting complimentary contributions of these two overlapping long-axis modes. Considered together, analyses in the main and replication datasets indicated a role of G2 topography in memory across the adult lifespan, independent of age. A similar association with G1 was only evident across the entire sample in the replication dataset, whereas results in the main sample seemed to emphasize a role of youthlike G1 topography in memory performance. In line with previous research, memory was successfully predicted by G1 topography in young adults(30), and similarly predicted by G1 in older adults exhibiting a youth-like functional profile.”

      (4) Please share the data and code and add a description of data and code availability in the manuscript.

      We have now made our code available, and added a statement on data and code availability in the revised manuscript.

      On page 37: “Data from the DyNAMiC study are not publicly available. Access to the original data may be shared upon request from the Principal investigator, Dr. Alireza Salami. The Matlab, R, and FSL codes used for analyses included in this study are openly available at https://github.com/kristinnordin/hcgradients. Computation of gradients was done using the freely available toolbox ConGrads: https://github.com/koenhaak/congrads.”

      Reviewer #3 (Recommendations For The Authors):

      Please see the comments in the public review.

      We thank the Reviewer for their comments and recommendations, and have addressed them in the “Public review” section.

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    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this study, Yue et al. re-processed publicly available DNA methylation data (published in 2012 and 2017 from the Meissner lab) from pre- and post-implantation mouse embryos. Against the global wave of genome-wide reduction of DNA methylation occurring during pre-implantation development, they detected a slight increase (~1% on average) of DNA methylation at gene promoter regions during the transition from 8-cell to blastocyst stage. They claim that many such promoters are located in the X chromosome. Subsequently, they knocked down Dnmt3b (presumably because of its upregulation during the transition from the 8-cell to blastocyst stage) and detected the aberrant patterning of H3K27me3 in the mutant female embryos. Based on this observation, they claim that imprinted X-chromosome inactivation is impaired in the Dnmt3b-Kd pre-implantation embryos. Finally, they propose a model where such an increase of DNA methylation together with H3K27me3 regulates imprinted X-chromosome inactivation in the pre-implantation embryos. While their observation is of potential interest, the current version of the work fails to provide enough evidence to support their conclusions. Below are suggestions and comments on the manuscript.

      Major issues:

      (1) Sex of the embryos of the genome-wide bisulfite-sequencing data

      The authors re-analyzed publicly available genome-wide DNA methylation data from the Meissner lab published in 2012 and 2017. The former used reduced representation bisulfite sequencing (RRBS) and the latter used whole-genome bisulfite sequencing (WGBS). Based mainly on the RRBS data, Yue et al. detected de novo DNA methylated promoters during the transition from 8-cell to blastocyst against the global wave of genome-wide DNA demethylation. They claim that such promoter regions are enriched at the "inactive" X chromosome. However, it would be difficult to discuss DNA methylation at inactive X-chromosomes as the RRBS data were derived from a mixture of male and female embryos. It would also be notable that the increase of DNA methylation at these promoter regions is ~1% on average. Such a slight increase in DNA methylation during pre-implantation development could also be due to the developmental variations between the embryos or between the sexes of embryos.

      Thanks so much for your insightful comments. Whether de novo DNA methylation occurs in a sex-dimorphic manner would be of significance for our study. Based on your comments, we have added a reanalysis based on a publicly available single cell multi-omics sequencing (COOL-seq) data of mouse early embryos (Guo et al., 2017). The results showed that both male and female embryonic cells gain DNA methylation during the transition from the 8-cell to ICM (Figure 1—figure supplement 1C-D; Lines 112-115 in the revised manuscript).

      With regards to the increase in the promoter region, many previous studies have revealed that promoter and overlapping CGI regions, especially high CpG promoters, always showed low levels of DNA methylation (Auclair et al., 2014; Borgel et al., 2010; Dahlet et al., 2020). The relatively lower basal levels make the increase seem relatively slight. Thus, we added relevant statements to clarify this information and rewritten the sentences in the revised manuscript (Lines 116-118, 125-127 in the revised manuscript).

      In addition, using the single cell COOL-seq data, we also specifically reanalyzed the DNA methylation changes on the X chromosome in female embryos. The X chromosome showed a more notable increase than that on autosomes, and the female X chromosome showed a higher DNA methylation level than that of the male (Figure 3—figure supplement 2A-B; Lines 203-206 in the revised manuscript).

      Thanks again for your insightful and constructive comments that significantly strengthen our evidence. We have added these results in the revised manuscript.

      (2) Imprinted X-chromosome inactivation and evaluation of H3K27me3 (related to Figures 2C, D; 3F; Figure2-supplement 2 F, G; Figure3-supplement 3G)

      Based on the slight change in the H3K27me3 signals in the Dnmt3b-Kd blastocysts, the authors claim that imprinted X-chromosome inactivation is impaired in the mutant embryo. It would be not easy to reach this conclusion from such a rough analysis of H3K27me3 presented in Figure 2C, D. Rigorous quantification/evaluation of the H3K27me3 signals in the Dnmt3b-Kd embryos should be considered. Additional evidence for the impairment of H3K27me3 in the mutant embryos should also be provided (expression of a subset of X-linked genes by RNA-FISH or RT-PCR etc.). Though technically challenging, high-resolution genome-wide approach such as ChIP-seq of H3K27me3 in the Dnmt3b-kd female embryos (with traceable SNPs between maternal and paternal X chromosome to distinguish inactive and active X-chromosome) could more precisely evaluate regions that lose H3K27me3 in the X-chromosome (de novo DNA methylated promoters from 8-cell to blastocyst, for example).

      Thanks so much for your insightful comments that make our results more convincing. The H3K27me3 domain is a classic marker for establishment of XCI by achieving X chromosome wide heterochromatinization of transcriptional depression (Chow and Heard, 2009; Heard et al., 2004; Huynh and Lee, 2005). Thus, in the present study, we have performed immunostaining for H3K27me3 domains to evaluate the iXCI status in the blastocysts, as previously reported (Fukuda et al., 2014; Gontan et al., 2018; Inoue et al., 2010; Tan et al., 2016). Base on your comments, we have added another statistical method to quantify the establishment of iXCI, i.e. the percentage of H3K27me3-positive and -negative cells to total trophoblast cells in female blastocysts subject to Dnmt3b knockdown or not. The result also indicated that Dnmt3b knockdown led to a significant loss of H3K27me3 domains from total trophoblast cells. Similarly, new data based on statistical analyses of total trophoblast cells, has also been added in the results of Dnmt3b knockout and 5-aza-dC (Figure 3F; Figure 3—figure supplement 3D, H in the revised manuscript).

      To clarify the significance and reliability of detecting H3K27me3 domains, we have added a schematic diagram depicting the process of iXCI initiation and establishment, as well as the experimental design and work flows, to make our results easier to be understood (Figure 3C in the revised manuscript).

      In addition, we agree with your comments that additional evidence will benefit the conclusion. Thus, we have reanalyzed the RNA-seq and H3K27me3 CHIP-seq data in extraembryonic ectoderm (ExE) of E6.5 single embryos that underwent Dnm3a/3b knockout because preimplantation iXCI status maintains extraembryonic cells (Chen et al., 2019; Galupa and Heard, 2015; Schulz and Heard, 2013). The results showed that Dnmt knockout-induced chromosome-wide loss of DNA methylation led to a nearly complete loss of H3k27me3 on paternal X chromosome (specifically inactivated in iXCI), along with a notable transcriptional upregulation cross the chromosome. By contrast, these changes cannot be not observed on maternal X chromosome.

      We have added this result in the revised manuscript (Lines 253-261; Figure 3—figure supplement 4A in the revised manuscript).

      (3) Analysis of the developmental potential of Dnmt3b-kd embryos

      While the authors claim that Dnmt3b-mediated de novo DNA methylation plays an important role in imprinted X-chromosome inactivation, it remains unclear whether the analysis presented in Figure 4 is derived from "female" embryos. This analysis seemed confusing as the authors claim that de novo DNA methylation in the promoter regions during the transition from 8-cell to blastocyst regulates imprinted X-chromosome inactivation, but this should not happen in the male embryos. Was the impairment of embryonic proliferation and differentiation observed in both male and female embryos? Or is this specific to the female embryos? We think that the sex of the embryos would be critical for the analysis presented in Figure 4.

      Thanks so much for your constructive comments to make our results smoother and clearer. The Figure 4 mainly presents the developmental role of minor de novo methylation based on the integrated analysis of DNA methylation and gene expression dynamics from the 8-cell to ICM. Because our data indicated that both male and female embryos undergo minor de novo methylation (Figure 1—figure supplement 1C-D in the revised manuscript). This section mainly focused on genome wide and general changes, but not on sex dimorphic consequence.

      To avoid the possible confusion, we have reorganized the RESULTS AND DISCUSSION section and presented this section as Figure 2 in the revised manuscript, before the chromosomal distribution analysis and subsequent detection relevant to iXCI.

      Reviewer #2 (Public Review):

      Summary:

      Here, Yue et al. set out to determine if the low DNMT3B expression that is observed prior to de novo DNA methylation (before the blastocyst stage) has a function. Re-analyzing existing DNA methylation data from Smith et al. (2012) they find a small DNA methylation gain over a subset of promoters and gene bodies, occurring between the 8-cell and blastocyst stages, and refer to this as "minor de novo DNA methylation". They attempt to assess the relevance/functionality of this minor DNA methylation gain, and report reduced H3K27me3 in Dnmt3b knockdown (KD) trophoblast cells that normally undergo imprinted X-chromosome inactivation (iXCI) before the blastocyst stage. In addition, they assess the proliferation, differentiation, metabolic function, implantation rate, and live birth rate of Dnmt3b KD blastocysts.

      Strengths:

      Working with early embryos is technically demanding, making the well-designed experiments from this manuscript useful to the epigenetics community. Particularly, the DNMT3B expression and 5-mC staining at different embryonic stages.

      Thanks for your positive evaluation, we have revised manuscript based on your comments, and the items need to be addressed in detail are explained in the point-by-point response to each comment.

      Weaknesses:

      - Throughout the manuscript, please represent DNA methylation changes as delta DNA methylation instead of fold change.

      Thanks so much for your constructive comments. We have represented DNA methylation changes as “ΔDNA methylation” (Figure 2—figure supplement 1A; Figure 3—figure supplement 1A; Figure 3—figure supplement 3I in the revised manuscript).

      - Detailed methods on the re-analysis of the DNA methylation data from Smith et al. 2012 are missing from the materials and methods section. Was a minimum coverage threshold used?

      Thanks so much for your reminder. We have added relevant statements and provided the detail of the coverage criteria in the subsection of Bioinformatics analysis in the Materials and methods section as follows: RRBS data of mouse embryos (2-cell embryos, 4-cell embryos, 8-cell embryos, ICM, and E6.5 embryos) were downloaded from the published article by Smith et al (Smith et al., 2012) (accession number: GSE34864). The methylation level was calculated as the number of “methylated” reads (reporting as C), divided by the total number of “methylated” and “unmethylated” read, which reporting as C or T. The genomic region information was downloaded from the mm9 Repeat Masker. As described in the published article, promoters were defined as 1 kb up- and downstream of the TSS and classified into high-density CpG promoter (HCP), intermediate-density CpG promoter (ICP) and low-density CpG promoter (LCP). Only CpG sites with at least fivefold coverage were included in the methylation analysis. We have added relevant information in the revised manuscript (Lines 462-470 in the revised manuscript).

      - Detailed methods on the establishment and validation of Dnmt3b KO blastocysts and 5-aza-dC treated blastocysts are missing (related to Figure 2).

      Thanks so much for your detailed reminder. In the present study, we used a well-established Dnmt3b-deficient mouse model (Okano et al., 1999) to validate the role of minor de novo DNA methylation in iXCI establishment. Heterozygous Dnmt3b<sup>+/-</sup> mice that carry one mutant locus of Dnmt3b, were obtained from the Mutant Mouse Resource & Research Centers (MMRRC, NIH). Homozygous embryos were obtained by intercrossing Dnmt3b<sup>+/-</sup> male and female mice. Genotyping assays of collected embryos was performed by PCR using primers that were designed based on the gene targeting strategy following the MMRRC genotyping protocol (https://www.med.unc.edu/mmrrc/genotyping-protocols/mmrrc-center-protocol-29886/). We have provided the detailed methods in the revised manuscript (Lines 350-354; 391-393 in the revised manuscript). In addition, we added a schematic diagram depicting the processes of embryo collection and detection (Figure 3—figure supplement 3A in the revised manuscript).

      Similarly, we have provided relevant details of 5-aza-dC supplementation in the revised manuscript (Lines 412-415 in the revised manuscript) and added a schematic diagram depicting the details of experimental design and processes (Figure 3—figure supplement 3E in the revised manuscript).

      - Detailed methods on the re-analysis of the ChIPseq data from Liu et al. 2016 are missing from the materials and methods section.

      Thank you for pointing this out. The bigwig files of H3K27me3 ChIP-seq data were downloaded from the published article by Liu et al (Liu et al., 2016)(accession number: GSE73952). These signal tracks were generated using the MACS2 (v2.0.10.20131216) pileup function and normalized to 1 million reads for visualization, as described in the original publication. We have added relevant information to the MATERIALS AND METHODS section in the revised manuscript (Lines 474-479 in the revised manuscript).

      - Some of the data represented in bar graphs does not look convincing/significant. Maybe this data can be better represented differently, such as in box plots or violin plots, which would better represent the data.

      Thanks so much for your comments that improve our result presentation, relevant results have been changed into box plots in the revised manuscript (Figure 3E; Figure 3—figure supplement 3C; Figure 3—figure supplement 3G in the revised manuscript). In addition, to strengthen our evidence, we have added alternative statistical method to quantify the establishment of iXCI, i.e. the percentage of H3K27me3-positive and -negative cells to total trophoblast cells in female blastocysts subject to Dnmt3b knockdown or not. (Figure 3F; Figure 3—figure supplement 3D, H in the revised manuscript).

      - The relevance and rationale for experiments using 5-aza-dC treatment is unclear.

      Thanks so much for reminding us to make our results more informative and convincing. 5-aza-dC is a well-established global DNA hypomethylating agent that efficiently inhibit the activity of all DNMTs, and thus has been frequently used to study the maintenance of DNA methylation and de novo DNA methylation (Maslov et al., 2012; Oka et al., 2005).

      In our study, to validate the function of minor de novo DNA methylation in iXCI, we take advantage of 5-aza-dC-induced DNMT inhibition, which allows us, despite its inhibitory effect common to various DNMTs, to transiently treat embryos specifically during the window of minor de novo DNA methylation (from the 8-cell to blastocyst stage). We have added these statements, as well as a schematic diagram depicting the experimental design, in the revised manuscript to make our experiments more rational and easier to be understood (Lines 183-188; Figure 3—figure supplement 3E in the revised manuscript).

      References

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      Borgel, J., Guibert, S., Li, Y., Chiba, H., Schubeler, D., Sasaki, H., Forne, T. and Weber, M. (2010). Targets and dynamics of promoter DNA methylation during early mouse development. Nat. Genet. 42, 1093-1100.

      Chen, Z., Yin, Q., Inoue, A., Zhang, C. and Zhang, Y. (2019). Allelic H3K27me3 to allelic DNA methylation switch maintains noncanonical imprinting in extraembryonic cells. Sci Adv 5, eaay7246.

      Chow, J. and Heard, E. (2009). X inactivation and the complexities of silencing a sex chromosome. Curr. Opin. Cell Biol. 21, 359-366.

      Dahlet, T., Argueso Lleida, A., Al Adhami, H., Dumas, M., Bender, A., Ngondo, R. P., Tanguy, M., Vallet, J., Auclair, G., Bardet, A. F., et al. (2020). Genome-wide analysis in the mouse embryo reveals the importance of DNA methylation for transcription integrity. Nat Commun 11, 3153.

      Fukuda, A., Tomikawa, J., Miura, T., Hata, K., Nakabayashi, K., Eggan, K., Akutsu, H. and Umezawa, A. (2014). The role of maternal-specific H3K9me3 modification in establishing imprinted X-chromosome inactivation and embryogenesis in mice. Nat Commun 5, 5464.

      Galupa, R. and Heard, E. (2015). X-chromosome inactivation: new insights into cis and trans regulation. Curr. Opin. Genet. Dev. 31, 57-66.

      Gontan, C., Mira-Bontenbal, H., Magaraki, A., Dupont, C., Barakat, T. S., Rentmeester, E., Demmers, J. and Gribnau, J. (2018). REX1 is the critical target of RNF12 in imprinted X chromosome inactivation in mice. Nat Commun 9, 4752.

      Guo, F., Li, L., Li, J., Wu, X., Hu, B., Zhu, P., Wen, L. and Tang, F. (2017). Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells. Cell Res. 27, 967-988.

      Heard, E., Chaumeil, J., Masui, O. and Okamoto, I. (2004). Mammalian X-chromosome inactivation: an epigenetics paradigm. Cold Spring Harb. Symp. Quant. Biol. 69, 89-102.

      Huynh, K. D. and Lee, J. T. (2005). X-chromosome inactivation: a hypothesis linking ontogeny and phylogeny. Nat. Rev. Genet. 6, 410-418.

      Inoue, K., Kohda, T., Sugimoto, M., Sado, T., Ogonuki, N., Matoba, S., Shiura, H., Ikeda, R., Mochida, K., Fujii, T., et al. (2010). Impeding Xist expression from the active X chromosome improves mouse somatic cell nuclear transfer. Science 330, 496-499.

      Liu, X. Y., Wang, C. F., Liu, W. Q., Li, J. Y., Li, C., Kou, X. C., Chen, J. Y., Zhao, Y. H., Gao, H. B., Wang, H., et al. (2016). Distinct features of H3K4me3 and H3K27me3 chromatin domains in pre-implantation embryos. Nature 537, 558-562.

      Maslov, A. Y., Lee, M., Gundry, M., Gravina, S., Strogonova, N., Tazearslan, C., Bendebury, A., Suh, Y. and Vijg, J. (2012). 5-aza-2'-deoxycytidine-induced genome rearrangements are mediated by DNMT1. Oncogene 31, 5172-5179.

      Oka, M., Meacham, A. M., Hamazaki, T., Rodic, N., Chang, L. J. and Terada, N. (2005). De novo DNA methyltransferases Dnmt3a and Dnmt3b primarily mediate the cytotoxic effect of 5-aza-2'-deoxycytidine. Oncogene 24, 3091-3099.

      Okano, M., Bell, D. W., Haber, D. A. and Li, E. (1999). DNA methyltransferases Dnmt3a and Dnmt3b are essential for de novo methylation and mammalian development. Cell 99, 247-257.

      Schulz, E. G. and Heard, E. (2013). Role and control of X chromosome dosage in mammalian development. Curr. Opin. Genet. Dev. 23, 109-115.

      Smith, Z. D., Chan, M. M., Mikkelsen, T. S., Gu, H. C., Gnirke, A., Regev, A. and Meissner, A. (2012). A unique regulatory phase of DNA methylation in the early mammalian embryo. Nature 484, 339-344.

      Tan, K., An, L., Miao, K., Ren, L., Hou, Z., Tao, L., Zhang, Z., Wang, X., Xia, W., Liu, J., et al. (2016). Impaired imprinted X chromosome inactivation is responsible for the skewed sex ratio following in vitro fertilization. Proc. Natl. Acad. Sci. U. S. A. 113, 3197-3202.

      Reviewer #1 (Recommendations For The Authors):

      Title

      It would be hard to understand what "co"-regulates means. Does this mean DNA methylation and H3K27me3 co-regulate imprinted X- X-chromosome inactivation? If so, the title can be reworded.

      Thanks for your insightful comments, the title has been corrected into “A wave of minor de novo DNA methylation initiates in mouse 8-cell embryos and co-regulates imprinted X- chromosome inactivation with H3K27me3” (Line 2 in the revised manuscript).

      Text

      (1) As DNA methylation analysis is a primary part of this study, how they processed DNA methylation data can be added to the "Bioinformatics analysis" in the MATERIALS AND METHODS section.

      Thanks for your kind reminder. We have added relevant information in the Materials and methods section in the revised manuscript (Lines 462-474 in the revised manuscript).

      (2) It seems that recent literature has not been cited in the manuscript. Specifically, none of the papers after 2018 were cited. Recent relevant papers should also be cited throughout the manuscript.

      Thanks so much for your reminder. We have added more recent literature to update the relevant information, such as the evidence supporting the causal role between DNA methylation and XCI (Lines 225-228, 264-265 in the revised manuscript); the concurrent enrichment of DNA methylation and H3K27me3 in genes subject to XCI (Lines 301-303 in the revised manuscript); the dominant role of de novo methylation in X chromosome (Lines 253-256 in the revised manuscript), etc.

      (3) Line 56: The first report that describes the dynamics of DNMT3B expression in pre-implantation embryonic development (Hirasawa et al., 2007) is missing. This paper should be cited.

      Sorry for our carelessness, we have added relevant references and rewritten the sentence in the revised manuscript (Lines 56-57 in the revised manuscript). I think you meant the report by Hirasawa et al in 2008, in which presented expression and subcellular localization of Dnmt3a and Dnmt3b in mouse oocytes and preimplantation embryos.

      (4) Line 98: It would be good to mention that the data were derived from reduced representation bisulfite sequencing as the authors used whole-genome bisulfite sequencing data from the same research group as well.

      Thanks for your kind reminder. As you have suggested, we have added the description in the revised manuscript to emphasize that these data were derived from reduced representation bisulfite sequencing, while another data were derived from whole-genome bisulfite sequencing, respectively. (Lines 98-99, 111 in the revised manuscript).

      (5) Line 101: We first... "the preferential target of DNMT3B (Auclair et al., 2014; Borgel et al., 2010)". More recent literature (Baubec et al., 2016, Duymich et al., 2016, for example) showed that the preferential target of DNMT3B is not a promoter but a gene body. This sentence should be reworded.

      Thanks so much for your detailed reminder. As you have pointed out, “preferential target” seems to be an inaccurate statement. Besides of promoters, gene bodies and other elements also undergo de novo DNA methylation (Auclair et al., 2014; Dahlet et al., 2020; Duymich et al., 2016).

      We have rewritten the sentence as follows in the revised manuscript: “Promoter regions are important target sites of DNMT3B (Choi et al., 2011). The acquisition of DNA methylation in promoters, especially in intermediate and low CpG promoters, during implantation is largely dependent on DNMT3B and plays an important role in regulating developmental genes (Auclair et al., 2014; Borgel et al., 2010; Dahlet et al., 2020). Thus, among genomic regions that may undergo de novo DNA methylation, we initially focused our analysis on DNA methylation dynamics of promoters...” (Lines 100-106 in the revised manuscript)

      (6) Lines 108-109: It would be good to mention that these data were derived from whole-genome bisulfite sequencing.

      Thanks for your kind reminder. As aforementioned, we have added a description in the revised manuscript to distinguish between data derived from reduced representation bisulfite sequencing and whole-genome bisulfite sequencing (Lines 98-99, 111 in the revised manuscript).

      (7) Line 141: rXCI should be defined.

      Thanks for your kind reminder. We have added full descriptions and more necessary information about iXCI and rXCI, to make our statements clearer and easier to be understood (Lines 210-213 in the revised manuscript). In addition, we carefully checked the relevant descriptions throughout the manuscript, and each abbreviation (such as “ICM”) has been defined at its first occurrence. Additionally, we have replaced abbreviations that appears only once in the manuscript with their full terms (Lines 122, 212 in the revised manuscript).

      (8) Lines 145-149: The role of DNA methylation for imprinted X-inactivation has already been reported (Chiba et al., 2008). The relevant sentences should be reworded.

      Thanks so much for reminding us the important earlier literature that explores the relationship between DNA methylation and XCI. However, the primary aim and hypothesis of the study by Chiba et al. are different from those of our study. Chiba et al focused on whether DNA methylation is the imprinting mark responsible for monoallelic expression of Xist (the initiation event of iXCI), while our study focused on the role of DNA methylation in achieving X chromosomal heterochromatinization (the late event of iXCI).

      In detail, the study by Chiba et al. mainly focused on exploring why Xist is specifically expressed from paternal allele and iXCI occurs specifically on the paternal X chromosome in mouse preimplantation embryos. Because Previous studies have suggested that genomic imprinting of Xist is established during oogenesis (Oikawa et al., 2014; Tada et al., 2000), Chiba et al. wanted to test whether the DNA methylation imprinting established during oogenesis is responsible for the monoallelic expression of Xist in preimpantaiton embryos. Analyses of DNA methyltransferase maternal knockout embryos revealed that oocyte DNA methylation is dispensable for Xist imprinting (Chiba et al., 2008). Follow-up study by Inoue et al. identified a broad H3K27me3 enrichment within the Xist 5’region established during oocyte growth and persists through preimplantation development, as the imprinting mark of Xist (Inoue et al., 2017). These series of studies are very important and allows us to understand the mechanism underlying paternal allele-specific iXCI in mouse preimplantation embryos and extraembryonic tissues.

      However, the hypothesis is different in our study. Based on the finding of minor de novo DNA methylation and its preferential distribution on the X chromosome, we have speculated that the minor de novo methylation, which occurs from the 8-cell to blastocyst stage, may participate in achieving X chromosomal heterochromatinization. Although DNA methylation is essential for maintaining X chromosome-wide transcriptional silence of rXCI, its role in iXCI remains controversial and it is even plausibly thought that DNA methylation is not required for achieving iXCI because preimplantation embryos undergo global and massive DNA demethylation.

      We have reorganized this paragraph, relevant statements have been added to make the background and discussion clearer and easier to be understood. (Lines 217-234 in the revised manuscript)

      (9) Lines 164-165: Information regarding Dnmt3b KO is missing. Did the authors generate an original KO line or use an already published one? It should be explicitly stated.

      Thank you so much for your kind reminder. The Dnmt3b heterozygous mice were obtained from the Mutant Mouse Resource & Research Centers (MMRRC), and Dnmt3b knockout (KO) embryos were generated by mating Dnmt3b heterozygous females with heterozygous males. The genotyping of Dnmt3b KO embryos was performed by PCR following the MMRRC genotyping protocol (https://www.med.unc.edu/mmrrc/genotyping-protocols/mmrrc-center-protocol-29886/). The relevant information has been added to the MATERIALS AND METHODS section in the revised manuscript (Lines 350-354; 391-393 in the revised manuscript).

      (10) Line 165: chemical-induced inhibition of DNMT3B. As 5-aza-dC also blocks DNMT3A and DNMT1, this sentence should be reworded.

      Thank you for your valuable comments. 5-aza-dC is a well-established global DNA hypomethylating agent that efficiently inhibit the activity of all DNMTs, and has been frequently used to study the maintenance of DNA methylation and de novo DNA methylation (Maslov et al., 2012; Oka et al., 2005). Thus, despite its inhibitory effect common to various DNMTs, chemical-induced inhibition of DNMTs has the advantage of allowing us to transiently treated embryos specifically during the window of minor de novo DNA methylation (the 8-cell to blastocyst stage). We have rewritten the relevant sentences in the revised manuscript (Lines 183-188 in the revised manuscript).

      (11) Lines 171-174: "The role of de novo methylation in iXCI...". This possibility was already tested in the previous study from the Sasaki lab (Chiba et al., 2008).

      As mentioned above, the primary aim and hypothesis of the study by Chiba et al. are different from those of our study. Chiba et al. mainly focused on exploring why Xist is specifically expressed from paternal allele and iXCI occurs specifically on the paternal X chromosome in mouse preimplantation embryos, so they tested whether the DNA methylation imprinting established during oogenesis is responsible for this monoallelic expression of Xist in preimplantation embryos (the initiation event of iXCI).

      By contrast, based on the finding of minor de novo DNA methylation and its preferential distribution on X chromosome, our study has speculated that the minor de novo DNA methylation, which occurs from the 8-cell to blastocyst stage, may participate in achieving X chromosomal heterochromatinization (the late event of iXCI).

      Thanks so much for reminding us this important literature, to make our discussion more informative. We have reorganized this paragraph by rewriting or adding relevant statements to make the background and discussion clearer and easier to be understood (Lines 217-231 in the revised manuscript). In addition, to avoid repeated statement and make our discussion more concise, we have removed the similar sentences at the end of this paragraph.

      (12) Lines 198-200: "Given DNA methylation...". These citations mention a general relationship between DNA methylation and H3K27me3 in cells in culture. As I believe the authors focus on X-chromosome inactivation in the female embryos, more relevant papers that discuss the order of the events for the establishment of H3K27me3 and DNA methylation in the inactive X-chromosome can be cited.

      Thanks so much for your comment to improve our discussion. It has been thought that during the late phase of rXCI in fully differentiated cells, gene silencing is achieved by PRC2 complex-induced H3K27me3, and then is further stably maintained by the redundant action of multiple layers of epigenetic modifications, including DNA methylation, to reach the maximum level of chromatin compaction (Chow and Heard, 2009; Heard et al., 2004; Pintacuda and Cerase, 2015). In line with this, a recent multifaceted analysis showed that DNA methylation and H3K27me3 are concurrently enriched in genes subject to XCI (Balaton and Brown, 2021). We have added these statements in the revised manuscript (Lines 295-303 in the revised manuscript).

      (13) Line 241: As 5-aza-dC blocks both de novo and maintenance DNA methylation, this sentence should be reworded.

      Thank you for your kind reminder. As you have mentioned above, 5-aza-dC is a well-established global DNA hypomethylating agent that efficiently inhibit the activity of all DNMTs, and has been frequently used to study the maintenance of DNA methylation and de novo DNA methylation (Maslov et al., 2012; Oka et al., 2005). Thus, despite its inhibitory effect common to various DNMTs, chemical-induced inhibition of DNMTs has the advantage of allowing us to transiently treated embryos specifically during the window of minor de novo DNA methylation (the 8-cell to blastocyst stage). We have rewritten the relevant sentences in the revised manuscript (Lines 183-188 in the revised manuscript).

      Figures

      (1) Figure 1C, D: Do the rows in C and D show the corresponding genes?

      Figure 1C and D represent the DNA methylation changes of promoters (C) and gene bodies (D) respectively, during the transition from the 8-cell to blastocyst stage. Two data were analyzed independently, and rows did not show the corresponding genes. Since we have focused on the minor de novo methylation in promoter regions, to avoid confusion, the results of the gene body have been removed from the revised manuscript.

      (2) Figure 1G: Yy2 promoter gained DNA methylation during the transition from 8-cell to the blastocyst stage. Is this a representative locus for the de novo methylated promoters that are shown in Figure 1F where an increase of DNA methylation is about ~1% on average? Another representative locus could be shown instead of this gene promoter.

      Thanks so much for you detailed reminder. The inconsistency between the global methylation change and bisulfite sequencing analysis of Yy2, may be due to the details of methodologies, such C-T conversion efficiency, the number of picked colonies, etc. Since we have confirmed the presence of minor de novo DNA methylation using different publicly available data, to avoid ambiguity, we have removed this result in revised manuscript.

      (3) Figures 2C and 3A: It would be helpful to mention what the arrowheads mean.

      Thanks so much for you detailed reminder. In Figure 2C, the arrowhead indicates the H3k27me3 domain and the blank arrowhead indicates the blastomere without the H3k27me3 domain. In Figure 3A, the arrowhead indicates Xist RNA domain and the blank arrowhead indicates the blastomere without Xist RNA domain. We have added the information in the revised manuscript (Lines 736-738, 747-749 in the revised manuscript).

      (4) Figure 3-figure supplement 2B: It would be hard to see whether H3K27me3 is enriched at the promoter regions of presented genes. It would be helpful to show the values for the Y-axis as in panel A.

      Thanks for your helpful reminder. We have added the scales to the figure to improve the result presentation (Figure 4—figure supplement 2B in the revised manuscript).

      (5) Figure 4-figure supplement 2: 5-aza-dC blocks not only the activity of DNMT3B but also DNMT1, and DNMT3A (all these DNMTs are expressed during pre-implantation embryos, see Hirasawa et al., 2007). This part can be omitted from the manuscript.

      Thanks for your insightful comments. As you have mentioned above, the relevance and rationale for experiments using 5-aza-dC treatment should be clarified. 5-aza-dC is a well-established global DNA hypomethylating agent that efficiently inhibit the activity of all DNMTs, and thus has been frequently used to study the maintenance of DNA methylation and de novo DNA methylation (Maslov et al., 2012; Oka et al., 2005).

      In our study, to validate the function of minor de novo DNA methylation in iXCI and blastocyst development, we take advantage of 5-aza-dC-induced DNMT inhibition, which allows us to transiently treated embryos specifically during the window of minor de novo DNA methylation (the 8-cell to blastocyst stage), despite its non-specificity to various DNMTs.

      Based on these considerations, we hope to retain this result, and wish to get your understanding.

      We have added these statements in the revised manuscript to make our experiments more rational and easier to be understood (Lines 183-188 in the revised manuscript) and added a schematic diagram depicting the experimental design (Figure 3—figure supplement 3E in the revised manuscript).

      Reviewer #2 (Recommendations For The Authors):

      Recommendations/concerns in the text:

      - Line 106, it is unclear what is meant by "in line with this"? Gene body DNA methylation is a characteristic of active transcription, so why would a gain in DNA methylation at promoters be in line with a gain in DNA methylation over gene bodies?

      Thank you so much for your comments that pointed out our ambiguous statement. We meant both the promoter and gene body regions, albeit accounting for small proportions, gain DNA methylation during the transition from the 8-cell to blastocyst stage. Based on the comment by Reviewer#1, since we have focused on the minor de novo methylation in promoter regions, to avoid confusion, the results of the gene body have been removed from the revised manuscript.

      - Line 111 & 114, can 6% DNA methylation really be considered "relatively hypermethylated" compared to 3% DNA methylation that is referred to as "more hypomethylated"?

      We apologize for our unclear and ambiguous statements. Here we focused on the promoter regions. Many previous studies have revealed that compared with gene bodies and other genome elements, promoter and overlapping CGI regions, especially high CpG promoters, always showed low levels of DNA methylation. We have added relevant statements to clarify this information, and rewritten the sentences in the revised manuscript (Lines 100-106, 116-118, 121, 124 in the revised manuscript).

      - Line 124, there are a number of processes identified, why only mention one in the text? Suggest changing writing to be more accurate, indicating what was included for the GO analysis and using the words "enriched for ... processes". Saying it may be linked to a process is an overstatement and not supported by further experiments/data.

      Thank you so much for your detailed comments that make our results more informative. We have checked the relevant description and addressed your suggestions as follows: By performing gene ontology enrichment analysis of genes that undergo minor or major de novo DNA methylation respectively, we noticed that besides of many important basic processes common to two waves of de novo DNA methylation, genes subject to minor de novo DNA methylation were enriched in processes such as organic substance transport, chromosome organization, and cell fate specification (Lines 129-134 in the revised manuscript).

      - Lines 149 - 152: sentence/message unclear.

      We apologize for the ambiguous description. We have corrected the relevant descriptions as follows: To identify the biological function of minor de novo DNA methylation in iXCI, we knocked down Dnmt3b in preimplantation embryos by microinjecting Dnmt3b siRNA into zygotes (Lines 234-236 in the revised manuscript).

      - Lines 162-164: the data in Figure 2C/D does not support this statement, as it does not show H3K27me3 loss specifically at the inactive X-chromosome.

      Thanks so much for your insightful comments. Despite the global enrichment of H3K27me3, the H3K27me3 domain detected by immunostaining is a classic marker for establishment of XCI by achieving X chromosome wide heterochromatinization of transcriptional depression (Chow and Heard, 2009; Heard et al., 2004; Huynh and Lee, 2005). Thus, we have used immunostaining for H3K27me3 domains to evaluate the iXCI establishment in the blastocysts, as previously reported (Fukuda et al., 2014; Gontan et al., 2018; Inoue et al., 2010; Tan et al., 2016). To make our results more convincing, we have added another statistical method to quantify the establishment of iXCI, i.e., the percentage of H3K27me3-positive and -negative trophoblast cells to total trophoblast cells in female blastocysts subject to Dnmt3b knockdown or not.

      In addition, we have added a schematic diagram depicting the process of iXCI initiation and establishment, as well as the experimental design and work flows, to make the result easier to be understood.

      In addition, we agree with your comments that additional evidence will benefit the conclusion. To strengthen the evidence, and test whether DNA methylation loss leads to a prolonged effect on iXCI, we have reanalyzed the RNA-seq and H3K27me3 CHIP-seq data in extraembryonic ectoderm (ExE) of E6.5 single embryos that underwent Dnm3a/3b knockout because preimplantation iXCI status maintains extraembryonic cells (Chen et al., 2019; Galupa and Heard, 2015; Schulz and Heard, 2013). The results showed that chromosome-wide loss of DNA methylation led to a nearly complete loss of H3k27me3 on paternal (specifically inactivated in iXCI), along with a notable transcriptional upregulation cross the chromosome. By contrast, these changes cannot be not observed on maternal X chromosome. (Lines 253-261; Figure 3—figure supplement 4A in the revised manuscript)

      - Lines 169-174: sentence/message unclear.

      As aforementioned, we have reorganized this paragraph by rewriting or adding relevant statements relevant to the DNA methylation and XCI, to make the background and discussion clearer and easier to be understood (Lines 217-234 in the revised manuscript). In addition, to avoid repeated statement and make our discussion more concise, we have removed the similar sentences at the end of this paragraph.

      - Lines 177-179: this statement is too bold. The data does not support "direct evidence".

      Thank you for your detailed reminder. We have rewritten the sentence to avoid confusion and overstatement (Lines 262-268 in the revised manuscript).

      - Line 198: these are not all enzymes, but could be referred to as chromatin modifiers.

      We apologize for the ambiguous description. As you suggested, we have corrected “enzymes” to “chromatin modifiers” (Lines 284, 287 in the revised manuscript).

      - Line 199: this statement is not correct in all contexts. There are many studies showing antagonism between DNA methylation and H3K27me3.

      Thanks so much for you careful reviewing. As you have pointed out, the relationship of DNA methylation and H3K27me3 are divergent and largely controversial among studies. Under certain circumstances, DNA methylation shows antagonistic effect to H3K27me3 at promoters, via excluding the binding of PRC2 (the main complex responsible for H3K27me3 deposition) components to their targets (Bartke et al., 2010; Jermann et al., 2014), while other studies have presented alternative evidence that PRC2 (the main complex responsible for H3K27me3 deposition) and DNA methylation cooperate to achieve silencing (Hagarman et al., 2013; Vire et al., 2006). Thus, it has been thought that the relationship between DNA and methylation and histone modifications is complex, possibly in a cell-type and/or genomic region-specific manner. Both antagonism and coordination can be observed in different regulatory elements in mouse ES cells (King et al., 2016).

      We apologize our incomplete statement because we mainly focused on their synergistic relationship. We have refined this section by rewriting relevant sentences and adding necessary statements (Lines 288-303 in the revised manuscript).

      - Lines 228-230: the developmental significance of DNA methylation homeostasis is already well-established. Please reference relevant papers showing this here.

      Thank you for this helpful suggestion. We have reorganized this section. Relevant references that highlight the developmental significance of DNA methylation homeostasis have added. The sentence has been rewritten and moved to the end of this paragraph, in the revised manuscript (Lines 159-161 in the revised manuscript).

      - Line 238: an explanation/rationale for looking at energy metabolism is lacking.

      Thank you for your comments to make our results earlier to be understood. The detection of energy metabolism is mainly based on the integrated analysis of DNA methylation and gene expression from the 8-cell embryos to ICM, to test the potential short-and long-term developmental consequences of minor de novo DNA methylation. Bioinformatic analysis suggested that many basic processes, such as cell differentiation, cell cycle and metabolic regulation, may be regulated by minor de novo DNA methylation. Among the enriched genes, several are related energy metabolism. In addition, because energy metabolism is crucial for supporting embryo differentiation and development, and oxidative phosphorylation (OXPHOS) metabolism is highly activated during the blastocyst stage (Zhao et al., 2021), we next examined the energy metabolism, particularly OXPHOS activity, of Dnmt3b-KD embryos. We have refined the section by rewritten relevant sentence and added necessary statements (Lines 175-179 in the revised manuscript).

      - Lines 246-248: Looking at the data in Figure 2 figure supplement 2, this statement is simply not true with regards to DNMT3B protein, and also global DNA methylation level is reduced in the Dnmt3b KD blastocyst, which could lead to defective major de novo DNA methylation.

      Thanks for your careful reviewing, we have rewritten the sentence to make our statement more accurate and avoid overstatement (Lines 188-190 in the revised manuscript).

      Recommendations/concerns relating to figures:

      Figure 1:

      - Of all genic promoters, how many were included in the analysis (contained sufficient coverage)? What cut-off/thresholds were used to consider DNA methylation gain at a promoter?

      Thanks for your comments. In total, 11662 promoters were analyzed. Given that promoter methylation is generally at low level, particularly at the 8-cell stage at which minor de novo methylation is just initiated. The relatively lower basal levels make the increase before the blastocyst, seem considerably slight. To capture the slight changes, we have used the relaxed threshold based on ΔDNA methylation. Only CpG sites with at least fivefold coverage were included in the methylation analysis based on data from Smith et al. (Smith et al., 2012)., ΔDNA methylation greater or less than 0 was defined as gain or loss of DNA methylation. We have added this information in the revised manuscript (Lines 462-470 in the revised manuscript).

      - Does an average methylation level of 0.02 represent 2% DNA methylation? Presuming yes, is the average 1.5% DNA methylation gain at promoters real? And meaningful? Especially compared to the gain in DNA methylation that takes place between ICM and E6.5 (Figure 1 Figure Supplement 1 D)

      As you have pointed out, an average methylation level of 0.02 represent 2% DNA methylation. As aforementioned, promoters exhibited an average of 1.5% DNA methylation gain during the transition from 8-cell stage to ICM. The slight increase may be mainly due to the relatively lower basal levels. As you expected, compared with the comprehensive de novo DNA methylation during implantation, preimplantation de novo methylation occurs more slightly, at a small proportion of promoter regions, so designated it as minor de novo DNA methylation. It should be also mentioned that a proportion of these promoters continue to gain massive DNA methylation during implantation. We have refined the relevant sentences to provide more detailed information of our results (Lines 125-127 in the revised manuscript).

      - Why is there a focus on promoters (which are not the preferential target of DNMT3B)?

      Thanks so much for your detailed reminder. As you have pointed out, “preferential target” seems to be an inaccurate statement. besides of promoters, gene bodies and other elements also undergo de novo DNA methylation (Auclair et al., 2014; Dahlet et al., 2020; Duymich et al., 2016). We have focused on the promoter regions based on the following considerations: (1) Promoter regions are important target sites of DNMT3B (Choi et al., 2011); (2) The acquisition of DNA methylation in promoters, especially in intermediate and low CpG promoters, during implantation is largely dependent on DNMT3B and plays an important role in regulating developmental genes (Auclair et al., 2014; Borgel et al., 2010; Dahlet et al., 2020). We have rewritten the relevant sentence in the revised manuscript (Lines 100-106 in the revised manuscript).

      - Figure 1H shows that promoters that gain DNA methylation during the "minor de novo DNA methylation" continue to gain DNA methylation during "de novo DNA methylation". Is the ~1.5% DNA methylation gain just the slow start of the main de novo DNA methylation wave?

      Your comments is very helpful to improve the description of our results. In the present study, our analysis indicated that a small proportion of promoters initially gain methylation during the transition from the 8-cell to ICM. The finding challenges current knowledge: (1) de novo DNA methylation occurs during implantation, by which globally hypomethylated blastocysts acquire genome-wide DNA methylation (Borgel et al., 2010; Dahlet et al., 2020; Smith et al., 2012); (2) during preimplantation development, embryos undergo massive and global DNA demethylation.

      To distinguish the current knowledge of the timing and dynamics of DNA methylation during the early development, we have designated our finding during the transition from the 8-cell to blastocyst stage, as minor de novo DNA methylation.

      We agree with your notion that among the promoters undergoing minor de novo methylation, most of them continue to gain DNA methylation during implantation, as revealed in Fig. 1F. We have added refine the relevant statement in revised manuscript (Lines 125-127 in the revised manuscript).

      - The GO analysis performed for Figure 1H, what was used as input? Promoters of genes that gain DNA methylation as identified in 1C?

      Thank you for your comments. For the GO analysis shown in Figure 1H, we used genes with promoter regions that gained or lost DNA methylation during the transition from the 8-cell to ICM respectively (identified in Figure 1C, as input), respectively. This information has been clarified in the revised manuscript to ensure accuracy (Lines 129-134 in the revised manuscript).

      - Figure 1 figure supplement 1, is there only a fold change as threshold or also a calculated significance (eg. p-value/FDR)?

      Thanks for your valuable comments. Considering the relatively low DNA methylation levels at promoter regions, and the slightly changes occurring during the preimplantation embryo development, we used the relaxed threshold based on ΔDNA methylation. Only CpG sites with at least fivefold coverage were included in the methylation analysis based on data from Smith et al. (Smith et al., 2012), ΔDNA methylation greater or less than 0 was defined as gain or loss of DNA methylation. We have replaced relevant figures and added this information in the revised manuscript (Figure 1—figure supplement 1D-E; Lines 125-127 in the revised manuscript).

      - To confirm DNMT3B is responsible for the DNA methylation gain: DNMT3B KD/KO followed by promoter DNA methylation analysis to confirm the promoters that gain DNA methylation between 8 cell and ICM don't gain DNA methylation in the absence of DNMT3B.

      We agree with your comments that additional evidence will benefit the conclusion. To strengthen the evidence, we have reanalyzed the RNA-seq and H3K27me3 CHIP-seq data in extraembryonic ectoderm (ExE) of E6.5 single embryos that underwent Dnm3a/3b knockout because preimplantation iXCI status maintains extraembryonic cells (Chen et al., 2019; Galupa and Heard, 2015; Schulz and Heard, 2013). The results showed that chromosome-wide loss of DNA methylation led to a nearly complete loss of H3k27me3 on paternal (specifically inactivated in iXCI), which showed a notable transcriptional upregulation cross the chromosome. By contrast, these changes cannot be not observed on maternal X chromosome. We have added this result in the revised manuscript (Lines 253-261; Figure 3—figure supplement 4A in the revised manuscript).

      Figure 2:

      - Figure 2A: label missing for what the numbers on the y-axis represent.

      Thank you for pointing this out. We apologize for the oversight. We have added the label of y-axis in Figure 2A to clarify what the numbers represent, making it easier to be understood (Figure 3A in the revised manuscript).

      - Figure 2B: y-axis is % of methylated promoters compared to all promoters?

      Thank you for your suggestion. The y-axis in Figure 2B indeed represents the percentage of de novo methylated promoters relative to all promoters. As you have suggested, we have clarified this labeling in the revised manuscript (Figure 3B in the revised manuscript).

      - What is the delta DNA methylation gain specifically for X-linked promoters?

      Thanks so much for your reminder. To provide more convincing evidence. We have reanalyzed a single cell COOL-seq data, we also specifically reanalyzed the DNA methylation changes on the X chromosomal promoter in female embryos. The X chromosome showed a more notable increase in the de novo methylated promoters than that on autosomes, and the female X chromosome showed higher DNA methylation levels than that of the male (Figure 3—figure supplement 2A-B; Lines 203-206 in the revised manuscript).

      - Figure 2C: include representative images of separate channels to better see the signal of CDX2 and H3K27me3. Quantification would be better represented with box plots.

      Thank you for your helpful suggestions. We have added separate channel images in the revised manuscript. Additionally, we have adjusted the quantification to be represented as box plots, as you have suggested, to improve the accuracy and interpretability of the data presentation (Figure 3D-F in the revised manuscript).

      - Figure 2C: Does the H3K27me3 signal overlap with the location of the inactive X-chromosome (is there maybe denser DAPI or do IF combined with Xist RNA-FISH)?

      Thanks so much for your insightful comments. Despite the global enrichment of H3K27me3, the H3K27me3 domain detected by immunostaining is a classic marker for establishment of XCI by achieving X chromosome wide heterochromatinization of transcriptional depression (Chow and Heard, 2009; Heard et al., 2004; Huynh and Lee, 2005). Thus, we have used immunostaining for H3K27me3 domains to evaluate the iXCI establishment in the blastocysts, as previously reported (Fukuda et al., 2014; Gontan et al., 2018; Inoue et al., 2010; Tan et al., 2016). We have taken effort to perform co-staining of H3K27me3 IF and Xist FISH, but was hindered by the technical challenge, we wish to get your understanding. However, as we aforementioned, H3K27me3 is a well-accepted maker to clarify the XCI status.

      In addition, to make our results more convincing, we have added an alternative statistical method to quantify the establishment of iXCI, i.e., the percentage of H3K27me3-positive and -negative trophoblast cells to total trophoblast cells in female blastocysts subject to Dnmt3b knockdown or not (Figure 3F; Lines 243-244 in the revised manuscript)

      - Figure 2 figure supplement 2A: relative expression of Dnmt3b?

      Thanks for your detailed reminder. The data represent the relative expression level of Dnmt3b, as noted in the original figure legend. Based on your comments, we have added the gene name in the label of the Y-axis. Similarly, the protein name has been also added to make the results more informative (Figure 2 figure supplement 2A, C, E in the revised manuscript).

      - Figure 2 figure supplement 2B/C: in the text, line 153, it is stated that "Dnmt3b mRNA and protein levels were significantly reduced in morulae, but not in blastocysts compared to those of negative control (NC) group". These figures do not support that statement. The IF images show a loss of DNMT3B in the Dnmt3b KD blastocysts. The IF quantification seems to have fewer datapoints for the blastocyst, and looking at the bar graphs, there seems to be a trend towards reduced DNMT3B in both the morula and blastocyst, which would also explain the reduction in DNA methylation in both stages as shown in Figure 2 figure supplement 2D/E.

      Thanks so much for your careful reviewing that makes our statements more accurate. We have rewritten the sentence in the revised manuscript as follows: Dnmt3b mRNA and protein levels were significantly reduced in morulae, and tended to be lower in blastocysts compared to those of the negative control (NC) group. In addition, we have removed “transient” from the original statement “The transient inhibition of Dnmt3b” (Lines 168-170 in the revised manuscript).

      - Figure 2 figure supplement 2F/G: include representative IF images with separation of all channels and the merged image.

      Thank you for your suggestion. We have added the representative immunofluorescence (IF) images with separate channels and merged image in the revised manuscript (Figure 3—figure supplement 3B, F in the revised manuscript).

      - Figure 2 figure supplement 2H: Instead of showing log2FC in methylation levels, delta methylation would be more informative. Are these genes already inactivated at the 8-cell stage? Or are they active and become inactivated by the gain in DNA methylation? Doing qPCR for these genes, or looking at published RNAseq data would be informative. What happens to the expression of these genes in the Dnmt3b KD?

      Thanks for your suggestions. We have represented DNA methylation changes as “ΔDNA methylation”. During mouse preimplantation development, iXCI is initiated in earlier cleavage female embryos dependent on Xist upregulation around 4-8-cell stage, and then Xist specifically coats paternal X chromosome and finally leads to chromosome-wide silencing via heterochromatinization in early blastocysts. Thus, these non-escaping genes, which are subject to XCI, would not be inactivated at 8-cell stage

      Author response image 1.

      The processes of iXCI initiation and establishment (left panel), and dynamics of total expression levels of X chromosome in male and female preimplantation embryos (right panel, note that X-dosage is balanced between sexes until the early blastocyst stage).

      As you expected, most of these representative non-escaping is downregulated upon the transition of 8-cell to blastocyst stage, consistent with their gain of DNA methylation. Additionally, since preimplantation iXCI status maintains extraembryonic cells (Galupa and Heard, 2015; Schulz and Heard, 2013), we further reanalyzed the published RNA-seq data in extraembryonic ectoderm (ExE) of E6.5 single embryos that underwent DNA methyltransferase knockout (Chen et al., 2019). The results showed that chromosome-wide loss of DNA methylation led to a chromosome-wide transcriptional upregulation, including the locus of these non-escaping genes, on paternal X chromosome. We have added this result in the revised manuscript (Figure 3—figure supplement 3J; Figure 3—figure supplement 4A-B; Lines 253-261 in the revised manuscript).

      Figure 3:

      - Figure 3 figure supplement 1: representative IF image missing.

      Thanks for your kind reminder. We have added the representative IF images in the revised manuscript to provide a clearer illustration of the data (Figure 4—figure supplement 1A in the revised manuscript).

      - Figure 3 figure supplement 2B: scales are missing for the H3K27me3 ChIP-seq data (are the 8-cell and ICM tracks set to the same scale?). It looks like the ICM track is cut off at the top (peaks not fully displayed) and the data looks very sparse. A more informative analysis would be to do peak calling over promoters and compare 8-cell with ICM.

      Thanks for your detailed reminder. We apologize for the missing of scale bars in the H3K27me3 ChIP-seq data. The 8-cell and ICM tracks were set to the same scale, and we have now added scales to the figure in the revised manuscript to improve the result presentation. As you have speculated, the visual effect of the flatted peak is not caused by track cutting off, but rather by zooming into a specific region in the extended IGV files.

      These results are based on the reanalysis of publicly available data of pooled embryos, which just provided suggestive but not direct evidence to support the role of DNA methylation in promoting X-linked H3K27me3 enrichment in iXCI.

      To provide more convincing evidence. we have reanalyzed the RNA-seq and H3K27me3 CHIP-seq data in extraembryonic ectoderm (ExE) of E6.5 female embryos that underwent Dnmt3a/3b knockout because preimplantation iXCI status maintains extraembryonic cells (Chen et al., 2019; Galupa and Heard, 2015; Schulz and Heard, 2013). The results showed that Dnmt knockout led to a nearly complete loss of H3k27me3 on paternal (specifically inactivated in iXCI), which showed a notable transcriptional upregulation cross the chromosome. By contrast, these changes cannot be not observed on maternal X chromosome (Figure 3—figure supplement 4 in the revised manuscript). We have added these results in the revised manuscript.

      - Figure 3E: Given all tested proteins give a positive signal, it would have been good to include a negative control chromatin protein that is known to not interact with DNMT3B. Given both PRC2 and DNMT3B are chromatin-binding proteins, can the signal be a result of close proximity instead of a direct interaction?

      In the present study, to test the interaction between DNMT3B and PRC2 core components, we have used in situ proximity ligation assay (PLA), an increasingly popular technique for detecting the close proximity of two proteins in fixed samples using two primary antibodies (Alsemarz et al., 2018).

      Author response image 2.

      Schematic diagram of the principle of the in situ PLA.

      Compared with classical co-Immunoprecipitation (Co-IP) method, in situ PLA has advantages in (1) detecting low input samples or proteins expressed at low levels, which is extremely difficult using Co-IP; (2) providing in situ or subcellular information of protein-protein interaction. However, it should be noted that the maximal distance allowing this reaction is 40 nm, which is not quite small enough to demonstrate a physical interaction between the two antigens, but sufficient to support a very close “proximity”.

      In our study, in situ PLA, including the experimental design of negative control, was performed in the accordance with the manufacturer’s instruction of Duolink® In Situ Red Starter Kit (MilliporeSigma): “Technical negative controls included incubation with each primary antibody separately and no primary antibody”. We have refined the relevant sentence in the revised manuscript (Lines 308-310 in the revised manuscript)

      - Figure 3G: It would have been good to include a negative control, and DNase/benzonase to exclude DNA/RNA-mediated protein interaction.

      - (Of note, there have been previous studies reporting an interaction between PRC2 and DNMT3B in other cell types, such as in Weigert et al. 2023, but unfortunately, they don't seem to use DNase/benzonase either).

      The Co-IP analysis of DNMT3B and PRC2 core components in differentiated female ES cells was presented as additional supportive evidence. Because the Co-IP analysis is extremely difficult for preimplantation embryos, we have used in situ PLA to detect their interaction. However, the maximal distance allowing in situ PLA reaction is 40 nm, which is not quite small enough to demonstrate a physical interaction (Alsemarz et al., 2018). Thus, we have added a Co-IP analysis using differentiated female ES cells, in which rXCI occurs upon the differentiation.

      Based on this consideration of the importance and contribution of this result, we have moved this result from the main figure, to the supplemental figure (Figure 4—figure supplement 3H in the revised manuscript).

      - Figure 3 figure supplement 3G: what were the ESCs differentiated into? Did the Dnmt3b KO or Dnmt3a/b DKO show any differentiation defect?

      The mouse ESC line PGK12.1 was a well-established ex vivo model of rXCI. Under the standard culture condition, PGK12.1 is normally fated to neuroectodermal commitment.

      Author response image 3.

      Immunostaining of NESTIN, a neuroectodermal stem cell marker molecule, and NANOG in undifferentiated and differentiated PGK12.1 ESCs respectively.

      No differentiation defects have been observed in either Dnmt3b KO or Dnmt3a/3b DKO ESCs in our study. Dnmt KO/DKO/TKO ES cell lines have been successfully used as the model of interaction of DNA methylation and H3K27me3 deposition (King et al., 2016).

      Figure 4:

      - Figure 4B: Is there an explanation for seeing similar total cell numbers in Figure 4B, but showing decreased proliferation in Figure 4A?

      Thank you for your insightful comments. The EdU cell proliferation assays labels cells during the S phase of cell cycle, as the 5-ethynyl 2´-deoxyuridine (EdU) is incorporated into newly synthesized DNA. This labeling identifies cells undergoing DNA synthesis, but these cells may not have completed mitosis at the time of detection. As a result, the total cell number may not immediately reflect the decrease in proliferation observed in the treated group. To address this point, we have rewritten the sentences in the revised manuscript (Lines 174-175 in the revised manuscript).

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    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment 

      This study presents valuable finding regarding the role of life history differences in determining population size and demography. The evidence for the claims is still partially incomplete, with concerns about generation times and population structure. Nonetheless, the work will be of considerable interest to biologists thinking about the evolutionary consequences of life history changes.  

      Thank you. We have addressed the generation time and population structure issues in detail in our revision and hope that you, like us, find them to be of sufficiently low concern (i.e., they are not driving the results) that they do not overshadow the main findings and conclusions.

      The opportunity to make in-depth revisions also helped the manuscript in two ways unanticipated by both us and the reviewers. First, KW made a mistake in the original analysis of phylogenetic signal, and catching that error simplifies that aspect of the study (there is none in our measured variables). Second, in June 2024 Hilgers et al. (2024; https://doi.org/10.1101/2024.06.17.599025) posted an important manuscript to bioRxiv noting the possibility of false population size peaks in PSMC analyses using the standard default settings. Our results had three of those, which we have eliminated. N<sub>e</sub>ither of these issues affect the overall conclusions, but their resolution improves the work.  

      Public Reviews: 

      Reviewer #1 (Public Review): 

      Summary: 

      This interesting study applies the PSMC model to a set of new genome sequences for migratory and nonmigratory thrushes and seeks to describe differences in the population size history among these groups. The authors create a set of summary statistics describing the PSMC traces - mean and standard deviation of N<sub>e</sub>, plus a set of metrics describing the shape of the oldest N<sub>e</sub> peak - and use these to compare across migratory and resident species (taking single samples sequenced here as representative of the species). The analyses are framed as supporting or refuting aspects of a biogeographic model describing colonization dynamics from tropical to temperate North and South America. 

      Strengths: 

      At a technical level, the sequencing and analysis up through PSMC looks good and the paper is engaging and interesting to read as an introduction to some verbal biogeographic models of avian evolution in the Pleistocene.

      The core findings - higher and more variable N<sub>e</sub> in migratory species - seem robust, and the biogeographic explanation is plausible.  

      Thanks. We thought so as well. Our analyses go beyond being simply descriptive and test some simple hypotheses, including a biogeographic+ecological expansion opportunity gained in some lineages through the adoption of a seasonal migration life-history strategy.  

      Weaknesses: 

      I did not find the analyses particularly persuasive in linking specific aspects of clade-level PSMC patterns causally to evolutionary driving forces. To their credit, the authors have anticipated my main criticism in the discussion. This is that variation in population size inferred by methods like PSMC is in "effective" terms, and the link between effective and census population size is a morass of bias introduced by population structure and selection so robustly connecting specific aspects of PSMC traces to causal evolutionary forces is somewhere between extremely difficult and impossible.  

      As R1 notes, we do not attempt to link effective population sizes and census sizes (though we do discuss this), and we are also careful to discuss correlated rather than causative factors when going beyond the overarching hypotheses regarding life-history strategy.

      Population structure is the most obvious force that can generate large N<sub>e</sub> changes mimicking the census-sizefocused patterns the authors discuss. The authors argue in the discussion that since they focus on relatively deep time (>50kya at least, with most analyses focusing on the 5mya - 500kya range) population structure is "likely to become less important", and the resident species are usually more structured today (true) which might bias the findings against the observed higher N<sub>e</sub> in migrants.  

      To clarify, the patterns we discuss are entirely related to effective population size, not census size. But, yes, this is why we’ve given population structure its own section in the Discussion.

      But is structure really unimportant in driving PSMC results at these specific timescales? There is no numerical analysis presented to support the claim in this paper. The biogeographic model of increased temperate-latitude land area supporting higher populations could yield high N<sub>e</sub> via high census size, but shifts in population structure (for example, from one large panmictic population to a series of isolated refugial populations as a result of glaciation-linked climate changes) could plausibly create elevated and more variable N<sub>e</sub>. Is it more land area and ecological release leading to a bigger and faster initial N<sub>e</sub> bump, or is it changes in population connectivity over time at expanding range edges, or is the whole single-bump PSMC trace an artifact of the dataset size, or what? The authors have convinced me that the N<sub>e</sub> history of migratory thrushes is on average very different from nonmigrant thrushes, but beyond that it's unclear what exactly we've learned here about the underlying process.  

      We do not argue that population structure is unimportant, only that it is less important as one goes into deeper time. Further, we agree with the reviewer’s observation above that structure is more likely to bias nonmigrant estimates of N<sub>e</sub>. In other words, following Li & Durbin’s (2011) simulations, we interpret that an inflated N<sub>e</sub> due to structure should occur more often among residents. We have clarified this in the revision. We also agree that what we’ve learned about the underlying process is not entirely clear, but as we stated, population structure does not seem to be the main driver, and there is evidence that both biogeographic and ecological factors are involved. With this being the first time that these questions have been asked, we think we’ve made an important advance and that we’ve opened a number of avenues for future study.

      It also important to consider the time scales involved and the sampling regime. Glacial-interglacial cycles averaged ~100 Kyr back to 0.74 Mya and then averaged ~41 Kyr from then back to 2.47 Mya; about 50-60 of these cycles occurred (Lisiecki & Raymo 2005: fig. 4). This probably caused a lot of population structuring and mixing in these lineages. In addition, in the PSMC output from one of our lineages, C. ustulatus swainsonii, we find that there are 54 time segments sampled for the Pleistocene, indicating the inadequacy of this method to reflect fine-scale changes and suggesting that each estimate is capturing a lot of both phenomena, structuring and mixing. We have added this to the revision.

      I generally agree with the authors that "at present there is no way to fully disentangle the effects of population structure and geographic space on our results". But given that, I think there are two options - either we can fully acknowledge that oversimplified demographic models like PSMC cannot be interpreted as supporting evidence of any particular mechanistic or biogeographic hypothesis and stop trying to use them to do that, or we have to do our best to understand specifically which models can be distinguished by the analyses we're employing. 

      Short of developing some novel theory deep in the PSMC model, I think readers would need to see simulations showing that the analyses employed in this paper are capable of supporting or refuting their biogeographic hypothesis before viewing them as strongly supporting a specific biogeographic model. Tools like msprime and stdpopsim can be used to simulate genome-scale data with fairly complex biogeographic models. Running simulations of a thrush-like population under different biogeographic scenarios and then using PSMC to differentiate those patterns would be a more convincing argument for the biogeographic aspects of this paper. The other benefit of this approach would be to nail down a specific quantitative version of the taxon cycles model referenced in the abstract, and it would allow the authors to better study and explain the motivation behind the specific summary statistics they develop for PSMC posthoc analysis.  

      These could very well be fruitful pursuits for future work, but they are beyond the scope of this paper. The impossibility of reconstructing ranges through deep time makes anything other than the very general biogeographic hypothesis we’ve posed an uncertain pursuit. Also, a purely biogeographic approach neglects the likelihood of ecological expansion also being involved. We get at the importance of the latter in the “Geography and evolutionary ecology” section of the Discussion. Below, the editor states that discussions among reviewers indicate that simulations are not warranted at this time. We agree that the complexities involved are substantial, to the point of making direct relevance to this empirical study uncertain (especially in such an among-lineage context). Regarding taxon cycles, we merely point out that that conceptual framework seems relevant given our findings. This was not even remotely anticipated at the outset of the study, so we are reluctant to do anything more than point out its possible relevance in several aspects of the results. Finally, the motivation for the study’s summary statistics were entirely driven by the hypotheses, as given in Methods, and due to an earlier error (noted above), there are no post-hoc analyses in the revision. Sorry for the needless confusion.

      Reviewer #2 (Public Review): 

      Summary: 

      Winker and Delmore present a study on the demographic consequences of migratory versus resident behavior by contrasting the evolutionary history of lineages within the same songbird group (thrushes of the genus Catharus). 

      Strengths: 

      I appreciate the test-of-hypothesis design of the study and the explicit formulation of three main expectations to test. The data analysis has been done with appropriate available tools. 

      Weaknesses: 

      The current version of the paper, with the case study chosen, the results, and the relative discussion, is not satisfying enough to support or reject the hypotheses here considered.  

      Given the stated strengths, the weaknesses noted seem a little incongruous, but we understand from the comments below that the reviewer would like to see the study redesigned and expanded.  

      The authors hypothesized that the wider realized breeding and ecological range characterising migrants versus resident lineages could be a major drive for increased effective population size and population expansion in migrants versus residents. I understand that this pattern (wider range in migrants) is a common characteristic across bird lineages and that it is viewed as a result of adapting to migration. A problem that I see in their dataset is that the breeding grounds range of the two groups are located in very different geographic areas (mainly South versus North America). The authors could have expanded their dataset to include species whose breeding grounds are from the two areas, regardless of their migratory behaviour, as a comparison to disentangle whether ecological differences of these two areas can affect the population sizes or growth rates.

      Because the questions are about the migratory life history strategy and the best way to get at this is in a phylogenetic framework, we’re not sure how we could effectively add species “regardless of their migratory behavior.” Further, we know that migration causes lineages to experience variable ecological conditions that include breeding, migration, and wintering conditions. Obligate migrants are going to have different breeding ranges from their close relatives, and the more distantly related species are, the less likely it is that they respond to particular ecological conditions the same way. So we do not think that an approach that included miscellaneous species from northern and southern regions would strengthen this study. Here, the comparative framework of closely related lineages that possess or lack the trait of interest is a study design strength. We do agree, however, that future work is needed that does encompass more lineages (we would argue in a phylogenetic context), and that disentangling the effects of geography and ecology will also be an important future endeavor. 

      As I understand from previous literature, the time-scale to population growth and estimates of effective population sizes considered in the present paper for the resident versus migratory clades seem to widely predate the times to speciation for the same lineages, which were reported in previous work of the same authors (Everson et al 2019) and others (Termignoni-Garcia et al 2022). This piece of information makes the calculation of species-specific population size changes difficult to interpret in the light of lineages' comparison. It is unclear what the authors consider to be lineage-specific in these estimates, as the clades were likely undergoing substantial admixture during the time predating full isolation.  

      We do recognize that timing estimates vary among studies. Differences among studies in important variables like markers, methods, generation time, and mutation or substitution rates create much of this uncertainty. Also, we are not confident in prior dating efforts in this group, largely because of gene flow and its effects on bringing estimates closer to the present. As we point out (line 485), differences among studies on these issues do not detract from the strengths here for within-study, among-lineage contrasts. In short, the timing could be off in an among-study context (and likely is with prior work, given gene flow), but relative performance of among-lineage N<sub>e</sub> differences is less susceptible to these factors. This was shown fairly well in Li & Durbin’s initial use of the method among human populations. Regarding substantial admixture, PSMC curves often unite at their origins with sister lineages (when they were the same lineage). A good example is with the two C. guttatus E & W curves in Fig. S3, which still have substantial gene flow today (they are subspecies and in contact), yet they show remarkably different N<sub>e</sub> curves through their history. It is not possible to mark a cutoff point for each lineage that represents the cessation of admixture with another lineage (e.g., Everson et al. 2019 showed substantial admixture between three full species in this group); that period can be very long (Price et al. 2008), varies among lineages, and will not be available for deeper lineage divergences in the phylogeny. We therefore chose to use all of the time intervals retrievable from the genomic data in each lineage, considering that this uniform treatment is the best approach for our among-lineage comparison. And note that we were careful to label these as “the lineages’ PSMC inception” (line 190).  

      Regarding the methodological difficulties in interpreting the impact of population structure on the estimates of effective population sizes with the PSMC approach, I would think that performing simulations to compare different scenarios of different degrees of structured populations would have helped substantially understand some of the outcomes.  

      The complexities of such modeling in a system like this are daunting. The different degrees of structuring among all of these lineages across just a single glacial-interglacial cycle would necessitate a lot of guesswork; projecting that back across 50-60 such cycles just in the Pleistocene would probably end up being fiction. Disentangling the effects of structure versus changes in N<sub>e</sub> in a system like this would probably not be possible with that approach and these data. As noted above and below, there was agreement among reviewers and the editor that simulations in this case are not warranted for revision. We have added the nature of the glacialinterglacial cycles and the PSMC sampling time segments to help readers understand this better (see above in response to R1, and lines 272-278).

      Additionally, I have struggled to understand if migratory behaviour in birds is considered to be acquired to relieve species competition, or as a consequence of expanded range (i.e., birds expand their range but their feeding ground is kept where speciation occurred as to exploit a ground with higher quality and abundance of seasonal local resources).  

      The origins of migration have been a struggle for researchers since the subject was taken up. But how the trait was acquired among these species does not really matter for our study. Here, migratory lineages possess different biogeographic+ecological attributes than their close relatives that are sedentary. Our focus is on the presence and absence of this life-history trait.

      The points raised above could be considered to improve the current version of the paper. 

      Thank you. We appreciate the opportunity to guide our revision using your comments.  

      Reviewer #3 (Public Review): 

      Summary: 

      This paper applies PSMC and genomic data to test interesting questions about how life history changes impact long-term population sizes. 

      Strengths: 

      This is a creative use of PSMC to test explicit a priori hypotheses about season migration and N<sub>e</sub>. The PSMC analyses seem well done and the authors acknowledge much of the complexity of interpretation in the discussion. 

      Weaknesses: 

      The authors use an average generation time for all taxa, but the citations imply generation time is known for at least some of them. Are there differences in generation time associated with migration? I am not a bird biologist, but quick googling suggests maybe this is the case (https://doi.org/10.1111/1365-2656.13983). I think it important the authors address this, as differences in generation time I believe should affect estimates of N<sub>e</sub> and growth.  

      Good point. The study cited by the reviewer encompasses a much higher degree of variation in body size and thus generation time. Differences in generation time in similarly sized close relatives, as in our study, should be small, and our approach has been to average those that are known. Unfortunately, generation times are not known for all of these species, but given their similarity in size we can have reasonable confidence in their being similar. We used data from the life-history research available (as cited) to obtain our average; there are not appropriate data for the residents, though. However, there is thought to be a generation time cost to seasonal migration in birds, and Bird et al. (2020) included this in their estimates to provide modeled values for all of the lineages we studied. We’re leery of using modeled values where good data for the nonmigrants in this group don’t exist (and the basis for quantifying this cost is tiny), but we recognize that this second approach is available and could leave some doubt in our results if not pursued. So we re-did everything with the modeled generation times of Bird et al. (2020). As expected, most of the differences are time-related. Importantly, our overall results are not different. We present them as Table S2 and have added the details on this to the Methods.

      The writing could be improved, both in the introduction for readers not familiar with the system and in the clarity and focus of the discussion.  

      We have added a phylogeny (new Fig. 1) to help readers better understand the system, and we’ve re-worked the Discussion to make it clearer what is clarified by our results and what remains unclear.  

      Recommendations for the authors:

      Reviewing Editor comment: 

      I note that discussion among the reviewers made clear that simulations are probably not the right answer given the complexity of the modeling required.  

      We appreciate this conclusion, with which we agree.  

      Reviewer #2 (Recommendations For The Authors): 

      Apologies for the delay with the review, which came at a very busy time. I hope you will find my comments helpful.

      Thanks. Your comments are helpful, and we fully understand how reviews (and our revisions!) have to wait until more pressing needs are addressed.

      I enjoyed reading the manuscript but I believe that the discussion sections could be heavily rewritten for better clarity. The discussion is sometimes redundant and lacks some flow/clarity. In a nutshell, I had the feeling that a bit of everything is thrown in the discussion but clear conclusions are not made.  

      Yes, the Discussion has been difficult to write, because more issues arose in the Results than we anticipated at the outset. We feel that discussing them is relevant, but we agree that much remains unclear. This coupling of paleodemographics with geography and ecology is a new area, which opens some important new (and relevant) areas to consider. So clarity is not possible in some areas. We’ve revised to point out where we do have clarity (e.g., in migrant lineages having different paleodemographic attributes than nonmigrants) and where only further study can provide clarity (e.g., in the roles of geography versus ecology). The journal format does not seem to have secondary subheaders, but we’ve used bold in one place to highlight ‘ecological mechanisms’ to offset that section, one of the more complex. We’ve also added a paragraph in the conclusions to clarify where we have clear takeaways and where uncertainties remain. 

      Reviewer #3 (Recommendations For The Authors): 

      The introduction should engage the reader with biology, not the use of demographic methods or genomics (both of which have been around for more than a decade). I would drop the first paragraph and considerably expand the second. What has previous research on ecology/behavior/genetics found regarding the demographic effects of seasonal migration?

      There are two important aspects to our study: 1) using paleodemographic methods to test hypotheses about adoption of a major life-history trait—an important biological question regardless of system, and so far (surprisingly) unaddressed; and 2) using this novel approach to study the effects of one such trait, seasonal migration. At these timescales, nothing exists on this subject, so there is really nothing to expand with. If there is relevant literature that we’ve missed, we’d be happy to add it.

      What is the missing bit of information or angle the current study addresses (other than just doing it larger and fancier with genomics)?  

      The effects of major life-history traits on paleodemographics has not been addressed before, to our knowledge. The whole context is new, so we’re not doing something “larger and fancier” with genomics. We are doing something that has not been done before: testing hypotheses about the effects of a major life-history trait on population sizes in evolutionary time. We’re not sure how this can be made clearer. To us this seems like a very engaging biological question with wide applicability. We hope that this study is just the first of many to come, in a diversity of biological systems.

      A figure showing the phylogenetic relationships of these taxa which are migratory would help the reader immensely. Although this is shown in Fig S3 I think it might be nice to have a map of the species and their ranges alongside a phylogeny as a main figure early on.  

      Thank you. This is a good suggestion. We can’t fit a phylogeny and all the distribution maps (Fig. S1) onto a page, but we can include a phylogeny as one of the main figures with nonmigrants highlighted. We’ve inserted this as a new Fig. 1. 

      If I understand correctly, the authors' arguments for why migratory species should show more growth hinge on large range size and geographic expansion. Yet they argue in the discussion that these forces are unlikely to be important (L226). I found the discussion on this confusing (e.g. L231 then says maybe it does matter). I think more clarity here would be helpful.

      Our argument and predictions are based both on geographic and ecological expansion. This was clearly stated as our third prediction “3) early population growth would be higher as seasonal migration opens novel ecological and geographic space…” We have gone back through and reiterated the coupling of these two factors. The line mentioned concludes the first paragraph in the section ‘Geography and evolutionary ecology,’ which focuses on the difficulty of decoupling these in this system. As the paragraph relates, geography alone does not seem to be driving our results (we do not argue that it is unimportant). 

      I also would have liked more time in the discussion addressing why variation in N<sub>e</sub> may be higher in migratory lineages.

      In addition to re-clarifying this in the Introduction, we have touched back on this now at line 221: “We attribute the higher variation in N<sub>e</sub> among migrants to be the result of the relative instability of northern biomes compared with tropical ones through glacial-interglacial cycles (e.g., Colinvaux et al., 2000; Pielou, 1991).”

      Minor comments: 

      L 62: Presumably PSMC is limited by the coalescent depth of the genelaogy, which may be younger or older than population "origins" depending on the history of colonization, lineage splitting, gene flow, etc.  

      We were careful to phrase these as “the lineages’ PSMC inception” (line 190), and responded to this issue in more detail above in response to R2’s public review. 

      L 338: I think a few more details on PSMC would be helpful. Was no maskfile used?  

      We did not use a maskfile, choosing instead to generate data of decent coverage and aligning reads to a single closely related relative. 

      Did the consensus fasta include all species?  

      No, we used a single reference high-quality fasta of Catharus ustulatus , as reported (lines 434-37). We have added that “Identical treatment of all lineages in these respects should provide a strong foundation for a comparative study like this among close relatives.” 

      L 361: Fair to assume the authors used a weighted average of N<sub>e</sub> from the output, rather than just averaging the N<sub>e</sub> values from each time segment?  

      No – we used all the values of N<sub>e</sub> produced by PSMC output. The PSMC method uses nonoverlapping portions of the genome in its analyses (which we’ve added to make that clear), and portions in juxtaposition will often provide data for very different periods in the time segments. Further, time segments are uneven within and among taxa, so it is not clear how a uniform and comparable weighting scheme could be implemented. We consider a uniform approach to be of primary importance, including for future comparisons among studies. 

      L 383 "delta" typo

      Thank you for catching this.

      L 93: I'd be tempted to present the questions (how does seasonal migration affect population size trajectory, means, and variation) and rationale before presenting the hypotheses. I found myself reading the hypotheses and wondering "why?"  

      We’ve tried this change in the revision. It makes the hypotheses a little harder to pull out (they are no longer numbered in a short sequence), but it is shorter and solves this concern.  

      L 337 read depth is usually expressed as X (e.g. "23X") rather than bp.

      Changed.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This important study further validates DNAH12 as a causative gene for asthenoteratozoospermia and male infertility in humans and mice. The data supporting the notion that DNAH12 is required for proper axonemal development are generally convincing, although more experiments would solidify the conclusions. This work will interest reproductive biologists working on spermatogenesis and sperm biology, as well as andrologists working on male fertility.

      We thank the editor and the two reviewers for their time and careful evaluation of our manuscript. We sincerely appreciate their encouraging feedback and insightful guidance on improving our study. In the revised manuscript, we have performed additional experiments and provided quantitative data regarding the reviewers' comments.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Even though this is not the first report that the mutation in the DNAH12 gene causes asthenoteratozoospermia, the current study explores the sperm phenotype in-depth. The authors show experimentally that the said mutation disrupts the proper axonemal arrangement and recruitment of DNALI1 and DNAH1 - proteins of inner dynein arms. Based on these results, the authors propose a functional model of DNAH12 in proper axonemal development. Lastly, the authors demonstrate that the male infertility caused by the studies mutation can be rescued by ICSI treatment at least in the mouse. This study furthers our understanding of male infertility caused by a mutation of axonemal protein DNAH12, and how this type of infertility can be overcome using assisted reproductive therapy.

      Strengths:

      This is an in-depth functional study, employing multiple, complementary methodologies to support the proposed working model.

      Thank you for your recognition of the strength of this study. Your positive feedback motivates us to continue refining our research and methodological rigor in future studies.

      Weaknesses:

      The study strength could be increased by including more controls such as peptide blocking of the inhouse raised mouse and rat DNAH12 antibodies, and mass spectrometry of control IP with beads/IgG only to exclude non-specific binding. Objective quantifications of immunofluorescence images and WB seem to be missing. At least three technical replicates of western blotting of sperm and testis extracts could have been performed to demonstrate that the decrease of the signal intensity between WT and mutant was not caused by a methodological artifact.

      Thank you for your comments. In order to study in-depth, we have analyzed the protein sequence features of DNAH12 protein, 1-200 amino acids of DNAH12 were selected as the ideal antigen considering its good performance (1. high immunogenicity; 2. High hydrophilicity; 3. Good Surface Leakage Groups; 4. Sequence homology analysis to avoid unspecific recognition to other proteins;). The two different anti-DNAH12 antibodies were developed with the help Dia-An Biotech company in 2022, we have tried to acquire the polypeptide fragments of target proteins to do peptide blocking but the material were discard after the service. Luckily, we have got the target band of DNAH12 protein in western blotting experiment while the band was not detected in knockout mice group; the immunofluorescence signals of DNAH12 were strong but not present in knockout mice group. Besides, we have tested that the inhouse raised rabbit antibody were suitable for IP experiment. The IP experiment also showed the raised rabbit antibody were able to immunoprecipitated the DNAH12 band in the Dnah12<sup>+/+</sup> mice but not in Dnah12<sup>-/-</sup> mice. Collectively, these data could support the specificity of the raised DNAH12 antibodies.  In IP assay, we have added the IgG group in the IP-mass spectrometry to exclude non-specific binding. And the experimental design was described in Figure 6B. The raw data were deposited in iProX partner repository (accession number: PXD051681), and we have coordinated with the repository manager to make the data publicly accessible (https://www.iprox.cn/page/subproject.html?id=IPX0008674001).  

      Besides, we have conducted replicates of western blotting of sperm and testis extracts at least 3 times and added the objective quantifications of immunofluorescence signals and WB images. The quantifications of the blot were shown in figures to help readers understand these results easily.

      Reviewer #2 (Public Review):

      Summary:

      The authors first conducted whole exome sequencing for infertile male patients and families where they co-segregated the biallelic mutations in the Dynein Axonemal Heavy Chain 12 (DNAH12) gene.

      Sperm from patients with biallelic DNAH12 mutations exhibited a wide range of morphological abnormalities in both tails and heads, reminiscing a prevalent cause of male infertility, asthenoteratozoospermia. To deepen the mechanistic understanding of DNAH12 in axonemal assembly, the authors generated two distinct DNAH12 knockout mouse lines via CRISPR/Cas9, both of which showed more severe phenotypes than observed in patients. Ultrastructural observations and biochemical studies revealed the requirement of DNAH12 in recruiting other axonemal proteins and that the lack of DNAH12 leads to the aberrant stretching in the manchette structure as early as stage XI-XII. At last, the authors proposed intracytoplasmic sperm injection as a potential measure to rescue patients with DNAH12 mutations, where the knockout sperm culminated in the blastocyst formation with a comparable ratio to that in WT.

      Strengths:

      The authors convincingly showed the importance of DNAH12 in assembling cilia and flagella in both human and mouse sperm. This study is not a mere enumeration of the phenotypes, but a strong substantiation of DNAH12's essentiality in spermiogenesis, especially in axonemal assembly.

      The analyses conducted include basic sperm characterizations (concentration, motility), detailed morphological observations in both testes and sperm (electron microscopy, immunostaining, histology), and biochemical studies (co-immunoprecipitation, mass-spec, computational prediction). Molecular characterizations employing knockout animals and recombinant proteins beautifully proved the interactions with other axonemal proteins.

      Many proteins participate in properly organizing flagella, but the exact understanding of the coordination is still far from conclusive. The present study gives the starting point to untangle the direct relationships and order of manifestation of those players underpinning spermatogenesis. Furthermore, comparing flagella and trachea provides a unique perspective that attracts evolutional perspectives.

      Thank you for your thoughtful and positive feedback. We are delighted that you found our study to be a strong substantiation of DNAH12's essential role in spermiogenesis, particularly in axonemal assembly. We believe that this study represents a meaningful step toward unraveling the intricate coordination of axonemal proteins during spermatogenesis, and your comments further inspire us to continue exploring these complex mechanisms in future work. Thank you once again for your valuable insights and summary of this work.

      Weaknesses:

      Seemingly minor, but the discrepancies found in patients and genetically modified animals were not fully explained. For example, both knockout mice vastly reduced the count of sperm in the epididymis and the motility, while phenotypes in patients were rather milder. Addressing the differences in the roles that the orthologs play in spermatogenesis would deepen the comprehensive understanding of axonemal assembly.

      This is an interesting question. Actually, it seems that although humans and mice share the male infertility phenotypes with deficiency in dynein proteins essential for sperm flagellar development, they are different in some ways. For instance, it has been reported that deficiency in DNAH17 (Clin Genet. 2021. PMID: 33070343) or DNAH8 (Am J Hum Genet. 2020. PMID: 32619401; PMCID: PMC7413861), two other members of Dynein Axonemal Heavy Chain family, also cause more severe phenotype in mice, comparing with that of human patients carrying bi-allelic DNAH17 or DNAH8 loss-of-function mutations. In knockout mice, sperm counts are lower, and the proportion of abnormal sperm morphology is higher, whereas the phenotypes in human patients tend to be milder. These observations suggest that orthologs may influence spermatogenesis to slightly different extents in humans and mice. We plan to investigate the mechanisms underlying these discrepancies in future studies, which will provide deeper insights into axonemal assembly and the evolutionary aspects of spermatogenesis. Thank you again for bringing up this important issue.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      This reviewer is impressed by the study's depth and the extent of the methodology used in the study. The study is well-designed, and the results are very interesting. The reviewer's enthusiasm was reduced by the lack of some controls (provided that the reviewer did not miss them). Further are point-to-point suggestions that this reviewer believes will increase the merit of the present study.

      Title:

      (1) Why a "special" dynein? What makes it special when compared to other dyneins? I suggest removing the word special.

      Through phylogenetic and protein domain analyses of the DNAH family, we found that DNAH12 is the shortest member and the only one that lacks a typical microtubule-binding domain (MTBD) in the DNAH family, thus we want to describe it as a “special” dynein. We have fully considered your valuable suggestion and decided to remove it from the title.

      Abstract:

      (2) L23: same as above, why special?

      We identified DNAH12 as the shortest member of the DNAH family and uniquely lacking the typical microtubule-binding domain (MTBD). This distinct characteristic prompted us to describe it as a 'special' dynein in the abstract part.

      (3) L37: the reviewer did not find a figure (neither main nor supplementary) that would demonstrate the proper organization of microtubules in cilia. Figure S11 only shows the presence of cilia in DNAH12-/- mouse. A TEM image of cilia is required to confirm or reject the claim that DNAH12 does not play a crucial role in proper microtubule organization in cilia.

      We have now added TEM images of cilia in wild-type and Dnah12<sup>-/-</sup> mice. The ultra-structures of cilia axonemes were comparable in wild-type and Dnah12<sup>-/-</sup> groups, suggesting that DNAH12 may not play crucial role in proper microtubule organization. The results have now been added to Supplemental Figure 11F.

      (4) L122-6: Did the authors also confirm these structures by cryo-EM? If not, this needs to be pointed out as a shortcoming in the discussion, that the structures and interactions are predicted in silico only.

      Thank you for your comment. Due to resource limit, we do not perform cryo-EM to confirm these structures. We will pursue the structures details at an atomic resolution structure in further study. We understand this point and now we have addressed this as a shortcoming in the discussion part.

      (5) L134: Be more specific about what characteristics of DNAH12 were analyzed.

      Thank you for your comment. We have now updated these in the method part. The characteristics of the DNAH12 including its region immunogenicity, hydrophilicity, surface leakage groups, and sequence homology were analyzed.

      (6) L137: Be more specific about how the antibodies validated were. Were the antibodies validated for both immunofluorescence and western blotting? I suggest doing peptide blocking of the antibody, for instance for ICC, preincubation of ab with immunizing peptide followed by primary ab incubation with studied cells/tissues.

      Thank you for your comments and suggestions. We validated the antibodies for both immunofluorescence and western blotting to ensure their effectiveness in our experiments. The two different anti-DNAH12 antibodies were developed with the help Dia-An Biotech company in 2022, we have attempted to acquire the polypeptide fragments of target proteins to do peptide blocking but the material were disposed after the service. Luckily, we have got the target band of DNAH12 protein which showed strong signal in western blotting experiment and the band was not detected in knockout mice group; the immunofluorescence signals of DNAH12 were strong but not present in knockout mice group. Besides, the IP experiment also showed the raised rabbit antibody were able to immunoprecipitated the DNAH12 band in the Dnah12<sup>+/+</sup> mice but not in Dnah12<sup>-/-</sup> mice. Collectively, these data could support the specificity of the raised DNAH12 antibodies. We sincerely admire your suggestion and will require for the peptide material if we develop new antibodies.

      (7) L142: This reviewer is unfamiliar with using TRIzol for sperm protein extraction. Is there a specific reason for not using PAGE loading buffer for human sperm protein extraction?

      Thanks for your suggestions. TRIzol reagent can be used for small amounts of samples (5×10<sup>6</sup> cells) as well as large amounts of samples (>10<sup>7</sup> cells). It is suitable for extraction of RNA and proteins at the same time. Our lab has adopted these methods in our previous work (Hum Reprod Open. 2023; PMID: 37325547; PMCID: PMC10266965.). This method is very useful to process valuable small amounts of samples for scientific work. The human sperm protein extraction was added with SDS-sample buffer [PAGE loading buffer] before SDS-PAGE separation. We have added this detail in the method part. We are sorry for making this misunderstanding.

      (8) L144: Were these the final concentrations of the SDS loading buffer? 1 × Laemmli buffer contains 62.5 mM TRIS, 2% (w/w) SDS, 10 % (w/v) glycerol, and 5% 2-mercaptoethanol. Please, amend accordingly.

      Thanks for your suggestions.  We apologized for incorrect labelling of concentrations (The previous one is 3× SDS loading buffer).  We have now amended the SDS loading buffer to 1 × Laemmli buffer as suggested.

      (9) L151: Table S2 contains other homemade antibodies than DNAH12. Please, include references to the studies where the generation and validation of these antibodies is described.

      Thank you for your suggestions. We have developed a DNAH1 antibody for use in Western blot assays, with its generation and validation detailed in Frontiers in Endocrinology (Lausanne), 2021 (PMID: 34867808; PMCID: PMC8635859). Additionally, we have produced a DNAH17 antibody for both immunofluorescence (IF) and Western blot, as described in Journal of Experimental Medicine, 2020 (PMID: 31658987; PMCID: PMC7041708). These references have now been included.

      (10) L167: Please, spell out ICR at its first appearance.

      Done as suggested, Thank you. The full name of ICR is Institute of Cancer Research.

      (11)L169: This reviewer is confused. It seems that the mouse encodes DNAH12 on exons 5 and 18 simultaneously. Each mouse model has only one exon targeted for a knockout. Would not this mean that the expression of DNAH12 in both models is not completely knocked down? Please, give more background in this paragraph for those less familiar with CRISPR/Cas9.

      Thank you for your insightful comment. We appreciate your attention to detail. To clarify, while the mouse model does indeed encode DNAH12 on exons 5 and 18 simultaneously, we specifically targeted the key exon 5 or exon 18 in each model to achieve different knockout strategies. This approach allows us to assess the functional implications of the remaining DNAH12 expression in both models. We have checked the DNAH12 expression in both models, and the result showed both models present with undetected DNAH12 proteins, indicating both models were completely knocked out of DNAH12 proteins. Additionally, we will revise the manuscript to include further details on the CRISPR/Cas9 methodology, ensuring accessibility for readers less familiar with this technique. Thank you again for your valuable feedback, which we believe will greatly enhance our manuscript.

      (12) L201: 50 % PBS? As in 0.5 x concentrated PBS? Please, rewrite for clarity.

      The term "50% PBS" refers to a 1:1 dilution of phosphate-buffered saline (PBS) with an appropriate diluent, resulting in a final concentration of 0.5x PBS. We will revise the text to explicitly clarify this, ensuring it is clear to all readers. Thank you for highlighting this point.

      (13) L224: Please, state what beads those were (magnetic/agarose, conjugated to protein A/G...) Include catalog # and manufacturer.

      Thank you for your suggestion. We have updated the manuscript to include this information. The beads used were Protein A/G Magnetic Beads (Catalog #B23202, Bimake, Texas, USA).

      (14) L227: What was the reason for adding a proteasomal inhibitor? What concentration was used? Please, add this information to the text.

      We adding MG132 in cell immunoprecipitation (IP) experiments is to inhibit proteasomal activity, thereby preventing the degradation of the target protein. This helps maintain the stability of the target protein during the experiment (Sci Adv. 2022. PMID: 35020426; PMCID: PMC8754306.), enhancing its detectability in subsequent analyses. MG132 (5 μM) was added. We have added this information in the revised the manuscript

      (15) L233: in vivo IP of mouse testis lysate? This does not make sense. I suggest removing "in vivo".

      Thank you for your careful review and comments on our manuscript. We have modified as suggested.

      (16) L317: Supplemental Figure 6 precedes Supplemental Figure 5 in the text, which is neither logical nor orderly.

      Thank you for your suggestion. Since the N-terminal DNAH12 antibody is already described in the Methods section (L317), we propose removing Supplemental Figure 6 from the content to improve the logical flow and maintain an orderly presentation.

      (17) L345 and elsewhere: how did the authors quantify the decrement of the signal? This needs to be measured objectively.

      Thank you for your valuable suggestion. We quantified the signal intensity using Fiji (Nat Methods. 2012. PMID: 22743772; PMCID: PMC3855844), which allows for precise analysis of pixel intensity. The results are presented in the figures to effectively illustrate the decrement in signal intensity. We appreciate your suggestion, and we have provided a description of the method in our methodology section.

      (18) L371: I recommend: ...and elongated spermatids; the abnormal...

      Done as suggested. Thank you.

      (19) L412-4: Cilia in both Dnah12<sup>mut/mut</sup> and Dnah12<sup>-/-</sup> are developed, but are they motile or immotile? This needs to be investigated. Is the DNAH12 in cilia truncated while still fulfilling its function?

      Thanks for your comment. We have checked the ciliary motility using an inverted microscope, and no significant difference of ciliary motility were observed between the knockout group and the control group. These results indicated that the ciliary motility was not affected by DNAH12 deficiency. The N-terminal DNAH12 antibody was developed to detect whether a truncated protein in mice tissues while we do not detect DNAH12 signals through immunofluorescence assay on trachea sections of the Dnah12<sup>-/-</sup> mice. These results indicate that DNAH12 may exert little influence on cilia, comparing to its important function in flagella.

      (20) L414-6: The results do not support this claim as the authors do not show that cilia are motile.

      Thanks for your comment. The supplemental videos 3-4 of trachea live of Dnah12<sup>+/+</sup> and Dnah12<sup>-/-</sup> mice have been uploaded to support this conclusion.

      (21) L421-3: Did the authors perform a negative test, where they let the testis lysate interact with beads/IgG only and performed the MS to identify non-specific binding? This is a crucial specificity test for this approach.

      We have performed negative test. In IP assay, we have added the IgG group in the IP-mass spectrometry to exclude non-specific binding. And the experimental design was described in Figure 6B. The raw data were deposited in iProX partner repository (PXD051681), which we have required the manager soon to update the status to public, so it will be visible to readers. 

      (22) L462: same as #18 the authors need to show that cilia are also motile. The mere presence of cilia in DNAH12-/- as shown in Fig S11C&D is not sufficient to conclude that the mice do not manifest PCD symptoms.

      Thanks for your comment. We do not observe obvious differences between the cilia of Dnah12<sup>+/+</sup> and Dnah12<sup>-/-</sup> mice.  The supplemental videos 3-4 of trachea live of Dnah12<sup>+/+</sup> and Dnah12<sup>-/-</sup> mice have been uploaded to show the motility of the trachea.

      (23) L529: MTBD region instead of domain, as "domain" is already part of the abbreviation.

      Done as suggested

      (24) L875: Sperm is both the singular and plural form. Spermatozoon vs spermatozoa can be used where the distinction between singular and plural needs to be made.

      Thanks for your suggestion. We have checked and changed this usage.

      (25) Figure 3H: Is there a specific reason why P11 is not shown?

      Because limited smear slides of P11 were available, the P11 were not stained for DNAH17 antibody previously. We have now updated the experiment, which showed that DNAH17 expression were not affected in patient P11. We have now added this result to Figure 3H.

      (26) Figure 8H: The authors in their MS do not describe what is happening to N-DRC proteins, yet they suggest in their model that it's unaffected in the mutant mouse/human. Please, address this in the MS and clearly state in the model that N-DRC needs further exploration in future studies.

      Thanks for your suggestion, we have checked the MS data but do not observe the enrichment of nexin-dynein regulatory complex (N-DRC) protein, just one known N-DRC protein DRC1 present with only 1 unique peptide. Instead, enrichment of inner dynein arm proteins and radial spoke proteins were observed. However, we cannot determine the N-DRC structures maybe affected or not. We have stated this in the discussion part and will pursue this with high resolution technology like cryo-EM in the future.

      (27) Figure 5F: Is it possible to choose a different Dnah12<sup>-/-</sup> spermatozoon to see a reduced level of DNALI1 so that it corresponds with the WB detection in Fig 5B?

      Thanks for your suggestion, we have chosen a Dnah12<sup>-/-</sup> spermatozoon with faint remnants of the DNALI1 signal as the representative picture.

      (28) Figure S2 and elsewhere: How were the authors able to resolve and calibrate 356 kDa protein using SDS PAGE? Agarose electrophoresis protein electrophoresis is more suitable for resolution of high molecular proteins. Most of the protein standards have as high molecular standard as 250 kDa.

      We have found that high molecular proteins (like 356kDa) were able to resolve in concentration 4-12% gradient gel of polyacrylamide gels and employ appropriate voltages and more time during electrophoresis to improve resolution of high molecular weight proteins. The DNAH12 proteins were calibrated by the using of a HiMark™ Pre-Stained High Molecular Weight Protein Standard (30-460 kDa). We have now updated the blot images to show the size of the DNAH12 protein (Fig S6B,). The target band is obvious between 268 kDa and 460 kDa, which make it easy to calculate the target band of DNAH12 antibody elsewhere. Thanks for your suggestion.

      (29) Figure S5: similar to #24: Why P10 and P11 are not shown?

      Because limited smear slides of P10 or P11 were available, we did not stain ODF2 antibody previously. We have now updated the experiments, which showed that ODF2 expression were not affected in patient P10 or P11. We have now added this result to Figure S5.

      (30) Figure S6B: The specificity of the anti-DNAH12 antibody against mouse DNAH12 seems to be questionable since the authors detect multiple bands on WB. I recommend doing peptide blocking to show that these are non-specific binding as opposed to off-target binding.

      Thank you for your comments. In order to study in-depth, we have analyzed the protein sequence features of DNAH12 protein, 1-200 amino acids of DNAH12 were selected as the ideal antigen considering its good performance (1. high immunogenicity; 2. High hydrophilicity; 3. Good Surface Leakage Groups; 4. Sequence homology analysis to avoid unspecific recognition to other proteins;). The two different anti-DNAH12 antibodies were developed with the help Dia-An Biotech company in 2022, we have attempted to acquire the polypeptide fragments of target proteins to do peptide blocking but the material were disposed after the service. Luckily, we have got the target band of DNAH12 protein which showed strong signal in western blotting experiment and the band was not detected in knockout mice group; the immunofluorescence signals of DNAH12 were strong but not present in knockout mice group. Besides, we have tested that the inhouse raised rabbit antibody was suitable for IP experiment. The IP experiment also showed the raised rabbit antibody were able to immunoprecipitated the DNAH12 band in the Dnah12<sup>+/+</sup> mice but not in Dnah12<sup>-/-</sup> mice. Collectively, these data could support the specificity of the raised DNAH12 antibodies. We admire your suggestion and will require for the peptide material if we develop new antibodies.

      Reviewer #2 (Recommendations For The Authors):

      Recruitment of DNAH1 and DNALI1 to the flagella is dependent on DNAH12 expression, according to the data. What would be the mechanism that locates DNAH12 which lacks MTBD to the flagella?

      Thank you for your insightful question. We are currently investigating the mechanisms that facilitate the loading of DNAH12 to the flagella. Based on existing data, we hypothesize that CCDC39 and/or CCDC40 may play a critical role in the recruitment of DNAH12 to sperm flagella during spermiogenesis (Nat Genet. 2011, PMID: 21131972; PMCID: PMC3509786; Nat Genet. 2011, PMID: 21131974; PMCID: PMC3132183). Furthermore, a structural study by Walton et al. showed that DNAH12 associates with CCDC39/CCDC40 proteins (Nature. 2023, PMID: 37258679; PMCID: PMC10266980). These findings suggest that CCDC39 and/or CCDC40 may play a role in facilitating the localization of DNAH12 to the flagella. Additional studies are needed to identify other potential factors involved in this process and to further elucidate the mechanisms underlying this complex biological phenomenon.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This important study reports the transcriptomic and proteomic landscape of the oviducts at four different preimplantation periods during natural fertilization, pseudopregnancy, and superovulation. The data presented convincingly supported the conclusion in general, although more analyses would strengthen the conclusions drawn. This work will interest reproductive biologists and clinicians practicing reproductive medicine. 

      We appreciate the concise summary and agree that additional experiments can reinforce the fidelity of predictions made by our robust bioinformatic characterization of the oviduct. Our robust bioinformatic model appears reproducible as similar pathway trends have been produced in all three datasets, lending confidence for future researchers to establish testable hypotheses more effectively.  

      Reviewer #1 (Public review):

      The paper demonstrated through a comprehensive multi-omics study of the oviduct that the transcriptomic and proteomic landscape of the oviduct at 4 different preimplantation periods was dynamic during natural fertilization, pseudopregnancy, and superovulation using three independent cell/tissue isolation and analytical techniques. This work is very important for understanding oviductal biology and physiology. In addition, the authors have made all the results available in a web search format, which will maximize the public's access and foster and accelerate research in the field.

      Strengths:

      (1) The manuscript addresses an important and interesting question in the field of reproduction: how does the oviduct at different regions adapt to the sperm and embryos for facilitating fertilization and preimplantation embryo development and transport?

      (2) Authors used cutting-edge techniques: Integrated multi-modal datasets followed by in vivo confirmation and machine learning prediction.

      (3) RNA-seq, scRNA-seq, and proteomic results are immediately available to the scientific community in a web search format.

      (4) Substantiated results indicate the source of inflammatory responses was the secretory cell population in the IU region when compared to other cell types; sperm modulate inflammatory responses in the oviduct; the oviduct displays immuno-dynamism.

      We sincerely thank you for your thorough and insightful review of our manuscript. Your comprehensive summary accurately captures the essence of our multi-omics study on oviductal biology, highlighting its importance in understanding reproductive physiology. We are particularly grateful for your recognition of our study's strengths. In the revised manuscript, we have added another searchable scRNA-seq data on our public website; https://genesearch.org/winuthayanon/Oviduct_pregnancy/. We have also addressed the weaknesses in the response below in our revised manuscript.  

      Weaknesses:

      (1) The rationale for using the superovulation model is not clear. The oviductal response to sperm and embryos can be studied by comparing mating with normal and vasectomized mice and comparing pregnancy vs pseudopregnancy (induced by mating with vasectomized males). Superovulation causes supraphysiological hormone levels and other confounding conditions.

      We agree with this assessment that superovulation changes the hormonal levels and could have a confounding impact on the oviduct function. As such, for all experiments involving pseudopregnant datasets, pseudopregnancy was induced by mating females with vasectomized males without superovulation. Our oviductal luminal protein content analysis was collected from oviductal fluid from pregnant females with and without superovulation. This allowed us to directly compare the impact of superovulation on protein abundance and profile. In the revised manuscript, we have provided clarifying statements on using superovulation in our Method section, which reads 

      “Datasets from the natural cycle and SO allowed us to directly compare the impact of exogenous hormone treatments on protein abundance and profile distinct from the physiological levels of hormones”.

      One exception for using superovulation in the absence of a “natural mating” group for comparison is the scRNA-seq dataset. As single-cell libraries should be performed in a single run to avoid batch effects, we need to ensure that a sufficient number of females were pregnant for single-cell isolation (we used ~4 mice/time point). Therefore, superovulation was used to synchronize and ensure that the females were receptive to mating. At the time of our sample collection, single nuclei isolation methods (freeze tissue now, isolate nuclei later) had not been reliable or standardized. We tried to synchronize females using the male bedding without superovulation. However, we would still need to set up at least 12-15 females per pregnancy timepoint to mate with male mice, totaling ~48-60 mice each night. Due to budget constraints and vivarium space limitations, we were not able to do so. We have included a similar statement to clarify the justifications in the revised Methods, which reads,

      “Mating and tissue collection protocols were similar to bulk RNA isolation described above, with the exception that female mice were superovulated using the protocol described previously (73) to ensure sufficient numbers of female mice at each timepoint could be harvested for single cell isolation and library preparation within the same day (n= 3-4 mice/group)”.

      (2) This study involves a very complex dataset with three different models at four time points. If possible, it would be very informative to generate a graphic abstract/summary of their major findings in oviductal responses in different models and time points

      Thank you for this suggestion. We have now included the graphical abstract to accompany our final version of the manuscript.

      (3) The resolution of Figures 3A-3C in the submitted file was not high enough to assess the authors' conclusion.

      We have now used a higher magnification of images in Figures 3A-C in the revised version.

      (4) The authors need to double-check influential transcription factors identified by machine learning. Apparently, some of them (such as Anxa2, Ift88, Ccdc40) are not transcription factors at all.

      We appreciate the recognition of this oversight. In the revised manuscript, we have clarified and stated the distinction between ‘influential transcripts’ and ‘influenced proteins’, which now reads,

      “The top 25 “influential” transcripts (ITs) with the highest attention scores from all the transcription factors present in bulk RNA-seq data were extracted for every potentially influenced protein (IP) in the empirical proteomics datasets”. 

      Recommendations for the authors:

      (1) What are the stained debris/nuclei surrounding oocytes/fertilized eggs in Figure 1A? Please indicate in figure legends. 

      We have edited Figure 1A with black arrows that highlight the stained cumulus cells surrounding the ovulated eggs/fertilized eggs, together with a revised Figure legend, which now reads, “Arrows indicate cumulus cells surrounding the eggs/fertilized eggs called cumulus-oocyte complexes”.

      (2) "Then, oviducts were sectioned into IA and IU regions" The Ampulla region is quite a long tube. Could authors provide details about the cutting border between IA and IU regions? 

      We have now included a literature defining the number of turns in the coiled mouse oviduct and how we cut between the IA and IU regions in the Method section, which reads,

      “We defined the IA region by including the infundibulum and cutting at turn three of the oviductal coil (the end of ampulla) (5). Turn four to eleven was considered the IU region, which was stripped of uterine tissue enveloping the colliculus tubaris of the UTJ region (5)”. 

      (3) "In this experiment, superovulation (SO) using exogenous gonadotropins was used due to technical limitations of sample collection for single-cell processing." It was not clear. What was the technical limitation of sample collections? 

      As indicated in response to the public review above, we have now clarified that we used superovulation for scRNA-seq analysis to ensure that a sufficient number of females were pregnant for singlecell isolation (we used ~4 mice/time point). Therefore, superovulation was used to synchronize, making sure that females were receptive to mating, thereby providing enough cell numbers for the experiment.

      (4) Ephx2+ cluster (only present at SO 0.5 dpc and SO estrus) was very interesting. Could the author provide more information about this gene and the potential cell type this cluster represents? 

      We appreciate the reviewer’s interest in this cell-type cluster. We have now included the discussion regarding this gene, which reads, “Interestingly, the Ephx2+ cluster was mainly present in the SO 0.5 dpc and SO estrus samples. Ephx2 encodes epoxide hydrolase 2, which converts epoxides to dihydrodiols. Recent findings suggest that EPHX2 may play a role in primary hypertension in humans (52). However, the reproductive-related functions of EPHX2 have not yet been investigated. Therefore, we believe this presents an opportunity for future research to define the role of Ephx2 in the oviduct in response to SO during preimplantation embryo development.” However, as it is beyond the scope of the research provided in this manuscript, we did not further investigate the roles of Ephx2 in our current study. 

      (5) "we elucidated whether exogenous hormone treatment impacts protein secretion in the oviduct. There were 298, 354, and 163 differentially abundant proteins when compared between SO estrus vs. SO 269 0.5 dpc". Which hormone?? FSH/LH? Or high estrogens due to more mature follicles; or more embryos instead of hormones? Again, the rationale for using the superovulation model need to be better explained with the consideration of other possibilities. 

      Thank you for pointing this out. We have clarified that “exogenous hormone treatment” was the superovulation (SO), which is now corrected in the statement, which reads, “we elucidated whether SO treatment impacts protein secretion in the oviduct”. 

      The justification for the superovulation has now been included in the revised manuscript as indicated in the responses to reviewers above. A detailed description of gonadotropin treatment was included in the Material and Methods section. As the reviewer suggested, we have revised in the Discussion, including the caveat and possibility of the other factors that could lead to biological changes we observed subsequent to SO, which reads, 

      “As SO increases the number of mature follicles (therefore, estrogen levels), ovulated eggs, and follicular fluid, it is also likely that these biological alterations could lead to changes in the protein abundance in the oviduct”.

      (6) "we used RNAScope in situ hybridization staining of Tlr2, Ly6g (leukocytes), and Ptprc (common immune cell marker)." Please indicate what cell types Tlr2 marker was for. 

      We have now corrected the statement to include the cell types with Tlr2+ staining, which reads, “we used RNAScope in situ hybridization staining of Tlr2 (epithelium, stroma, and myosalpinx), ”.

      (7) In which cell types are P38 and p-P38 expressed?  

      Based on our scRNA-seq searchable dataset, which has been included in the revised manuscript (https://genesearch.org/winuthayanon/Oviduct_pregnancy/), we found that Mapk14 (encoding P38) was highly expressed in the immune cells in mice (red arrows in the UMAPs below).

      Author response image 1.

      In humans, scRNA-seq data published by Ulrich et al. (PMID: 35320732) showed that MAPK14 was present in most cell types in the Fallopian tubes at low levels (see violin plot below).

      Author response image 2.

      (8) "Our findings showed an influx of Ptrprc+ cells to the stromal layer, and subsequently penetration into the epithelial layer in the presence of sperm at 0.5 dpc in the UTJ." The authors didn't have results for tracking the influx Ptrprc+ cell to the stromal layer. 

      Thank you for pointing this out. We agreed with the reviewer’s assessment, as we did not have the results of the tracking of the influx of Ptprc+ cells. We have corrected and removed the “influx” statement, which now reads, “Our findings showed that Ptrprc+ cells were present in the stromal and epithelial layers in the presence of sperm at 0.5 dpc in the UTJ.”

      Reviewer #2 (Public review):

      The manuscript investigates oviductal responses to the presence of gametes and embryos using a multi-omics and machine learning-based approach. By applying RNA sequencing (RNA-seq), single-cell RNA sequencing (sc-RNA-seq), and proteomics, the authors identified distinct molecular signatures in different regions of the oviduct, proximal versus distal. The study revealed that sperm presence triggers an inflammatory response in the proximal oviduct, while embryo presence activates metabolic genes essential for providing nutrients to the developing embryos. Overall, this study offers valuable insights and is likely to be of great interest to reproductive biologists and researchers in the field of oviduct biology. However, further investigation into the impact of sperm on the immune cell population in the oviduct is necessary to strengthen the overall findings.

      We appreciate the concise summary, strengths, and weaknesses highlighted. We have addressed all comments made by the reviewer concerning superovulation, figure recommendations, and additional analysis in our revised manuscript. We have included a new analysis of scRNA-seq datasets from human Fallopian tube tissues collected from hydrosalpinx patients and healthy subjects by Ulrich et al. (PMID: 35320732). The evaluation of this human data helped distinguish between different inflammatory pathways stimulated by sperm vs. general inflammation, as well as species differences (more details in responses below). In future studies, we will follow up on a detailed description of immune cell types present at 0.5 dpc using FACS analysis. This is mainly due to a lack of expertise and technical limitations in our lab on immune cell investigation. Nevertheless, we have already recruited two immunologists to facilitate our future immune cell studies. We have also provided a clear justification for superovulation, especially in the scRNA-seq analysis in the revised manuscript (please see response to Reviewer 1 above). 

      Recommendations for the authors:

      (1) In Figure 3A and 3B, the authors should provide higher contrast and high-resolution images for the expression of the selected immune cell markers at 0.5 dpc and 0.5 dpp. For better clarity and flow, 0.5 dpc & 0.5 dpp, as well as 1.5 dpc & 1.5 dpp, should be merged into a single panel.  

      Thank you for this suggestion. As shown in the response to Reviewer 1 above, we have now used a higher-magnification image for Fig. 3. We have also changed the panel in the quantification graphs to better reflect the immunofluorescent images and improve clarity and flow.

      (2) The authors demonstrated that sperm induces an inflammatory response in the oviduct by presenting IF for selected immune cells. However, FACS analysis should be included to dissect the various immune cell populations further. 

      We appreciate the recommendation and agree that FACS analysis should provide a more detailed description of the immune cell types present at 0.5 dpc. However, our current work primarily offers initial investigations, confirming that three bioinformatic models (bulk RNA-seq, scRNA-seq, and proteomic analyses) can be validated by IF staining. Our future research using analysis should provide additional characterization of immune cell types at 0.5 dpc in the oviduct.

      (3) In Figure 2, the authors performed proteomic analysis at different stages of implantation. They observed similar alterations in the pro-inflammatory Reactome, as seen with RNA-seq and sc-RNA-seq analyses. It would be interesting to examine the types of proteins induced by embryo presence and how their expression changes at 1.5 and 2.5 dpc. Similarly, are sperm-interacting proteins induced in response to sperm presence at 0.5 dpc? Are these proteins uniquely present in the isthmus compared to the ampulla? 

      We sincerely appreciate the reviewer’s insightful comments regarding the findings in Figure 2 and the potential avenues for further exploration of the proteomic analysis during different stages of embryo preimplantation. We found that during 1.5 dpc, enriched Reactome included Innate Immune System and RHO GTPase (Fig. S4A). In comparison, Reactome at 2.5 dpc were enriched for Keratinization, Metabolism of Protein, and Post-translational Protein Modification (Fig. S4B). Therefore, the pro-inflammatory Reactome profile appeared to have completely subsided at 2.5 dpc. This statement has now been included in the results section, which reads, “Lastly, differential protein abundance at 1.5 dpc and 2.5 dpc indicated the enrichment for Ras Homolog (RHO) GTPase signaling pathway and changes in epithelial remodeling (keratinization) (Fig. S4A and B), respectively. Therefore, the pro-inflammatory Reactome profile appeared to have completely subsided at 2.5 dpc”.

      And yes, we detected sperm-interacting proteins (such as OVGP1, ANXA1, HSPA5, and PDIA6, etc.) from our 0.5 dpc proteomic datasets (see examples from images below taken from our dataset:

      https://genes.winuthayanon.com/winuthayanon/oviduct_proteins/). We noticed that all of these sperminteracting protein levels were lower at 0.5 dpc compared to other timepoints. We speculated that these proteins bind to the sperm and were washed out together with the sperm during the pre-processing centrifugation prior to mass spectrometry analysis. However, we could not distinguish the original location (ampulla vs. isthmus) of proteins as the luminal fluid was flushed from the entire oviduct.

      Author response image 3.

      (4) Given that salpingitis is associated with inflammation of the fallopian tubes, the authors should consider comparing the gene signatures from this study with publicly available salpingitis datasets. 

      Thank you for this insightful suggestion. We have reanalyzed the human data from scRNA-seq of Fallopian tube tissues collected from hydrosalpinx (inflamed and dilated tube) and healthy patients by Ulrich et al. (PMID: 35320732). From this published human dataset, we have evaluated GO biological pathways enriched in the differentially expressed genes (DEGs) in hydrosalpinx compared to healthy Fallopian tubes. We have added these new data in the revised Results, Fig. 5 and Supplementary Dataset S5. The new data now read,  

      “Evaluation of human hydrosalpinx Fallopian tubes compared to sperm-induced inflammation genes

      To determine whether sperm-induced inflammatory responses in the mouse oviduct are similar to or different from human inflammation conditions, we reanalyzed publicly available scRNA-seq data from hydrosalpinx samples by Ulrich et al (50). We found that some of the sperm-induced inflammatory genes identified from our mouse study were present and upregulated in hydrosalpinx samples compared to healthy subjects (Fig. 5A). However, the differentially expressed levels, for example the CCL2 gene, appeared to be marginal between healthy vs. hydrosalpinx samples (Fig. 5_B-C_ and Supplemental Datasets S5). Nevertheless, the top five most enriched GOBPs related to inflammatory responses were Regulation of Complement Activation, Positive Regulation of Macrophage Migration Inhibitory Factor Signaling Pathway, MHC Class II Protein Complex Assembly, Positive Regulation of NK Cell Chemotaxis, and Negative Regulation of Metallopeptidase Activity (Fig. 5D). These GOBPs differed from those identified in sperm-exposed mouse oviducts at 0.5 dpc, which were enriched for neutrophil-related pathways, unlike macrophages or NK cells in hydrosalpinx samples”.

      We have also added a revised Discussion, which now reads, 

      “Lastly, we found that sperm-induced inflammatory conditions in the oviduct were potentially different than those of chronic inflammatory conditions in human Fallopian tubes. The inflammatory responses observed in mice and humans exhibited significant distinction based on immune cell involvement, mechanisms, and context. In mice, acute inflammation after sperm exposure could be primarily characterized by the activation of neutrophils, which serve as the first responders to injury or foreign bodies. In contrast, human Fallopian tubes with hydrosalpinx conditions displayed chronic inflammatory conditions predominantly involving macrophages and NK cells, suggesting a more complex and sustained immune response. It is also possible that inflammation in the oviduct differs between mice and humans. Understanding these species-specific variations is crucial for developing effective therapeutic strategies, as findings from murine models may not accurately translate to human inflammatory conditions due to the distinct immune dynamics at play”.

      (5) In Line 259, the authors should clarify why SO females were chosen for luminal fluid collection at different points. 

      Thank you for pointing this out. We wanted to clarify that the proteomic analysis from the luminal fluid was performed in both naturally mated with and without SO. We have revised the statement in the Results section, which now reads,

      “To validate our transcriptomics data at a translational level, LC-MS/MS proteomic analysis was performed on secreted proteins in the oviductal luminal fluid at estrus, 0.5, 1.5, and 2.5 dpc with or without SO. As we also aim to address whether changes in proteomic profiles in the oviduct are governed by hormonal fluctuations, the SO was performed using exogenous gonadotropins. Therefore, the comparison was assessed in the following groups: estrus, 0.5 dpc, 1.5 dpc, 2.5 dpc, SO estrus, SO 0.5 dpc, SO 1.5 dpc, and SO 2.5 dpc”.

      In addition, we have now provided additional clarification in the Method section, which reads,

      “In this context, our SO approach facilitates multi-dimensional analysis comparisons among naturally cycling bulk RNA-seq, SO scRNA-seq, and natural luminal proteomic biological replicates, enhancing confidence between different methods. This experimental design also reflects adaptive responses in the oviduct during natural fertilization and preimplantation development, influenced by PMSG and hCG treatments at both RNA and protein levels. Furthermore, SO is commonly used in female reproduction to synchronize estrus cycles in animals, thus reducing variables at each collection timepoint.”.

      (6) The authors should include scale bars in all fluorescent images. 

      We apologize for this oversight. In all applicable figures, we have provided a scale bar for all immunofluorescent images.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this study, the authors showed that enalapril was able to reduce cellular senescence and improve health status in aged mice. The authors further showed that phosphorylated Smad1/5/9 was significantly elevated and blocking this pathway attenuated the protection of cells from senescence. When middle-aged mice were treated with enalapril, the physiological performance in several tissues, including memory capacity, renal function, and muscle strength, exhibited significant improvement.

      Strengths:

      The strength of the study lies in the identification of the pSMAD1/5/9 pathway as the underlying mechanism mediating the anti-senescence effects of enalapril with comprehensive evaluation both in vitro and in vivo.

      Thanks very much for your insightful evaluation and the constructive suggestions. We have thoroughly studied the comments and a provisional point-to-point response is shown as follows.

      Weaknesses:

      The major weakness of the study is the in vivo data. Despite the evidence shown in the in vitro study, there is no data to show that blocking the pSmad1/5/9 pathway is able to attenuate the anti-aging effects of enalapril in the mice. In addition, the aging phenotypes mitigation by enalapril is not evidenced by the extension of lifespan.

      Thanks for your comment. As suggested, we will feed LDN193189 to mice while using LDN193189 to block pSmad1/5/9, and will assess age-related phenotypes in the mice to demonstrate that the anti-aging effect of enalapril in mice is mediated through pSmad1/5/9.

      We only assess the improvement in the health status of the aging mice, which indicate that enalapril can extend the healthy lifespan of aging mice. This is because we believe that lifespan is controlled by genetics. Therefore, this study focuses solely on the improvement of health phenotypes in aging mice by enalapril.

      If it is necessary to show that NAC is able to attenuate enalapril effects in the aging mice. In addition, it would be beneficial to test if enalapril is able to achieve similar rescue in a premature aging mouse model.

      Thanks for your suggestion. To our knowledge, NAC is an inhibitor of ROS, which is consistent with the antioxidant effect of enalapril. Therefore, we believe that NAC will not diminish the effect of enalapril.

      For the premature aging mouse models, we examined the effect of enalapril on Lmna<sup>G609G</sup> mice and other premature aging models and found that the effect was relatively modest. This may be due to differences in the genetic background of premature aging mice, leading to a less pronounced effect of enalapril compared to its impact on naturally aged mice.

      Reviewer #2 (Public review):

      This manuscript presents an interesting study of enalapril for its potential impact on senescence through the activation of Smad1/5/9 signaling with a focus on antioxidative gene expression. Repurposing enalapril in this context provides a fresh perspective on its effects beyond blood pressure regulation. The authors make a strong case for the importance of Smad1/5/9 in this process, and the inclusion of both in vitro and in vivo models adds value to the findings. Below, I have a few comments and suggestions which may help improve the manuscript.

      Thanks very much for your insightful evaluation and the constructive suggestions. We have thoroughly studied the comments and a provisional point-to-point response is shown as follows.

      A major finding in the study is that phosphorylated Smad1/5/9 mediates the effects of enalapril. However, the manuscript focused on the Smad pathway relatively abruptly, and the rationale behind targeting this specific pathway is not fully explained. What makes Smad1/5/9 particularly relevant to the context of this study?

      Thanks for your comment. As stated in the manuscript, after we found that enalapril could improve the cellular senescence phenotype, we screened and examined key targets in important aging-related signaling pathways, such as AKT, mTOR, ERK (Fig. S2A), Smad2/3 and Smad1/5/9 (Fig. 2A). We found that only the phosphorylation levels of Smad1/5/9 significantly increased after enalapril treatment. Therefore, the subsequent focus of this study is on pSmad1/5/9.

      Furthermore, their finding that activation of Smad1/5/9 leads to a reduction of senescence appears somewhat contradictory to the established literature on Smad1/5/9 in senescence. For instance, studies have shown that BMP4-induced senescence involves the activation of Smad1/5/8 (Smad1/5/9), leading to the upregulation of senescence markers like p16 and p21 (JBC, 2009, 284, 12153). Similarly, phosphorylated Smad1/5/8 has been shown to promote and maintain senescence in Ras-activated cells (PLOS Genetics, 2011, 7, e1002359). Could the authors provide more detailed mechanistic insights into why enalapril seems to reverse the typical pro-senescent role of Smad1/5/9 in their study?

      Thanks for your comment. The downstream regulatory network of BMP-pSmad1/5/9 is highly complex. The BMP-SMAD-ID axis has been mentioned in many studies, and its downstream signaling inhibits the expression of p16 and p21 (PNAS, 2016, 113(46), 13057-13062; Cell, 2003, 115(3), 281-292). Additionally, studies have also found that the Smad1-Stat1-P21 axis inhibits osteoblast senescence (Cell Death Discovery, 2022, 8:254). In our study, enalapril was found to increase the expression of ID1, which is a classic downstream target of pSmad1/5/9 (Cell Stem Cell, 2014, 15(5), 619-633). Therefore, pSmad1/5/9 inhibits cellular senescence markers such as p16, p21 and SASP through ID1, thereby promoting cell proliferation (Fig. 3). Furthermore, we also found that pSmad1/5/9 increases the expression of antioxidant genes and reduces ROS levels, exerting antioxidant effects (Fig. 4). Together, ID1 and antioxidant genes enable pSmad1/5/9 to exert its anti-aging effects.

      While the authors showed that enalapril increases pSmad1/5/9 phosphorylation, what are the expression levels of other key and related factors like Smad4, pSmad2, pSmad3, BMP2, and BMP4 in both senescent and non-senescent cells? These data will help clarify the broader signaling effects.

      Thanks for your suggestion. We observed an increase in Smad4 expression, while the levels of pSmad2 and pSmad3 remained unchanged after enalapril treatment (Fig. 2A). We will supplement data on the expression changes of these key factors in both senescent and non-senescent cells.

      They used BMP receptor inhibitor LDN193189 to pharmacologically inhibit BMP signaling, but it would be more convincing to also include genetic validation (e.g., knockdown or knockout of BMP2 or BMP4). This will help confirm that the observed effects are truly due to BMP-Smad signaling and not off-target effects of the pharmacological inhibitor LDN.

      Thanks for your suggestion. We will use shRNA or siRNA to knockdown BMP and examine the related changes to clarify the role of BMP-Smad signaling.

      I don't see the results on the changes in senescence markers p16 and p21 in the mouse models treated with enalapril. Similarly, the effects of enalapril treatment on some key SASP factors, such as TNF-α, MCP-1, IL-1β, and IL-1α, are missing, particularly in serum and tissues. These are important data to evaluate the effect of enalapril on senescence.

      Thanks for your comment. As for the markers p16 and p21, we observed no change in p16, while the changes in p21 varied across different organs and tissues. (Author response image 1). Nevertheless, behavioral experiments and physiological and biochemical indicators at the individual level consistently demonstrated the significant anti-aging effects of enalapril (Fig. 6).

      Author response image 1.

      p21(Cdkn1a) expression levels in organs of mice after enalapril feeding.

      We also examined the changes in SASP factors in the serum of mice after enalapril treatment. Notably, SASP factors such as CCL (MCP), CXCL and TNFRS11B showed significant decreases (Fig. 5C). The expression changes of SASP factors varied across different organs. In the liver, kidneys and spleen, the expression of IL1a and IL1b decreased, while TNFRS11B expression decreased in both the liver and muscles (Fig. 5B). Additionally, CCL (MCP) levels decreased in all organs (Fig. 5B).

      Given that enalapril is primarily known as an antihypertensive, it would be helpful to include data on how it affects blood pressure in the aged mouse models, such as systolic and diastolic blood pressure. This will clarify whether the observed effects are independent of or influenced by changes in blood pressure.

      Thanks for your comment. We measured the blood pressure in mice, and found no significant change in blood pressure after enalapril treatment, which has also been validated in other studies (J Gerontol A Biol Sci Med Sci, 2019, 74(8), 1149–1157). Therefore, our results are independent of changes in blood pressure.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1:

      Regarding the manuscript's clarity, the sentence on page 5, "We also stained VTA sections for Tyrosine hydroxylase (TH) to estimate the rate of ChR2 colocalization with DA neurons," reads awkwardly. Removing the word "rate" could improve clarity.

      We have made the recommended clarifying edit (page 5, lines 30-31).

      Additionally, the anatomical data and findings are largely non-quantitative in nature. However, solid microscopy images are presented to support each claim. Additional quantification would strengthen the paper, specifically the quantification of projection density for each population and the proportion of each subpopulation that projects to their regions of interest.

      To rigorously quantify the projection density of each subpopulation would require a level of exhaustivity our study was not designed for. This is because during microscopy we focused efforts on imaging regions containing dense signals but did not exhaustively image regions receiving apparently weak or no input. While we considered including a semi-quantitative table of projection density, based on the data available we could not discriminate with confidence between, e.g., regions recipient of minimal input versus no input from VTA populations. Thus, while we stand by our descriptive statements we do not expand on those further.

      The authors should consider discussing the possibility that subpopulations of these cells could still be true interneurons especially if cells were looked at the single neuron level of resolution.

      We agree that some of the VTA populations we studied could include subpopulations that are bona fide interneurons. The identification of alternate markers or combinations of markers, or use of single-cell imaging approaches may indeed support this possibility in future. This is discussed in the context of currently available evidence on page 5 lines 32-34, page 11 lines 2-4, page 12 lines 2-11, and page 12 lines 15-16.

      Overall, the paper is well-written and important for the field and beyond.

      Thank you!

      Reviewer #2:

      Weaknesses:

      While the authors use several Cre driver lines to identify GABAergic projection neurons, they then use wild-type mice to show that projection neurons synapse onto neighboring cells within the VTA. This does not seem to lend evidence to the idea that previously described "interneurons" are projection neurons that collateralize within the VTA.

      We think the use of WT mice is a strength because it allows us to measure both GABA and non-GABA synapses made by VTA projections on to the same cells within VTA. However, we have also done this experiment targeting NAc-projecting VTA VGAT-Cre neurons, and VP-projecting VTA MOR-Cre neurons. Consistent with the WT dataset, we find that these defined projection neurons also make intra-VTA synapses. These data are now included as Figure 7.

      More broadly. Our review of the literature finds very little evidence to support the notion of a VTA interneuron as we define it: VTA neurons that makes only local connections. But the absence of evidence need not imply evidence of absence, thus we do not claim that all VTA neurons previously presumed to be interneurons must be projection neurons. We do express confidence in our findings that VTA projection neurons (that include GABA-releasing neurons) make local synapses in VTA. We argue that in the absence of compelling positive evidence for the existence of VTA interneurons, such as a selective marker, “we”, “the field”, should not presume their existence.

      Other suggestions:

      (1) While the authors present evidence that some projection neurons also synapse locally, there is no quantification as to the proportion of each neuronal subtype that collateralizes within the VTA. This would be a useful analysis.

      We agree this would be useful information. But our experiments were not designed to answer this question. Indeed, we have not conceived of a feasible method to discriminate between collateralizing and non-collateralizing VTA projection neurons at the single-cell level, thus we do not know how we would calculate such proportions.

      (2) There is significant interest in the molecular heterogeneity and spatial topography of the VTA. Additional analyses of the spatial topography of labeled projectors would be useful. For example, knowing if Pvalb+ projection neurons are distributed throughout the VTA or located along the midline would be a useful analysis.

      Prior studies and public databases (e.g., Allen brain atlas, GENSAT) allow one to visualize the location of VTA neurons positive for Pvalb and the other markers we investigated (Olson & Nestler, 2007). However, these label the entire population of neurons and thereby include those that project to any of the various projection targets. There are also studies that have used retrograde labeling approaches to map the distribution of labeled VTA cells projecting to one or another target (Beier et al., 2015; Lammel et al., 2008; Margolis et al., 2006). For example, finding that LHb-projecting neurons (a major target of Pvalb+ VTA neurons) are enriched in medial VTA (Root et al., 2014). From this evidence we might infer that Pvalb+ VTA neurons that project to LHb are likely to be medially biased. Future studies may more carefully map the intersection of specific projection targets for each VTA subpopulation.  

      Reviewer #3 (Recommendations For The Authors):

      Weaknesses:

      This study has a few modest shortcomings, of which the first is likely addressable with the authors' existing data, while the latter items will likely need to be deferred to future studies:

      (1) Some key anatomical details are difficult to discern from the images shown. In Figure 1, the low-magnification images of the VTA in the first column, while essential for seeing what overall section is being shown, are not of sufficient resolution to distinguish soma from processes. A supplemental figure with higher-resolution images could be helpful.

      We uploaded a higher resolution file for figure 1.

      Also, where are the insets shown in the second column obtained from? There is not a corresponding marked region on the low-magnification images. Is this an oversight, or are these insets obtained from other sections that are not shown?

      This was an oversight, we added the corresponding marked region to the low-magnification images.

      Lastly, there is a supplemental figure showing the NAc injection sites corresponding to Figure 5, but not one showing VP or PFC injection sites in Figure 6. Why not?

      We added a figure with histology examples for the VP and the PFC injection sites as done for Figure 5, included as Supplemental Figure 3.

      (2) Because multiple ChR2 neurons are activated in the optogenetic experiments, it is not clear how common is it for any specific projection neuron to make local connections. Are the observed synaptic effects driven by just a few neurons making extensive local collateralizations (while other projection neurons do not), or do most VTA projection neurons have local collaterals? I realize this is a complex question, that may not have an easy answer.

      This is a great question but, indeed, we don’t know the answer. As mentioned in response to Reviewer #2, we are not convinced there is a currently feasible way to discriminate between collateralizing and non-collateralizing cells at the single cell level.

      (3) There is something of a conceptual disconnect between the early and later portions of this paper. Whereas Figures 1-4 examine forebrain projections of genetic subtypes of VTA neurons, the optogenetic studies do not address genetic subtypes at all. I do realize that is outside of the scope of the author's intent, but it does give the impression of somewhat different (but related) studies being stitched together. For example, the MOR-expressing neurons seem to project strongly to the VP, but it is not addressed whether these are also the ones making local projections. Also, after showing that PV neurons project to the LHb, the opto experiments do not examine the LHb projection target at all.

      This too was raised by Reviewer #2. While addressing this question for all the populations we investigated feels redundant, we now include optogenetic data showing that NAc-projecting VTA VGAT-Cre and VP-projecting VTA MOR-Cre neurons also make local collaterals (Figure 7). We think this allows us to connect the two approaches to a greater degree. Based on our findings using a dual virus approach to express Syn:Ruby in each population of VTA projection neuron, we think it very likely that we’d continue to find similar results using optogenetics-assisted slice electrophysiology for each population.

      Other suggestions:

      (1) I appreciated the extensive and high-quality anatomical figures shown in Figures 2-4. However, the layout was sometimes left-to-right, and sometimes right-to-left, which felt distracting. At some point, the text refers to "Fig. 3KJ", i.e. with the letters being in backward alphabetical order, and Figures 3I and 3L do not appear mentioned anywhere in the main text, leading me to wonder if that text was intended to read "Fig. 3I-L".

      Thank you for noting this. We have harmonized the layout of Figures 2-4 and adjusted the in-text Figure call-outs.

      Also, the inset in Figure 3J appears to show local collaterals of NTS neurons in the VTA, since there is no soma in that inset. This is interesting, and worth reporting, but is not explained in either the main text or Figure legend.

      We added a more complete description in the result section (page 6 line 25-30).

      (2) Perhaps I missed it, but I could not find any mention of the intensity of the LED light delivered during the optogenetic experiments. While acknowledging that this can be variable, do the authors have at least a rough range?

      We have added this information to the methods, page 17 line 8.

      Editor's Note:

      Should you choose to revise your manuscript, please double check that you have fully reported all statistics including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals.

      We confirm that we have fully reported all statistics including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals.

      Note to Editor and Readers

      While reanalyzing our data for resubmission, we discovered that some of the short-latency optogenetic evoked postsynaptic currents (oPSCs) we detected were erroneously categorized. Specifically, some VTA cells that showed large outward currents (oIPSCs) when held at 0 mV, also had small inward currents when held at -60 mV. These small inward currents were initially categorized as oEPSCs, suggesting these VTA cells received input from populations of VTA projection neurons that released GABA and/or glutamate. However, the kinetics of these small inward currents were slow and aligned with the within-cell kinetics of the oIPSCs, indicating that these were very likely mediated by GABA<SUB>A</SUB> receptors. In one case the opposite was apparent, with a small PSC initially miscategorized as an oIPSC. These miscategorized oEPSCs and oIPSC were presumably detected because our holding potentials were not precisely identical to the reversal potentials for GABA<SUB>A</SUB> and AMPA receptors, respectively. For this reason, we removed these 14 oEPSCs and 1 oIPSCs from our analyses in the revised version. The revised dataset suggests that VTA glutamate projection neurons may be less likely to collateralize widely within VTA compared to GABA projection neurons. But, importantly, this correction does not affect any of our conclusions.

      Citations:

      Beier, K. T., Steinberg, E. E., DeLoach, K. E., Xie, S., Miyamichi, K., Schwarz, L., Gao, X. J., Kremer, E. J., Malenka, R. C., & Luo, L. (2015). Circuit Architecture of VTA Dopamine Neurons Revealed by Systematic Input-Output Mapping. Cell, 162(3), 622-634. https://doi.org/10.1016/j.cell.2015.07.015

      Lammel, S., Hetzel, A., Hackel, O., Jones, I., Liss, B., & Roeper, J. (2008). Unique properties of mesoprefrontal neurons within a dual mesocorticolimbic dopamine system. Neuron, 57(5), 760-773. https://doi.org/10.1016/j.neuron.2008.01.022

      Margolis, E. B., Lock, H., Chefer, V. I., Shippenberg, T. S., Hjelmstad, G. O., & Fields, H. L. (2006). Kappa opioids selectively control dopaminergic neurons projecting to the prefrontal cortex. Proc Natl Acad Sci U S A, 103(8), 2938-2942. https://doi.org/10.1073/pnas.0511159103

      Olson, V. G., & Nestler, E. J. (2007). Topographical organization of GABAergic neurons within the ventral tegmental area of the rat. Synapse, 61(2), 87-95. https://doi.org/10.1002/syn.20345

      Root, D. H., Mejias-Aponte, C. A., Zhang, S., Wang, H. L., Hoffman, A. F., Lupica, C. R., & Morales, M. (2014). Single rodent mesohabenular axons release glutamate and GABA. Nat Neurosci, 17(11), 1543-1551. https://doi.org/10.1038/nn.3823

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reply to Reviewer #1 (Public Review):

      The post-processing increases number of putative neoantigens. As shown in Author response image 1, this is done through data augmentation or “mutations” of individual amino acids in a sequence by their most similar amino acid in the BLOSUM62 embedding. If most of the mutations result in a positive prediction (which we binarize through a >0.5 score) the sequence changes its prediction.

      Author response image 1.

      Post-processing pipeline to increase the number of putative neoantigens. Sequences can either be predicted using the forward method, for which a raw score is produced, or it can be introduced to a majority-vote prediction of the ensemble prediction of similar protein sequences.

      In this article, we obtain the following candidates after post-processing.

      Author response table 1.

      Sequence Symbol Gene Prediction FPKM

      As mentioned, the prediction column shows a binary label. The full list contained 402 sequences did not include any other sequences that met the majority vote criteria.

      As noted by the reviewer, the Table 3 of our original paper includes the scores of the direct prediction, which has four sequences in common with the post-processing criteria (*Pnp, *Adar, *Lrrc28 and *Nr1h2). * indicates the mutated form of the peptide, i.e neoantigen.

      We selected the top 4 predicted antigens (present both by direct prediction and after post-processing; (*Pnp, *Adar, *Lrrc28 and *Nr1h2) (Wert-Carvajal et al. 2021), but we encountered difficulty in synthesizing, *Nr1h2 (Mutated Nr1h2), and thus it could not be included in the study.

      We also decided to evaluate the immunogenicity of *Wiz, which was identified as a potential TNA only after postprocessing. *Wiz exhibited lower levels of immunogenicity compared to *Pnp, *Adar, and *Lrrc28. However, unlike these, *Wiz is highly expressed in the tumor, and vaccination with *Wiz provided the strongest protection levels. These findings led us to incorporate post-processingg into the NAPCNB platform.

      We chose *Herc6 as a mutated antigen predicted not to be a TNA over other candidates because its expression in the tumor was similar to that of *Wiz.

      Depending on the experiment we used 4 or 5 animals per group (this is now clarified in the revised version).

      The software used for statistical analysis was GraphPad Prism.

      Reply to Reviewer #2 (Public Review):

      This is true, binding affinity does not always predict immune responses but in most cases, high affinity peptides are immunogenic. There are of course other parameters that drive the effective priming of tumor-reactive CD8+ T cells through antigen crosspresentation, but the mechanisms of antigen presentation are yet not completely understood. High affinity peptides are desirable as good candidates in neoantigen-based vaccines.

      Other comments of the reviewers

      Reviewer #1 (Recommendations For The Authors):

      - Please decipher all abbreviations when they appear for the first time, e.g. NAP-CNB, PBS, CFA, FIA, and so on.  

      Done in the revised version.

      - Please be consistent with the capitalization of gene names (WIZ vs Wiz, TRP2 vs Trp2, and so on), and why there is an asterisk.

      Done in the revised version.

      - Please be clear about where you use cell lines or mice as a model. It's not clear.

      All work is done in mice, or cells isolated from vaccinated mice.

      - Why there is an asterisk in front of gene names?

      Explained in the revised version; The * indicates the peptides that are the mutated version.

      - Please add a reference for the following statement in the Introduction: "However, the response rates of these therapies remain low and relapses are common."

      Done in the revised version.

      - Also please add a reference for the use of TRP2 as a positive control.

      Done in the revised version.

      Reviewer #2 (Recommendations For The Authors):

      - It may be helpful to validate a larger pool of antigens. This is not necessary however and could be done in a follow-up study.

      We are doing it for other studies with excellent results.

      - The negative PBS control should be included in Figure 1.

      Done in the modified figure 1C in the revised version.

      - Stats should be clearly indicated in Figure 2.

      Done in the revised version.

      - Some nuances should be discussed. Is a threshold of neoantigen expression required or is there a correlation with tumor control? On the flip side, these neoantigens that are not likely to elicit immune responses but are highly expressed are also not likely to mediate tumor control.

      These points have been discussed. Based on our data, strategies for designing antitumor therapies should prioritize antigens that are highly expressed in tumors, even if they are not the most immunogenic. However, it is worth noting that even low-expressed antigens can still elicit an antitumor immune response. If possible one should define strategies attacking multiple antigens in order to minimize tumor scape. Whenever possible, strategies should be developed to target multiple antigens simultaneously, aiming to minimize tumor escape.

      - This study focuses on CD8 T cell responses but CD4s are also important in tumor control. This could be mentioned in the discussion.

      This is true, but this article focuses on validating a platform that predicts the antigenicity of antigens presented in the context of MHC-I.

      - Ideally, we would want to see that these responses are not elicited with adjuvant alone as an additional control.

      The non-vaccinated control animals received PBS and adjuvant. This clarification has now been included in the text.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      This is by far the phylogenetic analysis with the most comprehensive coverage for the Nemacheilidae family in Cobitoidea. It is a much-lauded effort. The conclusions derived using phylogenetic tools coincide with geological events, though not without difficulties (Africa pathway).

      Strengths:

      Comprehensive use of genetic tools

      Weaknesses:

      Lack of more fossil records

      Thank you for appreciating the comprehensiveness of our study.

      We agree that additional nemacheilid fossils would have provided valuable support for reconstructing the evolutionary history of the family. However, the nemacheilid fossil used in our study is currently the only fossil species of the family, which precludes the possibility of including more. To address this limitation, we incorporated fossils from closely related fish families, as well as a geological event, to calibrate the time tree. We have added further details on this point in “Divergence time estimations and ancestral range reconstruction” section of the Methods. The reconstruction of the pathway by which loaches reached northeast Africa, is further complicated by the extensive aridification of the Arabian Peninsula and the Nile valley, leaving no fossil or extant Nemacheilidae species of Nemacheilidae to provide insights into the distribution of the family during late Miocene.

      Reviewer #2 (Public review):

      Summary:

      The authors present the results of molecular phylogenetic analysis with very comprehensive samplings including 471 specimens belonging to 250 species, trying to give a holistic reconstruction of the evolutionary history of freshwater fishes (Nemacheilidae) across Eurasia since the early Eocene. This is of great interest to general readers.

      Strengths:

      They provide very vast data and conduct comprehensive analyses. They suggested that Nemacheilidae contain 6 major clades, and the earliest differentiation can be dated to the early Eocene.

      Weaknesses:

      The analysis is incomplete, and the manuscript discussion is not well organized. The authors did not discuss the systematic problems that widely exist. They also did not use the conventional way to discuss the evolutionary process of branches or clades, but just chronologically described the overall history.

      In the revised version, we address the systematic issues within Nemacheilidae in a new paragraph. The polyphyly of the genus Schistura and the polyphyly or paraphyly of many other nemacheilid genera are wellknown challenges in ichthyology. However, the large size of the family Nemacheilidae and the absence of a clear basal classification system has made systematic work difficult.

      The chronological concept in the description of events is in accordance with the sequence in which the events occurred over time and corresponds with Figure 8. Additionally, a clade-by-clade description would make it challenging to capture the periods before all clades were formed. As a compromise, the revised version includes a new table where each clade is represented by a column, allowing readers to trace the history of each clade in a clear overview. With this table, we make both the chronological and clade-by-clade perspectives to enhance reader understanding

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      I have no major comments, except for Figure 8, where the colour code for Sunda is not consistent, appearing as light purple and then dark purple. I was trying to locate the colour legend, maybe include this for all figures or refer to it.

      Figure 8 has been revised to improve matching of the colours.

      Reviewer #2 (Recommendations for the authors):

      (1) It is better to discuss the evolutionary history of the major inner groups. For example, why the Branch A and B differentiated? How are the 6 major clades differentiated?

      As mentioned above, the new table provides an overview of the evolutionary history of the major clades and, where known, the mechanism that led to their differentiation. For branches A and B, the underlying causes of differentiation remain known. Currently, the extensive morphological variability within each clade prevents a definitive morphological diagnosis, but such a study is planned for the future.

      (2) In this study, there are still some phylogenetic or systematic problems unresolved. For example, the Genus Schistura remains polyphyletic even in different major clades. The situation is similar for the Genus Tripophysa though not so serious. These need to be discussed or at least partially solved before discussing the evolutionary history.

      We discuss these topics now in a new paragraph ‘Taxonomic implications’.

      (3) In Table S1, what is the meaning of "-". Does this mean no data available? If so, how do the authors treat this in their phylogenetic analysis?

      Indeed, in Table S1, a ‘-‘ indicates that no sequence was available for the given species and gene. In the phylogenetic analyses, these cases were treated as missing data.

      (4) What is the source of Figure 8? There are different opinions on the geological events. The authors need to indicate the source of their information.

      The sources of Fig. 8 are now provided in the figure caption.

      (5) The Eastern Clade forms continuous distribution in Figure 6, but discontinuous in Figure 8. Is this correct?

      Figure 6 does not display the distribution areas for the clades, but illustrates the biogeographic regions used in the biogeographic analysis.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Reviewer #2 (Recommendations for the authors):

      A good number of sentences in the introduction, page two, refer to a figure, 'Fig. 2a', which appears to be the copy-paste effect of these sentences from another location (please see below):

      "Notably, SPHK2 does not directly contribute to levels of secreted S1P (Thuy et al., 2022), nor is it annotated in the chick genome. S1P can be exported from cells by a transporter (MFSD2A and SPNS2) or converted to sphingosine by a phosphatase (SGPP1) (Fig. 2a). Levels of sphingosine are increased by ASAH1 by conversion of ceramide or decreased by CERS2/5/6 by conversion to ceramide (Fig. 2a). S1P is known to activate G-protein coupled receptors, S1PR1 through S1PR5 (Fig. 2a). S1PRs are known to activate different cell signaling pathways including MAPK and PI3K/mTor, and crosstalk with pro-inflammatory pathways such as NFκB (Fig. 2a) (Hu et al., 2020)."

      We have removed references to Fig. 2a, which was from a previous draft of this manuscript.

      Please correct the typo in the following sentence (Fid.)

      "S1PR1 was most prominently expressed by resting MG and MG returning to a resting state, whereas S1PR3 was detected in relatively few scattered cells in clusters of MG, ganglion cells, horizontal cells, bipolar cells, amacrine cells, photoreceptors, oligodendrocytes, microglia and NIRG cells (Fid. 1d).

      We have corrected this typo_._

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Weaknesses: 

      It is not always clear what the novel findings are that this manuscript is presenting. It appears to be largely similar to the analysis done by McKey et al. (2022) but with more time points and molecular markers. The novelty of the present study's findings needs to be better articulated. 

      The previous study focused on placing the Rete Ovarii in the context of ovarian development. The current study focuses on the novel findings that the EOR is a active structure that sends fluid/information to the ovary. We show this by characterizing the presence of secretory proteins in the RO epithelial cells, by dye injections into the EOR and observing transport of the dye to the ovary, and by collection of EOR fluid followed by proteomic analysis. We also show that RO is embedded in an elaborate vascular network and contacted by neurons. None of this data was not discussed in the McKey 2022 paper. 

      Reviewer #2 (Public Review):

      Clarifications: 

      (1) Is there any comparative data on the proteomics of RO and rete testis in early development? With some molecular markers also derived from rete testis, it would be better to provide the data or references.

      To the best of our knowledge, there are no available proteomic datasets of the embryonic or early postnatal mouse Rete Testis or Epididymis. The authors agree that having this information would be very useful. 

      (2) Although the size of RO and its components is quite small and difficult to operate, the researchers in this article had already been able to perform intracavitary injection of EOR and extract EOR or CR for mass spectrometry analysis. Therefore, can EOR, CR, or IOR be damaged or removed, providing further strong evidence of ovarian development function?

      We attempted to genetically ablate the RO by expressing the diphtheria toxin receptor (DTR) in RO cells and adding DT. This approach was not successful in ablating the RO. We also tried to use Pax2/8 homo- and heterozygous mutants for ablation (as used in the McKey 2022 paper), but so far, we cannot find a genetic combination that ablates the RO, but not the oviduct, uterus and/or kidneys. We have also embarked on a study to surgically remove the RO. This assay is taking some time to optimize. The goal of the current study was to characterize the cells along the length of the RO and to present evidence that it is a secretory appendage of the ovary.

      (3) Although IOR is shown on the schematic diagram, it cannot be observed in the immunohistochemistry pictures in Figure 1 and Figure 3. The authors should provide a detailed explanation.

      An annotation has been added to Figure 1 to indicate the IOR. As the images within the panels are of maximum intensity projections, it is often difficult to clearly see the IOR as it is deeper within the ovary. In Figure 3, the view of the ovary is from the ventral side:  this view does not allow for clear visualization of the IOR.

      Reviewer #3 (Public Review):

      Weaknesses: 

      There is a lack of conclusive data supporting many conclusions in the manuscript. Therefore, the paper's overall conclusions should be moderated until functional validations are conducted.

      We have moderated the conclusions where appropriate

      Reviewer #1 (Recommendations For The Authors):

      (1) The introduction is relatively brief and does not mention some historical data/hypotheses on the role of the RO in ovarian function (e.g. regulation of meiotic entry) or development (e.g. Mayère et al., 2022).

      Mayere 2022 was cited in line 57. Steins hypothesis about entry into meiosis has been added line 58.

      (2) L82-84: It is stated that KRT8 was first identified as a potential RO marker by sc/snRNAseq (Anbarci et al., 2023) and then validated in this manuscript. However, KRT8 was used by McKey et al. (2022) as a RO marker, and they noted there that KRT8 was enriched in the EOR. It is not clear why McKey et al. is not cited as the primary reference validating KRT8 as an EOR marker.

      The embryonic and neonatal timecourse description from KRT8 expression is first identified in this paper. McKey 2022 only highlights KRT8 at E18.5 A reference has been added to address this line 85

      (3) Figure 1: Can the IOR be seen in these images? If so, please label. 

      The label has been added.

      (4) L107: It is hypothesized that "the RO may respond to or interpret homeostatic cues." Can transcriptomics data shed light on what signals the RO may be capable of responding to? E.g. what receptors are expressed by cells of the RO (e.g. ER, LHCGR, FSHR)?

      The RO expresses ESR1, PGR, INSR, IGF1R. The IOR exclusively expresses LHCGR and FSHR.This has been added to the manuscript line 309

      (5) L152: Mass spec was used to identify proteins secreted into the lumen of the RO. These proteins were then compared to the mammalian secretome to filter out possible nonsecreted protein contaminants. Finally, the candidates were compared to the RO scRNAseq data from Anbarci et al., (2023). This method gives a very conservative candidate list. However, it may also be informative to compare the sc/snRNA-seq gene list directly to the secretome to ID other possible candidate-secreted proteins that may not have been detected in the mass spec data set. 

      There are quite a number of secreted proteins that are also not actively secreted. This is a good suggestion for future analysis. For the current study we wanted to take a more conservative approach, and chose to do proteomics to determine proteins that are actively secreted. 

      (6) L195: It is not clear if IGFBP2 is expressed by both OR and granulosa cells or only granulosa cells. It would be informative to know what ovarian cell types express both IGFBP2 and IGF1R (e.g. from sc/snRNA-seq)? This information is referenced in the discussion (L285-287) but would be better to reference it in the results section for clarity.

      Both RO and granulosa cells express IGFBP2 and IGF1R. A sentence has been added to results for clarity. (Line 197)

      (7) L295: "...the RO participates in endocrine signaling..." might be more accurate to say "...the RO responds to endocrine signaling...".

      The authors agreed that this statement is more accurate and the changes have been made. 

      Reviewer #3 (Recommendations For The Authors): 

      Several issues significantly affect the paper's quality in the current version. Firstly, there is a lack of conclusive data supporting many conclusions in the manuscript. For instance, the assertion in line 105 that "EOR was directly innervated by neurons" lacks substantial evidence beyond basic immunofluorescent staining. 

      We agree that the term “innervated” might be a step too far since we rely on IF evidence.  We changed the wording of this sentence to say, “The EOR was directly contacted by neurons”.

      In another pivotal experiment illustrated in Figure 3, the provided images lack temporal continuity and quantitative analysis, suggesting the incorporation of time-lapse imaging for improved sequential presentation in Figure 3.

      The microscope where we can perform injections cannot record movies.  We have tried moving the rete to another microscope after injection, but so far, we have been unable to capture dextran moving through the RO. We therefore believe that transport is rapid, but future experiments will be needed to optimize this imaging.

      Moreover, relying solely on proteomics analysis, as seen in lines 188-189, makes it challenging to assert conclusions such as "EOR actively secretes proteins." Therefore, the paper's overall conclusions should be moderated until functional validations are conducted. 

      The findings that (1) the cells of the EOR express SNARE complex proteins at their apical surfaces and (2) luminal fluid expelled from the EOR contains abundant secreted proteins strongly suggest that the RO is involved in active secretion. We use the word “suggest” in this sentence, lines 188-189 as we realize that further experiments should be done to validate this conclusion.

      Furthermore, the predominant methods in this study involve immunostaining and imaging. However, the current images exhibit a notable inconsistency in color definitions for different markers by the authors. For instance, in Figure 2.A/C, PAX8 is portrayed as cyan, while in D, it is represented in yellow. Similarly, in Figure 4, E-CAD is depicted using both cyan and yellow. Utilizing different colors for the same protein within a figure can significantly confuse readers' interpretation of the experiments. Rectifying these inconsistencies is essential to enhance the clarity and comprehension of the experimental results.

      These colors were chosen to be visible to those with color image impairments. We typically used cyan and magenta to emphasize the most important markers in the image. When E-Cad and KRT8 were often used to emphasized or landmark a structure by localization of these protein. When KRT8 and E-Cad were highlighted, they were represented in cyan and magenta for visibility. When these proteins were used as a landmark to orient the reader and not as the main point, they were labeled in yellow.

      At last, many markers in this study are derived from bulk and single-cell sequencing of developing RO. However, it seems that these important data were separated into another paper as a preprint. If this data were incorporated into the current manuscript, the manuscript would become more comprehensive for guiding future research on the RO.

      Since we have single cell and single nuclei data from fetal and adult estrus and metestrus stages, we found that incorporating all this data into the present manuscript was overwhelming. Instead, we devoted another manuscript to presenting and validating that data. We believe a quick look at the sequencing manuscript will make this clear.

    1. Author response:

      We appreciate the reviewers’ thoughtful and constructive feedback, which has provided valuable insights to refine our manuscript. Below, we outline the planned revisions in response to the public reviews.

      Response to Reviewer #1

      We are grateful for the reviewer’s recognition of our methodological approach and the potential significance of CD47 as a novel MSC marker for cartilage repair. To address the concerns raised:

      (1) Clarifying the proteomics data supporting CD47 as an MSC marker

      · The manuscript will be revised to clearly indicate where the proteomics data demonstrate elevated CD47 expression in MSCs compared to non-MSCs.

      · Additional figure annotations or a supplemental figure may be included to enhance clarity.

      (2) Providing further details on CD47hi and CD47lo MSC populations

      · Information on the number of isolated CD47hi and CD47lo cells, along with any necessary expansion steps before in vivo use, will be explicitly detailed.

      (3) Expanding the characterization of CD47hi MSCs in vitro

      · A more comprehensive analysis of the chondrogenic differentiation capacity of CD47hi MSCs will be incorporated to strengthen the findings.

      (4) Clarifying experimental details of the in vivo rat OA model

      · The methodology section will be updated to specify the number of injected cells and their labeling strategies.

      · Representative histological images will be added to support the results.

      · To further substantiate the cartilage repair potential of CD47hi MSCs, additional staining for Collagen Type II will be included alongside Sox9 expression.

      Response to Reviewer #2

      We appreciate the reviewer’s enthusiasm for the study and recognition of its rigor and translational significance. The following revisions are planned to address the feedback:

      (1) Addressing additional assessments for OA phenotype in the rat model

      · While this study primarily relied on histology, the limitations of this approach will be acknowledged in the discussion.

      · The absence of microCT and behavioral assessments will be explained, with suggestions for incorporating these methods in future studies.

      (2) Justifying the focus on CD47

      · The rationale behind prioritizing CD47 over other proteomics-identified markers will be expanded to provide better context for this choice.

      (3) Clarifying MSC engraftment patterns

      · The manuscript will include a discussion on whether CD47hi MSCs specifically engraft in articular cartilage or contribute to ectopic cartilage formation (e.g., osteophytes).

      (4) Contextualizing findings within recent research on synovial progenitors

      · Additional discussion will highlight recent studies on DPP4+ PI16+ CD34+ stromal cells and how the identified MSC populations may relate to these universal fibroblasts.

      We are confident that these revisions will strengthen the manuscript and enhance its clarity and impact. The reviewers’ insights have been invaluable, and we look forward to refining the study accordingly.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary

      The authors describe a method for gastruloid formation using mouse embryonic stem cells (mESCs) to study YS and AGM-like hematopoietic differentiation. They characterise the gastruloids during nine days of differentiation using a number of techniques including flow cytometry and single-cell RNA sequencing. They compare their findings to a published data set derived from E10-11.5 mouse AGM. At d9, gastruloids were transplanted under the adrenal gland capsule of immunocompromised mice to look for the development of cells capable of engrafting the mouse bone marrow. The authors then applied the gastruloid protocol to study overexpression of Mnx1 which causes infant AML in humans.

      In the introduction, the authors define their interpretation of the different waves of hematopoiesis that occur during development. 'The subsequent wave, known as definitive, produces: first, oligopotent erythro-myeloid progenitors (EMPs) in the YS (E8-E8.5); and later myelo-lymphoid progenitors (MLPs - E9.5-E10), multipotent progenitors (MPPs - E10-E11.5), and hematopoietic stem cells (HSCs - E10.5-E11.5), in the aorta-gonadmesonephros (AGM) region of the embryo proper.' Herein they designate the yolk sac-derived wave of EMP hematopoiesis as definitive, according to convention, although paradoxically it does not develop from intraembryonic mesoderm or give rise to HSCs.

      The apparent perplexity of the Reviewer with our definition of primitive and definitive waves is somewhat surprising, as it is widely used in the field (e.g. PMID: 18204427; PMID: 28299650; PMID: 33681211). Definitive haematopoiesis, encompassing EMP, MLP, MPP and HSC, highlights their origin from haemogenic hendothelium, generation of mature cells with adult characteristics from progenitors with multilineage potential and direct and indirect developmental contributions to the intra-embryonic and time-restricted generation of HSCs.

      General comments

      The authors make the following claims in the paper:

      (1) The development of a protocol for hemogenic gastruloids (hGx) that recapitulates YS and AGM-like waves of blood from HE.

      (2) The protocol recapitulates both YS and EMP-MPP embryonic blood development 'with spatial and temporal accuracy'.

      (3) The protocol generates HSC precursors capable of short-term engraftment in an adrenal niche.

      (4) Overexpression of MNX1 in hGx transforms YS EMP to 'recapitulate patient transcriptional signatures'.

      (5) hGx is a model to study normal and leukaemic embryonic hematopoiesis.

      There are major concerns with the manuscript. The statements and claims made by the authors are not supported by the data presented, data is overinterpreted, and the conclusions cannot be justified. Furthermore, the data is presented in a way that makes it difficult for the reader to follow the narrative, causing confusion. The authors have not discussed how their hGx compares to the previously published mouse embryoid body protocols used to model early development and hematopoiesis. the data is presented in a way that makes it difficult for the reader to follow the narrative, causing confusion. The authors have not discussed how their hGx compares to the previously published mouse embryoid body protocols used to model early development and hematopoiesis.

      Specific points

      (1) It is claimed that HGxs capture cellularity and topography of developmental blood formation. The hGx protocol described in the manuscript is a modification of a previously published gastruloid protocol (Rossi et al 2022). The rationale for the protocol modifications is not fully explained or justified. There is a lack of novelty in the presented protocol as the only modifications appear to be the inclusion of Activin A and an extension of the differentiation period from 7 to 9 days of culture. No direct comparison has been made between the two versions of gastruloid differentiation to justify the changes.

      The Reviewer paradoxically claims that the protocol is not novel and that it differs from a previous publication in at least 2 ways – the patterning pulse and the length of the protocol. Of these, the patterning pulse is key. As documented in Fig. S1, we cannot obtain Flk1-GFP expression in the absence of Activin A. Expression of Flk1 is a fundamental step in haemato-endothelial specification and, accordingly, we do not see CD41 or CD45+ cells in the absence of Activin A. Also, in our hands, there is a clear time-dependent progression of marker expression, with sequential acquisition of CD41 and CD45, with the latter not detectable until 192h (Fig. 1C-D), another key difference relative to the Rossi et al (2022) protocol. The 192h-timepoint, we argue in the manuscript, and present further evidence for in this rebuttal, corresponds to the onset of AGM-like haematopoiesis. We have empirically extended the protocol to maximise the CD45+ cell output (Fig. S1B-D).

      The inclusion of Activin A at high concentration at the beginning of differentiation would be expected to pattern endoderm rather than mesoderm. BMP signaling is required to induce Flk1+ mesoderm, even in the presence of Wnt.

      Again, we call the Reviewer’s attention to Fig. S1 which clearly shows that Activin A (with no BMP added) is required for induction of Flk1 expression, in the presence of Wnt. Activin A in combination with Wnt, is used in other protocols of haemato-endothelial differentiation from pluripotent cells, with no BMP added in the same step of patterning and differentiation (PMID: 39227582; PMID: 39223325). In the latter protocol, we also call the Reviewer’s attention to the fact that a higher concentration of Activin A precludes the need for BMP4 addition. Finally, one of us has recently reported that Activin A, on its own, will induce FLK1, as well as other anterior mesodermal progenitors (https://www.biorxiv.org/content/10.1101/2025.01.11.632562v1)..) In addressing the Reviewer’s concerns with the dose of Activin A used, we titrated its concentration against activation of Flk1, confirming optimal Flk1-GFP expression at the 100ng/ml dose used in the manuscript.

      Author response image 1.

      Dose-dependent requirement of Activin A for induction of Flk1 expression in haemogenic gastruloids. Composite GFP and brightfield live imaging of Flk1-GFP haemogenic gastruloids at 96h. Images were acquired using a Cytation5 instrument (Thermo). Images are representative of 12 gastruloids per condition.

      FACS analysis of the hGx during differentiation is needed to demonstrate the co-expression of Flk1-GFP and lineage markers such as CD34 to indicate patterning of endothelium from Flk1+ mesoderm. The FACS plots in

      Fig. 1 show c-Kit expression but very little VE-cadherin which suggests that CD34 is not induced. Early endoderm expresses c-Kit, CXCR4, and Epcam, but not CD34 which could account for the lack of vascular structures within the hGx as shown in Fig. 1E.

      We were surprised by the Reviewer’s comment that there are no endothelial structures in our gastruloids. The presence of a Flk1-GFP+ network is visible in the GFP images in Fig.1B, from 144h onwards, also shown in Author response image 2A. In addition, our single-cell RNA-seq data, included in the manuscript, confirms the presence of endothelial cells with a developing endothelial, including arterial, programme. This can be seen in Fig. 2B, F of the manuscript and is represented in Author response image 2B. In contrast with the Reviewer’s claims that no endothelial cells are formed, the data show that Kdr (Flk1)+ cells co-express Cdh5/VE-Cadherin and indeed Cd34, attesting to the presence of an endothelial programme. Arterial markers Efnb2, Flt1, and Dll4 are present. A full-blown programme, which also includes haemogenic markers including Sox17, Esam, Cd44 and Mecom is clear at early (144h) and, particularly at late (192h) timepoints in cells sorted on detection of surface c-Kit (Author response image 2B). Further to the data shown in B, already present in the manuscript, we also document co-expression of Flk1-GFP and CD34 by flow cytometry (Author response image 2C).

      Author response image 2.

      Haemogenic gastruloids have a branched vascular network. A. Whole-mount confocal imaging of 144h-haemogenic gastruloids. B. Differentiation of an arterial endothelial programme in haemogenic gastruloids; singlecell RNA-seq data of differentiating haemogenic gastruloids, sorted on cell surface expression of c-Kit at 144 and 192h; gene expression colour scale from yellow (low) to orange (high); grey = no detectable expression. C. Flow cytometry plots of 216h-haemogenic gastruloids showing detection of haemato-endothelial marker CD34.

      (2) The protocol has been incompletely characterised, and the authors have not shown how they can distinguish between either wave of Yolk Sac (YS) hematopoiesis (primitive erythroid/macrophage and erythro-myeloid EMP) or between YS and intraembryonic Aorta-Gonad-Mesonephros (AGM) hematopoiesis. No evidence of germ layer specification has been presented to confirm gastruloid formation, organisation, and functional ability to mimic early development. Furthermore, differentiation of YS primitive and YS EMP stages of development in vitro should result in the efficient generation of CD34+ endothelial and hematopoietic cells. There is no flow cytometry analysis showing the kinetics of CD34 cell generation during differentiation. Benchmarking the hGx against developing mouse YS and embryo data sets would be an important verification.

      The Reviewer is correct that we have not provided detailed characterisation of the different germ layers, as this was not the focus of the study. In that context, we were surprised by the earlier comment assuming co-expression of c-kit, Cxcr4 and Epcam, which we did not show, while overlooking the endothelial programme reiterated above, which we have presented.

      Given our focus on haemato-endothelial specification, we have started the single-cell RNA-seq characterisation of the haemogenic gastruloid at 120h and have not looked specifically at earlier timepoints of embryo patterning.

      This said, we show the presence of neuroectodermal cells in cluster 9; on the other hand, cluster 7 includes hepatoblast-like cells, denoting endodermal specification. We are happy to include this characterisation, to the extent that it is present, in a revised version of the manuscript. However, in the absence of earlier timepoints and given the bias towards mesodermal specification, we expect that specification of ectodermal and endodermal programmes may be incomplete.

      In respect of the contention regarding the capture of YS-like and AGM-like haematopoiesis, we have presented evidence in the manuscript that haemogenic cells generated during gastruloid differentiation, particularly at late 192h and 216h timepoints project onto highly purified c-Kit+ CD31+ Gfi1-expressing cells from mouse AGM (PMID: 38383534), providing support for the recapitulation of the corresponding developmental stage. In distinguishing between YS-like and AGM-like haematopoiesis, we call the Reviewer’s attention to the replotting of the single-cell RNA-seq data already in the manuscript, which we provided in response to point 1 (Author response image 2B), which highlights an increase in Sox17, but not Sox18, expression in the 192h haemogenic endothelium, which suggests an association with AGM haematopoiesis (PMID: 20228271). A significant association of Cd44 and Procr expression with the same time-point (Fig. 2F in the manuscript), further supports an AGM-like endothelial-to-haematopoietic transition at the 192h timepoint.

      Following on the Reviewer’s comments about CD34, we also inspected co-expression of CD34 with CD41 and CD45, the latter co-expression present in, although not necessarily exclusive to, AGM haematopoiesis.

      Reassuringly, we observed clear co-expression with both markers (Author response image 3), in addition to a CD41+CD34-population, which likely reflects YS EMP-independent erythropoiesis. Interestingly, marker expression is responsive to the levels of Activin A used in the patterning pulse, with the 100ng/ml Activin A used in our protocol superior to 75ng/ml.

      Author response image 3.

      Association of CD34 with CD41 and CD45 expression is Activin A-responsive and supports the presence of definitive haematopoiesis. A. Flow cytometry analysis of CD34 and CD41 expression in 216h-haemogenic gastruloids; two doses of Activin A were used in the patterning pulse with CHI99021 between 48-72h. FMO controls shown. B. Flow cytometry analysis of CD34 and CD45 at 216h in the same experimental conditions.

      We agree that it remains challenging to identify markers exclusive to AGM haematopoiesis, which is operationally equated with generation of transplantable haematopoietic stem cells. While HSC generation is a key event characteristic of the AGM, not all AGM haematopoiesis corresponds to HSCs, an important point in evaluating the data presented in the manuscript, and indeed acknowledged by us.

      Author response image 4.

      Clustering of haemogenic gastruloid cells sorted on the basis of haemato-endothelial surface markers CD41, C-Kit and CD45. A. Leiden clustering to single-cell RNA-seq data. B. Time stamps of sorted haemogenic gastruloid cells in A. C. Surface marker stamps of cells in A.

      Given the centrality of this point in comments by all the Reviewers, we have conducted projections of our single-cell RNA-seq data against two studies which (1) capture arterial and haemogenic specification in the para-splanchnopleura (pSP) and AGM region between E8.0 and E11 (Hou et al, PMID: 32203131), and (2) uniquely capture YS, AGM and FL progenitors and the AGM endothelial-to-haematopoietic transition (EHT) in the same scRNA-seq dataset (Zhu et al, PMID: 32392346).

      Focusing the analysis on the subsets of haemogenic gastruloid cells sorted as CD41+ (144h) CKit+ (144h and 192h) and CD45+ (192h and 216h) (Author response image 4AC), we show:

      (1) That a subset of haemato-endothelial cells from haemogenic gastruloids at 144h to 216h project onto intra-embryonic cells spanning E8.25 to E10 (Author response image 5A-B). This is in agreement with our interpretation that 216h are no later than the MPP/pre-HSC state of embryonic development, requiring further maturation to generate long-term engrafting HSC.

      (2) That haemogenic gastruloids contain YS-like (including EMP-like) and AGM-like haematopoietic cells (Author response image 6A-B). Significantly, some of the cells, particularly c-Kit-sorted cells with a candidate endothelial and HE-like signature project onto AGM pre-HE and HE, as well as IAHC, and later, predominantly 216h cells, have characteristics of MPP/LMPP-like cells from the FL.

      Altogether, the data support the notion that haemogenic gastruloids capture YS and AGM haematopoiesis until E10, as suggested by us in the manuscript. We thought it was important to share this preliminary data with the Editors at an early stage, and we will incorporate a deeper analysis in a revised version of the manuscript.

      Single-cell RNA sequencing was used to compare hGx with mouse AGM. The authors incorrectly conclude that ' ..specification of endothelial and HE cells in hGx follows with time-dependent developmental progression into putative AGM-like HE..' And, '...HE-projected hGx cells.......expressed Gata2 but not Runx1, Myb, or Gfi1b..' Hemogenic endothelium is defined by the expression of Runx1 and Gfli1b is downstream of Runx1.

      As a hierarchy of regulation, Gata2 precedes and drives Runx1 expression at the specification of HE (PMID: 17823307; PMID: 24297996), while Runx1 drives the EHT, upstream of Gfi1b in haematopoietic clusters (PMID: 34517413).

      Author response image 5.

      Projection of sorted haemogenic gastruloid cells onto Hou et al dataset (PMID: 32203131) analysing development of mouse intra-embryonic haematopoiesis. A. Time signatures of Hou et al data. B. Projection of Leiden clusters in Author response image 4A. Methodology as described in our manuscript; 68% gastruloid cells projected.

      Author response image 6.

      Projection of sorted haemogenic gastruloid cells onto Zhu et al dataset (PMID: 32392346), capturing arterial endothelial and haemogenic endothelial development, in reference to YS, AGM and FL haematopoietic progenitors. A. Functional cluster classification as per Zhu et al. B. Projection of Leiden clusters in Author response image 4A. Methodology as detailed in our manuscript; 58% gastruloid cells projected. Haematopoietic clusters annotated as in A.

      (3) The hGx protocol 'generates hematopoietic SC precursors capable of short-term engraftment' is not supported by the data presented. Short-term engraftment would be confirmed by flow cytometric detection of hematopoietic cells within the recipient bone marrow, spleen, thymus, and peripheral blood that expressed the BFP transgene. This analysis was not provided. PCR detection of transcripts, following an unspecified number of amplification cycles, as shown in Figure 3G (incorrectly referred to as Figure 3F in the legend) is not acceptable evidence for engraftment.

      We provide the full flow cytometry analysis of spleen engraftment in the 5 mice which received implantation of 216h-haemogenic gastruloids in the adrenal gland; an additional (control) animal received adrenal injection of PBS (Author response image 7). The animals were analysed at 4 weeks. In this experiment, the bone marrow collection was limiting, and material was prioritised for PCR.

      We had previously provided only representative plots of flow cytometry analysis of bone marrow and spleen in Fig. S4E, which we described as low-level engraftment. The analysis was complemented with genomic DNA PCR, where detection was present in only some of the replicates tested per animal. We confirm that PCR analysis used conventional 40 cycles; the sensitivity was shown in Fig. S4F. As shown in Fig. 3 A-C, no more than 7 CD45+CD144+ multipotent cells are present per haemogenic gastruloid, with 3 haemogenic gastruloids implanted in the adrenal gland of each transplanted animal. We argue that the low level of cytometric and molecular engraftment at 4 weeks, from haemogenic gastruloid-derived progenitors that have not progressed beyond a stage equivalent to E10 Author response image 5A-B) and that we have described as requiring additional maturation in vivo, are not surprising.

      Author response image 7.

      BFP engraftment of Nude recipient mice 4 weeks after unilateral adrenal implantation of 216h-haemogenic gastruloids. Flow cytometry analysis of spleen engraftment. Genomic PCR analysis is shown in Fig. 3G of the manuscript.

      Transplanted hGx formed teratoma-like structures, with hematopoietic cells present at the site of transplant only analysed histologically. Indeed, the quality of the images provided does not provide convincing validation that donor-derived hematopoietic cells were present in the grafts.

      As stated in the text, the images mean to illustrate that the haemogenic gastruloids developed in situ. The observation of donor-derived blood cells in the implanted haemogenic gastruloids would not correspond to engraftment, as we have amply demonstrated that they have generated blood cells in vitro. There is no evidence that there are remaining pluripotent cells in the haemogenic gastruloid after 9 days of differentiation, and it is therefore not clear that these are teratomas

      There is no justification for the authors' conclusion that '... the data suggest that 216h hGx generate AGM-like pre-HSC capable of at least short-term multilineage engraftment upon maturation...'. Indeed, this statement is in conflict with previous studies demonstrating that pre-HSCs in the dorsal aorta of the mouse embryo are immature and actually incapable of engraftment.

      We have clearly stated that we do not see haematopoietic engraftment through transplantation of dissociated haemogenic gastruloids, which reach the E10 state containing pre-HSC (Author response image 5). Instead, we observed rare myelo-erythroid (in the manuscript) and myelo-lymphoid (Author response image 9 below, in response to Reviewer 2) engraftment upon in vivo maturation of haemogenic gastruloids with preserved 3D organisation. These statements are not contradictory.

      The statement '...low-level production of engrafting cells recapitulates their rarity in vivo, in agreement with the embryo-like qualities of the gastruloid system....' is incorrect. Firstly, no evidence has been provided to show the hGx has formed a dorsal aorta facsimile capable of generating cells with engrafting capacity. Secondly, although engrafting cells are rare in the AGM, approximately one per embryo, they are capable of robust and extensive engraftment upon transplantation.

      We are happy to rephrase the statement to simply say that “…the data suggest that 216h haemogenic gastruloids contain candidate AGM-like progenitors with some short-term engraftment potential but incomplete functional maturation.” To be clear, with our existing statement we meant to highlight that the production of definitive AGM-like haematopoietic progenitors (not all of which are engrafting) in haemogenic gastruloids does not correspond to non-physiological single-lineage programming. We did not claim that we achieved production of HSC, which would be long-term engrafting.

      (4) Expression MNX1 transcript and protein in hematopoietic cells in MNX1 rearranged acute myeloid leukaemia (AML) is one cause of AML in infants. In the hGX model of this disease, Mnx1 is overexpressed in the mESCs that are used to form gastruloids. Mnx1 overexpression seems to confer an overall growth advantage on the hGx and increase the serial replating capacity of the small number of hematopoietic cells that are generated. The inefficiency with which the hGx model generates hematopoietic cells makes it difficult to model this disease. The poor quality of the cytospin images prevents accurate identification of cells. The statement that the kit-expressing cells represent leukemic blast cells is not sufficiently validated to support this conclusion. What other stem cell genes are expressed? Surface kit expression also marks mast cells, frequently seen in clonogenic assays of blood cells. Flow cytometric and gene expression analyses using known markers would be required.

      The haemogenic gastruloid model generates haematopoietic and haemato-endothelial cells. MNX1 expands Kit+ cells at 144h, which we show to have a haemato-endothelial signature (manuscript Fig. 2B, which we replotted in Author response image 2B).

      Serial replating of CFC assays is a conventional in vitro assay of leukaemia transformation. Critically, colony replating is not maintained in EV control cells, attesting to the transformation potential of MNX1.

      Although we have not fully-traced the cellular hierarchy of MNX1-driven transformation in the haemogenic gastruloid system, the in vitro replating expands a Kit+ cell (Fig. 5E), which reflects the surface phenotype of the leukaemia, also recapitulated in the mouse model initiated by MNX1-overexpressing FL cells. Importantly, it recapitulates the transcriptional profile of MNX1-leukaemia patients (Fig. 6C), which is uniquely expressed by MNX1144h and replated colony cells, but not to MNX1 216h gastruloid cells, arguing against a generic signature of MNX1 overexpression (Fig. 6B). Importantly, the MNX1-transformation of haemogenic gastruloid cells is superior to the FL leukaemia model at capturing the unique transcriptional features of MNX1-driven leukaemia, distinct from other forms of AML in the same age group (Fig S7). It is possible that this corresponds to a preleukaemia event, and we will explore this in future studies, which are beyond the proof-of-principle nature of this paper.

      (5) In human infant MNX1 AML, the mutation is thought to arise at the fetal liver stage of development. There is no evidence that this developmental stage is mimicked in the hGx model.

      We never claim that the haemogenic gastruloid model mimics the foetal liver. We propose that susceptibility to MNX1 is at the HE-to-EMP transition. Moreover, and importantly, contrary to the Reviewer’s statement, there is no evidence in the literature that the mutation arises in the foetal liver stage, just that the mutation arises before birth (PMID: 38806630), which is different. In a mouse model of MNX1 overexpression, the authors achieve leukaemia engraftment upon MNX1 overexpression in foetal liver, but not in bone marrow cells (PMID: 37317878). This is in agreement with a vulnerability of embryonic / foetal, but not adult cells to the MNX1 expression caused by the translocation. However, haematopoietic cells in the foetal liver originate from YS and AGM precursors, so the origin of the MNX1-susceptible cells can be in those locations, rather than the foetal liver itself.

      Reviewer #2 (Public review):<br /> Summary:<br /> In this manuscript, the authors develop an exciting new hemogenic gastruloid (hGX) system, which they claim reproduces the sequential generation of various blood cell types. The key advantage of this cellular system would be its potential to more accurately recapitulate the spatiotemporal emergence of hematopoietic progenitors within their physiological niche compared to other available in vitro systems. The authors present a large set of data and also validate their new system in the context of investigating infant leukemia.<br /> Strengths:<br /> The development of this new in vitro system for generating hematopoietic cells is innovative and addresses a significant drawback of current in vitro models. The authors present a substantial dataset to characterize this system, and they also validate its application in the context of investigating infant leukemia.<br /> Weaknesses:<br /> The thorough characterization and full demonstration that the cells produced truly represent distinct waves of hematopoietic progenitors are incomplete. The data presented to support the generation of late yolk sac (YS) progenitors, such as lymphoid cells, and aortic-gonad-mesonephros (AGM)-like progenitors, including pre-hematopoietic stem cells (pre-HSCs), by this system are not entirely convincing. Given that this is likely the manuscript's most crucial claim, it warrants further scrutiny and direct experimental validation. Ideally, the identity of these progenitors should be further demonstrated by directly assessing their ability to differentiate into lymphoid cells or fully functional HSCs. Instead, the authors primarily rely on scRNA-seq data and a very limited set of markers (e.g., Ikzf1 and Mllt3) to infer the identity and functionality of these cells. Many of these markers are shared among various types of blood progenitors, and only a well-defined combination of markers could offer some assurance of the lymphoid and pre-HSC nature of these cells, although this would still be limited in the absence of functional assays.<br /> The identification of a pre-HSC-like CD45⁺CD41⁻/lo c-Kit⁺VE-Cadherin⁺ cell population is presented as evidence supporting the generation of pre-HSCs by this system, but this claim is questionable. This FACS profile may also be present in progenitors generated in the yolk sac such as early erythro-myeloid progenitors (EMPs). It is only within the AGM context, and in conjunction with further functional assays demonstrating the ability of these cells to differentiate into HSCs and contribute to long-term repopulation, that this profile could be strongly associated with pre-HSCs. In the absence of such data, the cells exhibiting this profile in the current system cannot be conclusively identified as true pre-HSCs.

      At this preliminary response stage, we present 2 additional pieces of evidence to support our claims that we capture YS and AGM stages of haematopoietic development. In future experiments, we can complement these with functional assays, including co-culture with OP9 and OP9-DL stroma.

      Author response image 8.

      EZH2 inhibition affects CD41+ cellular output in haemogenic gastruloids at 144, but not 216h. A. Flow cytometry analysis of CD41 expression in 144h-haemogenic gastruloid treated with 0.5μM EZH2 inhibitor GSK126 from 120h. DMSO (0.05%), vehicle. 1 of 2 independent experiments (average CD41+: DMSO, 21.20%; GSK126, 12.10%; CD45 not detected). B. Flow cytometry analysis of CD41 and CD45 expression in 216h gastruloids, treated with DMSO or GSK216. (DMSO: average CD41+, 15.28%; average CD45+ 0.46%. GSK126: average CD41+, 23.78%; average CD45+, 2.08%).

      In Author response images 5 and 6, we project our single-cell RNA-seq data onto (1) developing intra-embryonic pSP and AGM between E8 and E11 (Author response image 5) and (2) a single-cell RNA-seq study of HE development which combines haemogenic and haematopoietic cells from the YS, the developing HE and IAHC in the AGM, and FL (Author response image 6). Our data maps E8.25-E10 (Author response image 5) and captures YS EMP and erythroid and myeloid progenitors, as well as AGM pre-HE, HE and IAHC, with some cells matching HSPC and LMPP (Author response image 6), as suggested by the projection onto the Thambyrajah et al data set (Fig. S3 in the manuscript).

      Given the difficulty in finding markers that specifically associate with AGM haematopoiesis, we inspected the possibility of capturing different regulatory requirements at different stages of gastruloid development mirroring differential effects in the embryo. Polycomb EZH2 is specifically required for EMP differentiation in the YS, but does not affect AGM-derived haematopoiesis; it is also not required for primitive erythroid cells (PMID: 29555646; PMID: 34857757). We treated haemogenic gastruloids from 120h onwards with either DMSO (0.05%) or GSK126 (0.5μM), and inspected the cellularity of gastruloids at 144h, which we equate with YS-EMP, and 216h – putatively AGM haematopoiesis (Author response image 8). We show that EZH2 inhibition / GSK126 treatment specifically reduces %CD41+ cells at 144h (Author response image 8A), but does not reduce %CD41+ or %CD45+ cells at 216h (Author response image 8B).

      Although preliminary, these data, together with the scRNA-seq projections described, provide evidence to our claim that 144h haemogenic gastruloids capture YS EMPs, while CD41+ and CD45+ cells isolated at 216h reflect AGM progenitors. We cannot conclude as to the functional nature of the AGM cells from this experiment.

      The engraftment data presented are also not fully convincing, as the observed repopulation is very limited and evaluated only at 4 weeks post-transplantation. The cells detected after 4 weeks could represent the progeny of EMPs that have been shown to provide transient repopulation rather than true HSCs.

      We clearly state that there is low level engraftment and do not claim to have generated HSC. We describe cells with short-term engraftment potential. Although the cells we show in the manuscript at 4 weeks could be EMPs (Author response image 7 and Fig. 3 and S3), we now have 8-week analysis of implant recipients, in which we observed, again low-level, engraftment of the recipient bone marrow in 1:3 animals (Author response image 9). This engraftment is myeloid-lymphoid and therefore likely to have originated in a later progenitor. To be clear, we do not claim that this corresponds to the presence of HSC. It nevertheless supports the maturation of progenitors with engraftment potential.

      Author response image 9.

      Flow cytometry BFP engraftment of recipient bone marrow 8-weeks post implantation of 216hhaemogenic gastruloids in the adrenal gland of Nude mice. 1:3 animals show BFP CD45+ engraftment in the myeloid (Mac1+) and B-lymphoid (B220+) lineages. 3 haemogenic gastruloids were implanted unilaterally in the adrenal gland of each animal. A. Engrafted animal, showing CD45+ BFP cells of myeloid (CD11b) and B-lymphoid affiliation (B220). B. Non-engrafted mouse recipient of haemogenic gastruloid implants.

      Reviewer #3 (Public review):<br /> In this study, the authors employ a mouse ES-derived "hemogenic gastruloid" model which they generated and which they claim to be able to deconvolute YS and AGM stages of blood production in vitro. This work could represent a valuable resource for the field. However, in general, I find the conclusions in this manuscript poorly supported by the data presented. Importantly, it isn't clear what exactly are the "YS" and the "AGM"-like stages identified in the culture and where is the data that backs up this claim. In my opinion, the data in this manuscript lack convincing evidence that can enable us to identify what kind of hematopoietic progenitor cells are generated in this system. Therefore, the statement that "our study has positioned the MNX1-OE target cell within the YS-EMP stage (line 540)" is not supported by the evidence presented in this study. Overall, the system seems to be very preliminary and requires further optimization before those claims can be made.<br /> Specific comments below:<br /> (1) The flow cytometric analysis of gastruloids presented in Figure 1 C-D is puzzling. There is a large % of c-Kit+ cells generated, but few VE-Cad+ Kit+ double positive cells. Similarly, there are many CD41+ cells, but very few CD45+ cells, which one would expect to appear toward the end of the differentiation process if blood cells are actually generated. It would be useful to present this analysis as consecutive gating (i.e. evaluating CD41 and CD45 within VE-Cad+ Kit+ cells, especially if the authors think that the presence of VE-Cad+ Kit+ cells is suggestive of EHT). The quantification presented in D is misleading as the scale of each graph is different.

      Fig. 1C-D provide an overview of haemogenic markers during the timecourse of haemogenic gastruloid differentiation, and does indeed show a late up-regulation of CD45, as the Reviewer points out would be expected. The %CD45+ cells is indeed low. However, we should point out that the haemogenic gastruloid protocol, although biased towards mesodermal outputs, does not aim to achieve pure haematopoietic specification, but rather place it in its embryo-like context. Consecutive gating at the 216h-timepoint is shown and quantified in Fig. 3A-B. We refute that the scale is misleading. It is a necessity to represent the data in a way that is interpretable by the reader: the gates (in C) are truly representative and annotated, as are the plot axes (in D).

      (2) The imaging presented in Figure 1E is very unconvincing. C-Kit and CD45 signals appear as speckles and not as membrane/cell surfaces as they should. This experiment should be repeated and nuclear stain (i.e. DAPI) should be included.

      We include the requested images below (Author response image 10).

      Author response image 10.

      Confocal images of haematopoietic production in haemogenic gastruloids. Wholemount, cleared haemogenic gastruloids were stained for CD45 (pseudo-coloured red) and c-Kit antigens (pseudo-coloured yellow) with indirect staining, as described in the manuscript. Flk1-GFP signal is shown in green. Nuclei are contrasted with DAPI. (A) 192h. (B) 216h.

      (3) Overall, I am not convinced that hematopoietic cells are consistently generated in these organoids. The authors should sort hematopoietic cells and perform May-Grunwald Giemsa stainings as they did in Figure 6 to confirm the nature of the blood cells generated.

      It is factual that the data are reproducible and complemented by functional assays shown in Fig. 3, which clearly demonstrate haematopoietic output. The single-cell RNA-seq data also show expression of a haematopoietic programme. Nevertheless, we include Giemsa-Wright’s stained cytospins obtained at 216h to illustrate haematopoietic output (Reviewer Fig. 11). Inevitably, the cytospins will be inconclusive as to the presence of endothelial-to-haematopoietic transition or the generation of haematopoietic stem/progenitor cells, as these cells do not have a distinctive morphology.

      Author response image 11.

      Cytospin of dissociated haemogenic gastruloids at 216h. Cytospins were stained with Giemsa-Wright’s stain and are visualised with a 40x objective. Annotated are cells in the monocytic (dashed open arrow), granulocytic (solid open arrow), megakaryocytic (solid arrow) and erythroid (asterisk) lineages; arrowheads indicate cells with a non-specific blast-like morphology. Representative image.

      (4) The scRNAseq in Figure 2 is very difficult to interpret. Specific points related to this:<br /> - Cluster annotation in Figure 2a is missing and should be included.<br /> - Why do the heatmaps show the expression of genes within sorted cells? Couldn't the authors show expression within clusters of hematopoietic cells as identified transcriptionally (which ones are they? See previous point)? Gene names are illegible.<br /> - I see no expression of Hlf or Myb in CD45+ cells (Figure 2G). Hlf is not expressed by any of the populations examined (panels E, F, G). This suggests no MPP or pre-HSC are generated in the culture, contrary to what is stated in lines 242-245. (PMID 31076455 and 34589491).<br /> Later on, it is again stated that "hGx cells... lacked detection of HSC genes like Hlf, Gfi1, or Hoxa9" (lines 281-283). To me, this is proof of the absence of AGM-like hematopoiesis generated in those gastruloids.

      Author response image 12.

      Expression of endothelial, haemogenic and haematopoietic genes in haemogenic gastruloid cells sorted at 144h, 192h and 216h. UMAP as in Author response image 4. Pecam (CD31) and CD34 represent endothelial genes also detected in haemogenic endothelium. CD44 is specifically enriched at the endothelial-to-haemogenic transition. Mecom is detected in haemogenic endothelium and haematopoietic progenitors. Mllt3 and Runx1 are haematopoietic markers. Hoxa9 and Hlf are associated with haematopoietic stem and progenitor cells and their detection is rare in haemogenic gastruloids at 216h.

      For a combination of logistic and technical reasons, we performed single-cell RNA-seq using the Smart-Seq2 platform, which is inherently low throughput. We overcame the issue of cell coverage by complementing whole-gastruloid transcriptional profiling at successive time-points with sorting of subpopulations of cells based on individual markers documented in Fig. 1. We clearly stated which platform was used as well as the number and type of cells profiled (Fig. S2A and lines 172-179 of the manuscript), and our approach is standard. We will review our representation of the data in a revised manuscript. Nevertheless, at this stage, we provide plots of the expression of key haematopoietic markers over UMAPs of haemogenic gastruloid timecourse (Author response image 12). We also show preliminary qRT-PCR data with increased Hlf expression upon extension of the protocol to 264h (Author response image 13), further confirming haematopoietic specification, including of candidate definitive progenitor cells, in the haemogenic gastruloid model.

      Author response image 13.

      Hlf expression is up-regulated in late stage haemogenic gastruloids. Quantitative RT-PCR analysis of Hlf expression in unfractionated haemogenic gastruloids cultured for 264h. From 168h onwards, haemogenic gastruloids were cultured in N2B27 in the presence of VEGF, SCF, FLT3L and TPO, all recombinant mouse cytokines, as described in the manuscript. Shown are mean±standard deviation of n=5 replicates from 2 mouse ES cell lines, respectively Flk1-GFP and Rosa26-BFP::Flk1-GFP, reported in the manuscript; 2-tailed unpaired t-test with Welch correction.

      (5) Mapping of scRNA-Seq data onto the dataset by Thambyrajah et al. is not proof of the generation of AGM HE. The dataset they are mapping to only contains AGM cells, therefore cells do not have the option to map onto something that is not AGM. The authors should try mapping to other publicly available datasets also including YS cells.

      We have done this and the data are presented in Author response image 5 and 6. As detailed in response to Reviewer 1, we have conducted projections of our single-cell RNA-seq data against two studies which (1) capture arterial and haemogenic specification in the para-splanchnopleura (pSP) and AGM region between E8.0 and E11 (Hou et al, PMID: 32203131) (Author response image 5), and (2) uniquely capture YS, AGM and FL progenitors and the AGM endothelial-to-haematopoietic transition (EHT) in the same scRNA-seq dataset (Zhu et al, PMID: 32392346) (Author response image 6). Specifically in answering the Reviewers’ point, we show that different subsets of haemogenic gastruloid cells sorted on haemogenic surface markers c-Kit, CD41 and CD45 cluster onto pre-HE and HE, intra-aortic clusters and FL progenitor compartments, and to YS EMP and erythroid and myeloid progenitors. This lends support to our claim that the haemogenic gastruloid system specifies both YS-like and AGM-like cells.

      (6) Conclusions in Figure 3, named "hGx specify cells with preHSC characteristics" are not supported by the data presented here. Again, I am not convinced that hematopoietic cells can be efficiently generated in this system, and certainly not HSCs or pre-HSCs.

      We have provided evidence, both in the manuscript and in this response to Reviewers, that there is haematopoietic specification, including of progenitor cells, in the haemogenic gastruloid system (Fig. 3 and Author response image 7,9). We have added data in this response that supports the specification of YS-like and AGM-like cells (Author response image 5-6, 8). Importantly, we have never claimed that haemogenic gastruloids generate HSC. We accept the Reviewer’s comment that we have not provided sufficient evidence for the specification of pre-HSC-like cells. We will re-phrase Fig. 3 conclusion as “Haemogenic gastruloids specify cells with characteristics of definitive haematopoietic progenitors”.

      - FACS analysis in 3A is again very unconvincing. I do not think the population identified as c-Kit+ CD144+ is real. Also, why not try gating the other way around, as commonly done (e.g. VE-Cad+ Kit+ and then CD41/CD45)?

      There is nothing unconventional about our gating strategy, which was done from a more populated gate onto the less abundant one to ensure that the results are numerically more robust. In the case of haemogenic gastruloids, unlike the AGM preparations the Reviewer may be referring to, CD41 and CD45+ cells are more abundant as there is no circulation of more differentiated haematopoietic cells away from the endothelial structures. This said, we did perform the gating as suggested (Author response image 14), indeed confirming that most VE-cad+ Kit+ cells are CD45+. Interestingly VE-cad+Kit- are predominantly CD41+, reinforcing the true haematopoietic nature of these cells.

      Author response image 14.

      Flow cytometry analysis of VE-cadherin+ cells in haemogenic gastruloids at 216h of the differentiation protocol, probing co-expression of CD45, CD41 and c-Kit.

      - The authors must have tried really hard, but the lack of short- or long-engraftment in a number of immunodeficient mouse models (lines 305-313) really suggests that no blood progenitors are generated in their system. I am not familiar with the adrenal gland transplant system, but it seems like a very non-physiological system for trying to assess the maturation of putative pre-HSCs. The data supporting the engraftment of these mice, essentially seen only by PCR and in some cases with a very low threshold for detection, are very weak, and again unconvincing. It is stated that "BFP engraftment of the Spl and BM by flow cytometry was very low level albeit consistently above control (Fig. S4E)" (lines 337-338). I do not think that two dots in a dot plot can be presented as evidence of engraftment.

      We have presented the data with full disclosure and do not deny that the engraftment achieved is low-level and short-term, indicating incomplete maturation of definitive haematopoietic progenitors in the current haemogenic gastruloid system. However, we call the Reviewer’s attention to the fact that detection of BFP+ cells by PCR and flow cytometry in the recipient animals at 4 weeks is consistent between the 2 methods (Author response image 7).

      Furthermore, we have now also been able to detect low-level myelo-lymphoid engraftment in the bone marrow 8 weeks after adrenal implantation, again suggesting the presence of a small number of definitive haematopoietic progenitors that potentially mature from the 3 haemogenic gastruloids implanted (Author response image 9).

      (7) Given the above, I find that the foundations needed for extracting meaningful data from the system when perturbed are very shaky at best. Nevertheless, the authors proceed to overexpress MNX1 by LV transduction, a system previously shown to transform fetal liver cells, mimicking the effect of the t(7;12) AML-associated translocation. Comments on this section:<br /> - The increase in the size of the organoid when MNX1 is expressed is a very unspecific finding and not necessarily an indication of any hematopoietic effect of MNX1 OE.

      We agree with the Reviewer on this point; it is nevertheless a reproducible observation which we thought relevant to describe for completeness and data reproducibility.

      - The mild increase of cKit+ cells (Figure 4E) at the 144hr timepoint and the lack of any changes in CD41+ or CD45+ cells suggests that the increase in Kit+ cells % is not due to any hematopoietic effect of MNX1 OE. No hematopoietic GO categories are seen in RNA seq analysis, which supports this interpretation. Could it be that just endothelial cells are being generated?

      The Reviewer is correct that the MNX1-overexpressing cells have a strong endothelial signature, which is present in the patients (Fig. 4A). We investigated a potential link with c-Kit by staining cells from the replating colonies during the process of in vitro transformation with CD31. We observed that 40-50% of c-Kit+ cells (20-30% total colony cells) co-expressed CD31(Author response image 15), at least at early plating. These cells co-exist with haematopoietic cells, namely Ter119+ cells, as expected from the YS-like erythroid and EMP-like affiliation of haematopoietic output from 144h-haemogenic gastruloids (Fig. 5F).

      Author response image 15.

      Endothelial affiliation of MNX1-oe replating cells from haemogenic gastruloid. A. Representative flow cytometry plot of plate 1 CFC from MNX1-overexpressing haemogenic gastruloids at 144h. B. Quantification of the proportion of CD31+c-Kit+ cells in plates 1 and 2 of MNX1-oe-driven in vitro transformation.

      (8) There seems to be a relatively convincing increase in replating potential upon MNX1-OE, but this experiment has been poorly characterized. What type of colonies are generated? What exactly is the "proportion of colony forming cells" in Figures 5B-D? The colony increase is accompanied by an increase in Kit+ cells; however, the flow cytometry analysis has not been quantified.

      Given the inability to replate control EV cells, there is not a population to compare with in terms of quantification. The level of c-Kit+ represented in Fig. 5E is achieved at plate 2 or 3 (depending on the experiment), both of which are significantly enriched for colony-forming cells relative to control (Fig. 5B, D).

      (9) Do hGx cells engraft upon MNX1-OE? This experiment, which appears not to have been performed, is essential to conclude that leukemic transformation has occurred.

      For the purpose of this study, we are satisfied with confirmation of in vitro transformation potential of MNX1 haemogenic gastruloids, which can be used for screening purposes. Although interesting, in vivo leukaemia engraftment from haemogenic gastruloids is beyond the scope of this study.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      While CRISPR/Cas technology has greatly facilitated the ability to perform precise genome edits in Leishmania spp., the lack of a non-homologous DNA end-joining (NHEJ) pathway in Leishmania has prevented researchers from performing large-scale Cas-based perturbation screens. With the introduction of base editing technology to the Leishmania field, the Beneke lab has begun to address this challenge (Engstler and Beneke, 2023).

      In this study, the authors build on their previously published protocols and develop a strategy that:

      (1) allows for very high editing efficiency. The cell editing frequency of 1 edit per 70 cells reported in this study represents a 400-fold improvement over the previously published protocol,

      (2) reduces the negative effects of high sgRNA levels on parasite growth by using a weaker T7 promoter to drive sgRNA transcription.

      The combination of these two improvements should open the door to exciting large-scale screens and thus be of great interest to researchers working with Leishmania and beyond.

      We thank reviewer #1 for these encouraging comments.

      Reviewer #2 (Public Review):

      Summary:

      Previously, the authors published a Leishmania cytosine base editor (CBE) genetic tool that enables the generation of functionally null mutants. This works by utilising a CAS9-cytidine deaminase variant that is targeted to a genetic locus by a small guide RNA (sgRNA) and causes cytosine to thymine conversion. This has the potential to generate a premature stop codon and therefore a loss of function mutant.

      CBE has advantages over existing CAS-based knockout tools because it allows the targeting of multicopy gene families and, potentially, the easier generation of pooled loss of function mutants in complex population experiments. Although successful, the first generation of this genetic tool had several limitations that may have prevented its wider adoption, especially in complex genome-wide screens. These include nonspecific toxicity of the sgRNAs, low transfection efficiencies, low editing efficiencies, a proportion of transfectants that express multiple different sgRNAs, and insufficient effectivity in some Leishmania species.

      Here, the authors set out to systematically solve each of these limitations. By trialling different transfection conditions and different CAS12a cut sites to promote sgRNA expression cassette integration, they increase the transfection efficiency 400-fold and ensure that only a single sgRNA expression cassette integrates that edits with high efficiencies. By trialling different T7 promoters, they significantly reduce the non-specific toxicity of sgRNA expression whilst retaining high editing efficiencies in several Leishmania species (Leishmania major, L. mexicana and L. donovani). By improving the sgRNA design, the authors predict that null mutants will be more efficiently produced after editing.

      This tool will find adoption for producing null mutants of single-copy genes, multicopy gene families, and potentially genome-wide mutational analyses.

      Strengths:

      This is an impressive and thorough study that significantly improves the previous iteration of the CBE. The approach is careful and systematic and reflects the authors' excellent experience developing CRISPR tools. The quality of data and analysis is high and data are clearly presented.

      Weaknesses:

      Figure 4 shows that editing of PF16 is 'reversed' between day 6 and day 16 in L. mexicana WTpTB107 cells. The authors reasonably conclude that in drug-selected cells there is a mixed population of edited and non-edited cells, possibly due to mis-integration of the sgRNA expression construct, and non-edited cells outcompete edited cells due to a growth defect in PF16 loss of function mutants. However, this suggests that the CBE tool will not work well for producing mutants with strong fitness phenotypes without incorporating a limiting dilution cloning step (at least in L. mexicana and quite possibly other Leishmania species). Furthermore, it suggests it will not be possible to incorporate genes associated with a growth defect into a pooled drop-out screen as described in the paper. This issue is not well explored in the paper and the authors have not validated their tool on a gene associated with a severe growth defect, or shown that their tool works in a mixed population setting.

      We would like to thank reviewer #2 for this helpful comment and valid point. We have now included a small-scale loss-of-function screen in L. mexicana, targeting nine known essential genes with 24 CBE sgRNAs and 15 non-targeting control sgRNAs. This approach successfully detected all known included growth-associated phenotypes in a pooled screening format. This experiment is now shown in Figure 5 and described in section “Detection of fitness-associated phenotypes in a pooled loss-of-function screen”.

      In addition, we would like to re-iterate our initial public response to this comment. We believe that escapes or reversals of mutant phenotypes can be observed also with other genetic tools used for loss-of-function screening, including lentiviral CRISPR approaches in mammalian systems and RNAi in Trypanosoma brucei (e.g. Ariyanayagam et al., 2005 and Schlecker et al., 2005). Notably, in lentiviral delivered CRISPR screens, sgRNA expression cassettes are integrated in random places within the genome and multiple cassettes can be integrated depending on the viral titre. In these type of screens, cells can escape phenotypes through various mechanisms, such as promoter silencing or selection of non-deleterious mutations. Additionally, not every CRISPR guide is efficient in generating a mutant phenotype, and RNAi constructs can also vary in their effectiveness. Despite these challenges, genome-wide loss-of-function screens have been successfully carried out in mammalian cells and Trypanosoma parasites. Therefore, we believe that the observed escape of one mutant phenotype does not preclude the detection of growth-associated or other phenotypes in pooled screens. Moreover, we did not observe a reversal of the mutant phenotype in L. mexicana, L. donovani, and L. major parasites expressing tdTomato from an expression cassette integrated into the 18S rRNA SSU locus (Figure 4). Our now included small scale fitness screen (Figure 5) confirms these assumptions and shows that we can detect “strong” growth associated phenotypes. We would also like to point out that we have recently successfully conducted several genome-wide loss-of-function screens in vivo and in vitro, ultimately confirming the feasibility of this type of screen on a genome-wide scale (manuscript in preparation).

      We have included a discussion of these points under section “Integration of CBE sgRNA expression cassettes via AsCas12a ultra-introduced DSBs increase editing rates” and section “Detection of fitness-associated phenotypes in a pooled loss-of-function screen” in our revised manuscript.

      Although welcome, the improvements to the crRNA CBE design tool are hypothetical and untested.

      We agree that the improvements to the CBE sgRNA design are currently hypothetical. We plan to systematically test our guide design principles in future studies. Since this will require testing hundreds of guides to draw robust conclusions, we believe that this aspect is beyond the scope of the current study. In section “Improved CBE sgRNA design to prioritize edits resulting only in STOP codons” of our revised manuscript we now discuss these future plans.

      The Sanger and Oxford Nanopore Technology analyses on integration sites of the sgRNA expression cassette integration will not detect the mis-integration of the sgRNA expression construct into an entirely different locus.

      We have now re-analysed our ONT data and have extracted all ONT contigs that match the CBE sgRNA expression cassette. All extracted contigs align to the 18S rRNA SSU locus, showing integration of the cassette into this locus. It is important to note that here a population was sequenced and not a clone. Despite this, no contigs could be found that would link the CBE sgRNA expression cassettes to another locus. This is now shown in Figure 4 S2 and described in section “Cas12a-mediated DSB ensures the integration of one CBE sgRNA per L. mexicana transfectant”.

      Reviewer #3 (Public Review):

      Genetic manipulation of Leishmania has some challenges, including some limitations in the DNA repair strategies that are present in the organism and the absence of RNA interference in many species. The senior author has contributed significantly to expanding the available routes towards Leishmania genetic manipulation by developing and adapting CRISPR-Cas9 tools to allow gene manipulation via DNA double-strand break repair and, more recently, base modification. This work seeks to improve on some limitations in the tools previously described for the latter approach of base modification leading to base change.

      The work in the paper is meticulously described, with solid evidence for most of the improvements that are claimed: Figure1 clearly describes reduced impairment in the growth of parasites expressing sgRNAs via changes in promoters; Figures 2 and 3 compellingly document the usefulness of using AsCas12a for integration after transformation; and Figures 1 and 4 demonstrate the capacity of the combined modifications to efficiently edit a gene in three different Leishmania species. There is little doubt these new tools will be adopted by the Leishmania community, adding to the growing arsenal of approaches for genetic manipulation.

      There are two weaknesses the authors may wish to address, one smaller and one larger.

      (1) The main advance claimed here is in this section title: 'Integration of CBE sgRNA expression cassettes via AsCas12a ultra-introduced DSBs increase editing rates', with the evidence for this presented in Figure 4. It is hard work in the submission to discern what direct evidence there is for editing rates being improved relative to earlier, Cas9-based approaches. Did they directly compare the editing by the new and old approach? If not, can they more clearly explain how they are able to make this claim, either by adding text or a new figure? A side-by-side comparison would emphasise the advance of the new approach more clearly.

      We would like to thank reviewer #3 for this helpful comment. We have directly compared our improved method to our previous base editing method in Figures 1E and 4, demonstrating higher editing rates in a much shorter time. Especially the L. major panel in Figure 4B shows that in a direct comparison between the previously published (Engstler and Beneke, eLife 2023) and our here presented new system, editing can be only observed with the version presented here. However, to clarify the improvements we made, we compare now data from our previous screen done in Engstler and Beneke, eLife 2023 with a loss-of-function screen carried out with our updated method (see Figure 5 and section “Detection of fitness-associated phenotypes in a pooled loss-of-function screen”).

      In addition, we also feel that our title might have been misleading in a sense that we claim that Cas12a editing is more efficient than other Cas9 based approaches, which is something that we don’t want to state here. Given that we have now included a small scale CRISPR screen and given that we generally show improved base editing compared to our previous method (improved in terms of less toxicity, more editing in shorter time, higher transfection rates and less species specific variation), we have rephrased our title to: “Improved base editing and functional screening in Leishmania via co-expression of the AsCas12a ultra, a T7 RNA Polymerase, and a cytosine base editor”. 

      (2) The ultimate, stated goal of this work is (abstract) to 'enable a variety of loss-of-function screens', as the older approach had some limitations. This goal is not tested for the new tools that have been developed here; the experiment in Figure 5 merely shows that they can, not unexpectedly, make a gene mutant, which was already possible with available tools. Thus, to what extent is this paper describing a step forward? Why have the authors not run an experiment - even the same one that was described previously in Engstler and Beneke (2023) - to show that the new approach improves on previous tools in such a screen, either in scale or accuracy?

      We have now included a small-scale loss-of-function screen in L. mexicana, targeting nine known essential genes with 24 CBE sgRNAs and 15 non-targeting control sgRNAs. This approach successfully detected all known included growth-associated phenotypes in a pooled screening format. This experiment is now shown in Figure 5 and described in section “Detection of fitness-associated phenotypes in a pooled loss-of-function screen”. We believe that this underscores our claims made here and believe therefore that our updated toolbox will indeed enable a variety of loss-of-function screens.

      As pointed out in the comment to reviewer #2, we have recently successfully conducted several genome-wide loss-of-function screens in vivo and in vitro, ultimately confirming the feasibility of this type of screen on a genome-wide scale (manuscript in preparation). Without the improvements presented here, such as the higher transfection and base editing rates, these genome-wide screens could have not been carried out.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      I would like to compliment Tom Beneke and his lab on their continued efforts to develop tools to facilitate genome editing in Leishmania.

      I have no doubt that the toolkit presented in this study will be very useful for the community. The submitted paper is very well written and contains all the necessary controls to support the author's claims. There is only one point that left me a bit concerned if this strategy is to be used for large-scale screens, and that is the potential for integration of multiple sgRNA expression cassettes in a single cell.

      We would like to thank reviewer 1 for helpful comments. We have addressed the major concern raised by including a small-scale loss-of-function screen in our revised manuscript. By targeting nine known essential genes with 24 CBE sgRNAs and 15 non-targeting control sgRNAs, this approach successfully detected growth-associated phenotypes in a pooled format (see section “Detection of fitness-associated phenotypes in a pooled loss-of-function screen” and Figure 5). Regarding the point of multiple sgRNA expression cassette integration, please see the next comment below.

      Major points:

      Integration of multiple sgRNA expression cassettes:

      While Illumina-based gDNA-seq is well suited to determine changes in ploidy, I don't think it is sensitive enough to draw conclusions about possible double integration in a small percentage of cells. In fact, the data shown in Figure 4 S1D show a normalized coverage >1.5 for sgRNA cassette and NeoR, suggesting that they may have integrated >1 times in some cells.

      To verify that the integration of the CBE sgRNA expression cassette is specific, we have re-analysed our ONT results and confirmed that only ONT contigs can be detected that link the CBE sgRNA expression to the 18S rRNA locus. No other integration sites can be found. We also do not detect any contigs containing multiple CBE sgRNA expression cassettes. This is now shown in Figure 4 S2 and described in section “Cas12a-mediated DSB ensures the integration of one CBE sgRNA per L. mexicana transfectant”.

      Nevertheless, it is a valid concern that the sequencing depth is not sufficient to detect small percentage of cells that have integrated the CBE sgRNA expression multiple times. However, in this case we also like to make the point that this small percentage of cells within a screen is likely to be not relevant and we therefore now added a small scale pooled loss-of-function screen, targeting essential genes, to the manuscript (see new Figure 5) to proof our claim. If the integration of multiple sgRNAs into one cell would have any measurable combinatorial effect, the non-targeting controls in our screen would have been depleted as well. However, there is no detectable difference between all 15 included controls in our small-scale screen.

      We have addressed all points in sections “Cas12a-mediated DSB ensures the integration of one CBE sgRNA per L. mexicana transfectant“ and “Detection of fitness-associated phenotypes in a pooled loss-of-function screen”.

      To avoid double integration, wouldn't it be easiest to just create an allele-specific "landing pad" on one chromosome? I believe that a double integration rate of ~20% could severely complicate the analysis of any large-scale screen later on.

      We thank the reviewer for this suggestion but we have tried to use an allele-specific "landing pad" and described this already in our first manuscript version (see section “DSBs introduced by AsCas12a ultra increase integration rates of donor DNA constructs”). Specifically, we integrated CBE sgRNA expression cassettes into the neomycin resistance marker contained in the tdTomato expression cassette (Figure 2 S1D, Cas12a crRNA-5 and 6) but this resulted in lower transfection rates (Figure 2F: crRNA-5 1 in ~47,000; crRNA-6 1 in ~32,000) then when using a Cas12a crRNA that targets the 18S rRNA locus directly (Figure 2F: crRNA-4 1 in ~2,000). As we believe a high transfection rate is key for pooled large-scale screens, we therefore pursued further experiments with crRNA-4. However, since a different crRNA can be easily selected for our tool, simply by just changing the Cas12a crRNA during transfection, users can chose a different integration site or other “landing pads” if they want to. We have updated section “Cas12a-mediated DSB ensures the integration of one CBE sgRNA per L. mexicana transfectant” to clarify these details.

      Also, it is not clear to me how the integration of tdTomato could affect the integration of the sgRNA expression cassette 400 bp downstream.

      As said above, our ONT data clearly shows that we can only see integration into one locus (Figure 4 S1 and S2). Given that the recognition site of crRNA-4 is contained in the homology flank used to integrate tdTomato into the 18S rRNA locus, this may contribute to the effect we observe. But since the homology sequences match the original sequences within the locus, the reasons to why this affects integration of the CBE sgRNA expression cassettes remain also elusive to us. We try to discuss this better now in the section “Cas12a-mediated DSB ensures the integration of one CBE sgRNA per L. mexicana transfectant”.

      Data accessibility:

      The Illumina and ONT data should be made publicly available.

      ONT and Illumina fastq reads are now available at the European Nucleotide Archive (ENA Accession Number: PRJEB83088)

      Minor point:

      Line 30: It would be easier for readers if the authors could briefly explain what bar-seq is.

      We have added more details:[…] and bar-seq screens, which involve individually deleting, barcoding, and pooling mutants for analysis, have facilitated […].

      Lines 114, 120: I think the authors are referring to Figures 1E and F, not Figures 2E and F.

      Many thanks for picking this up, we have corrected the Figure reference.

      Reviewer #2 (Recommendations For The Authors):

      This has the potential to be a valuable tool for the community if it is efficiently distributed. If the authors have not yet done so they should make their plasmids available to the community via Addgene.

      We have started the deposit process with Addgene and plasmids will be available soon. In the meantime, all plasmid maps are available on our website www.leishbaseedit.net and can be requested for shipment from our lab.

      Line 162-165, 400-401: The potential for using AsCAS12a's intrinsic RNase activity for "multiplexing" would benefit from a little more explanation (i.e. how this would work, and what multiplexing means in this context).

      We have added further details on multiplexing with Cas12a and point out potential applications.

      “For example, Cas12a crRNA arrays with four or more guides can be assembled and transfected to introduce multiple DSBs within one gene. Since Cas12a generates sticky DNA ends that facilitate recombination via microhomology-mediated end joining and homologous recombination (Zhang et al., 2021), this approach could effectively disrupt target genes without requiring the addition of donor DNA and this may provide an alternative approach to our here presented base editing method in the future. Moreover, CBE sgRNAs could be multiplexed by interspacing them with Cas12a direct repeats (DRs), enabling simultaneous targeting of multiple genes in one cell.”

      Line 193-194: can the authors offer an explanation for the reduction in mNG editing observed with 30nt homology flanks?

      We assume this is caused by imprecise recombination events in some cells and have revised the original sentence.

      In several places in the manuscript, it is unclear if an analysis has been done on an individual clone or a population derived from multiple transfected cells. If on mixed population, clarify this and calculate the number of clones that the mixture represents. E.g. lines 195-196 and 221-223 (Sanger sequencing of integration site); Line 333-352 (ONT analysis of CBE expression cassette integration).

      Only when we tested whether multiple CBE sgRNAs are integrated, we generated and analysed clones (Figure 4 S3). In all other experiments we analysed parasite populations. For better clarity, we have where possible indicated this in the revised manuscript (e.g. at the lines requested). 

      Line 259: "site by site" should presumably be "side by side".

      Many thanks for pointing this out. We have changed this typo.

      Lines 315-317: Clarify why the mis-integration of the CBE sgRNA expression cassette might cause a lack of editing (e.g. lack of expression?).

      We have added: “This could potentially result in the silencing of the CBE sgRNA expression or even lead to the deletion of the guide cassette”

      Line 364 - 367: it is unlikely there is the statistical power to state that 2/10 represents lower than the previously observed 38% of double integrants.

      We agree that the statistical power is low and have therefore changed our phrasing to an overall estimation.

      Reviewer #3 (Recommendations For The Authors):

      I suggest that the authors make clearer to the reader the evidence for improved editing efficiency in the new CBE system described here relative to the system described in Engstler and Beneke, 2023. Such clarification could be as simple as an extra paragraph or figure, clearly comparing the editing rates with the two systems in, as far as possible, equivalent conditions.

      We have directly compared our improved method to our previous base editing method in Figures 1E and 4, demonstrating higher editing rates in a much shorter time. Especially the L. major panel in Figure 4B shows that in a direct comparison between the previously published (Engstler and Beneke, eLife 2023) and new system, editing can be only observed with the version presented here. However, to clarify the improvements we made, we compare now data from our previous screen done in Engstler and Beneke, eLife 2023 with a loss-of-function screen carried out with our updated method (see Figure 5 and section “Detection of fitness-associated phenotypes in a pooled loss-of-function screen”).

      The significance of this work would be improved by running the type of loss of fitness screen described previously in Engstler and Beneke (2023), thereby showing that the new approach improves on previous tools. Without such data, questions remain about potential confounding effects that might not be anticipated from the targeted experiments provided in the current manuscript.

      We thank the reviewer for this suggestion. The requested experiment is now presented in Figure 5 and described in section “Detection of fitness-associated phenotypes in a pooled loss-of-function screen”.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      eLife Assessment

      This important study provides empirical evidence of the effects of genetic diversity and species diversity on ecosystem functions across multi-trophic levels in an aquatic ecosystem. The support for these findings is solid, but a more nuanced interpretation of the results could make the conclusions more convincing. The work will be of interest to ecologists working on multi-trophic relationships and biodiversity.

      Thanks for this new assessment. Here below we reply to the comments that you and the reviewer have made. We understand the critics related to the issue of the interpretation of causal relationships from observational data. We now added an entire paragraph (in the second paragraph of the Discussion) that explicitly call for a cautionary interpretation of our results. We also tried to refrain the use of certain words (e.g., “we demonstrate”) when we think it is hard to conclude. This a tricky exercise as on the one hand we gathered a large and strong database (which had been underlined by the reviewers) that should supposedly strengthen statistical inferences, but on the other hands, the inferences we’ve made are based from observational data, which obviously comes from biases (even if partially controlled statistically). We hope that you’ll find our adding appropriate to find the good balance between a strong dataset and fragile interpretation.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This work used a comprehensive dataset to compare the effects of species diversity and genetic diversity within each trophic level and across three trophic levels. The results stated that species diversity had negative effects on ecosystem functions, while genetic diversity had positive effects. Additionally, these effects were observed only within each trophic level and not across the three trophic levels studied. Although the effects of biodiversity, especially genetic diversity across multi-trophic levels, have been shown to be important, there are still very few empirical studies on this topic due to the complex relationships and difficulty in obtaining data. This study collected an excellent dataset to address this question, enhancing our understanding of genetic diversity effects in aquatic ecosystems.<br /> Strengths:

      The study collected an extensive dataset that includes species diversity of primary producers (riparian trees), primary consumers (macroinvertebrate shredders), and secondary consumers (fish). It also includes genetic diversity of the dominant species in each trophic level, biomass production, decomposition rates, and environmental data. The writing is logical and easy to follow.

      Weaknesses:

      The two main conclusions-(1) species diversity had negative effects on ecosystem functions, while genetic diversity had positive effects, and (2) these effects were observed only within each trophic level, not across the three levels-are overly generalized. Analysis of the raw data shows that species and genetic diversity have different effects depending on the ecosystem function. For example, neither affected invertebrate biomass, but species diversity positively influenced fish biomass, while genetic diversity had no effect. Furthermore, Table S2 reveals that only four effect sizes were significant (P < 0.05): one positive genetic effect, one negative genetic effect, and two negative species effects, with two effects within a trophic level and two across trophic levels. Additionally, using a P < 0.2 threshold to omit lines in the SEMs is uncommon and was not adequately justified. A more cautious interpretation of the results, with acknowledgment of the variability observed in the raw data, would strengthen the manuscript.

      There is actually no objective justification for having chosen p<0.20. This is a subjective threshold that has been chosen to simplify the visual interpretation of causal graphs while highlighting the most biologically relevant links. We have now added a sentence stating explicitly the subjective nature of the threshold. We understand the point you raised regarding the cautionary interpretation of the results. We have now added a paragraph (just before the detailed discussion) explicitly calling for a cautionary interpretation of the results (see l. 414-424). We think this paragraph prevails for the entire discussion. Our message in this paragraph is that inferences that we’ve made can arise from both a biological reality and statistical artefacts. We can not really tease apart at this stage, and our interpretation of the results therefore has to be taken with care. We hope you’ll find the statement adequate.  We prefer advertising the readers from the start rather than including cautionary note all over the discussion. We feel it was more logical and comfortable. We have also modified the text from place to place to avoid strong statement such as “we demonstrated” when we think the demonstration can not be considered as solid.

      Recommendations for the authors:

      Reviewing Editor:

      In addition to the comments from the reviewer, we have the following comments on your paper:

      (1) It would be important to clarify that there could be different interpretations about one of the major findings: for within-trophic BEF relationships, genetic and species diversity have the opposite effects on ecosystem functions (i.e., positive and negative effects for genetic and species diversity, respectively). (1) One possibility is that for each specific ecosystem function, genetic and species diversity have the opposite effects. (2) The other possibility is that genetic diversity has positive effects on some functions, while species diversity has negative effects on other functions. These two possibilities can have quite different implications about the generalizability of the conclusion, mechanisms involved, and practices for ecosystem management. Therefore, it would be important to clarify that the findings from this paper are more about the second rather than the first possibility both in the discussion and conclusion sections.

      Yes, true, this is an important distinction and we agree with your conclusion. We have added a section in the Discussion (l. 537-545) and a note in the Conclusion (l. 625-627).

      (2) Please take special caution when comparing the findings from this observational study vs. previous experimental works. (1) The different ranges of diversity in the observational vs. experimental works, together with the nonlinear nature of the BEF relationship challenge the direct comparisons of their results. That is, even if their true BEF relationship are identical, focusing on different sections of a nonlinear curve can give us different results of the estimated BEF relationships. This challenge is further aggravated when involving both genetic and species diversity because these two facets have different biological meanings as the authors have already noted. Using standardized effect size or explained variance, as this paper did, may partially get around but not truly resolve this issue. It would be important to add clarifications to make the comparisons between genetic and species diversity effects more understandable in a biological or ecological context. One possibility could be to state that both genetic and species diversity measured in this study well represent their natural gradients in this aquatic ecosystem, so that the standardized effect sizes quantify how these natural diversity gradients associate with ecosystem functions. This further points to the issue about the representatives of the genetic diversity sampled from up to 32 individuals for each species per site, which would also need clarification. We suggest the authors to identify these challenges in the discussion, so that future studies can be aware of these or even find alternative solutions. (2) The species diversity effects have quite different meanings between this study and previous observational and experimental studies. The negative effects are for the biomass of one target species from this study, while the species diversity effects are usually for the biomass of all species within a community. These two scenarios are not directly comparable. The negative relationship between species diversity and a target species' biomass can simply arise from a sampling process, for example, given the same community biomass, the more species occur in a community, the less biomass allocated to a single species, without assuming any biological interactions or species differences. And this study cannot exclude this possibility. Note that this null, sampling process is not equal to a negative covariance between biomass of a focal species and biomass of the community involving the species as stated in lines 446-448. To avoid possible mis-interpretation, we suggest the authors to revise or remove the comparison appearing in the paragraph starting from line 515.

      Thanks for these comments. Although we agree with the two points raised by the Editor, we must admit that we found them difficult to answer properly.  See our detailed responses hereafter.

      Point (1): this is true that comparisons with previous studies is tricky, especially when these comparisons also include both genetic and species components. This is a problem (a limit) for almost all comparisons in biology. We added a few lines to warn readers that these comparisons are not without any limits (see l. 414-424). Regarding the fact that « genetic and species diversity measured in this study well represent their natural gradients in this aquatic ecosystem »: all is about scales. The genetic and species diversity measured in this study are obviously representative of communities and populations of the upstream (piedmont) part of the Garonne River basin as our sampling design covers all the east-west gradient. On the other hand, these communities and populations are not representative of the entire Garonne River basin, as we lack all the downstream part of the network. We added a sentence to specify that the sampling communities are specific of this specific ecosystem (rivers from the piedmont, see l. 224-226). Regarding « the issue about the representatives of the genetic diversity sampled from up to 32 individuals », we must admit that we are surprised by this comment as it is a very classical way for estimating genomic diversity. Although there is no clear rule, 30 individuals per site is generally assumed (and has been shown) to be an appropriate sample size (especially given that we used here a genome-wide approach). We added a reference to justify the sample size.

      Point (2): We understand the point raised by the Editors. Regarding your note “Note that this null, sampling process is not equal to a negative covariance between biomass of a focal species and biomass of the community involving the species as stated in lines 446-448.”: this is true, we rephrase this sentence to be more neutral. Regarding the paragraph starting l. 515 (now 550), we refrained to remove this paragraph as it provides some mechanistic explanation for underlying patterns, which we think is important even if incomplete or speculative. The confusion probably arises because here we discuss all type of negative BEFs, including the effect of species diversity on the biomass of the community, on the biomass of focal species (including those from other trophic levels) and the litter degradation. Our discussion is very general, whereas you seem to focus on a specific case of negative species-BEFs. To highlight this further and warn readers about possible conclusions, we added the following sentence: “Given the empirical nature of our study and the fact that our meta-regressive approach includes several types of BEFs (e.g., species richness acting either on the biomass of a single focal species or on the biomass of an entire focal community), it is hard to tease apart specific and underlying mechanisms” (l. 573-576).

      (3) Please clarify how you derived the 95% CI in Fig. 5. For example, how did you involve the uncertainties of each raw effect size (e.g. each black triangle in Fig. 5a) when calculating their mean and 95% CI in each group (e.g., the red triangles and error bars in Fig. 5a)?

      Estimates and 95%-CI from Figure 5 are derived from the mixed-effect models described from l. 314. They are hence marginal effects derived from the models, and 95%-CI include all error terms (fixed and random). We now specify in the Figure caption that estimates and 95%-CI are marginal effects derived from the mixed-effect models.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      The authors examined whether aberrantly projecting retinal ganglion cells in albino mice innervate a separate population of thalamocortical neurons, as would be predicted for Hebbian learning rules. The authors find support for this hypothesis in correlated light and electron microscopy (CLEM) reconstructions of retinal ganglion cell axons and thalamocortical neurons. In a second line of investigation, the authors ask the same question about retinal ganglion cell innervation of local inhibitory interneurons of the mouse LGN. The authors conclude that these connections are less specific.

      Strengths:

      The authors make good use of CLEM to test a circuit-level hypothesis, and they find an interesting difference in RGC synaptic innervation patterns for thalamocortical neurons vs. local interneurons.

      Weaknesses:

      The conclusions about the local interneuron innervation are a little more difficult to interpret. One would expect to only capture a small part of the local interneuron dendritic field, as compared to the smaller thalamocortical neurons, right? Doesn't that imply that finding some evidence of promiscuous connectivity means that other dendrites that were not observed probably connect to many different RGCs?

      We will try to clarify this point

      Reviewer #2 (Public review):

      In this article, the authors examined the organization of misplaced retinal inputs in the visual thalamus of albino mice at electron-microscopic (EM) resolution to determine whether these synaptic inputs are segregated from the rest of the retinogeniculate circuitry.

      The study's major strengths include its high resolution, achieved through serial EM and confocal microscopy, which enabled the identification of all synaptic inputs onto neurons in the dorsolateral geniculate nucleus (dLGN).

      The experiments are very precise and demanding; thus, only the synaptic inputs of a few neurons were fully reconstructed in one animal. A few figures could be improved in their presentation.

      Despite this, the authors clearly demonstrate the synaptic segregation of misrouted retinal axons onto dLGN neurons, separate from the rest of the retinogeniculate circuitry.

      This finding is impactful because retinal inputs typically do not segregate within the mouse dLGN, and it was previously thought that this was due to the nucleus's small size, which might prevent proper segregation. The study shows that in cases where axons are misrouted and exhibit a different activity pattern than surrounding retinal inputs, segregation of inputs can indeed occur. This suggests that the normal system has the capacity to segregate inputs, despite the limited volume of the mouse dLGN.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Please include page numbers and line numbers in future submissions.

      Done

      (2) I am red-green colorblind, and I had a lot of trouble seeing the red channels when they were mixed with green. I recommend using magenta when possible.

      Thanks for the heads up. We have switched to green and magenta where possible. In the tinted EM where switching colors did not seem helpful, we added an asterisk to RGC boutons so that red and green would not be the only identifiers.

      (3) It would help if the figure captions also stated the conclusions that can be drawn from the figures. I recommend stating the main conclusion in the first sentence of the caption, rather than stating only what we are viewing. Similarly, the last sentence of the caption can help summarize what has been seen.

      We have included summary sentences at the beginning and end of figure legends.

      (4) In the text when discussing Figure 2J, do the authors mean to cite Supplementary Figure 2?

      Yes, thanks.

      (5) I don't think TC was ever defined (or I didn't find it).

      Corrected

      (6) In the subsection "An exclusive set..." cite Liang et al. as more evidence of non-specific innervation.

      We cite Liang et al in the discussion, but I don’t see a good place to cite it in the referenced results section. Please elaborate if we are missing something.

      (7) Supplementary Figure 3 is never cited.

      We have added the citation to Figure 3.

      (8) I found myself unsure of what to conclude after the results on LIN. A few more sentences of interpretation and restating what was found would help.

      We have added additional clarification in the Results:

      “The LIN results are consistent with our prediction that shaft dendrites would be indifferent to island/non-island boundaries while individual targeted dendrites would target either the island or non-island RGC boutons. However, the restriction of the targeted dendrites to one or the other RGC field does not appear to be an absolute rule. Rather the scale of targeted dendrite exploration and the size of the exclusion zone is likely to reduce the chances that a targeted dendrite would find partners on both in the island and outside of the island. This matching between the exploration of targeted LIN dendrites and the segregation of retinogeniculate connectivity means that targeted LIN dendrites will have an RGC input profile (island/non-island) that matches the TCs they innervate.”

      Reviewer #2 (Recommendations for the authors):

      (1) The abbreviation TC is used in the text without a definition.

      Corrected

      (2) The features that allow for labeling the different dendrites/cells (TC and LIN) in Serial EM data (Figure 1) are necessary. While the explanation is provided for RGC boutons, the labeling for thalamic cells is not discussed.

      We added the sentence:

      “Thalamocortical dendrites were distinguished from local inhibitory neuron dendrites by the presence of spines and the absence of synaptic outputs.”

      (3) Image 2C (EM) appears blurry or pixelated. Enhancing its resolution could improve clarity.

      Image 2C is a demonstration of how much we felt we could sacrifice image quality and still reconstruct TC arbors and RGC inputs.

      (4) The gray circles that show the innervation of TC17 in Figure 2E are barely visible, especially on-screen without high magnification. A more contrasting color and wider lines would enhance visibility. It would also be helpful to indicate TC17 in Figure 2H and 2G, as this cell is special and highlighted in the main text.

      We have made the requested changes

      (5) A TC with no RGC input is mentioned. Have you identified other synaptic inputs, potentially related to SC or the cortex?

      Both TC17 (a few exclusion zone RGC inputs) and TC5 (no RGC inputs) were innervated by some large, dark mitochondria boutons that could be SC inputs.  However, we did not perform enough reconstruction of the axons to confidently describe their non-RGC input profile. I have previously observed occasional TCs in the same region of the dLGN where RGC inputs are almost entirely replaced by SC inputs, so finding two such cells was not surprising.

      (6) Two fully reconstructed TCs are mentioned. Please specify their exact number in the text, as citing Figure 2J or Supplementary Figure 1 alone is not sufficient for identification.

      Clarified as “(TC3, TC4, Figure 2J, Supplementary Figure 2,3).”

      (7) A correlation between the position of the dendrites and the location of RGC inputs would provide additional insights. This is somewhat reminiscent of the dendrite orientation of Layer IV spiny stellate neurons in the somatosensory cortex that receive inputs from the thalamocortical axons and could be mentioned in the discussion.

      We believe that the images provided are a strong argument for TC arbors being shaped by RGC bouton distributions. We agree that reporting the correlation between dendrites and RGC boutons would be useful, but we found this correlation difficult to quantify. One of the challenges is that we would need to perform several-fold more reconstruction of dendrites and RGC boutons to have an unbiased mapping of both. Currently, most of the reconstructions stop when the dendrites assume a distal morphology and stop interacting with RGC boutons. Likewise, the EM of the RGC boutons are only those that innervate the reconstructed cells. We considered simply quantifying the asymmetry of the TC arbors relative to a symmetrical distribution and a random distribution, but we felt that quantification would be difficult to interpret without a similar analysis performed in the same region of dLGN on wild-type TCs.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This paper is an incremental follow-up to the authors' recent paper which showed that Purkinje cells make inhibitory synapses onto brainstem neurons in the parabrachial nucleus which project directly to the forebrain. In that precedent paper, the authors used a mouse line that expresses the presynaptic marker synaptophysin in Purkinje cells to identify Purkinje cell terminals in the brainstem and they observed labeled puncta not only in the vestibular and parabrachial nuclei, as expected, but also in neighboring dorsal brainstem nuclei, prominently the central pontine grey. The present study, motivated by the lack of thorough characterization of PC projections to the brainstem, uses the same mouse line to anatomically map the density and a PC-specific channelrhodopsin mouse line to electrophysiologically assess the strength of Purkinje cell synapses in dorsal brainstem nuclei. The main findings are (1) the density of Purkinje cell synapses is highest in vestibular and parabrachial nuclei and correlates with the magnitude of evoked inhibitory synaptic currents, and (2) Purkinje cells also synapse in the central pontine grey nucleus but not in the locus coeruleus or mesencephalic nucleus.

      Strengths:

      The complementary use of anatomical and electrophysiological methods to survey the distribution and efficacy of Purkinje cell synapses on brainstem neurons in mouse lines that express markers and light-sensitive opsins specifically in Purkinje cells is the major strength of this study. By systematically mapping presynaptic terminals and light-evoked inhibitory postsynaptic currents in the dorsal brainstem, the authors provide convincing evidence that Purkinje cells do synapse directly onto pontine central grey and nearby neurons but do not synapse onto trigeminal motor or locus coeruleus neurons. Their results also confirm previously documented heterogeneity of Purkinje cell inputs to the vestibular nucleus and parabrachial neurons.

      Weaknesses:

      Although the study provides strong evidence that Purkinje cells do not make extensive synapses onto LC neurons, which is a helpful caveat given previous reports to the contrary, it falls short of providing the comprehensive characterization of Purkinje cell brainstem synapses which seemed to be the primary motivation of the study. The main information provided is a regional assessment of PC density and efficacy, which seems of limited utility given that we are not informed about the different sources of PC inputs, variations in the sizes of PC terminals, the subcellular location of synaptic terminals, or the anatomical and physiological heterogeneity of postsynaptic cell types. The title of this paper would be more accurate if "characterization" were replaced by "survey".

      Several of the study's conclusions are quite general and have already been made for vestibular nuclei, including the suggestions in the Abstract, Results, and Discussion that PCs selectively influence brainstem subregions and that PCs target cell types with specific behavioral roles.

      We agree that we did not provide an in-depth characterization of PC synapses onto all identified types of brainstem neurons. With so many types of neurons in the brainstem, this would be a monumental task. Despite this limitation we prefer to keep our original title, since our study makes the following advances:

      • We provide a comprehensive map of all PC synaptic boutons across the brainstem, and corresponding maps of PC synaptic input sizes. The input sizes vary widely, but are often multiple nanoamps, indicating that the cerebellum is an important regulator of activity in these regions. These maps will be indispensable for future investigations of cerebellar outputs.

      • We find that PC projections and the synapses they make are spatially restricted within most target nuclei such as the vestibular and parabrachial nuclei. This suggests that the influence of the cerebellum is spatially segregated within these nuclei, and likely allows the cerebellum to regulate specific behaviors.  While some aspects of these gradients have been described previously, our study is comprehensive, and has a higher degree of specificity than can be achieved with immunohistochemistry. 

      • We discover that PCs form functional synapses in the pontine central grey and nearby nuclei. Much of this region’s function is unknown, but certain subregions are important for micturition and valence. PCs make large synapses onto a small fraction of cells in this region, which suggests that PCs may target specific cell types to control novel nonmotor behaviors.

      • We provide clarification regarding PC projections to the locus coeruleus. Multiple high-profile, highly influential studies using rabies tracing (Schwarz et al., Nature 2015; Breton-Provencher and Sur, Nature Neuroscience 2019; and others) described a prominent PC input to the locus coeruleus. We showed that this projection is essentially nonexistent, both anatomically and functionally. We previously addressed this issue, but the PC-specific optogenetic approach we used here provides the most compelling evidence against a prominent PC-LC connection. This is an important finding for the cerebellum and a cautionary tale for conclusions based solely on viral tracing methods. We will expand on this issue in response to the comments of reviewer #3.

      Reviewer #2 (Public review):

      Summary:

      While it is often assumed that the cerebellar cortex connects, via its sole output neuron, the Purkinje cell, exclusively to the cerebellar nuclei, axonal projections of the Purkinje cells to dorsal brainstem regions have been well documented. This paper provides comprehensive mapping and quantification of such extracerebellar projections of the Purkinje cells, most of which are confirmed with electrophysiology in slice preparation. A notable methodological strength of this work is the use of highly Purkinje cell-specific transgenic strategies, enabling selective and unbiased visualization of Purkinje terminals in the brainstem. By utilizing these selective mouse lines, the study offers compelling evidence challenging the general assumption that Purkinje cell targets are limited to the cerebellar nuclei. While the individual connections presented are not entirely novel, this paper provides a thorough and unambiguous demonstration of their collective significance. Regarding another major claim of this paper, "characterization of direct Purkinje cell outputs (Title)", however, the depth of electrophysiological analysis is limited to the presence/absence of physiological Purkinje input to postsynaptic brainstem neurons whose known cell types are mostly blinded. Overall, conceptual advance is largely limited to confirmatory or incremental, although it would be useful for the field to have the comprehensive landscape presented.

      Strengths:

      (1) Unsupervised comprehensive mapping and quantification of the Purkinje terminals in the dorsal brainstem are enabled, for the first time, by using the current state-of-the-art mouse lines, BAC-Pcp2-Cre and synaptophysin-tdTomato reporter (Ai34).

      (2) Combinatorial quantification with vGAT puncta and synaptophysin-tdTomato labeled Purkinje terminals clarifies the anatomical significance of the Purkinje terminals as an inhibitory source in each dorsal brainstem region.

      (3) Electrophysiological confirmation of the presence of physiological Purkinje synaptic input to 7 out of 9 dorsal brainstem regions identified.

      (4) Pan-Purkinje ChR2 reporter provides solid electrophysiological evidence to help understand the possible influence of the Purkinje cells onto LC.

      Weaknesses:

      (1) The present paper is largely confirmatory of what is presented in a previous paper published by the author's group (Chen et al., 2023, Nat Neurosci). In this preceding paper, the author's group used AAV1-mediated anterograde transsynaptic strategy to identify postsynaptic neurons of the Purkinje cells. The experiments performed in the present paper are, by nature, complementary to the AAV1 tracing which can also infect retrogradely and thus is not able to demonstrate the direction of synaptic connections between reciprocally connected regions. Anatomical findings are all consistent with the preceding paper. The likely absence of robust physiological connections from the Purkinje to LC has also been evidenced in the preceding paper by examining c-Fos response to Purkinje terminal photoinhibition at the PBN/LC region.

      We agree that we previously dealt with the issue of PC-LC synapses (Chen et al., 2023, Nat Neurosci), but our conclusions differed from several high-profile publications (Schwarz et al., Nature 2015; Breton-Provencher and Sur, Nature Neuroscience 2019), and still met considerable resistance. We felt that the optogenetic approach provided the most definitive means of evaluating the presence and strength of PC-LC synapse that will hopefully settle this issue. These experiments also set a standard for future studies assessing the presence of PC synapses onto other target neurons in the brainstem.

      (2) Although the authors appear to assume uniform cell type and postsynaptic response in each of the dorsal brainstem nuclei (as noted in the Discussion, "PCs likely function similarly to their inputs to the cerebellar nuclei, where a very brief pause in firing can lead to large and rapid elevations in target cell firing"), we know that the responses to the Purkinje cell input are cell type dependent, which vary in neurotransmitter, output targets, somata size, and distribution, in the cerebellar and vestibular nuclei (Shin et al., 2011, J Neurosci; Najac and Raman, 2015, J Neurosci; Özcan et al., 2020, J Neurosci). This consideration impacts the interpretation of two key findings: (a) "Large ... PC-IPSCs are preferentially observed in subregions with the highest densities of PC synapses (Abstract)". For example, we know that the terminal sparse regions reported in the present paper do contain Floccular Targeted Neurons that are sparse yet have dense somatic terminals with profound postinhibitory rebound (Shin et al.). Despite their sparsity, these postsynaptic neurons play a distinct and critical role in proper vestibuloocular reflex. Therefore, associating broad synaptic density with "PC preferential" targets, as written in the Abstract, may not fully capture the behavioral significance of Purkinje extracerebellar projections. (b) "We conclude ... only a small fraction of cell. This suggests that PCs target cell types with specific behavioral roles (Abstract, the last sentence)". Prior research has already established that "PCs target cell types with specific behavioral roles in brainstem regions". Also, whether 23 % (for PCG), for example, is "a small fraction" would be subjective: it might represent a numerically small but functionally important cell type population. The physiological characterization provided in the present cell type-blind analysis could, from a functional perspective, even be decremental when compared to existing cell typespecific analyses of the Purkinje cell inputs in the literature.

      We now cite the papers suggested by the reviewer (Shin et al., 2011, J Neurosci; Najac and Raman, 2015, J Neurosci; Özcan et al., 2020, J Neurosci) and add to the discussion.

      (3) The quantification analyses used to draw conclusions about

      (a) the significance of PC terminals among all GABAergic terminals and the fractions of electrophysiologically responsive postsynaptic brainstem neurons may have potential sampling considerations:.

      (a.i) this study appears to have selected subregions from each brainstem nucleus for quantification (Figure 2). However, the criteria for selecting these subregions are not explicitly detailed, which could affect the interpretation of the results.

      Additional explanation has been added to results in the section, “Quantification of PC synapses in the brainstem.”  

      (a.ii) the mapping of recorded cells (Figure 3) seems to show a higher concentration in terminal-rich regions of the vestibular nuclei.

      In Figure 3, we strived to record in an unbiased manner. However, there may have been a slight bias to recordings in areas of lower myelination where patching is easier. We now clarify this issue in the text.

      Reviewer #3 (Public review):

      Summary:

      The manuscript by Chen and colleagues explores the connections from cerebellar Purkinje cells to various brainstem nuclei. They combine two methods - presynaptic puncta labeling as putative presynaptic markers, and optogenetics, to test the anatomical projections and functional connectivity from Purkinje cells onto a variety of brainstem nuclei. Overall, their study provides an atlas of sorts of Purkinje cell connectivity to the brainstem, which includes a critical analysis of some of their own data from another publication. Overall, the value of this work is to both provide neural substrates by which Purkinje cells may influence the brainstem and subsequent brain regions independent of the deep cerebellar nuclei and also, to provide a critical analysis of viral-based methods to explore neuronal connectivity.

      Strengths:

      The strengths lie in the simplicity of the study, the number of cells patched, and the relationship between the presence of putative presynaptic puncta and electrophysiological results. This type of study is important and should provide a foundation for future work exploring cerebellar inputs and outputs. Overall, I think that the critique of viral-based methods to define connectivity, and a more holistic assessment of what connectivity is and how it should be defined is timely and warranted, as I think this is under-appreciated by many groups and overall, there is a good deal of research being published that do not properly consider the issues that this manuscript raises about what viral-based connectivity maps do and do not tell us.

      We thank the reviewer for highlighting this important aspect of this work, and for agreeing with our thesis concerning viral-based connectivity maps.

      Weaknesses:

      While I overall liked the manuscript, I do have a few concerns that relate to interpretation of results, and discussion of technological limitations. The main concerns I have relate to the techniques that the authors use, and an insufficient discussion of their limitations. The authors use a Cre-dependent mouse line that expresses a synaptophysin-tomato marker, which the authors confidently state is a marker of synapses. This is misleading. Synaptophysin is a vesicle marker, and as such, labels axons, where vesicles are present in transit, and likely cell bodies where the protein is being produced. As such, the presence of tdtomato should not be interpreted definitively as the presence of a synapse. The use of vGAT as a marker, while this helps to constrain the selection of putative pre-synaptic sites, is also a vesicle marker and will likely suffer the same limitations (though in this case, the expression is endogenous and not driven by the ROSA locus). A more conservative interpretation of the data would be that the authors are assessing putative pre-synaptic sites with their analysis. This interpretation is wholly consistent with their findings showing the presence of tdtomato in some regions but only sparse connectivity - this would be expected in the event that axons are passing through. If the authors wish to strongly assert that they are specifically assessing synapses, a marker better restricted to synapses and not vesicles may be more appropriate.

      We agree that synaptophysin-tdTomato is an imperfect marker, although it is vastly superior to cytosolic tdTomato.  We found that viral expression of synaptophysin-GFP gives much more punctate labelling, but an appropriate synaptophysin-GFP line is not available. We carefully point out this issue, and threshold the images to avoid faint labeling associated with fibers of passage.  The intersection of VGAT labelling and of the synaptophysin-tdTomato labelling provides us with superior identification of PC boutons.  We will add additional clarification to point out that these are putative presynaptic boutons, but that alone this does not establish the existence or the strength of functional synapses.

      Similarly, while optogenetics/slice electrophysiology remains the state of the art for assessing connectivity between cell populations, it is not without limitations. For example, connections that are not contained within the thickness of the slice (here, 200 um, which is not particularly thick for slice ephys preps) will not be detected. As such, the absence of connections is harder to interpret than the presence of connections. Slices were only made in the coronal plane, which means that if there is a particular topology to certain connections that is orthogonal to that plane, those connections may be under-represented. As such, all connectivity analyses likely are under-representations of the actual connectivity that exists in the intact brain. Therefore, perhaps the authors should consider revising their assessments of connections, or lack thereof, of Purkinje cells to e.g., LC cells. While their data do make a compelling case that the connections between Purkinje cells and LC cells are not particularly strong or numerous, especially compared to other nearby brainstem nuclei, their analyses do indicate that at least some such connections do exist. Thus, rather than saying that the viral methods such as rabies virus are not accurate reflections of connectivity - perhaps a more circumspect argument would be that the quantitative connectivity maps reported by other groups using rabies virus do not always reflect connectivity defined by other means e.g., functional connections with optogenetics. In some cases, the authors do suggest this (e.g."Together, these findings indicate that reliance on anatomical tracing experiments alone is insufficient to establish the presence and importance of a synaptic connection"), but in other cases, they are more dismissive of viral tracing results (e.g. "it further suggests that these neurons project to the cerebellum and were not retrogradely labeled"). Furthermore, some statements are a bit misleading e.g., mentioning that rabies methods are critically dependent on starter cell identity immediately following the citation of studies mapping inputs onto LC cells. While in general, this claim has merit, the studies cited (19-21) use Dbh-Cre to define LC-NE cells which does have good fidelity to the cells of interest in the LC. Therefore, rewording this section in order to raise these issues generally without proximity to the citations in the previous sentence may maintain the authors' intention without suggesting that perhaps the rabies studies from LC-NE cells that identified inputs from Purkinje cells were inaccurate due to poor fidelity of the Cre line. Overall, this manuscript would certainly not be the first report indicating that the rabies virus does not provide a quantitative map of input connections. In my opinion, this is still under-appreciated by the broad community and should be explicitly discussed. Thus, an acknowledgment of previous literature on this topic and how their work contributes to that argument is warranted.

      We have a different take on connectivity and the use of optogenetics.  Based on our years of experience studying synapses in brain slice, axons survive very well even when they are cut. It is not necessary to preserve intact axons that extend for long distances. It is also true that activation of these axons, with either extracellular electrical stimulation or with optogenetics, is sufficient to evoke synaptic inputs. Robust synaptic responses are evoked with optogenetic activation regardless of the slice orientation. We thank the reviewer for raising this issue, and we have added a couple of sentences to clarify this point under the section “Characterization of functional properties of PC synapses in the brainstem.”

      The discussion on starter cell specificity was not referring to the specificity of cre in transgenic animals, but the TVA/G helper proteins that are introduced by AAV and used in conjunction with the rabies virus. The issues related to this have recently been discussed in Elife (Beier, 2022) in addition to citations 58 and 59 in the manuscript. We have more explicitly highlighted this issue in the revised manuscript in the section “Lack of significant PC inputs to LC neurons.”

      Recommendations for the authors:  

      Reviewer #1 (Recommendations for the authors):

      (1) Methods need detail to be replicable, particularly in how PC synapses were identified and automatically counted. It is not clear what was the variation within subregions across mice. How were neurons selected or rejected for recordings and analyses? Was each subregion sampled at equal spacing? Methods for anatomy should mention sagittal sections.

      Wording in Methods section, “Anatomy” was changed to better reflect how PC synapses were identified as colabeled segments of vGAT and tdTomato labeling. 

      Each datapoint in Figure 2D-F was quantification of a region for each section and each mouse. The color of the data point indicates the anterior posterior location of the section. The violin plot quantifies the median and quartile value for all points across sections and mice. The variability captured by the violin point reflects variability across the anterior-posterior axis. 

      Neurons were mostly randomly selected in each slice, and rejected based on unstable holding current or access resistance. Cell locations were recorded and updated with each experiment so that we minimized oversampling easier to patch regions.

      Sagittal sections were added in methods.

      (2) Figure 2D-F what is the black line and grey region?

      Additional text was added in the caption for Figure 2D-F

      (3) MEV is confusing given LAV stands for lateral vestibular - perhaps call it ME5?

      We will remain consistent with the abbreviations in the Allen Brain Reference Atlas.

      Reviewer #2 (Recommendations for the authors):

      (1) What are the criteria for distinguishing large, small, and non-responders?

      Large are in the nA range, small are in the hundreds of pA, and non-responders are effectively zero. Manual curation of these responses indicated that a current amplitude threshold of 45 pA clearly separated non-responders from responders. To be clear, the average response (as stated in text and displayed in Figure 3D) includes all cells.

      (2) p1. "Unexpectedly": it would not be unexpected, rather, expected, because it was reported in Chen et al., 2023, Nat Neurosci.

      The PCG was hinted at, but an actual functional, anatomical connection was not reported in our previous manuscript.

      (3) p1. "We combined electrophysiological recordings with immunohistochemistry to assess the molecular identities of these PC targets": please clarify "these" here. It could be read that it refers to "pontine central gray and nearby subnuclei" but it doesn't make sense. Immuno has only been performed for MeV and LC.

      Corrected

      (4) p1. "but only inhibit a small fraction of cells in many nuclei": as far as I read Fig.3, it seems that ~50% for PBN/VN and ~25% for PCG: would this be "a small fraction"?

      The small fraction of cells was in reference to subnuclei within the PCG, but we agree this statement is too broad to be useful and have eliminated it.

      (5) p2. "conventional tracer": viral tracer is becoming a standard, so dye tracer could be better here.

      Corrected

      (6) p3. "rostral/cauda": typo.

      Corrected.  

      (7) p3. Quantification of PC synapses in the brainstem: it would be helpful to introduce why synapto-tdT alone is not sufficient, and the purpose of adding vGAT immunostaining.

      We have added more on vGAT labeling putative presynaptic sites and quantifying only synaptic labeling instead of axonal tdTomato in the Results, “Quantification of PC synapses in the brainstem.” In addition, vGAT staining allows us to examine the PC contribution to total inhibition in each region.

      (8) p7. "PB and are": typo.

      Corrected. And all instances of PBN were changed to PB

      (9) p7. "they are likely a mix of excitatory and inhibitory inputs 54,55": Bagnall et al., 2009, J Neurosci, would be critically relevant here.

      Added, thank you

      (10) Figures 2-3: Yellow/Blue color scheme is hard to distinguish, and having two colors could be read as implying two distinct regions.

      We are unsure what the reviewer is referring to exactly here, but the colors refer to the sections in 2C (see the color bar on the bottom right of each atlas schematic). The points represent an individual section that was quantified, and thus do represent distinct samples from distinct regions.

      (11) Figure 2D-F: what is indicated by each point?

      Each data point is the number of PC bouton (D), density of bouton (E), or percentage of synaptophysin/vGAT (F) quantified for each region per section. Each color represents a coronally distinct section of a region. Additional text was added into the captions to clarify this and point 10.

      (12) Figure 3E, right: what is the correlation coefficient?

      The correlation coefficient was found to be 0.74

      Reviewer #3 (Recommendations for the authors):

      Some minor grammatical errors and typos need to be cleaned up (e.g. "To quantifying the densities...", "The medial-ventral region of the PBN...have extensive...".

      These errors have been corrected

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      Insulin is crucial for maintaining metabolic homeostasis, and its release is regulated by various pathways, including blood glucose levels and neuromodulatory systems. The authors investigated the role of neuromodulators in regulating the dynamics of the adult Drosophila IPC population. They showed that IPCs express various receptors for monoaminergic and peptidergic neuromodulators, as well as synaptic neurotransmitters with highly heterogeneous profiles across the IPC population. Activating specific modulatory inputs, e.g. dopaminergic, octopaminergic or peptidergic (Leucokinin) using an optogenetic approach coupled with in vivo electrophysiology unveiled heterogeneous responses of individual IPCs resulting in excitatory, inhibitory or no responses. Interestingly, calcium imaging of the entire IPC population with or without simultaneous electrophysiological recording of individual cells showed highly specific and stable responses of individual IPCs suggesting their intrinsic properties are determined by the expressed receptor repertoire. Using the adult fly connectome they further corroborate the synaptic input of excitatory and inhibitory neuronal subsets of IPCs. The authors conclude that the heterogeneous modulation of individual IPC activity is more likely to allow for flexible control of insulin release to adapt to changes in metabolic demand and environmental cues.

      Strengths:

      This study provides a comprehensive, multi-level analysis of IPC properties utilizing single-nucleus RNA sequencing, anatomical receptor expression mapping, connectomics, electrophysiological recordings, calcium-imaging and an optogeneticsbased 'intrinsic pharmacology' approach. It highlights the heterogeneous receptor profiles of IPCs, demonstrating complex and differential modulation within the IPC population. The authors convincingly showed that different neuromodulatory inputs exhibit varied effects on IPC activity and simultaneous occurrence of heterogeneous responses in IPCs with some populations exciting a subset of IPCs while inhibiting others, showcasing the intricate nature of IPC modulation and diverse roles of IPC subgroups. The temporal dynamic of IPC modulation showed that polysynaptic and neuromodulatory connections play a major role in IPC response. The authors demonstrated that certain neuromodulatory inputs, e.g. dopamine, can shift the overall IPC population activity towards either an excited or inhibited state. The study thus provides a fundamental entry point to understanding the complex influence of neuromodulatory inputs on the insulinergic system of Drosophila.

      We thank the reviewer for endorsing our study as a fundamental entry point to understanding the complex neuromodulation of the insulin system.

      Weakness:

      GPCRs are typically expressed at low levels and while the transcriptomic and reporter expression analysis was comprehensive, both approaches have the caveat that they do not allow validating protein level expression. Thus, some receptors might have been missed while others might be false positives. The authors acknowledged the challenges in accurately accessing receptor expression in complex modulatory systems indicating there are limitations in full understanding of the receptor profiles of IPCs.

      We agree with the reviewer and acknowledge that both the transcript and protein expression need to be examined in order to obtain higher confidence in receptor expression profiles. The T2A-GAL4 lines used in our anatomical analyses do in fact provide insights into which of the receptor transcripts are translated. We added the following statement to the discussion section to clarify this approach “The singlenucleus transcriptome analysis reveals which receptor transcripts are expressed whereas the T2A-GAL4 lines used in our anatomical analyses provide insights on which of the receptor transcripts are translated. This is based on the fact that T2A peptides induce ribosome skipping during translation. Therefore, GAL4 protein is only produced when the receptor protein is produced(42,88).”

      While this study provides valuable insights into the heterogeneity of IPC responses and receptor expression, it will require future studies to elucidate how these modulatory inputs affect insulin release and transcriptional long-term changes. The authors further analyzed male and female snRNAseq data and claimed that the differences in receptor expression were minimal. The experimental analyses used mated females only and while the study is very complete in this respect, it would have been extremely interesting to compare male flies in terms of their response profiles.

      We thank the reviewer for acknowledging that long-term effects on release and transcript levels go beyond the scope of this study and agree that these questions should be addressed in future investigations. Concerning the differences between females and males: we did not find significant differences in the snRNAseq data between the two sexes. Moreover, a parallel study from our lab found no differences between males and females in IPC baseline activity (Bisen et al. 2024, eLife https://doi.org/10.7554/eLife.98514.1). We therefore did not follow this path for the present study. We explained our reasoning in the results section of our paper, by adding: “Since there were little differences in receptor expression between males and females (Fig. S1C), we used the transcriptomes from both sexes for all subsequent analyses.” in the transcriptome section, and “Since baseline recordings from IPCs, in addition to our transcriptomic analysis, revealed no significant difference between male and female flies(26), we only used mated females for our physiological experiments.” in the transition to the physiology section of our manuscript.

      Lastly as also pointed out by the authors, their approach of using optogenetically driven excitation of modulatory neuronal subsets limits the interpretation of the results due to the possibly confounding direct or indirect effect of fast synaptic transmission on IPC excitation/inhibition, and the broad expression of some neuromodulatory lines used in this analysis.

      We agree that our results are limited to general effects of neuronal populations rather than individual neurons or specific inputs, and that it is generally hard to untangle effects of fast transmitters from those of modulatory inputs. However, we believe that we are careful in presenting and interpreting our results in this regard.

      Overall, however, the conclusions of this study are well supported by the data provided by the authors. Moreover, their detailed and thorough analysis of IPC modulation will have a significant impact on the field of metabolic regulation to understand the complex regulatory mechanism of insulin release, which can now be studied further to provide insight about metabolic homeostasis and neural control of metabolic processes.

      We thank the referee kindly for these comments!

      Reviewer #2 (Public review):

      Summary:

      Held et al. investigated the distinct activities of Insulin-Producing Cells (IPCs) by electrophysiological recordings and calcium imaging. In the brain of the fruit fly Drosophila melanogaster, there are approximately 14 IPCs that are analogous to mammalian pancreatic beta cells and provide a good model system for monitoring their activities in vivo. The authors performed single-nucleus RNA sequencing analysis to examine what types of neuromodulatory inputs are received by IPCs. A variety of neuromodulatory receptors are expressed heterogeneously in IPCs, which would explain the distinct activities of IPCs in response to the activations of neuromodulatory neurons. The authors also conducted the connectome analysis and G-protein prediction analysis to strengthen their hypothesis that the heterogeneity of IPCs may underlie the flexible insulin release in response to various environmental conditions.

      Strengths:

      The authors succeeded patch-clamp recordings and calcium imaging of individual IPCs in living animals at a single-cell resolution, which allows them to show the heterogeneity of IPCs precisely. They measured IPC activities in response to 9 types of neurons in patch-clamp recordings and 5 types of neurons in calcium imaging, comparing the similarities and differences in activities between two methods. These results support the idea that the neuromodulatory system affects individual IPC activities differently in a receptor-dependent manner.

      We thank the reviewer for emphasizing how our in vivo experiments allow for a precise characterization of the IPC responses to modulatory inputs.

      Weaknesses:

      One concern is how much extent the heterogeneity of IPC activities in a short time scale is relevant to the net output, a release of insulin-like peptides in response to metabolic demands in a relatively longer time scale. The authors can test their hypothesis by manipulating the heterogeneous expressions of receptor genes in IPCs and examining IPC activities on a longer time scale. Moreover, while the authors focus on IPC activities, they did not show the activation of the neuromodulatory inputs and the net output of insulin levels in the data. The readers might want to know which neurons are indeed activated to send signals to IPCs and how IPC activities result in the secretion of insulin peptides.

      We agree with the reviewer that the two experiments described, manipulating receptor expression before long-term recordings and measuring insulin levels after activating modulatory inputs, would deliver exciting insights into the interplay of modulatory inputs, IPC population activity, and insulin release. However, currently available methods for monitoring insulin release do not allow us to perform these experiments with a temporal resolution that would match the sensitivity and time resolution of our physiological experiments and are therefore not suited for a direct comparison. We also acknowledge that it would be extremely exciting to characterize the modulatory populations providing input to IPCs in terms of their sensitivity to internal state changes and external inputs. However, this clearly goes beyond the scope of our study. Essentially, one would have to perform experiments on a similar scale and breadth as we have done for IPCs here for the other populations. We aim to perform some of these experiments in follow up projects to this work.

      Reviewer #1 (Recommendations for the authors):

      (1) The authors used a 5% expression cutoff initially, which seems arbitrary. Can you explain the rationale for using this cutoff? If I interpret the authors' logic correctly and given there are 14 IPCs per animal, at 5% there is a 70% chance that 1 cell expresses that receptor.

      We used a 5% cutoff to reduce false positives in our transcriptomic analysis. This threshold translates to expression in 0.8 out of 16 IPCs found in an individual fly on average. Hence, this cutoff ensures that receptors are expressed in at least 1 cell. Based on 392 IPC transcriptomes used in our analysis, our 5% threshold means that any receptor expressed in less than 20 transcriptomes will be deemed to be absent. At the population level, this ensures that our expression analysis is based on cells from at least two flies. However, we expect the actual number of flies from which the IPC transcriptomes were derived from to be much higher. We added the following statement to the methods section to clarify this point: “To determine if a transcript is present in the IPC transcriptomes, we used a 5% cutoff to reduce false positives. This cutoff is equivalent to expression in 0.8 IPCs out of 16 on average in an individual fly, and hence less than one IPC in the entire population. Since we used 392 IPC transcriptomes in our analysis, this cutoff means that expression in less than 20 IPCs will be deemed false positive”

      (2) Were male and female brains examined separately and tested for divergent expression of T2A-reporter signals? While there were not many strong differences in the snRNAseq dataset, based on some discrepancies with the reporters it might be worthwhile to assess sex-specific differences that might account for the observed expression/non-expression of some receptors.

      We did not investigate sex-specific differences using anatomical mapping, since our scRNA analysis pointed against that being a major factor. We clarified our reasoning in the results section by adding “Since there were little differences in receptor expression between males and females (Fig. S1C), we used the transcriptomes from both sexes for all subsequent analyses.” in the transcriptome section, and “Since baseline recordings from IPCs, in addition to our transcriptomic analysis, revealed no significant difference between male and female flies(26), we only used mated females for our physiological experiments.” in the transition to the physiology section of our manuscript.

      (3) The anatomical reporter and transcriptome data for neuromodulatory receptor expression do not fully complement each other, e.g. in Fig1D Lkr is expressed only in one cluster but anatomical expression is observed in most IPCs. Ultimately, visualizing receptor expression at the protein level and functional analysis with genetic perturbation of the respective receptors is needed to draw strong conclusions.

      We agree with the reviewer that visualizing receptor expression at protein level could help clarify some of these differences since neuropeptide GPCR transcripts tend to be less abundant whereas we expect protein expression to be more stable. However, out of the 14 receptors examined in our study, antibodies are only available for two: DH31R and LKR. Since our DH31R-T2A-GAL4 line does not drive expression in IPCs, we did not pursue this further. We did perform preliminary experiments to validate LKR protein expression in IPCs. Unfortunately, we found that the LKR antibody labels cells in the pars intercerebralis in both the wild type and LKR mutants (see Author response image 1 below). Therefore, we do not think it suitable to monitor LKR protein expression. Thus, additional investigations must await future generations of neuropeptide receptor antibodies. One biological reason for the discrepancies could be that anatomical quantification is based on cumulative expression while transcriptomic analysis captures a brief snapshot. We included “One explanation for the discrepancies could be that transcriptomic analysis provides a single snapshot, whereas anatomical data is based on cumulative expression. Fluorescent markers persist long after transcription and translation has terminated. Therefore, a higher likelihood for receptor expression can be expected when it is quantified via anatomical techniques.” in our results part to give the readers more context.

      Author response image 1.

      (4) In Fig1E, As Dop2R reporter signal is not colocalizing with IPC whereas dop2R is expressed in all four clusters.

      We tested if additional transcript variants with different C-termini are the cause for the discrepancy between transcriptome data and anatomical mapping. However, using a Trojan-GAL4 line for Octa2R that should account for other transcript variants did also not show any expression. At this point, with the tools we have, we cannot conclusively determine what the cause of this discrepancy is. Since we only see them with Dop2R and Octa2R, a mismatch caused by more general differences,

      e.g. sex-specific differences, seems unlikely. A more plausible reason could be that for those lines, inadequate transgenes lead to failed expressions. We added “Hence, inadequate transgenes for Dop2R and Octα2R or the lack of protein translation are the likely cause for the discrepancy between transcriptome analysis and anatomical mapping.“ to our results part as a possible explanation for the discrepancy.

      (5) Moving the AstANs expression images to the main figure (Fig 1E) would make sense as the authors focus on AstAN rather than MsRT or Dop2R in the later parts of their work.

      We thank the reviewer for this suggestion and replaced the LKR image with an AstAR2 image, as suggested. We kept the other two receptors in the main figure as additional examples.

      (6) Have the authors considered gap junction coupling of IPCs, which might explain the simultaneous responses in some cases?

      We have indeed considered this exciting idea, as gap junctions between IPCs could potentially synchronize activity in connected IPC subpopulations. To test if gap junctions are a major factor in the IPC population, we performed experiments with patch-clamp recordings from a single IPC while performing calcium imaging of the IPC population (as demonstrated in Fig. 4J). In some of these experiments, we injected current into individual IPCs and tested for activity changes in the other IPCs. However, the preliminary data we acquired did not indicate that the current-induced train of action potentials was transmitted to others IPCs. Hence, it is unlikely that the IPCs are directly coupled by gap junctions. Given the challenging nature of these experiments, and the discouraging preliminary results, we have not followed up on the idea any further.

      Reviewer #2 (Recommendations for the authors):

      (1) Figure 3D was not described in the text.

      We thank the reviewer for pointing out this mistake, we included the panel in Figure 3C and added the reference in the text describing the results from multiple animals shown in the panel.

      (2) In Figure 4B, a scale of heat map is required. There is a blue spot with no ROI setting on the left side. On the right side of the photos, the ROI No.6 seemed to turn blue after activation. However, Figure 4D shows the ROI No.6 was inhibited.

      We are now using a simplified heatmap in Figure 4B and added a scalebar. We also changed the example images to avoid any confusion. Previously, we used a random snapshot from before LED onset, now we used a snapshot from the actual time window to which we normalized the traces. Regarding the spot where no ROI is depicted but a response is visible: in this area, a trachea made it difficult to clearly delimit the cell body underneath, and we therefore excluded this ROI. Occlusions by trachea are one reason why we can typically not image the entire IPC population in a single animal.

      (3) In Figure 4F, the regions of gray bars (baseline) contain blue and red colors to some extent, which makes me confused. Moreover, the description "within one cluster, the response seemed homogeneous, e.g., in fly #4 during the activation of DANs (Fig. 4F)." was not clear to me. How about fly #1, #2, and #3? It seems that the responses changed excitedly and inhibitory within a cluster. Although the authors tend to raise some consistent results with examples, it would not be so effective if I can see there are other counter-examples and exceptions in the results.

      We apologize for the confusion we caused. The gray bars indicate the time window we used for baseline subtraction: The median activity of each IPC in this window was subtracted from the activity of that IPC. Hence, the median activity in this window is zero, but individual frames can have positive or negative values.

      We thank the reviewer for pointing out the confusion about the homogeneous responses in one cluster. We clarified this part in the results, by adding “Recording from multiple IPCs at the same time uncovered that the activity of IPCs within a cluster was synchronized in some cases. For example, in fly #1 in the DAN activation experiment, the baseline activity pattern of the excited IPC cluster was already synchronized before the first activation (fly #1, cells 3-8). Furthermore, the excitation onset and duration during the activation of DANs was highly uniform in this cluster. However, in other flies, e.g. #2 and #3 in the DAN activation experiments, we did not observe this synchronicity. While all IPCs in the excited cluster displayed an excitatory response to the DAN activation in these flies, the onset and duration differed between individual IPCs. In addition, the IPCs also showed more variability in their baseline activity (Fig. 4F). These findings point towards a shared input that can lead to the synchronization of IPC activity in some clusters and time windows. One known such input is the behavioral state – flight strongly inhibits the activity of all IPCs with very short delays(22). The flies in our experiments were not flying, but this example illustrates the presence of strong, state-dependent inputs that can synchronize the IPC population activity.”

      (4) In Figure 4J, no explanations of arrowheads, gray boxes, or asterisks are available in the legend.

      We thank the reviewer for pointing out this omission. We added the missing information to the figure legend.

      (5) "IPCs form distinct clusters." Is this cluster located closely each other or distant from one another?

      We did not encounter a location-dependent relationship between the IPCs of one cluster in calcium imaging experiments, nor did the anatomical receptor mapping data or connectomics analysis give any indication for anatomical clusters. The location of individual IPC cell bodies is not stereotypical across flies. We clarified this point in the results by adding “IPCs form distinct functional clusters” and “However, we found no evidence in our anatomical data, calcium imaging experiments, or in the fly brain EM volume that these clusters are distinguishable based on IPC soma location in the pars intercerebralis.”

    1. Author response:

      The following is the authors’ response to the original reviews.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Abstract

      I don't think you need the first two sentences of the abstract. This is not a grant and your results are exciting enough to justify a full basic science-based approach.

      We fully understand this perspective.  However, we prefer to introduce the work in the broader context of sleep medicine.  This manuscript is part of our long-standing efforts to develop cavefish as a model for sleep disorders and we believe this provides important context.

      Last sentence of the abstract: the subject is missing. "That have developed..." who has developed?

      Thank you. We have corrected this error, the sentence now reads “...these findings suggest that cavefish have developed resilience to sleep loss...”

      Introduction

      First paragraph. Worth explaining in a sentence what is the link between DNA damage and ROS.

      We now state ‘Further, chronic sleep loss results in elevated reactive oxygen species (ROS), a known mediatior of DNA damage, in the gut and/or brain that contribute to mortality in Drosophila and mice [11,16].’

      "A. mexicanus exists as blind cave populations and an extant surface population that are interfertile". This needs rephrasing. As it is, it sounds like the surface population is infertile.

      We have rephrased for clarity; the line now reads: “while the surface and cave populations are geographically isolated, they remain interfertile and capable of hybridization in nature as well as laboratory settings”.

      "Further, the evolved differences in DNA repair genes, including links between mechanisms regulating sleep, light responsiveness, and DNA repair across all three cave populations studied to date [27,29]" This sentence is incomplete.

      We have corrected the phrasing, which now reads “...evolved differences in DNA repair genes have been identified across all three cave populations studied to date, including links between mechanisms regulating sleep, light responsiveness, and DNA repair”:

      Figure 1

      I recommend improving the legibility of the figure copying some of the information provided in the legend directly within the figure itself.

      A, B: label in the panel itself what is blue and what is green.

      Thank you, we have made this change.

      C: Make it clear in the figure itself that you are measuring yH2AX. Also, probably you have enough room in the figure to avoid abbreviations for Rhomb, mes, and tele. It may also help if you could add a little cartoon that explains what those three brain regions are.

      We have added text to the y axis indicating that yH2AX fluorescence is being measured, and replaced the abbreviations with eh full names of the regions.

      G: again, explain that DHE is being measured here. And perhaps pick a different colour choice to highlight the difference from C?

      We have added clarifiaction to the y-axis of the figure, but have retained the color scheme for consistency; in all surface-cave comparisons in the manuscript, gray is used for surface fish and red for cavefish.

      In the text: I would recommend adding some quantitative reminder of what is the difference in sleep amount between the two species (cave vs surface).

      We have added the following to highlight the magnitude of the difference in sleep: “Strikingly, cavefish sleep as little as 1-2 hours per day, in contrast to their surface counterparts, which sleep as much as 6-10 hours a day”

      "Together, these findings fortify the notion that cellular stress is elevated in the gut of cavefish relative to surface fish." Were the two populations fed the same diet and raised in the same lab conditions? If this is pinpointed to sleep amount, it's worth ruling out possible confounding factors.

      We have added a sentence to the results underlining this point: “Prior to imaging, both surface and cavefish had been reared in a temperature-controlled incubator, and relied solely on their yolk sac for nutrients; so, differences in gut ROS cannot be attributed to differences in rearing or feeding conditions.”

      Figure 2

      Spell out, somewhere in the figure itself, that the 30s and 60s refer to UV treatment protocols.

      We have added X-axis titles to clarify this in Fig 2 and supp. Fig 1.

      It would be worth providing a cartoon of the experimental setup that shows for instance what time of the day UV was given (it's only specified in the text) and which subsequent sleep period was selected for comparisons.

      We have added arrows to all sleep plots indicating the time of UV treatment, and brackets indicating the time period used for statistical comparisons, as well as text in the figure legends indicating this.

      Figure 3

      A. I don't think this is needed, to be honest, and if you want to keep it, it needs a better legend.

      We have edited the figure legend to increase clarity.

      B. I would make it clear in the figure that this refers to transcriptomics analysis. Perhaps you could change the order and show C, D, and then B.

      We have added text to the figure legend and the results text to more explicitly state that the PCA plot is of transcriptional response. We have however retained the original figure order, as well feel this figure is important to establish that both populations have strong, but distinct responses to the UV treatment.

      Figure 4

      A. Spell it out in the figure itself that you're staining for CPD.

      Thank you, we have made this change.

      B. You are using the same colour combination you had in Figure 1 but for yet another pairing. This is a bit confusing.

      Thank you for bringing this to our attention.  We have added descriptions of the colors in the figure legend.

      Discussion

      "Beyond the Pachón cavefish population, all three other cavefish populations have been found to have reduced sleep (Cite)." Citation missing here.

      Thank you.  We have now clarified this sentence and included a citation.

      Reviewer #2 (Recommendations For The Authors):

      Consideration of Environmental Conditions:

      Evaluate whether the lab conditions, which may more closely resemble surface environments, could influence the observed increase in neuronal DNA damage and gut ROS levels in cavefish. Adjusting these conditions or discussing their potential impact in the manuscript would strengthen the findings.

      We are very excited about these experiments.  We have a paper that will be submitted to BioRxiv this week where we record wild-caught fish, as well as fish in caves.  The conclusion is that sleep loss is present in both populations.  This field work took over 10 years to come together and still lacks the power of the lab based assays.  Nevertheless, we can conclusively say that the phenotypes we have observed for the last ~15 years in the lab are present in a natural setting.  We have included a statement about the need for future work to test these findings in a natural setting.

      Alternative Stressors:

      Given that cavefish are albino and blind (to my knowledge), consider using alternative sources of genotoxic stress beyond UV-induced damage. This could include chemical agents or other forms of environmental stress to provide a more comprehensive assessment of DDR.

      We agree and are enthusiastic about looking more generally at stress.  We note that we have previously found that cavefish rebound following sleep deprivation (McGaugh et al, 2020) suggesting that they are responsive to sleep disruption.  This will be a major research focus area moving forward.

      Broader Stress Responses:

      Investigate whether other forms of stress, such as dietary changes or temperature fluctuations, elicit similar differences in sleep patterns and DDR responses. This could provide additional insights into the robustness of the observed phenomena.

      We fully agree.  This will be the primary focus of this research area moving forward. We hypothesize that cavefish are generally less responsive to their environment.  Unpublished data reveals that temperature stress, circadian changes, and aging (presented here) to little to impact gene expression in surface fish.  We would like to test the hypothesis that transcriptional stability of cavefish contributes to their longevity.

      Potential Protective Mechanisms:

      Discuss the possibility that lower levels of gamma-H2AX in cavefish might be protective, as DDR can lead to cellular senescence or cancer. This perspective could add depth to the interpretation of the results.

      This was the hypothesis underlying this manuscript.  However, we found elevated levels of gamma-H2AX.  We believe there may be additional protective mechanisms that have evolved in cavefish, but cannot identify them to date.  Our hope is future functional studies by our group, as well as other groups’ access to this published work, may help address these questions.

      Strengthening the Sleep-DNA Damage Link:

      Further experiments are needed to directly link sleep differences to the observed variations in DNA damage and DDR. This could involve manipulating sleep patterns in surface fish and cavefish to observe corresponding changes in DNA repair mechanisms.

      We agree.  We have referenced work that conclusively showed this relationship in zebrafish. Our current methods for limiting sleep involves shaking, and this has too many confounds.  We are working on developing genetic tools, and applying the gentle rocking methods used previously in zebrafish to address these questions.

      Clarification of Causal Directionality:

      Address the potential that sleep patterns and DDR responses may both be downstream effects of a common cause or independent adaptations to the cave environment. Clarifying this in the manuscript would provide a more nuanced understanding of the evolutionary adaptations.

      Thank you for this suggestion.  We have now added a paragraph describing how these experiments (and the ones described above) are necessary for understanding the relationship between sleep and DDR.

      Clarification and Presentation:

      Fix the many typos, and improve the clarity of the figures and their legends to ensure they are easily interpretable. Additional context in the discussion section would help readers understand the significance and potential implications of the findings.

      Thank you, we have now included this.

      Reviewer #3 (Recommendations For The Authors):

      There are a number of suggestions that I have made in the public review, but there are a few things that I would like to add here.

      The methods section is missing many important details, for instance, the intensity of the illumination used in the UV exposure in larvae is not reported but is vital for the interpretation/replication of these experiments. In general, this section should be redone with a greater effort to include all important information. Similarly, the figure legends could be greatly improved, with important details like n-number and definition of significance thresholds defined (e.g. see Figures 1, C, and G.)

      We have added greater detail to the methods section to specify the spectral peak and power output of the bulbs used.

      There are a number of passages in the manuscript that do not make sense, which suggests that a future version of record should be carefully proofread. I know that this can be a case of reading multiple versions of a manuscript so many times that one doesn't really see it anymore, but, for example, phrases like "To differentiate between these two possibilities" are confusing to the reader when there has been no introduction of alternate possibilities.

      Thank you for this comment.  We have fixed this mistake and proofread the manuscript.

      Additionally, there are multiple examples of errors in citations/references. A few examples are below:

      "Further, chronic sleep loss results in elevated reactive oxygen species (ROS) in the gut and/or brain that contribute to mortality in Drosophila and mice [11, 16]". Reference 16 does not include mice at all, and reference 11 is Vaccaro et al. 2020, where Drosophila mortality is assessed, but mouse mortality is not.

      We have added the appropriate citations and revised this sentence.

      References 13 and 15 are the same.

      Thank you, we have fixed.

      References 24 and 26 are the same.

      Thank you, we have fixed.

      Public Reviews:

      Reviewer #1 (Publc Review):

      Summary:

      Lloyd et al employ an evolutionary comparative approach to study how sleep deprivation affects DNA damage repair in Astyanax mexicanus, using the cave vs surface species evolution as a playground. The work shows, convincingly, that the cavefish population has evolved an impaired DNA damage response both following sleep deprivation or a classical paradigm of DNA damage (UV).

      Strengths:

      The study employs a thorough multidisciplinary approach. The experiments are well conducted and generally well presented.

      Weaknesses:

      Having a second experimental mean to induce DNA damage would strengthen and generalise the findings.

      Overall, the study represents a very important addition to the field. The model employed underlines once more the importance of using an evolutionary approach to study sleep and provides context and caveats to statements that perhaps were taken a bit too much for granted before. At the same time, the paper manages to have an extremely constructive approach, presenting the platform as a clear useful tool to explore the molecular aspects behind sleep and cellular damage in general. The discussion is fair, highlighting the strengths and weaknesses of the work and its implications.

      We fully agree with this assessment.  We are currently performing experiments to test the effects of additional DNA damaging agents.  We hope to extend these studies beyond DNA-damage agents to look more generally at how animals respond to stress including ROS, sleep deprivation, and high temperature.  This will be a major direction of the laboratory moving forward.

      The manuscript investigates the relationship between sleep, DNA damage, and aging in the Mexican cavefish (Astyanax mexicanus), a species that exhibits significant differences in sleep patterns between surface-dwelling and cave-dwelling populations. The authors aim to understand whether these evolved sleep differences influence the DNA damage response (DDR) and oxidative stress levels in the brain and gut of the fish.

      Summary of the Study:

      The primary objective of the study is to determine if the reduced sleep observed in cave-dwelling populations is associated with increased DNA damage and altered DDR. The authors compared levels of DNA damage markers and oxidative stress in the brains and guts of surface and cavefish. They also analyzed the transcriptional response to UV-induced DNA damage and evaluated the DDR in embryonic fibroblast cell lines derived from both populations.

      Strengths of the Study:

      Comparative Approach:

      The study leverages the unique evolutionary divergence between surface and cave populations of A. mexicanus to explore fundamental biological questions about sleep and DNA repair.

      Multifaceted Methodology:

      The authors employ a variety of methods, including immunohistochemistry, RNA sequencing, and in vitro cell line experiments, providing a comprehensive examination of DDR and oxidative stress.

      Interesting Findings:

      The study presents intriguing results showing elevated DNA damage markers in cavefish brains and increased oxidative stress in cavefish guts, alongside a reduced transcriptional response to UV-induced DNA damage.

      Weaknesses of the Study:

      Link to Sleep Physiology:

      The evidence connecting the observed differences in DNA damage and DDR directly to sleep physiology is not convincingly established. While the study shows distinct DDR patterns, it does not robustly demonstrate that these are a direct result of sleep differences.

      We agree with this assessment.  We are currently working to apply tools developed in zebrafish to examine the physiology of sleep.  While this is important, and our results our promising, we will note that functional analysis of sleep physiology in fish has been limited to zebrafish.  We hope future studies will allow us to integrate approaches that examine the physiology of sleep.

      Causal Directionality:

      The study fails to establish a clear causal relationship between sleep and DNA damage. It is possible that both sleep patterns and DDR responses are downstream effects of a common cause or independent adaptations to the cave environment.

      We agree, however, we note that this could be the case for all animals in which sleep has been linked to DNA damage.  We believe the most likely explanation for Astyanax and other animals studied, is that sleep is that sleep and DDR are downstream/interface with the sleep homeostat.

      Environmental Considerations:

      The lab conditions may not fully replicate the natural environments of the cavefish, potentially influencing the results. The impact of these conditions on the study's findings needs further consideration.

      This is correct. We have considered this carefully.  After nearly a decade of effort,  we have completed analysis of sleep in the wild.  These will be uploaded to BioRxiv within the next week.

      Photoreactivity in Albino Fish:

      The use of UV-induced DNA damage as a primary stressor may not be entirely appropriate for albino, blind cavefish. Alternative sources of genotoxic stress should be explored to validate the findings.

      We have addressed this above.  Future work will examine additional stressors. Both fish are transparent at 6dpf and so it is unlikely that albinism impacts the amount of UV that reaches the brain.

      Assessment of the Study's Achievements:

      The authors partially achieve their aims by demonstrating differences in DNA damage and DDR between surface and cavefish. However, the results do not conclusively support the claim that these differences are driven by or directly related to the evolved sleep patterns in cavefish. The study's primary claims are only partially supported by the data.

      Impact and Utility:

      The findings contribute valuable insights into the relationship between sleep and DNA repair mechanisms, highlighting potential areas of resilience to DNA damage in cavefish. While the direct link to sleep physiology remains unsubstantiated, the study's data and methods will be useful to researchers investigating evolutionary biology, stress resilience, and the molecular basis of sleep.

      Reviewer #3 (Public Review):

      Lloyd, Xia, et al. utilised the existence of surface-dwelling and cave-dwelling morphs of Astyanax mexicanus to explore a proposed link between DNA damage, aging, and the evolution of sleep. Key to this exploration is the behavioural and physiological differences between cavefish and surface fish, with cavefish having been previously shown to have low levels of sleep behaviour, along with metabolic alterations (for example chronically elevated blood glucose levels) in comparison to fish from surface populations. Sleep deprivation, metabolic dysfunction, and DNA damage are thought to be linked and to contribute to aging processes. Given that cavefish seem to show no apparent health consequences of low sleep levels, the authors suggest that they have evolved resilience to sleep loss. Furthermore, as extended wake and loss of sleep are associated with increased rates of damage to DNA (mainly double-strand breaks) and sleep is linked to repair of damaged DNA, the authors propose that changes in DNA damage and repair might underlie the reduced need for sleep in the cavefish morphs relative to their surface-dwelling conspecifics.

      To fulfill their aim of exploring links between DNA damage, aging, and the evolution of sleep, the authors employ methods that are largely appropriate, and comparison of cavefish and surface fish morphs from the same species certainly provides a lens by which cellular, physiological and behavioural adaptations can be interrogated. Fluorescence and immunofluorescence are used to measure gut reactive oxygen species and markers of DNA damage and repair processes in the different fish morphs, and measurements of gene expression and protein levels are appropriately used. However, although the sleep tracking and quantification employed are quite well established, issues with the experimental design relate to attempts to link induced DNA damage to sleep regulation (outlined below). Moreover, although the methods used are appropriate for the study of the questions at hand, there are issues with the interpretation of the data and with these results being over-interpreted as evidence to support the paper's conclusions.

      This study shows that a marker of DNA repair molecular machinery that is recruited to DNA double-strand breaks (γH2AX) is elevated in brain cells of the cavefish relative to the surface fish and that reactive oxygen species are higher in most areas of the digestive tract of the cavefish than in that of the surface fish. As sleep deprivation has been previously linked to increases in both these parameters in other organisms (both vertebrates and invertebrates), their elevation in the cavefish morph is taken to indicate that the cavefish show signs of the physiological effects of chronic sleep deprivation.

      It has been suggested that induction of DNA damage can directly drive sleep behaviour, with a notable study describing both the induction of DNA damage and an increase in sleep/immobility in zebrafish (Danio rerio) larvae by exposure to UV radiation (Zada et al. 2021 doi:10.1016/j.molcel.2021.10.026). In the present study, an increase in sleep/immobility is induced in surface fish larvae by exposure to UV light, but there is no effect on behaviour in cavefish larvae. This finding is interpreted as representing a loss of a sleep-promoting response to DNA damage in the cavefish morph. However, induction of DNA damage is not measured in this experiment, so it is not certain if similar levels of DNA damage are induced in each group of intact larvae, nor how the amount of damage induced compares to the pre-existing levels of DNA damage in the cavefish versus the surface fish larvae. In both this study with A. mexicanus surface morphs and the previous experiments from Zada et al. in zebrafish, observed increases in immobility following UV radiation exposure are interpreted as following from UV-induced DNA damage. However, in interpreting these experiments it is important to note that the cavefish morphs are eyeless and blind. Intense UV radiation is aversive to fish, and it has previously been shown in zebrafish larvae that (at least some) behavioural responses to UV exposure depend on the presence of an intact retina and UV-sensitive cone photoreceptors (Guggiana-Nilo and Engert, 2016, doi:10.3389/fnbeh.2016.00160). It is premature to conclude that the lack of behavioural response to UV exposure in the cavefish is due to a different response to DNA damage, as their lack of eyes will likely inhibit a response to the UV stimulus.

      We believe that in A. mexicanus, like in zebrafish, it is highly unlikely that the effects of UV are mediated through visual processing. Even if this were the case, the timeframe of UV activation is very short compared to the time-scale of sleep measurements so this is unlikely to be a confound.

      Indeed, were the equivalent zebrafish experiment from Zada et al. to be repeated with mutant larvae fish lacking the retinal basis for UV detection it might be found that in this case too, the effects of UV on behaviour are dependent on visual function. Such a finding should prompt a reappraisal of the interpretation that UV exposure's effects on fish sleep/locomotor behaviour are mediated by DNA damage.

      We prefer not to comment on Zada et al, as that is a separate manuscript.

      An additional note, relating to both Lloyd, Xia, et al., and Zada et al., is that though increases in immobility are induced following UV exposure, in neither study have assays of sensory responsiveness been performed during this period. As a decrease in sensory responsiveness is a key behavioural criterion for defining sleep, it is, therefore, unclear that this post-UV behaviour is genuinely increased sleep as opposed to a stress-linked suppression of locomotion due to the intensely aversive UV stimulus.

      We understand this concern and are working on improved methodology for measuring sleep.  However, behavioral measurements are the standard for almost every manuscript that has studied sleep in zebrafish, flies, and worms to date. 

      The effects of UV exposure, in terms of causing damage to DNA, inducing DNA damage response and repair mechanisms, and in causing broader changes in gene expression are assessed in both surface and cavefish larvae, as well as in cell lines derived from these different morphs. Differences in the suite of DNA damage response mechanisms that are upregulated are shown to exist between surface fish and cavefish larvae, though at least some of this difference is likely to be due to differences in gene expression that may exist even without UV exposure (this is discussed further below).

      UV exposure induced DNA damage (as measured by levels of cyclobutene pyrimidine dimers) to a similar degree in cell lines derived from both surface fish and cave fish. However, γH2AX shows increased expression only in cells from the surface fish, suggesting induction of an increased DNA repair response in these surface morphs, corroborated by their cells' increased ability to repair damaged DNA constructs experimentally introduced to the cells in a subsequent experiment. This "host cell reactivation assay" is a very interesting assay for measuring DNA repair in cell lines, but the power of this approach might be enhanced by introducing these DNA constructs into larval neurons in vivo (perhaps by electroporation) and by tracking DNA repair in living animals. Indeed, in such a preparation, the relationship between DNA repair and sleep/wake state could be assayed.

      Comparing gene expression in tissues from young (here 1 year) and older (here 7-8 years) fish from both cavefish and surface fish morphs, the authors found that there are significant differences in the transcriptional profiles in brain and gut between young and old surface fish, but that for cavefish being 1 year old versus being 7-8 years old did not have a major effect on transcriptional profile. The authors take this as suggesting that there is a reduced transcriptional change occurring during aging and that the transcriptome of the cavefish is resistant to age-linked changes. This seems to be only one of the equally plausible interpretations of the results; it could also be the case that alterations in metabolic cellular and molecular mechanisms, and particularly in responses to DNA damage, in the cavefish mean that these fish adopt their "aged" transcriptome within the first year of life.

      This is indeed true.  However, one could also interpret this as a lack of aging.  If the profile does not change over time, the difference seems largely semantic.

      A major weakness of the study in its current form is the absence of sleep deprivation experiments to assay the effects of sleep loss on the cellular and molecular parameters in question. Without such experiments, the supposed link of sleep to the molecular, cellular, and "aging" phenotypes remains tenuous. Although the argument might be made that the cavefish represent a naturally "sleep-deprived" population, the cavefish in this study are not sleep-deprived, rather they are adapted to a condition of reduced sleep relative to fish from surface populations. Comparing the effects of depriving fish from each morph on markers of DNA damage and repair, gut reactive oxygen species, and gene expression will be necessary to solidify any proposed link of these phenotypes to sleep.

      We agree this would be beneficial.  We note that relatively few papers have sleep deprived fish.  While we done have this before in A. mexicanus the assay is less than ideal and likely induces generalizable stress.  We are working on adapting more recently developed methods in zebrafish.

      A second important aspect that limits the interpretability and impact of this study is the absence of information about circadian variations in the parameters measured. A relationship between circadian phase, light exposure, and DNA damage/repair mechanisms is known to exist in A. mexicanus and other teleosts, and differences exist between the cave and surface morphs in their phenomena (Beale et al. 2013, doi: 10.1038/ncomms3769). Although the present study mentions that their experiments do not align with these previous findings, they do not perform the appropriate experiments to determine if such a misalignment is genuine. Specifically, Beale et al. 2013 showed that white light exposure drove enhanced expression of DNA repair genes (including cpdp which is prominent in the current study) in both surface fish and cavefish morphs, but that the magnitude of this change was less in the cave fish because they maintained an elevated expression of these genes in the dark, whereas the darkness suppressed the expression of these genes in the surface fish. If such a phenomenon is present in the setting of the current study, this would likely be a significant confound for the UV-induced gene expression experiments in intact larvae, and undermine the interpretation of the results derived from these experiments: as samples are collected 90 minutes after the dark-light transition (ZT 1.5) it would be expected that both cavefish and surface fish larvae should have a clear induction of DNA repair genes (including cpdp) regardless of 90s of UV exposure. The data in Supplementary Figure 3 is not sufficient to discount this potentially serious confound, as for larvae there is only gene expression data for time points from ZT2 to ZT 14, with all of these time points being in the light phase and not capturing any dynamics that would occur at the most important timepoints from ZT0-ZT1.5, in the relevant period after dark-light transition. Indeed, an appropriate control for this experiment would involve frequent sampling at least across 48 hours to assess light-linked and developmentally-related changes in gene expression that would occur in 5-6dpf larvae of each morph independently of the exposure to UV.

      We agree that this would be useful, however, frequent sampling is not feasible given the experiments presented here and the challenges of working with an emerging model.

      On a broader point, given the effects of both circadian rhythm and lighting conditions that are thought to exist in A. mexicanus (e.g. Beale et al. 2013) experiments involving measurements of DNA damage and repair, gene expression, and reactive oxygen species, etc. at multiple times across >1 24 hour cycle, in both light-dark and constant illumination conditions (e.g. constant dark) would be needed to substantiate the authors' interpretation that their findings indicate consistently altered levels of these parameters in the cavefish relative to the surface fish. Most of the data in this study is taken at only single time points.

      Again, see comment above.  The goal was to identify whether there are differences in DNA Damage response between A. mexcicanus. Extending on this to examine interactions with the circadian system could be a useful path to pursue in the future.

      On a broader point, given the effects of both circadian rhythm and lighting conditions that are thought to exist in A. mexicanus (e.g. Beale et al. 2013) experiments involving measurements of DNA damage and repair, gene expression, and reactive oxygen species, etc. at multiple times across >1 24 hour cycle, in both light-dark and constant illumination conditions (e.g. constant dark) would be needed to substantiate the authors' interpretation that their findings indicate consistently altered levels of these parameters in the cavefish relative to the surface fish. Most of the data in this study is taken at only single time points.

      In summary, the authors show that there are differences in gene expression, activity of DNA damage response and repair pathways, response to UV radiation, and gut reactive oxygen species between the Pachón cavefish morph and the surface morph of Astyanax mexicanus. However, the data presented does not make the precise nature of these differences very clear, and the interpretation of the results appears to be overly strong. Furthermore, the evidence of a link between these morph-specific differences and sleep is unconvincing.

      In summary, the authors show that there are differences in gene expression, activity of DNA damage response and repair pathways, response to UV radiation, and gut reactive oxygen species between the Pachón cavefish morph and the surface morph of Astyanax mexicanus. However, the data presented does not make the precise nature of these differences very clear, and the interpretation of the results appears to be overly strong. Furthermore, the evidence of a link between these morph-specific differences and sleep is unconvincing.

    1. Author response:

      The following is the authors’ response to the original reviews.

      We thank all reviewers for their thorough and thoughtful comments. We have carefully addressed each point raised, conducting new experiments and analyses to strengthen the manuscript. Below is a summary:

      · Synchronous ensembles in new experiments: New experiments demonstrated synchronous ensembles during immobility in a novel environment (Figure 3-figure supplement 2) and revealed a significant reduction in such synchrony following familiarization training (Figure 4D).

      · Ripple-associated activity: We detected a much larger number of ripple events to confirm (a) the suppression of CA1PC spiking during ripples (Figure 4Ai) and (b) that synchronous ensembles mostly occur outside ripples (Figure 3-figure supplement 3). Additionally, spiking suppression was accompanied by decreased subthreshold membrane potentials (Figure 4Bi, Ci). Ripple-associated spiking and membrane potential dynamics shifted toward higher firing rates and more depolarization after familiarization training (Figure 4).

      · Public data analysis: Analysis of publicly available data identified thetaassociated synchronous ensembles, demonstrating the generalizability of our findings across different experimental conditions (Supplementary Figure 5).

      · Neuron morphology and algorithm validation: Images of recorded neurons after experiments confirmed their intact morphology. We also provided details on validating spike detection algorithms (Methods and Supplementary Figure 1).

      · Cell soma locations: New data and analyses illustrate the distribution of cells labeled at different embryonic days along the radial axis of the pyramidal layer (Supplementary Figure 1).

      · Analyses testing the robustness of synchronous ensembles: Additional analyses examined the impact of complex bursts and thetaphase locking, confirming the robustness of synchronous ensembles detection (Supplementary Figures 3 and 4).

      · Additional analyses and figures: We conducted further analyses and created new figures to address all remaining concerns (Response to Reviewer Figures 1-6).

      We believe these revisions have significantly enhanced the paper, and we sincerely thank all reviewers for their invaluable feedback.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      For many years, there has been extensive electrophysiological research investigating the relationship between local field potential patterns and individual cell spike patterns in the hippocampus. In this study, using state-ofthe-art imaging techniques, they examined spike synchrony of hippocampal cells during locomotion and immobility states. In contrast to conventional understanding of the hippocampus, the authors demonstrated that hippocampal place cells exhibit prominent synchronous spikes locked to theta oscillations.

      Strengths:

      The voltage imaging used in this study is a highly novel method that allows recording not only suprathreshold-level spikes but also subthreshold-level activity. With its high frame rate, it offers time resolution comparable to electrophysiological recordings. Moreover, it enables the visualization of actual cell locations, allowing for the examination of spatial properties (e.g., Figure 4G).

      We thank the reviewer for recognizing the strength of our study.

      Weaknesses:

      There is a notable deviation from several observations obtained through conventional electrophysiological recordings. Particularly, as mentioned below in detail, the considerable differences in baseline firing rates and no observations of ripple-triggered firing patterns raise some concerns about potential artifacts from imaging and analsyis, such as cell toxicity, abnormal excitability, and false detection of spikes. While these findings are intriguing if the validity of these methods is properly proven, accepting the current results as new insights is challenging.

      We appreciate the reviewer’s insightful comments regarding the apparent deviation of our observation from conventional understanding, which we address in the following sections.

      Reviewer #1 (Recommendations For The Authors):

      (1) I am not particularly inclined to strongly adhere to conventional insights, but the findings obtained through this imaging method seem significantly different from those known from conventional electrophysiological recordings. For instance, there are noticeable differences in several basic firing characteristics. First, the average firing rates of 2.3-4.3 Hz (Line 97) appear higher than the distribution of firing frequencies reported in many electrophysiological recordings of pyramidal cells (e.g., Mizuseki et al., Cell Rep, 2013).

      We understand that some of our findings differ from conventional insights. However, it is important to emphasize that many of our observations align closely with prior electrophysiological recordings. For instance, individual neurons in our study exhibit expected modulation by locomotion, spatial locations, novelty, and theta oscillations, all of which are hallmarks of normal hippocampal physiology.

      Regarding the firing rates, it is important to highlight the heterogeneity of the firing rates, which range from 0.01 to 10 Hz, with a skewed distribution toward lower frequencies(1). While our values (2.3-4.3Hz) are higher than those reported by Mizuseki et al. (2013)(1) in rats, our recordings were obtained from mice and aligned with studies using mice, including firing rates of 2.1 Hz reported by McHugh et al. (1996) and 2.4-2.6 Hz by Buzsaki et al. (2003)(2,3).

      In addition, our recordings were performed in a novel environment, which is known to enhance the firing rates of the hippocampal neurons(4). Consistent with this, our new recordings in a familiar environment demonstrate significantly lower firing rates (see below).

      Results (line 279)

      “Mean firing rates were significantly reduced in the familiar group compared to the novel group (Familiar group: 1.1 to 5.2 Hz (25<sup>th</sup>-75<sup>th</sup> percentiles), median=2.3 Hz, n\=66 cells, 6 sessions, 4 mice; Novel group: 1.7 to 6.0 Hz (25<sup>th</sup>-75<sup>th</sup> percentiles), median=4.2 Hz, n\=111 cells, 6 sessions, 6 mice, p\=0.0083, Wilcoxon signed-rank test).”

      Second, while this finding suggests that spike synchrony is entirely unrelated to ripple-triggered events, it is indeed difficult to comprehend (researchers who have analyzed electrophysiological data, at the very least, should have experienced some degree of correlation between ripples and spikes).

      We thank the reviewer for raising this important point. We, too, found it surprising that population synchrony appears largely unrelated to ripples. To ensure the robustness of this observation, we conducted new experiments under conditions optimized for ripple detection to (a) confirm that the lack of positive correlation is also observed under conditions where we can detect more ripples and (b) demonstrate that our imaging methods can detect a higher correlation between ripples and spikes in a familiar environment (see details below).

      Results (line 251)

      “It was puzzling that these CA1PCs exhibited robust spiking activities outside of ripples yet generated few spikes during ripples. To further investigate neuronal activities during ripples, we established a recording condition that allowed us to capture more ripple episodes. Specifically, we immobilized mice in a tube to promote behaviors favoring ripple generation. The mice were habituated to head fixation in a tube in a room distinct from the one where imaging experiments were conducted. On the imaging day, the mice were introduced to the recording room and head-fixed under the microscope for the first time.

      CA1PCs were labeled in utero on embryonic day (E) 14.5 (n\=56 cells from 3 sessions in 3 mice) and E17.5 (n\=55 cells from 3 sessions in 3 mice) and imaged in adult brains. Both neuronal populations exhibited prominent peaks in their grand average CCGs and significantly higher synchronous event rates compared to jittered data (Figure 3-figure supplement 2A, B). Approximately 40% of the recorded neurons participated in synchronous ensembles, indicating robust synchronous activity involving a substantial proportion of the recorded cells (Figure 3-figure supplement 2C).

      In total, 1052 synchronous ensembles and 174 ripple episodes were detected across these imaging sessions. Consistent with findings from walking animals, few synchronous ensembles occurred during ripples when animals were immobilized in a tube (Figure 3-figure supplement 3A, B). Moreover, no distinguishable ripple oscillations were observed in synchronous events, and the average firing rates during ripple episodes were near zero (Figure 3-figure supplement 3C, D). At the single-cell level, 90% of neurons showed significant negative spiking modulation during ripples, with ripple modulation indexes close to -1, indicating strong suppression of spiking (Figure 4Ai). This suppression extended to subthreshold membrane potentials, as nearly all cells exhibited decreased fluorescence during ripples compared to baseline (Figure 4Bi, Ci). These results demonstrate that spiking activity and subthreshold membrane potentials are robustly suppressed during ripples.

      Contextual novelty plays a critical role in shaping hippocampal neuronal activities. To assess its influence, we trained mice to become familiar with the imaging procedure and the recording environment over five days and recorded CA1PC activities on the final day. Mean firing rates were significantly reduced in the familiar group compared to the novel group (Familiar group:

      1.1 to 5.2 Hz (25<sup>th</sup>-75<sup>th</sup> percentiles), median=2.3 Hz, n\=66 cells, 6 sessions, 4 mice; Novel group: 1.7 to 6.0 Hz (25<sup>th</sup>-75<sup>th</sup> percentiles), median=4.2 Hz, n\=111 cells, 6 sessions, 6 mice, p\=0.0083, Wilcoxon signed-rank test). Additionally, 15% of the neurons in the familiar group exhibited significantly positive spiking modulation by ripples, while fewer cells showed negative modulation compared to the novel group (Figure 4A). During ripples, neurons in the novel group predominantly displayed hyperpolarizing membrane voltage responses, whereas a subset of neurons in the familiar group exhibited prominent depolarizing responses (Figure 4B). The mean fluorescence changes in the familiar group shifted toward depolarization compared to the novel group (Figure 4C). Finally, synchronous event frequencies were significantly lower in the familiar context, indicating weaker synchronous activities under familiar conditions (Figure 4D). These results demonstrate that hippocampal neuronal activities, particularly synchronous ensembles, are strongly influenced by contextual novelty.”

      Third, the fact that more than 40% of cells frequently exhibit synchronous firing other than during ripples has not been reported before, and if it were the case, many electrophysiologists would have likely noticed it. Overall, the excitability of cells seems too high.

      We thank the reviewer for raising this point. As discussed above, the reported spike rates are within the range expected from the previous electrophysiology recordings in mice, especially given that we record cells in a novel environment. In addition, our jittering procedure ensures that the observed synchrony exceeds what could be expected from the given level of spike rates alone. These analyses support the robustness of our observations.

      As mentioned below, there are concerns about experimental artifacts and analytical issues from optical imaging.

      (2) Method: In surgery, the cortical tissue above the hippocampus was aspirated, which is a general method for in vivo calcium imaging from the hippocampus. Furthermore, they use a CAG promoter to express the sensors. To my knowledge, this promoter is excessively strong and may sometimes be toxic to cells. In addition, for imaging, they use DMSO and Pluronic F-127, which are relatively toxic materials (please describe their concentrations). These conditions might be damaging to hippocampal neurons.

      We thank the reviewer for raising these comments. As the reviewer mentioned, cortical aspiration is a general method for in vivo imaging from the hippocampus and has been employed in numerous studies, including behavioral and systems-level investigations(5-15). For example, place cells are routinely recorded in both familiar and novel environments using this method and other approaches. Additionally, synchronous population activities have been observed and studied in the hippocampus both with and without cortical aspiration(6,15-18). These findings demonstrate that the hippocampal neuronal network generates place cells and synchronous activities regardless of whether the cortical tissue above it has been aspirated.

      DMSO and Pluronic F-127 are used as solvents for dissolving the JF<sub>552</sub>HaloTag ligand, and the resulting solution is injected into the bloodstream rather than directly into brain tissue. The concentrations of these reagents in the dye solution are now described in the text (see below). Assuming a blood volume of 2 ml in adult mice, the final concentrations of DMSO and Pluronic F-127 in the bloodstream are estimated to be 1% upon injection and then decrease rapidly while they are metabolized and excreted out of the body. Moreover, the effective concentrations in the brain tissue would be even lower. These low concentrations have been demonstrated to have minimal impact on cells and tissue(19-22).

      Methods (line 616)

      “JF<sub>552</sub>-HaloTag ligand (a generous gift from Dr. Luke Lavis) was first dissolved in DMSO (20 μl, Sigma) and then diluted in Pluronic<sup>TM</sup> F-127 (20 μl, P3000MP, Invitrogen) and PBS to achieve a final concentration of 0.83 mM of JF<sub>552</sub>-HaloTag ligand. The solution was then injected intravenously through the retro-orbital sinus. Imaging sessions were initiated 3 hours after the injection of the JF<sub>552</sub>-HaloTag ligand.”

      We understand that the CAG promoter may sometimes be toxic to cells if it drives high expression. However, it is important to note that we injected highly diluted virus (20x, final titer: 2.7x10<sup>12</sup> GC/ml) to avoid excessive expression levels. This titer was determined from serial dilution experiments to ensure an optimal expression level free from toxicity (see below). The same titer was used in a previous study(23) to label CA1 interneurons, which exhibited physiological spike rates and synchrony (see Abdelfattah 2023, Neuron, Figure 8). Furthermore, Voltron expression does not significantly affect key cellular properties, including membrane resistance, membrane capacitance, resting membrane potentials, spike amplitudes, and spike width (see Abdelfattah 2019, Science, Supplementary Figures 11 and 12). In our recordings, individual neurons exhibit the expected modulation by locomotion, spatial locations, novelty, and theta oscillations. We now include images of the recorded neurons to demonstrate their intact morphology and healthy appearance following imaging experiments (Supplementary Figure 1A, B), further supporting minimal cytotoxic effects.

      Methods (line 577)

      “A serial dilution experiment was conducted to determine an optimal titer of the virus carrying Voltron2 genes, minimizing cell toxicity, for use in this and in previous imaging experiments. A fine injection pipette (tip diameter 10-60 um) was used to inject AAV2/1-CAG-flex-Voltron2-ST (2.7x10<sup>12</sup> GC/ml, a generous gift from Dr. Eric Schreiter and the GENIE team at HHMI Janelia Research Campus) into the exposed regions at a depth of 200 μm (up to six injection sites and 100-200 nL of viral suspension).”

      (3) Another concern is the relatively low number of cells simultaneously recorded during imaging compared to typical hippocampal imaging such as Inscopix which often records several hundred cells. In this study, however, this number is 20 or fewer. This is likely because the visualized cells at baseline were limited to this extent. It is possible that these cells represent particularly too strong sensor expression, which may facilitate visualization and high signal-to-noise ratio in voltage imaging. Consequently, there is a possibility of abnormal activity occurring in these cells.

      The Inscopix studies use calcium imaging, which has a temporal resolution that is too slow to resolve fast synchrony central to our study. To enable highspeed voltage imaging at 2000 frames per second, we employed strategies to achieve sparse labeling and carefully limited the number of labeled cells to minimize out-of-focus contamination. In our analysis, we applied a criterion to include only cells separated by 70 μm or longer, reducing the potential for channel cross-talk among nearby neurons. These criteria limited the number of simultaneously imaged cells in our experiments. To address this issue, we have now included new data from 12 additional animals with 177 neurons to support our findings.

      Furthermore, despite the limited number of simultaneously imaged cells, population synchrony beyond what could be expected by chance can be detected using rigorous statistical procedures. As discussed earlier, neuronal activities were within the expected range; they were modulated by animals’ locomotion (Figure 2 and Supplementary Figure 2), exhibited place tuning, and were significantly reduced when the recording context became familiar, supporting the normal physiology of the recorded cells.

      (4) Analysis: There are some criteria for detecting spikes (described in the Methods), but there are concerns about whether these criteria truly extract only spike activity. When examining the traces in Figure 1 and Figure 2, there appear to be some activities that show fluorescence increases up to the level of putative spikes. How can we determine that these are indeed subthreshold changes? Conversely, some activities detected as spikes may also be subthreshold synaptic potential (this possibility concerns me). There is a need for more precise validation of spike detection analysis to ensure its accuracy.

      Regarding spike detection, we used validated algorithms(23-25) to ensure robust and reliable spike identification. Spiking activity was first separated from slower subthreshold potentials using high-pass filtering. This approach prevents slow fluorescence increases from being misinterpreted as spikes, even if their amplitude is large. We benchmarked this detection algorithm in our recent publication (Huang et al., 2024)(24), demonstrating its high sensitivity and specificity in spike detection (see the figure below). While we acknowledge that a small number of spikes, particularly those occurring later in a burst, might be missed due to their smaller amplitudes (as illustrated in Figures 1 and 2 of the manuscript), we anticipate that any missed spikes would lead to a decrease, rather than an increase, in synchrony between neurons. Overall, we are confident that spike detection is performed in a rigorous and reliable manner.

      Method (line 670)

      “Previous studies have described and validated the procedure for imaging preprocessing and spike detection. In short, the fluorescence intensities of individual neurons were calculated by averaging the fluorescence intensities of pixels from the same ROIs. Bleaching was corrected by calculating the baseline fluorescence (F<sub>0</sub>) at each time point as an average of the fluorescence intensities within ±0.5 seconds around the time point. The dF/F was calculated as the F<sub>0</sub> minus the fluorescence intensity of the same time point divided by F<sub>0</sub>. Positive fluorescence transients were detected to identify spikes from the high-passed dF/F traces created by subtracting the dF/F traces from the median-filtered version with a 5-ms window. To simulate the noise of recordings, high-passed dF/F traces were inverted, and the amplitudes of the transients detected from the inverted traces were used to construct a noise distribution of the spike amplitudes. A threshold was set by comparing the amplitudes of the detected transients with the noise distribution of the spike amplitudes to minimize the sum of type I and type II errors. Spikes were first detected when transients were larger than the threshold. Then, spike amplitudes smaller than half of the top 5% spike amplitudes were excluded. The signal-to-noise ratio (SNR) was calculated for each neuron as a ratio of the averaged spike amplitudes over the standard deviation of the high-passed dF/F traces, excluding points 2 ms before and 4 ms after each detected spike to estimate the quality of the recordings.”

      (5) If the authors aim to establish this new physiological phenomenon, it is necessary to compare it with electrophysiological data or verify if similar phenomena can be detected from electrophysiological data. Recently, various datasets have been made publicly available (e.g. CRCNS and Mendeley data), and these should be easily verifiable without the need for conducting experiments.

      We thank the reviewer for the suggestion. To address this, we analyzed a publicly available dataset (hc-11 on CRCNS), which contains hippocampal recordings from rats navigating novel mazes for water rewards. Using our algorithm, we detected significant population synchrony in the dataset (Supplementary Figure 5A). The synchronous event rates were 6.4-fold higher than those in jittered controls, demonstrating the reliability of our findings.

      Additionally, these synchronous events mostly occurred in the absence of ripples and were coupled to theta oscillations (Supplementary Figure 5B-D). These results not only validate our findings using independent datasets but also highlight the generalizability of synchronous ensembles as a distinct network phenomenon relevant to hippocampal function.

      Results (line 366)

      “To further investigate synchronous ensembles across different datasets, we analyzed publicly available hippocampal recordings ‘hc-11’ from the CRCNS repository, where rats navigated novel mazes for water rewards (see Method). Using our algorithm, we identified a significant number of synchronous ensembles during the first three minutes of novel navigation. On average, the rates of synchronous events were 6.4-fold higher than those detected in jittered controls (mean event rate: 2.0 ± 0.3 Hz for the original data vs. 0.32 ± 0.03 Hz for jittered data, n \= 8 sessions, p \= 0.0078, W \= 36, Wilcoxon signedrank test; Supplementary Figure 5A). To assess whether ripple oscillations were associated with these synchronous ensembles, we analyzed ripple event rates and their relationship to population synchrony. During this period, ripple events were infrequent (mean ripple rate: 0.02 ± 0.01, n \= 8 sessions), and ripple power peaked during ripple episodes but remained low at the timings of population synchrony (Supplementary Figure 5B). Nevertheless, LFP traces aligned to population synchrony revealed prominent theta oscillations (Supplementary Figure 5C). Synchronous ensembles were modulated by LFP theta oscillation (modulation strength: 0.30 ± 0.04, n \= 8 sessions, p < 0.001), and the timings of individual ensembles were consistently locked to the preferred phase of each session, suggesting a functional coupling of synchronous ensembles to theta oscillations important for information processing (Supplementary Figure 5D).”

      (6) Please describe exact statistical information (e.g. statistical values, degree of freedom, and test types) throughout the manuscript.

      Statistical values, degree of freedom and test types have been included in the manuscript. Please see below an example in the manuscript:

      Result (line 96)

      “Consistent with previous studies, neurons labeled on E14.5 located more on the deep side of the pyramidal layer than those labeled on E17.5 (t<sub>(601)</sub>=22.8, p<0.0001, Student’s t-test; Supplementary Figure 1C, D).”

      Minor comment - Figure 2A legend: what is "gray rectangles"?

      We apologize for the inconsistency in nomenclature in the figure legends. We have now corrected this issue and consistently use the term “gray vertical bars” to indicate the timings and durations of synchronous events throughout the article.

      Reviewer #2 (Public Review):

      Summary:

      This study employed voltage imaging in the CA1 region of the mouse hippocampus during the exploration of a novel environment. The authors report synchronous activity, involving almost half of the imaged neurons, occurred during periods of immobility. These events did not correlate with SWRs, but instead, occurred during theta oscillations and were phasedlocked to the trough of theta. Moreover, pairs of neurons with high synchronization tended to display non-overlapping place fields, leading the authors to suggest these events may play a role in binding a distributed representation of the context.

      We thank the reviewer for a thorough and thoughtful review of our paper.

      Strengths:

      Technically this is an impressive study, using an emerging approach that allows single-cell resolution voltage imaging in animals, that while head-fixed, can move through a real environment. The paper is written clearly and suggests novel observations about population-level activity in CA1.

      We thank the reviewer for pointing out the technical strength and the novelty of our study.

      Weaknesses:

      The evidence provided is weak, with the authors making surprising population-level claims based on a very sparse data set (5 data sets, each with less than 20 neurons simultaneously recorded) acquired with exciting, but less tested technology. Further, while the authors link these observations to the novelty of the context, both in the title and text, they do not include data from subsequent visits to support this. Detailed comments are below:

      We understand the reviewer’s concerns regarding the dataset size. In the revised manuscript, we have included additional data to further strengthen our conclusions and provide a more robust dataset. Specifically, we expanded our analysis by increasing the number of sessions and neurons recorded, ensuring that the findings are more representative and less likely to be influenced by sample sizes.

      Moreover, synchronous ensembles exceeding what could be expected by chance were detected in all examined data, validating our claims regarding population synchrony. We have also carefully considered the potential impact of the technology used in our experiments and included additional validation and comparison with results from other studies employing complementary techniques to support the reliability of our conclusions.

      Regarding the link to novelty, we have included data from subsequent visits, as suggested by the reviewer. These new data demonstrate that the observed changes in synchronous ensembles are context-dependent and significantly influenced by novelty. This confirms the novelty-related effects observed during initial visits and further supports the conclusions drawn in the manuscript. Please see below for our detailed replies to each of the reviewer’s points.

      (1) My first question for the authors, which is not addressed in the discussion, is why these events have not been observed in the countless extracellular recording experiments conducted in rodent CA1 during the exploration of novel environments. Those data sets often have 10x the neurons simultaneously recording compared to these present data, thus the highly synchronous firing should be very hard to miss. Ideally, the authors could confirm their claims via the analysis of publicly available electrophysiology data sets. Further, the claim of high extra-SWR synchrony is complicated by the observation that their recorded neurons fail to spike during the limited number of SWRs recorded during behavior- again, not agreeing with much of the previous electrophysiological recordings.

      We thank the reviewer for raising these important questions. To address the first question, it is possible that synchronous ensembles were not previously detected in extracellular recordings due to differences in detection methods or analysis approaches. To investigate this further, we analyzed a publicly available dataset (hc-11 on CRCNs), which contains hippocampal recordings from rats navigating novel mazes for water rewards. Using our algorithm, we detected robust synchronous ensembles in the dataset (Supplementary Figure 5). The rates of synchronous events were significantly higher than those in jittered controls, demonstrating the reliability and generalizability of these synchronous ensembles.

      Results (line 366)

      “To further investigate synchronous ensembles across different datasets, we analyzed publicly available hippocampal recordings ‘hc-11’ from the CRCNS repository, where rats navigated novel mazes for water rewards (see Method). Using our algorithm, we identified a significant number of synchronous ensembles during the first three minutes of novel navigation. On average, the rates of synchronous events were 6.4-fold higher than those detected in jittered controls (mean event rate: 2.0 ± 0.3 Hz for the original data vs. 0.32 ± 0.03 Hz for jittered data, n \= 8 sessions, p \= 0.0078, W \= 36, Wilcoxon signedrank test; Supplementary Figure 5A). To assess whether ripple oscillations were associated with these synchronous ensembles, we analyzed ripple event rates and their relationship to population synchrony. During this period, ripple events were infrequent (mean ripple rate: 0.02 ± 0.01, n \= 8 sessions), and ripple power peaked during ripple episodes but remained low at the timings of population synchrony (Supplementary Figure 5B). Nevertheless, LFP traces aligned to population synchrony revealed prominent theta oscillations (Supplementary Figure 5C). Synchronous ensembles were modulated by LFP theta oscillation (modulation strength: 0.30 ± 0.04, n \= 8 sessions, p < 0.001), and the timings of individual ensembles were consistently locked to the preferred phase of each session, suggesting a functional coupling of synchronous ensembles to theta oscillations important for information processing (Supplementary Figure 5D).”

      To address the second question, we conducted new experiments under conditions optimized for ripple generation. Specifically, we recorded neurons in mice head-fixed in a novel environment, resulting in 174 ripple episodes across six sessions. Consistent with our original findings, spiking rates were significantly suppressed and membrane potentials were hyperpolarized during ripples (Figure 4Ai-Ci of the manuscript). Despite this suppression, the same neurons exhibit rich synchronous activities outside of ripples (Figure 3-figure supplement 3 of the manuscript). These results confirm that these synchronous ensembles are distinct from ripple-related neuronal activity and strengthen our claim that the observed synchronous ensembles represent a distinct physiological phenomenon, consistent across different datasets and experimental conditions.

      Results (line 251)

      “It was puzzling that these CA1PCs exhibited robust spiking activities outside of ripples yet generated few spikes during ripples. To further investigate neuronal activities during ripples, we established a recording condition that allowed us to capture more ripple episodes. Specifically, we immobilized mice in a tube to promote behaviors favoring ripple generation. The mice were habituated to head fixation in a tube in a room distinct from the one where imaging experiments were conducted. On the imaging day, the mice were introduced to the recording room and head-fixed under the microscope for the first time.

      CA1PCs were labeled in utero on embryonic day (E) 14.5 (n\=56 cells from 3 sessions in 3 mice) and E17.5 (n\=55 cells from 3 sessions in 3 mice) and imaged in adult brains. Both neuronal populations exhibited prominent peaks in their grand average CCGs and significantly higher synchronous event rates compared to jittered data (Figure 3-figure supplement 2A, B). Approximately 40% of the recorded neurons participated in synchronous ensembles, indicating robust synchronous activity involving a substantial proportion of the recorded cells (Figure 3-figure supplement 2C).

      In total, 1052 synchronous ensembles and 174 ripple episodes were detected across these imaging sessions. Consistent with findings from walking animals, few synchronous ensembles occurred during ripples when animals were immobilized in a tube (Figure 3-figure supplement 3A, B). Moreover, no distinguishable ripple oscillations were observed in synchronous events, and the average firing rates during ripple episodes were near zero (Figure 3-figure supplement 3C, D). At the single-cell level, 90% of neurons showed significant negative spiking modulation during ripples, with ripple modulation indexes close to -1, indicating strong suppression of spiking (Figure 4Ai). This suppression extended to subthreshold membrane potentials, as nearly all cells exhibited decreased fluorescence during ripples compared to baseline (Figure 4Bi, Ci). These results demonstrate that spiking activity and subthreshold membrane potentials are robustly suppressed during ripples.”

      (2) The authors posit that these events are linked to the novelty of the context, both in the text, as well as in the title and abstract. However, they do not include any imaging data from subsequent days to demonstrate the failure to see this synchrony in a familiar environment. If these data are available it would strengthen the proposed link to novelty if they were included.

      Following the reviewer’s suggestion, we record neuronal activities in a familiar context to test the proposed link between synchronous activity and contextual novelty. We found that synchronous activity levels were significantly lower in the familiar context compared to the novel context, demonstrating that synchronous activity is strongly modulated by contextual novelty (Figure 4D of the manuscript). These findings provide further support for a link of the synchronous ensembles to novel environmental contexts.

      Result (line 277)

      “Contextual novelty plays a critical role in shaping hippocampal neuronal activities. To assess its influence, we trained mice to become familiar with the imaging procedure and the recording environment over five days and recorded CA1PC activities on the final day. Mean firing rates were significantly reduced in the familiar group compared to the novel group (Familiar group:

      1.1 to 5.2 Hz (25<sup>th</sup>-75<sup>th</sup> percentiles), median=2.3 Hz, n\=66 cells, 6 sessions, 4 mice; Novel group: 1.7 to 6.0 Hz (25<sup>th</sup>-75<sup>th</sup> percentiles), median=4.2 Hz, n\=111 cells, 6 sessions, 6 mice, p\=0.0083, Wilcoxon signed-rank test). Additionally, 15% of the neurons in the familiar group exhibited significantly positive spiking modulation by ripples, while fewer cells showed negative modulation compared to the novel group (Figure 4A). During ripples, neurons in the novel group predominantly displayed hyperpolarizing membrane voltage responses, whereas a subset of neurons in the familiar group exhibited prominent depolarizing responses (Figure 4B). The mean fluorescence changes in the familiar group shifted toward depolarization compared to the novel group (Figure 4C). Finally, synchronous event frequencies were significantly lower in the familiar context, indicating weaker synchronous activities under familiar conditions (Figure 4D). These results demonstrate that hippocampal neuronal activities, particularly synchronous ensembles, are strongly influenced by contextual novelty.”

      (3) In the discussion the authors begin by speculating the theta present during these synchronous events may be slower type II or attentional theta. This can be supported by demonstrating a frequency shift in the theta recording during these events/immobility versus the theta recording during movement.

      We thank the reviewer for the suggestion. As the reviewer points out, we did observe a frequency shift in synchrony-associated theta during immobility compared to locomotion (see Figure 5B, red vs. blue curves). We have now highlighted this result in the discussion section. Please refer to the text below.

      Discussion (line 471)

      “On the other hand, type 2 theta, or attentional theta, is slightly slower and is blocked by muscarinic receptor antagonists, emerging during states of arousal or attention, such as when entering a new environment. Consistent with these distinctions, the peak of the power spectrum density shows a distinctively slower theta frequency during immobility compared to locomotion (Figure 5B).”

      (4) The authors mention in the discussion that they image deep-layer PCs in CA1, however, this is not mentioned in the text or methods. They should include data, such as imaging of a slice of a brain post-recording with immunohistochemistry for a layer-specific gene to support this.

      We thank the reviewer for the constructive suggestion. In response, we have added images of slices from both E14.5 and E17.5 brains and analyzed soma locations along the radial axis of the pyramidal layer. The results are included in the main text, Methods, and Supplementary Figure 1 of the manuscript (see below).

      Result (line 96)

      “Consistent with previous studies, neurons labeled on E14.5 located more on the deep side of the pyramidal layer than those labeled on E17.5 (t<sub>(601)</sub>=22.8, p<0.0001, Student’s t-test; Supplementary Figure 1C, D).”

      Methods (line 563)

      “The injection resulted in Cre expression among neurons born on the day of injection, with earlier injection labeling neurons located on the deeper side of the cell layer.”

      Reviewer #3 (Public Review):

      Summary:

      In the present manuscript, the authors use a few minutes of voltage imaging of CA1 pyramidal cells in head-fixed mice running on a track while local field potentials (LFPs) are recorded. The authors suggest that synchronous ensembles of neurons are differentially associated with different types of LFP patterns, theta and ripples. The experiments are flawed in that the LFP is not "local" but rather collected in the other side of the brain, and the investigation is flawed due to multiple problems with the point process analyses. The synchrony terminology refers to dozens of milliseconds as opposed to the millisecond timescale referred to in prior work, and the interpretations do not take into account theta phase locking as a simple alternative explanation.

      We appreciate the reviewer’s feedback and acknowledge the concerns raised. However, we believe these concerns can be effectively addressed without compromising the validity of our conclusions. With this in mind, we respectfully disagree with the assessment that our experiments and investigation are flawed. Please allow us to address these concerns and offer additional context to support the validity of our study.

      Weaknesses:

      The two main messages of the manuscript indicated in the title are not supported by the data. The title gives two messages that relate to CA1 pyramidal neurons in behaving head-fixed mice: (1) synchronous ensembles are associated with theta (2) synchronous ensembles are not associated with ripples.

      There are two main methodological problems with the work: (1) experimentally, the theta and ripple signals were recorded using electrophysiology from the opposite hemisphere to the one in which the spiking was monitored. However, both signals exhibit profound differences as a function of location: theta phase changes with the precise location along the proximo-distal and dorso-ventral axes, and importantly, even reverses with depth. And ripples are often a local phenomenon - independent ripples occur within a fraction of a millimeter within the same hemisphere, let alone different hemispheres. Ripples are very sensitive to the precise depth - 100 micrometers up or down, and only a positive deflection/sharp wave is evident.

      We acknowledge the reviewer’s consideration regarding the collection of LFP from the contralateral hemisphere. While we acknowledge the limitation of this design, we believe these contralateral LFP recordings still provide valuable insights into the dynamics of synchronous ensembles. Despite potential variations in theta phases due to differences in recording locations and depths, the occurrence and amplitudes of theta oscillations are generally wellcoordinated across hemispheres (Buzsaki et al., 2003, Fig 5)(3). The presence of prominent contralateral LFP theta activity around the times of synchronous ensembles in our study (Figure 5A of the manuscript) strongly supports our conclusion about their association with theta oscillations, even with LFP collected from the opposite hemisphere.

      Additionally, we explicitly noted in the manuscript that the “preferred phases” varied between sessions, likely reflecting variability in recording locations (see below). Thus, we believe the concern about theta phase variability has already been adequately addressed in the current manuscript.

      Result (line 321)

      “Although the preferred phases varied from session to session due to differences in recording sites along the proximal-distal axis of the hippocampus, the timings of individual ensembles were consistently locked to the preferred phase of each session (Figure 5C).”

      While we acknowledge that ripple oscillations can sometimes occur locally, the majority of ripples occur synchronously in both hemispheres (up to 70%)(3,26), as demonstrated both in the literature (Szabo et al., 2022, Supplementary Figure 2) and by data from our lab (Huang et al., 2024, Figure S6). As a result, using contralateral LFP to infer ripple occurrence on the ipsilateral side is a well-established practice in the field, commonly employed by many studies published in reputable journals(26-29). Given the high co-occurrence of both theta and ripple oscillations across hemispheres, we maintain that the two main messages of our manuscript are supported by data, despite the concern regarding phase discrepancy mentioned by the reviewer.

      (2) The analysis of the point process data (spike trains) is entirely flawed. There are many technical issues: complex spikes ("bursts") are not accounted for; differences in spike counts between the various conditions ("locomotion" and "immobility") are not accounted for; the pooling of multiple CCGs assumes independence, whereas even conditional independence cannot be assumed; etc.

      We acknowledge the reviewer’s concern regarding spike train analysis. Complex bursts or differences in behavioral conditions can indeed lead to variations in spike counts, which could potentially affect the detection of synchronous ensembles. However, our jittering procedure is specifically designed to account for variations in spike counts. Notably, while the jittered spike trains retain the same spike count variations, we observed 7.8 times more synchronous events in our data compared to the jitter controls (Figure 1G of the manuscript). This indicates that the specific spike timings in the original data - disrupted in the jitter data – are responsible for the observed synchrony.

      To further address the concern that complex bursts might influence the observed synchrony, we performed additional analyses in which we excluded all later spikes in bursts, considering only single spikes and the first spikes of bursts. Importantly, this procedure did not affect the rate or size of synchronous ensembles and did not significantly alter the grand-average CCG (Supplementary Figure 3). These results explicitly demonstrate that complex bursts do not significantly impact the analysis of synchronous ensembles.

      Result (line 131)

      The observed population synchrony was not attributable to spikes in complex bursts, as synchronous event rates did not differ significantly with or without the inclusion of later spikes in bursts (Supplementary Figure 3).

      Beyond those methodological issues, there are two main interpretational problems: (1) the "synchronous ensembles" may be completely consistent with phase locking to the intracellular theta (as even shown by the authors themselves in some of the supplementary figures).

      We agree with the reviewer that the synchronous ensembles are indeed consistent with theta phase locking. However, it is important to note that theta phase locking alone does not necessarily imply population synchrony. In fact, previous research has demonstrated that theta phase locking can “reduce” population synchrony(30). Thus, the presence of theta phase locking cannot be considered a simple alternative explanation for the synchronous ensembles.

      The idea that theta phase locking does not necessarily lead to population synchrony is illustrated in Author response image 1A. In this example, while all three neurons are perfectly locked to specific theta phases, no synchrony among neurons is evident. In contrast, our data align with the scenario depicted in Figure 4B, where spikes occur not only at specific theta phases but also in the same cycles, thereby facilitating population synchrony.

      Author response image 1.

      Illustrative diagram of the relationship between theta phase coupling and population synchrony. Illustration of theta phase coupling with low population synchrony. Illustration of population synchrony with theta phase coupling.

      To directly assess the contribution of theta phase locking to synchronous ensembles, we performed a new analysis in which the specific theta cycles during which neurons spike were randomized while keeping the spike phases unchanged. This manipulation disrupts spike co-occurrence while preserving theta phase locking, allowing us to test whether theta phase locking alone can explain the population synchrony. We found that theta-cycle randomization significantly reduced the rate of synchronous events by 4.5 folds (Supplementary Figure 4). This new analysis demonstrates that theta phase locking alone cannot account for the population synchrony observed in our data.

      Result (line 358)

      “Correlated intracellular theta and theta-phase locking of the synchronous ensembles raise the question of whether population synchrony among CA1PCs extends beyond synchrony derived from these effects. To address this, we analyzed population synchrony after randomizing the theta cycles during which neurons spiked, while keeping their theta phases unchanged. Supplementary Figure 4 illustrates a significant reduction in synchronous event rates following theta cycle randomization. The finding indicates spiking at specific theta cycles plays a major role in driving population synchrony.”

      (2) The definition of "synchrony" in the present work is very loose and refers to timescales of 20-30 ms. In previous literature that relates to synchrony of point processes, the timescales discussed are 1-2 ms, and longer timescales are referred to as the "baseline" which is actually removed (using smoothing, jittering, etc.).

      Regarding the timescale of synchronous ensembles, we acknowledge that it varies considerably across studies and cell types. However, it is important to note that a timescale of dozens or even hundreds of milliseconds is commonly used in the context of synchrony terminology for CA1 pyramidal neurons(6,31-33). In fact, a timescale of 20-30 ms is considered particularly important for information transmission and storage in CA1, as it aligns with the membrane time constant of pyramidal neurons, the period of hippocampal gamma oscillations, and the time window for synaptic plasticity. Therefore, we believe this timescale is highly relevant and consistent with established practices in the field.

      Reviewer #3 (Recommendations For The Authors):

      (1) L19-20: "these synchronous ensembles were not associated with ripple oscillations" - this is a main fallacy in the present work (ripples are from the other side; there are not enough ripples to obtain sufficient statistical power to even test the hypothesis; etc.). The sentence should be removed.

      As we have addressed in the public review, most ripples occur synchronously in both hemispheres(3,26). Many studies have used contralateral LFP to infer ripple occurrence on the ipsilateral side(26-29). Moreover, our new data now support the dissociation between synchronous ensembles and ripples with a much larger number of ripples and rigorous statistical testing (Figure 3-figure supplement 3 of the manuscript). These findings support our conclusion that synchronous ensembles are not associated with ripple oscillations.

      Result (line 266)

      “In total, 1052 synchronous ensembles and 174 ripple episodes were detected across these imaging sessions. Consistent with findings from walking animals, few synchronous ensembles occurred during ripples when animals were immobilized in a tube (Figure 3-figure supplement 3A, B). Moreover, no distinguishable ripple oscillations were observed in synchronous events, and the average firing rates during ripple episodes were near zero (Figure 3-figure supplement 3C, D). At the single-cell level, 90% of neurons showed significant negative spiking modulation during ripples, with ripple modulation indexes close to -1, indicating strong suppression of spiking (Figure 4Ai). This suppression extended to subthreshold membrane potentials, as nearly all cells exhibited decreased fluorescence during ripples compared to baseline (Figure 4Bi, Ci). These results demonstrate that spiking activity and subthreshold membrane potentials are robustly suppressed during ripples.”

      (2) L135/Figure 1: panel C and elsewhere: show the same traces after removing (clipping) the spikes. You may be able to see the intracellular theta nicely, which may be very strongly synchronized between neurons and could then be supplemented by ticks (as in conventional raster plots). This will allow a clearer visualization of the spiking and their relations with Vm.

      We have created the plot as suggested (Author response image 2). As demonstrated in our figures (Figure 5 in the manuscript), the subthreshold membrane potentials of individual neurons are strongly correlated and coherent at theta frequency, consistent with the reviewer’s viewpoint.

      Author response image 2.

      Fluorescence traces of 19 simultaneously recorded cells with truncated spikes replaced by dots. Horizontal scale bar: 25 ms; vertical scale bar: -3%.

      (3) Related to the above comment, in general, a much more robust approach with the present dataset may be to derive an estimate of the LFP from the intracellular records. Extracellular theta is related to intracellular theta (approximately the negative), and extracellular ripples co-occur with intracellular high-frequency oscillations. However, because the precise transfer function (TF) between the two is not well established, ground truth data should first be collected. This may be done by voltage imaging of even a single neuron in parallel with an extracellular glass pipette placed in near proximity of the same cell, at the same depth. Such datasets have been collected in the past, so it may be sufficient to contact those authors and derive the TF from existing data. Alternatively, new experiments may be required. It is possible that the TF will not be well defined - in which case there are two options: (1) limit the analyses to the relation between spikes in Vm, or (2) record new datasets with true LOCAL field potentials in every case.

      We thank the reviewer for the insightful suggestion. Establishing a precise TF between intracellular and extracellular recordings is indeed crucial when exact phase information is required to draw conclusions. However, our goal is to understand the occurrence of specific network oscillation states surrounding these synchronous ensembles, rather than pinpointing the precise phase at which they occur. Therefore, we believe that the strong bilateral cooccurrence of both theta and ripple oscillations provides a practical and valid foundation for supporting our objective.

      While the approach suggested by the reviewer is an excellent idea, conducting simultaneous voltage imaging and local LFP recording is currently not feasible due to technical constraints associated with the implanted glass windows. Nevertheless, we recognize the potential value of this approach and plan to incorporate it into future experimental designs, which could provide further insights into the specific oscillatory phases associated with population synchrony.

      (4) L135/Figure 1: panel D and elsewhere: Account for second-order spike train statistics (e.g., bursts). The simplest way to do this is to remove all spikes that are not the first spike in a burst. Otherwise, the zero-lag bin of a pair-wise CCG will be filled with counts that are due e.g., to the first spike of the second neuron co-occurring with the last spike in a burst of the first neuron. In other words, without accounting for bursts, sequential activity may be interpreted as synchrony.

      We thank the reviewer for this insightful comment. As recommended, we have performed the suggested analysis by removing all spikes that are not the first spike in a burst (Supplementary Figure 3). The results demonstrate that, even after removing the subsequent spikes in bursts, the rates of synchronous events remain unchanged compared to the original data, and the sizes of the synchronous ensembles are also unaffected. These findings indicate that our conclusions are robust and not confounded by the presence of later spikes within bursts.

      Result (line131)

      “The observed population synchrony was not attributable to spikes in complex bursts, as synchronous event rates did not differ significantly with or without the inclusion of later spikes in bursts (Supplementary Figure 3).”

      (5) L135/Figure 1: panel D and elsewhere: Related to the previous comment: the "grand average" CCG of a single neuron with all the other simultaneouslyrecorded neurons is prone to a peak at zero lag ("synchrony") even if all pairs of neurons have pure mono-synaptic connections (e.g., at a 2 ms time lag). This is because neuron1 (N1) may precede N2, whereas then N3 may precede N2. In such a case, the pooled CCG will have two peaks - at e.g., 2 ms and -2 ms. However, if bursts occur (as is the case in CA1 and Figure 1C), there will also be non-zero counts around zero lag, which will accumulate as well. Together, these will build up to a peak around zero - even without any theta phase locking or any other alternative correlations.

      Please see our reply to comment #6 below.

      (6) L135/Figure 1: panel D and elsewhere: refrain from averaging "grand averages" over neurons. This problem is distinct from the above (where e.g., N2-N1 is averaged with N2-N3). In any case, all visualizations and measures should be derived from individual (pair-wise) CCGs, and not "grand averages"

      We thank the reviewer for the detailed comments and appreciate the opportunity to clarify our methods and analyses related to population synchrony. In response to the suggestion to replace grand average CCGs with pairwise CCGs, we have now included a heatmap to visualize individual pairwise CCGs for all recorded neuronal pairs that meet our inclusion criteria (497 pairs, Author response image 3). The heatmap provides a comprehensive view of the temporal relationships between neuron pairs.

      Author response image 3.

      Color-coded plot of pairwise CCGs for all cell pairs that meet our inclusion criteria.

      While we have chosen to keep the grand-average CCGs, we emphasize that they are served only to summarize the overall temporal scale of the population synchrony. Importantly, our conclusions regarding synchronous ensembles are not based on grand-average CCGs. Instead, we assess population synchrony using a rigorous approach: we compute spike counts across the population in 25-ms sliding windows and compare these counts to those derived from jittered data, where spike timings are randomly shifted by ±75 ms while preserving the overall spike count distribution. Synchrony is identified when the original spike counts exceed those from the jittered data by more than 4 standard deviations. This approach accounts for the potential accumulation of zero-lag counts arising from mixed mono-synaptic connections or bursting, as noted by the reviewer. By perturbing spike timings and preserving spike count distributions, our method identifies synchrony beyond what is expected by chance, ensuring robust and artifact-free conclusions.

      (7) L135/Figure 1: panel D and elsewhere: after deriving measures (peak lag, FWHM, synchrony strength, etc.) from individual pairwise CCGs, show the measures as a function of the spike counts. For a pair of neurons N1-N2, derive the geometric mean spike count (or the mean, or the max). For instance, if there are 500 pairs of neurons, show e.g., pairwise synchrony strength as a function of the spike count geometric mean. While little correlation is expected when the timescale is small (1-2 ms), the "synchrony" effect at a timescale of 20-30 ms is expected to be very strongly related to the spike counts. Because the spike counts may differ between the lower and higher speed "states", many results reported in the present manuscript may be an epiphenomenon of that relationship.

      We thank the reviewer for these valuable comments. In response, we analyzed pairwise synchronization strengths as a function of spike counts geometric mean of neuron pairs, as suggested. As shown in Author response image 4, the CCG peak counts in the original data (red dots) increase with the spike count geometric mean, consistent with the expected trend. However, this trend is also captured by the jitter control (black dots), which reflects synchrony levels expected by chance given the spike count levels.

      Importantly, the normalized synchronization strengths - defined as the ratio of CCG peak counts in the original data to the jitter control – are not positively correlated with spike counts and remain significantly greater than 1 across all spike count levels (Author response image 5). This demonstrates synchrony beyond what could be explained by spike count variations alone.

      While we understand the potential influence of state-dependent spike count variations, our jittering approach effectively controls for this by removing chance-level synchrony that could arise from these variations. This ensures that the observed synchrony reflects genuine neuronal interactions rather than an epiphenomenon of spike count variations between states.

      Author response image 4.

      Plot of peak spike counts of pairwise CCGs (red) and mean spike counts from jittered data (black) against geometric means of pair spike counts.

      Author response image 5.

      Plot of normalized synchronization strengths against spike count geometric means.

      (8) L135/Figure 1: show all CCGs in a color matrix.

      We have generated a color matrix visualization of all pairwise CCGs, as recommended (Author response image 3). This visualization highlights the consistency of our results across neuron pairs.

      (9) L168/Figure 2: the LFPO is nearly irrelevant - it is from the other hemisphere, and it is unclear whether the depth is the same as in the "deep" (closer to the brain surface) imaging plain used for the voltage recordings.

      As previously explained, the LFPO is relevant because it reveals the occurrence of theta and ripple states, which are highly synchronous across both hemispheres and serve as reliable indicators of network states relevant to our findings.

      (10) L222/Figure 3: The ripple-related analyses are completely irrelevant - ripples are a local phenomenon, and recording from the other hemisphere is completely irrelevant.

      We thank the reviewer’s suggestions. As we have explained in the public review, as well as in the reviewer’s comments #1 and #3, the occurrences of theta and ripple oscillations are well-coordinated across hemispheres. As our analyses only depend on the occurrences of these oscillations, our conclusions regarding the association of the synchronous ensembles with theta but not ripple oscillations are supported by data.

      (11) L292/Figure 4, panels A-E: please trigger Vm on the same-neuron spikes, not on the "synchrony events". This will already explain most of the observations. Some of this is already shown in the supplementary figures.

      As the reviewer correctly noted, we have already presented data triggered on same-neuron spikes in Figure 5-figure supplement 1C and D. The reason we show synchrony-triggered LFP and subthreshold Vm in the figure is to highlight the network dynamics during synchronous events. This approach provides a broader perspective on how neural networks function and interact during periods of synchrony, offering insights beyond individual neuron activity

      (12) L351/Figure 5, panel C: typo - should read "strength"

      The typo has been corrected.

      (13) L351/Figure 5: show "spatial tuning correlation" vs. inter-soma distance (as in Fig. 4G). This may explain part (if not all) of the observations

      We have followed the reviewer’s suggestion and generated the plot (Author response image 6). Consistent with the literature, the plot demonstrates that the spatial tuning correlations of place cell pairs exhibit little relationship with their inter-soma distances.

      Author response image 6.

      Plot of spatial tuning correlation vs. inter-soma distance (Spearman correlation coefficient=0.06, p\=0.54, n\=91 pairs).

      (14) L937/Figure S3: panel A: the ripples here appear to be recorded from the top part of the layer, i.e., the electrode is not in the center of the layer. Panel B: add statistical testing.

      We agree with the reviewer that this is possible, as we aimed to place our LFP electrodes in the stratum pyramidale. Regarding panel B of the figure, we verified the quality of LFP recordings by acquiring data from subsequent sessions following the initial imaging sessions. The detection of ripples in the same animals during these later sessions indicates that the absence of ripples during the first sessions is not due to deterioration in LFP recording quality. However, due to the small sample size, the statistical power is insufficient to demonstrate significance (n\=5 sessions, p\=0.06, Wilcoxon signed-rank test). Nevertheless, our conclusions are not contingent upon achieving statistical significance in this test.

      (15) L944/Figure S4: The "R=1" is very likely to be an outcome of n=1 spike. In other words, estimates of phase are unreliable when the spike count is very low. This is related to the problem referred to in Comment #7 above.

      We understand that phase estimates can be unreliable when the spike counts are low. We now highlight that this effect has been taken into account by a shuffling procedure that assesses the significance of phase modulation, and by excluding neurons with nonsignificant modulation strengths. Neurons with low spike count or inconsistent spike phases are typically excluded due to the non-significant strength of phase modulation.

      Method (line 828)

      “The significance of the modulation strength was tested by shuffling the spike timings and recalculating the modulation strength a thousand times to generate a distribution based on the shuffled spike timings. The original modulation strength was then compared to the distribution, with significance determined if it exceeded the 95% confidence interval of the shuffled values.

      Significant modulation strengths were plotted and compared across groups.”

      (16) L944/Figure S4: Putting the spike count issue (Comment #15) aside for a moment, the analyses in this figure are actually valid - they are carried out at the single-neuron level, with respect to the local (same-neuron) Vm. These findings provide a key alternative explanation to the observations purported in the main figures: (1) if spiking is locked to intracellular theta (occurring at the peak of Vm); and if (2) intra-cellular (Vm) theta is locked to extracellular theta (antiphase); and if (3) extracellular theta is similar for nearby neurons (the imaged neurons), then synchrony is a necessary outcome. The key question is then whether there is any EXTRA synchrony between the CA1PC - beyond that which necessarily derives from (1)+(2)+(3).

      We acknowledge the reviewer’s perspective. However, the factors (1)+(2)+(3) alone do not account for the synchrony we observed. As the reviewer points out (and as discussed in our response to the public review and in Supplementary Figure 4), theta phase locking does not necessarily imply population synchrony. To demonstrate that population synchrony extends beyond the contribution of (1)+(2)+(3), we performed an analysis where the theta cycles in which neurons spike were randomized, while the theta phases remained unchanged (Supplementary Figure 4). The analysis revealed that randomizing the theta cycles while preserving theta phases significantly reduces population synchrony. This finding indicates that spiking in specific theta cycles plays a major role in driving population synchrony.

      Result (line 358)

      “Correlated intracellular theta and theta-phase locking of the synchronous ensembles raise the question of whether population synchrony among CA1PCs extends beyond synchrony derived from these effects. To address this, we analyzed population synchrony after randomizing the theta cycles during which neurons spiked, while keeping their theta phases unchanged. Supplementary Figure 4 illustrates a significant reduction in synchronous event rates following theta cycle randomization. The finding indicates spiking at specific theta cycles plays a major role in driving population synchrony.”

      (17) L944/Fig. S4: Why 71 neurons in AB and only 59 in CD?

      In the previous version, panels A and B included 71 neurons, as we collected data from 71 cells across 5 mice (see the text below).

      Result (line 93)

      “…in total, 71 cells imaged from 5 fields of view in 5 mice; Figure 1B and

      Supplementary Figure 1A and 1B).”

      In the current version, we only include neurons with significant modulation strengths, reducing the number of cells from 71 to 65 in panel A and from 71 to 54 in panel B.

      Methods (line 828)

      “The significance of the modulation strength was tested by shuffling the spike timings and recalculating the modulation strength a thousand times to generate a distribution based on the shuffled spike timings. The original modulation strength was then compared to the distribution, with significance determined if it exceeded the 95% confidence interval of the shuffled values. Significant modulation strengths were plotted and compared across groups.”

      “Figure 5-figure supplement 1 Figure legend (line 1231)

      Polar plot comparing subVm theta modulation between spikes participating in synchronous ensembles (sync spikes) and spikes not participating in synchronous ensembles (other spikes) during immobility. Each dot represents the averaged modulation of a cell. Cells with modulation strengths that are not significant are excluded in the plot and in the comparison.”

      For panels C and D, we excluded neurons with four or fewer triggering events from the analysis, which reduced the number of cells from 71 to 59 (see the second text paragraph below).

      Method (line 835)

      “We extracted segments of fluorescence traces using a ±300 ms time window centered on the spike timings. To examine variations in fluorescence waveforms triggered by spikes within and outside synchronous events, we categorized the fluorescence traces based on whether the spikes occurred within or outside these events. Subsequently, we performed pairwise comparisons of the fluorescence values from the same neuron, concentrating on spikes occurring during corresponding behavioral states. Neurons with four or fewer triggering events in any of these categories were omitted from the analysis.”

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    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer 1:

      The main weaknesses of the paper are a lack of significance in key findings, and relatedly, concluding effects from insignificant findings. Additional elements could be improved to help strengthen this overall well-rounded and intriguing set of results.

      In the original manuscript, we reported that chemogenetic silencing of POA-social neurons (previously called POA-iso neurons; more details on rationale for renaming below in our responses to reviewer recommendations) tended to reduce mounting in both single-housed female and single-housed male mice, although these effects were non-significant. We have added samples to both datasets and now report that chemogenetic silencing of POA-social neurons significantly reduces the proportion of trials with mounting in both sexes (Fig. 2C and Fig. 6G). 

      We have also included new analyses to test whether optogenetic activation of POAsocial neurons in group-housed females promotes social investigation (in addition to USV production, as reported in the original manuscript). We now report that optogenetic activation of POA-social neurons significantly increases the probability of social investigation (Fig. 4E-F) and significantly increases the duration of social investigation bouts (Fig. 4G). 

      Additional recommendations from the reviewer are addressed in detail below. Thank you for your critical and insightful feedback.

      Reviewer 2:

      All the activity-dependent labeling experiments with TRAP mice, including the subsequent neural activity manipulation experiments (Figures 2, 3, 4, 5E-F), were conducted by labeling neurons only in socially isolated animals, not group-housed animals. The authors labeled neurons after 30-minute social interactions, raising the possibility that the labeled neurons simply represent a "social interaction/behavior population" (mediating mounting and USVs in females and males) rather than a set of neurons specific to social isolation.

      I strongly recommend including experimental groups that involve labeling neurons after 30minute social interactions in group-housed female or male mice and inhibit TRAPed neurons after social isolation or activate TRAPed neurons after group housing. If manipulating the grouphoused TRAP neurons has similar effects to manipulating the isolated TRAP neurons, it would suggest the current labeling paradigm is not isolating neurons specific to the effect of social isolation per se. Rather, the neurons may mediate more general social interaction or motivationrelated activities. Given the known role of POA in male mating behavior, a group-housed TRAP experiment in males with a female visitor is especially important for understanding the selectivity of the labeled cells.

      Without proper controls, referring to the labeled neurons as "POAiso" neurons is potentially misleading. The data thus far suggests these neurons may predominantly reflect a "POA social behavior" population rather than a set of cells distinctly responsive to isolated housing.

      We agree with the reviewer that the POA neurons we are studying regulate the production of social behaviors in females and males, rather than representing a set of cells distinctly responsive to single housing. To more clearly reflect our thinking, we have changed the name of the neurons from “POA-iso neurons” to “POA-social neurons”. Thank you for this helpful criticism.

      Our Fos data are consistent with the idea that the POA may regulate social behaviors in group-housed females (not just single-housed females). Namely, we found that counts of Fospositive POA neurons are significantly related to rates of social investigation (p = 0.01) and tend to be related to USV rates (p = 0.05) in group-housed females that engaged in same-sex interactions (Fig. S1C). We now include two new sets of experiments aimed at further testing the idea this idea. 

      First, we include 2 control groups in which TRAPing sessions were performed in grouphoused females following same-sex interactions. We find that chemogenetic silencing of grouphoused-TRAPed POA neurons fails to reduce social behaviors in females that are subsequently single-housed and given a same-sex social interaction (Fig. 5A-D), and that optogenetic activation of group-housed-TRAPed POA neurons fails to promote female social behavior (Fig. 5E-H). At face value, these findings do not support the idea that the POA contains neurons that regulate social behaviors in group-housed females.

      However, one important caveat is that group-housed females engage in low rates of social behaviors (low investigation time, no mounting, and few USVs), and thus TRAP-based labeling may not work efficaciously in these mice. There may be POA neurons that regulate social behaviors in group-housed females but that do not upregulate Fos following production of relatively low rates of social behaviors. To test this idea, we also include females in which POA neurons are chemogenetically silenced using a viral strategy that does not depend on activitydependent labeling. In this new experiment, we report that silencing of POA neurons significantly reduces USV production in group-housed females (Fig. 5J-L) and significantly reduces social investigation, mounting, and USV production when these same females are retested following single-housing (Fig. 5M-O). Together, these experiments suggest that the POA may regulate the production of social behaviors during same-sex interactions in group-housed females, but that these effects may be difficult to detect in some cases given the low rates at which group-housed females engage in social behaviors during same-sex interactions relative to single-housed females.

      Finally, we want to highlight an additional new dataset that supports the idea that POAsocial neurons regulate social behaviors, rather than encoding the “state” of social isolation. We now include a control group for the chemogenetic silencing of female POA-social neurons, in which females were single-housed but were not given a social interaction prior to 4-OHT treatment (N = 5 non-social controls). Rates of social behaviors were subsequently unaffected following CNO delivery in these females (Fig. S2D-G). These new data support the conclusion that POA-social neurons regulate the production of social behaviors, rather than encoding the state of social isolation. 

      Reviewer 3:

      While the authors should be commended for performing and reporting multiple circuit perturbation experiments (e.g., chemogenetics, ablation), the conflicting effects on behavior are hard to interpret without additional experiments. For example, chemogenetic silencing of the POA neurons (using DREADDs) attenuated all three behavioral measures but the ablation of the same POA neurons (using CASPACE) decreased mounting duration without impacting social investigation or USV production. Similarly, optogenetic activation of POA neurons was sufficient to generate USV production as reported in earlier studies but mounting or social investigation remained unaffected. 

      Do these discrepancies arise due to the efficiency differences between DREADD-mediated silencing vs. Casp3 ablation? Or does the chemogenetic result reflect off-manifold effects on downstream circuitry whereas a more permanent ablation strategy allows other brain regions to compensate due to redundancy? It is important to resolve whether these arise due to technical reasons or whether these reflect the underlying (perhaps messy) logic of neural circuitry. Therefore, while it is clear that POA neurons likely contribute to multiple behavioral readouts of social isolation, understanding their exact roles in any greater detail will require further experiments.

      We have added new analyses to consider the possibility that optogenetic activation of female POA-social neurons promotes social investigation. In the original manuscript, we analyzed the duration of social investigation bouts in POA-social-ChR2 females according to whether they overlapped with laser stimulation or whether they did not overlap. We realized that we made an error in this first analysis and inadvertently included social investigation bouts that occurred during the first 5 minutes of the social sessions, prior to any laser stimulation. Because these earlier bouts tend to be longer duration than later bouts, this mistake washed out the effect of laser stimulation on social bout duration. After correcting that error, we now report that optogenetic activation of female POA-social neurons lengthens social investigation bout duration (Fig. 4G). Inspired by this interesting finding, we also included analyses of the probability of social investigation following laser stimulation (Fig. 4E-F; excluding laser stimulations that were preceded by social investigation in the pre-laser baseline period). These analyses support the conclusion that optogenetic activation of POA-social neurons promotes both USV production and social investigation in group-housed females.  

      The majority of the females that we used in our TRAP2-based ablation experiments were heterozygous for TRAP2 (N = 11 of 15 POA-social-caspase subjects were TRAP2;Ai14 females), whereas all females used in our chemogenetic silencing experiments were homozygous for TRAP2. To test whether a more effective ablation of POA-social neurons might drive decreases in social investigation and USV production, we set up additional TRAP2 homozygous POA-social-caspase females and directly compare the effects of ablation between the two genotypes (Fig. S3; N = 11 hets in total and N = 9 homozygotes in total). These experiments revealed that effects on mounting were more pronounced following POA-social ablation in TRAP2 homozygotes vs. heterozygotes, but that neither group exhibited decreased social investigation or USV production following 4-OHT treatment.

      To ask whether caspase-mediated ablation in TRAP2 homozygotes was effective in eliminating neural activity associated with social behaviors in females, we performed Fos immunostaining in a subset of the POA-social-caspase TRAP2 homozygotes following a samesex interaction. We found that POA Fos expression was robustly reduced in these females relative to control group-housed and control single-housed females that also engaged in samesex interactions, down to levels seen in group-housed and single-housed females that did not engage in a social interaction (comparison shown in Fig. S3D; control female data same as in Fig. 1). Moreover, the remaining POA Fos in these TRAP2 homozygotes was no longer positively correlated to social investigation or USV production (Fig. S3E-F). Together, these findings lead us to favor the interpretation suggested by the reviewer below, that permanent ablation of POA-social neurons leads to compensation from other brain regions due to redundancy. In addition, our finding that optogenetic activation of POA-social neurons promotes both USV production and social investigation supports the idea that POA-social neurons directly regulate these behaviors. We agree with the reviewer that additional work is needed to understand the complex sex- and context-dependent role played by the POA in the regulation of mouse social behaviors.

      Recommendations for the Authors:

      Reviewer 1 Recommendations:

      (1) The largest issue is that many of the stated "key" behavioral findings are not statistically significant.

      (1a) Figure 2C is not significant and Figure 5G is not significant

      We have added N = 5 POA-social-hM4Di females, N = 3 POA-social-hM4Di males, and N = 3 POA-social-GFP males to the dataset. The decrease in mounting following chemogenetic silencing of POA-social neurons is now statistically significant in both sexes (p < 0.05 for both; see current Figs. 2C and 6G). We also simplified our statistical analysis of mounting in these experiments to consider the proportion of trials with and without resident-initiated mounting on saline vs. CNO days, using McNemar’s test for paired proportions. 

      (1b) Mounting graphs are completely omitted in Figure 4. 

      Given that mounting was only observed infrequently in POA-social-ChR2 females, we simply report this information in the Results text (lines 382-388). In our prior summary of the mounting results, we reported that mounting was observed in a total of 3 trials from 2 females, but we inadvertently included information from a duplicate trial from one of the POA-socialChR2 females in this summary (all other analyses of the POA-social-ChR2 females included one trial per female). We have corrected that error and now report that we observed mounting following laser stimulation in 1 trial from 1 POA-social-ChR2 female. We have expanded our consideration of potential effects of optogenetic activation of POA-social neurons on social investigation and include these new analyses as part of Figure 4 (Fig. 4E-G), following the existing analyses of USV production.

      (1c) Figure 3C shows a reduction of mounting following the ablation of POA (although no stats on the graph to denote significance), but this ablation approach can't resolve whether POA is required to encode the state produced by the short period of isolation, and/or whether it needs to be online at test.

      We have now added an asterisk in Fig. 3C to denote a p value less than 0.05. Thank you for catching our oversight.

      We designed our activity-dependent labeling experiments to TRAP and express viruses in POA neurons that increase their activity in conjunction with the production of social behaviors in single-housed females. We believe our findings our most consistent with the conclusion that these neurons regulate the production of social behaviors, rather than encoding the state of social isolation, and we have renamed these neurons as “POA-social” neurons to better reflect our thinking.

      We also now include control experiments (albeit chemogenetic inhibition, not caspase ablation) in which the TRAP2 strategy is used to express hM4Di in the POA of single-housed females that do not experience a social interaction prior to 4-OHT delivery (non-social controls, Fig. S2D-G). We report that chemogenetic inhibition of these neurons does not decrease social behavior in single-housed females during a subsequent same-sex interaction (p > 0.05 for saline vs. CNO rates of social investigation, mounting, and USVs). These additional findings support the idea that the activity of POA-social neurons is related to the production of social behaviors rather than to the state of social isolation. 

      The reviewer is correct that our ablation approach cannot resolve the question of whether POA-social neuronal activity is required online during testing, but our reversible chemogenetic inhibition experiments provide evidence that the activity of POA-social neurons is required online at the time of testing to regulate social behavior.

      (1d) A similar issue is seen regarding investigation (a general lack of significance with most of the LOF and GOF manipulations).

      As reported in the original manuscript, we find that chemogenetic inhibition of POAsocial neurons reduces social investigation in females, while caspase-mediated ablation of female POA-social neurons does not. Our original caspase dataset used mostly but not all TRAP2 heterozygous females (N = 11 TRAP2 heterozygotes (TRAP2;Ai14), generated by crossing TRAP2 mice with Ai14 mice, for the purpose of visualizing the absence of tdTomato labeling to estimate spread of the caspase virus; and N = 4 TRAP2 homozygotes). By adding to the TRAP2 homozygous caspase dataset and comparing the effects on female social behavior of ablation of POA-social neurons in TRAP2 heterozygous vs. TRAP2 homozygous females, we

      now provide evidence that the attenuation of mounting is more efficacious in TRAP2 homozygous females than in heterozygotes (Fig. S3B). Nonetheless, we fail to see effects on social investigation and USV production, even when caspase ablation of POA-social neurons is performed in TRAP2 homozygous females (Fig. S3A,C). 

      In spite of the lack of effect on these behaviors, we show that caspase-mediated ablation of POA-social neurons in TRAP2 homozygous females leads to a dramatic reduction in social interaction-induced Fos expression in the POA. POA Fos expression in these caspase females is reduced to the levels seen in control group-housed and single-housed females that are not given social interactions and are significantly lower than Fos expression in group-housed and single-housed females that are given a same-sex interaction (Fig. S3D). Moreover, the remaining POA Fos expression in the caspase females is no longer related to rates of social investigation (Fig. S3E), as is normally the case in group-housed and single-housed control females (Fig. S1C, left). Together, these data support the idea that some type of neuronal compensation outside of the POA is occurring following ablation of POA-social neurons, and this compensation permits normal levels of USV production and social investigation.

      As in the original manuscript, we report that chemogenetic inhibition of POA-social neurons in male mice reduces mounting but does not reduce social investigation (or USV production). We now include quantification of social behaviors produced by male and female POA-social-hM4Di mice in the TRAPing sessions that preceded 4-OHT delivery (Fig. S5). These measurements show that males spent significantly more time than females engaged in mounting, and we speculate that this bias in TRAPing session behavior might have led to a bias in TRAP-mediated viral labeling of male POA neurons that regulate mounting, at the expense of male POA neurons that regulate social investigation (or USV production).

      We have added new analyses to consider the possibility that optogenetic activation of female POA-social neurons promotes social investigation. In the original manuscript, we analyzed the duration of social investigation bouts in POA-social-ChR2 females according to whether they overlapped with laser stimulation or whether they did not overlap. We realized that we made an error in this first analysis and inadvertently included social investigation bouts that occurred during the first 5 minutes of the social sessions, prior to any laser stimulation. Because these earlier bouts tend to be longer duration than later bouts, this mistake washed out the effect of laser stimulation on social bout duration. After correcting that error, we now report that optogenetic activation of female POA-social neurons lengthens social investigation bout duration (Fig. 4G). Inspired by this encouraging finding, we also included analyses of the probability of social investigation following laser stimulation (Fig. 4E-F; excluding laser stimulations that were preceded by social investigation in the pre-laser baseline period). These analyses support the conclusion that optogenetic activation of POA-social neurons promotes both USV production and social investigation in group-housed females.

      (2) In Figure 1 and elsewhere, the authors use a Mann-Whitney U test, which should be used for non-parametric data, but in other places, they use statistical tests for normally distributed data. Why? How was the normality of distributions tested?

      We tested the normality of data distributions using the Shapiro-Wilk test. Parametric tests were used for analyses that contained normally distributed data, and non-parametric tests were used for analyses that contained non-normally distributed data. This information is included in the Methods (lines 997-1000), and full details of statistical analyses can be found in Table S1.

      (3) The method for "trapping" neurons that are part of the short-term isolation ensemble has some caveats that have not been adequately addressed. First, 4-OHT was administered after social interaction, but before 24 hours of isolation, making it unclear exactly WHAT is being trapped.

      i) Is it neurons that encode the recent 3-day iso experience? (seems unlikely, as this would have been hours after the end of that iso window)

      We now include a group of control females to directly test this possibility (Fig. S2D-G). These TRAP2 females were single-housed for 3 days but were not given a social interaction prior to 4-OHT treatment (N = 5 non-social controls). Presumably, POA neurons TRAPed in these females might encode the experience of short-term isolation. However, we found that chemogenetic inactivation of these TRAPed neurons during a subsequent same-sex interaction failed to decrease social behaviors in single-housed females (Fig. S2E-G; p > 0.05 for CNO vs. saline rates of social investigation, mounting, and USV production). These control experiments support the idea that we are TRAPing neurons whose activity is related to the production of social behaviors, and we have renamed the neurons as “POA-social” neurons to reflect this thinking.

      ii) Is it neurons that encode the recent behavior impacted by the 3-day iso? (this seems to be the goal, but the authors do not provide evidence that the time course of their injection is efficient enough to recruit the recently activated neurons, nor do they provide evidence that opening the trapping window directly after the behavior is better than directly before)

      We opted to perform IP injections of 4-OHT immediately following the behavior session, rather than behavior, due to concern that handling the mice and delivering IP injections prior to behavior sessions would stress the mice, leading to lower rates of social behaviors. The nonsocial female hM4Di experiments described above support the idea that we are TRAPing neurons related to the production of social behaviors, as the reviewer suggests. 

      iii) Is it trapping neurons active during the subsequent 24 hours of isolation? (seems possible, but this would mean that the authors are looking at a different population of neurons than they claim).

      If chemogenetic silencing of POA neurons that were TRAPed following 3-days of social isolation but in the absence of a social interaction (N = 5 non-social controls, Fig. S2D-G) does not alter social behaviors, there is no compelling reason to hypothesize that TRAPing POA neurons activated following the 24 hours of social isolation that follow a social interaction would do so. Moreover, in the original study characterizing the TRAP2 mice (DeNardo et al., 2019), the authors performed experiments to characterize the time course of TRAPing relative to 4-OHT treatment and concluded that the majority of TRAPing occurs within a 6-hour window centered around the 4-OHT injection.

      (4) Relatedly, the authors seem to find a fair bit of variability in their TRAP-mediated experiments. This begs the question - are the effects of their GOF and LOF approaches

      i) dependent on the iso-behaviors that were "trapped" for each animal (in other words, how does behavior at test 1 correlate with behavior at test 2)? 

      To test the reviewer’s idea, we compared rates of TRAPing session behaviors for the POA-social-hM4Di females to the subsequent effects of neuronal silencing on these behaviors (calculated as (CNO behavior – saline behavior). These correlations are shown in Fig. S2A-C and are all non-significant. We also include below for the reviewer the same types of correlations for the other datasets in our study (loss-of-function experiments: female POAsocial-caspase, male POA-social-hM4Di; and gain-of-function experiments: female POA-socialChR2).

      Author response image 1.

      The only loss-of-function experiment comparison in the above figure that reveals a negative and significant correlation is the mounting comparison for the POA-social-hM4Di males (time spent mounting during TRAPing session vs. (CNO time spent mounting -saline time spent mounting). This significant correlation likely reflects that fact that (1) no males mounted in the CNO session and (2) that mounting rates for individual males are relatively consistent over time (in comparison to female mounting, which is more variable; see Author response image 2 below of TRAPing session vs. saline mounting in male vs. female POA-social-hM4Di experiments). The correlation between TRAPing session and testing session mounting is significant for the POA-social-ChR2 females, but despite the significant correlation, we would want to see more instances of optogenetically-elicited mounting to make any claim about its relationship to TRAPing session behavior.

      Author response image 2.

      Nonetheless, we agree with the reviewer’s intuition that one would expect the effects of POA activity manipulations on different behaviors to scale with rates at which these behaviors were performed during the TRAPing session. We speculate that variability in the TRAPing process might have obscured such a relationship. There is inevitable variability in the exact body cavity placement of IP injections, which can affect drug absorption, and another point is that we delivered a fixed volume of 4-OHT (10 mg/mL 4-OHT in 150 uL filtered corn oil) to all mice in the study, regardless of their weight, which likely added variability in TRAPing efficacy from animal to animal. This detail was reported inaccurately in the Methods, and that error has been corrected (line 920). With regard to our male POA-social-hM4Di dataset, we find that these males spend more time mounting during their TRAPing sessions than female POA-socialhM4Di (Fig. S5; males also spent less time investigating and tended to produce fewer USVs than females), a fact that we hypothesize may have led to a bias toward TRAPing mountingrelated POA neurons in male subjects. In addition, however, the fact that male mice typically weigh more than females and would have received a slightly lower effective dosage of 4-OHT may also have contributed to the weaker effects on behavior in the male POA-social-hM4Di experiments relative to the female POA-social-hM4Di experiments.

      We also want to highlight that interpreting correlations for females between time spent mounting during the TRAPing session and time spent mounting during the test sessions can be complicated. For example, we see 2 cases in the female POA-social-hM4Di dataset in which the female did not mount in the TRAPing session, and then mounted on the saline day (12s and 10s total mounting for those 2 females) but not on the CNO day. One interpretation of the data from these 2 females is that mounting on the TRAPing day is not required to attenuate mounting on the later test days. However, female mounting behavior itself is variable, both across different females and across different tests of a given female, as noted above. If we consider all singlehoused females included in our dataset for which we quantified control behavioral data (i.e., behavior trials from unmanipulated females and TRAPing sessions from females that were later manipulated), we find that mounting is not observed in ~30% of the females (24 of 83). In ongoing behavioral experiments not included in this manuscript, we are investigating factors that regulate female mounting following single-housing. In that dataset, we also see little evidence that female mounting in one social interaction predicts mounting in a subsequent interaction

      (i.e., there don’t appear to stable “high mounters” and “low mounters” following single housing). Thus, the small number of cases in which females did not mount in the TRAPing session and then displayed mounting on the CNO only day are difficult to interpret. 

      Two additional considerations are that TRAPing may not be equally efficacious for POA neurons that regulate different behaviors, and that different behaviors may be differentially sensitive to perturbations of the POA. Previous elegant calcium imaging work has shown that different subsets of Esr1+ POA neurons exhibit activity that is “tuned” to specific behaviors (sniffing vs. mounting in males interacting with females; Yang et al., 2023). However, it is possible that these subsets of neurons display differential levels of Fos expression following the production of their preferred behavior and that some behavior-related subsets may thus be more easily TRAPed than others. It may also be the case that some behaviors are more easily disrupted by POA activity manipulations than others (e.g., perturbation in a smaller percentage of behavior-related POA neurons may be required to disrupt some behaviors relative to others). 

      Despite these caveats, we have two lines of evidence that the effects of chemogenetic silencing of POA-social neurons depends on the behaviors produced during the TRAPing sessions.

      (1) Social behavior is required during the TRAPing session to see subsequent effects on social behavior following chemogenetic silencing of TRAPed POA neurons. In control females that were single-housed but were not given a social interaction prior to 4OHT treatment, social behaviors are not reduced by chemogenetic silencing of TRAPed POA neurons (Figs. S2D-G).

      (2) To directly test whether mounting in the TRAPing session is required to see attenuation of mounting during subsequent chemogenetic silencing of POA-social neurons, we performed control experiments in which single-housed females interacted with a female visitor that was placed under a cup during the TRAPing session prior to 4-OHT treatment. Mounting was not possible in this context, and we also found that females produced lower rates of USVs during the TRAPing session relative to single-housed females engaged in free social interaction. However, subject females spent more time engaged in social investigation of the visitor relative to single-housed females engaged in free social interactions (see Author response image 3 below).

      Author response image 3.

      Unfortunately, none of the experimental females in this cohort displayed mounting in the CNO or saline sessions. Given that we could use this dataset to address the intended question, we did not include it in the manuscript. However, it is quite interesting that female subjects displayed higher than normal social investigation and lower than normal USV production in their TRAPing sessions (relative to single-housed females engaged in free interactions), and subsequently, chemogenetic inhibition of TRAPed POA neurons decreased social investigation but did not decrease USV production (Author response image 4 below). 

      Author response image 4.

      Together, we think our data support the idea that the POA neurons that are TRAPed are related to the social behaviors performed by the animals, but these relationships may be complex and difficult to detect from comparisons across animals within a single experimental group.

      And/or are they

      ii) influenced by the spread or amount of virus for each animal? These correlations could help shed light on what exactly is being trapped - is it specific behaviors or is it the "state" of shortterm isolation?

      Our control experiments with females that were single-housed but did not receive a social interaction prior to 4-OHT treatment provide evidence that the production of social behaviors is required to see subsequent effects on behavior following chemogenetic inhibition of TRAPed POA neurons (Figs. S2D-G).

      The same volume of virus was injected across all activity manipulation experiments (200 nL). Because of the trajectory of our POA viral injections (performed at a slight rostral angle relative to vertical), we did sometimes see viral labeling that spread into the AH caudal to the POA. For this reason, we included the AH TRAPed control group (Fig. 2), to rule out the possibility that viral spread into the AH could account for the effects of chemogenetic silencing of POA-social neurons on female social behaviors. Also because of the injection angle used, we don’t see substantial viral spread rostral to our injection coordinates. In short, there isn’t systematic variability in the targeting or spread of our POA viral injections that can account for variability in the effects on USV production and social investigation of our LOF and GOF manipulations (female hM4Di and female ChR2 experiments).

      In older lesion studies in male rodents and birds, there is some support for the idea that rostral vs. caudal POA neurons differentially regulate appetitive vs. consummatory sexual behaviors (as reviewed in Balthazart and Ball, 2007). However, all of our viral injections were placed in what that review paper would have considered ‘caudal’ POA. We also note that more recent imaging studies have reported that subsets of POA neurons are differentially tuned to male sniffing vs. male mounting (Yang et al.,2023), and these subsets must be relatively co-localized given that they are imaged in the same field of view. Whether distinct subsets of POA neurons regulate the production of different female social behaviors, and if so, how these subsets are localized within the POA, remains an important question for future study.

      (5) The authors label their region of interest as the "POA" but images throughout (e.g. their fos image, Figure 1E), look more like the MPO. Why label it POA?

      The POA neurons in our study are found in a band that spans the medial POA, as well as a bit of the lateral POA. To avoid over-specifying, we call this region the POA more generally.

      (6) In all the experiments, mice are isolated and then re-group housed with siblings. Do all the siblings in the group belong to the same experimental group, or are siblings naïve? This may be critical to help determine whether some of the effects observed may be "group" effects.

      In general, multiple (although not always all) mice in a cage belonged to the same experimental group. In our inhibitory DREADDs experiments, it is unclear how that could drive our observed effects on behavior, given that home cage behavior would only be expected to differ for a given mouse in the time period following their CNO session. 

      For the female POA-social-caspase mice, we cannot rule out the possibility that their home cage behaviors differed in the time period following 4-OHT treatment and re-grouphousing and prior to post-4-OHT behavior measurements. However, given that the only social behavior affected by ablation of POA-social neurons was mounting, and that rates of mounting would be expected to be very low in group-housed females within home cages, it is unclear how our experimental result could be attributed to group effects.

      If by “group” effects the reviewer means “litter” effects, we include a plot below that shows the CNO vs. saline behaviors for the POA-social-hM4Di females, separated by cage ID. There is no evidence that the effects of chemogenetic silencing of POA-social-hM4Di females are being driven by only certain cages (only social investigation and USVs are shown, because mounting was uniformly low (1 of 17 females mounted) in the CNO session).

      Author response image 5.

      (7) For chemogenetic experiments, the authors state that CNO and Saline were given in a counterbalanced order (eg line 189). Did the authors see any order effects?

      We did not see order effects, and we can include plots of those data below for the female and male POA-social-hM4Di groups, with mice plotted according to which treatment they received first.

      Author response image 6.

      (8) In the control experiments in Figure 2 where VMH or AH are chemogenetically silenced, it isn't clear whether these groups include mice that were subjected to 3 days of isolation. Please clarify.

      Yes, these female groups were also subjected to 3 days of isolation (first prior to the TRAPing session, and for a second time prior to the onset of the CNO/saline testing sessions). That information has been clarified in the Results section (line 214) and in the Methods (lines 935-938).

      (9) Line 312. The title for this section, "POA neurons increase their activity....." is somewhat misleading. It sounds like the authors imaged trapped neurons. I think what they mean is that more POA neurons are activated following opposite-sex interactions with males.

      Thanks for this catch. We have modified the section title, as well as the title of the first results sub-section.

      (10) Figure 5A, right panels. The authors fail to find an increase in the investigation of male-male pairs following the short-term isolation of one. This contrasts with the main finding in Matthews et al., 2016 Cell, where short periods of isolation are said to promote pro-social behaviors. The authors could comment on this discrepancy in their discussion (eg difference in testing apparatus/test type? Difference in the number of days of isolation? etc.).

      In current Fig. 6A, there is no significant interaction between the two main effects, but each main effect is significant: single-housed males spend more time investigating partners than group-housed males, and males spend more time investigating female partners than male partners. The significant main effect of housing condition is consistent with the findings of Matthews et al., 2016 and is included within the Results (lines 486-492). 

      (11) Figure 5F, the authors seem to have a main effect of virus (more overall investigation in dreadds mice). Nothing about this is addressed.

      We sometimes see differences in social behavior between cohorts of males when they are tested at different times and, correspondingly, with different groups of female social partners. Our POA-social-hM4Di and POA-social-GFP males were set-up and tested at largely non-overlapping times. We have added a brief note to the Results section to include this information (lines 535-539).

      Reviewer 2 Recommendations:

      (1) (C)ritical control experiments are missing to support this claim (that a population of preoptic hypothalamic neurons contribute to the effects of short-term social isolation on the social behaviors of female mice).  

      (1a) All the activity-dependent labeling experiments with TRAP mice, including the subsequent neural activity manipulation experiments (Figures 2, 3, 4, 5E-F), were conducted by labeling neurons only in socially isolated animals, not group-housed animals. The authors labeled neurons after 30-minute social interactions, raising the possibility that the labeled neurons simply represent a "social interaction/behavior population" (mediating mounting and USVs in females and males) rather than a set of neurons specific to social isolation behaviors of mice)… The data thus far suggests these neurons may predominantly reflect a "POA social behavior" population rather than a set of cells distinctly responsive to isolated housing.

      We agree with the reviewer that the POA neurons we are studying regulate the production of social behaviors in females and males, rather than representing a set of cells distinctly responsive to single housing. To more clearly reflect our thinking, we have changed the name of the neurons from “POA-iso neurons” to “POA-social neurons”. Thank you for this helpful criticism.

      Our Fos data are consistent with the idea that the POA may regulate social behaviors in group-housed females (not just single-housed females). Namely, we found that counts of Fospositive POA neurons are significantly related to rates of social investigation (p = 0.01) and tend to be related to USV rates (p = 0.05) in group-housed females that engaged in same-sex interactions (Fig. S1C). We now include two new sets of experiments aimed at further testing the idea this idea. 

      First, we include 2 control groups in which TRAPing sessions were performed in grouphoused females following same-sex interactions. We find that chemogenetic silencing of these group-housed-TRAPed POA neurons fails to reduce social behaviors in females that are subsequently single-housed and given a same-sex social interaction (Fig. 5A-D; GH-TRAPed POA hM4Di females), and that optogenetic activation of group-housed-TRAPed POA neurons fails to promote female social behavior (Fig. 5E-H; GH-TRAPed POA ChR2 females). At face value, these findings do not support the idea that the POA contains neurons that regulate social behaviors in group-housed females.

      However, one important caveat is that group-housed females engage in low rates of social behaviors (low investigation time, no mounting, and few USVs), and thus TRAP-based labeling may not work efficaciously in these mice. There may be POA neurons that regulate social behaviors in group-housed females but that do not upregulate Fos following production of relatively low rates of social behaviors. To test this idea, we also include females in which POA neurons are chemogenetically silenced using a viral strategy that does not depend on activitydependent labeling. In this new experiment, we report that silencing of POA neurons significantly reduces USV production in group-housed females (Fig. 5J-L) and significantly reduces social investigation, mounting, and USV production when these same females are retested following single-housing (Fig. 5M-O).

      (2) Please add strain background information of subject animals in the methods.

      This information has been added to the Animals section within the Methods (lines 788802).

      Responses to Reviewer 3 Recommendations:

      (1a) (T)he conflicting effects on behavior are hard to interpret without additional experiments….Similarly, optogenetic activation of POA neurons was sufficient to generate USV production as reported in earlier studies but mounting or social investigation remained unaffected. 

      We have added new analyses to consider the possibility that optogenetic activation of female POA-social neurons promotes social investigation. In the original manuscript, we analyzed the duration of social investigation bouts in POA-social-ChR2 females according to whether they overlapped with laser stimulation or whether they did not overlap. We realized that we made an error in this first analysis and inadvertently included social investigation bouts that occurred during the first 5 minutes of the social sessions, prior to any laser stimulation. Because these earlier bouts tend to be longer duration than later bouts, this mistake washed out the effect of laser stimulation on social bout duration. After correcting that error, we now report that optogenetic activation of female POA-social neurons lengthens social investigation bout duration (Fig. 4G). Inspired by this interesting finding, we also included analyses of the probability of social investigation following laser stimulation (Fig. 4E-F; excluding laser stimulations that were preceded by social investigation in the pre-laser baseline period). These analyses support the conclusion that optogenetic activation of POA-social neurons promotes both USV production and social investigation in group-housed females.

      (1b) Do these discrepancies (between hM4Di and caspase) arise due to the efficiency differences between DREADD-mediated silencing vs. Casp3 ablation? Or does the chemogenetic result reflect off-manifold effects on downstream circuitry whereas a more permanent ablation strategy allows other brain regions to compensate due to redundancy? It is important to resolve whether these arise due to technical reasons or whether these reflect the underlying (perhaps messy) logic of neural circuitry.  

      The possibility that the difference in effects on behavior between chemogenetic silencing and caspase ablation at face value seems inconsistent with the findings of previous experiments, in which ablation of large numbers of POA neurons failed to reduce USV production in male mice (POA lesions in Bean et al., 1981; ablation of VGAT+ POA neurons by Gao et al., 2018). These findings stand in contrast to those using chemogenetic silencing of large numbers of POA neurons, which report reduced USV production in male mice (VGAT+/Esr1+ in Karigo et al., 2021; Esr1+ in Chen et al., 2021).

      However, it is the case that the majority of the females that we used in our TRAP2-based ablation experiments were heterozygous for TRAP2 (N = 11 of 15 POA-social-caspase subjects were TRAP2;Ai14 females), whereas all females used in our chemogenetic silencing experiments were homozygous for TRAP2. To test whether a more effective ablation of POAsocial neurons might drive decreases in social investigation and USV production, we set up additional TRAP2 homozygous POA-social-caspase females and directly compare the effects of ablation between the two genotypes (Fig. S3; N = 11 hets in total and N = 9 homozygotes in total). These experiments revealed that effects on mounting were more pronounced following POA-social ablation in TRAP2 homozygotes vs. heterozygotes, but that neither group exhibited decreased social investigation or USV production following 4-OHT treatment.

      To ask whether caspase-mediated ablation in TRAP2 homozygotes was effective in eliminating neural activity associated with social behaviors in females, we performed Fos immunostaining in a subset of the POA-social-caspase TRAP2 homozygotes following a samesex interaction. We found that POA Fos expression was robustly reduced in these females relative to control group-housed and control single-housed females that also engaged in samesex interactions, down to levels seen in group-housed and single-housed females that did not engage in a social interaction (comparison shown in Fig. S3D; control female data same as in Fig. 1). Moreover, the remaining POA Fos in these TRAP2 homozygotes was no longer positively correlated to social investigation or USV production (Fig. S3E-F). Together, these findings lead us to favor the interpretation suggested by the reviewer below, that permanent ablation of POA-social neurons leads to compensation from other brain regions due to redundancy.

      Given the negative results above, we favor this possibility and indicate so in our Discussion. In addition, our finding that optogenetic activation of POA-social neurons promotes both USV production and social investigation supports the idea that POA-social neurons directly regulate these behaviors. We agree with the reviewer that additional work is needed to understand the complex sex- and context-dependent role played by the POA in the regulation of mouse social behaviors.

      (2) L 49: Please define Mesolimbic circuitry the first time it is mentioned.

      We have added a definition (lines 52-53).

      (3) L 210: In Figure 2C, the mounting duration baseline (saline) distribution seems lower than the same experimental baseline in Figures 1C and 3C. Does this reflect natural variability in the behavioral assay and might this be mitigated by additional sampling of animals?

      Yes, there is substantial variability in the display of mounting behavior by single-housed females, including in the proportion of trials with mounting as well as in the total duration of mounting. In the revised manuscript, we have simplified our analysis of mounting in our TRAPbased experiments to quantify the proportion of trials with mounting, rather than considering the total time spent mounting. After adding N = 5 additional females to the POA-social-hM4Di dataset, we now report a statistically significant decrease in the proportion of trials with mounting following chemogenetic silencing of POA-social neurons (Fig. 2C; McNemar’s test for paired proportions). 

      (4) L 310: The authors claim that "These findings suggest that a subset of POAiso neurons overlap with GABAergic, PAG-projecting POA neurons that have been demonstrated in previous work to promote USVs via disinhibition of excitatory PAG neurons important to USV production (Chen et al., 2021; Michael et al., 2020)." I think the data reported suggests the opposite since only 18.3% of all POA->PAG neurons are cFos+. Perhaps better rephrased as "A subset (18.3%) of POA->PAG neurons are labelled by cFos and that is sufficient to drive the production of USVs". Is it surprising?

      We modified the phrasing (lines 468-469), but a bit differently than suggested above, because although we suspect that optogenetic activation of the PAG-projecting neurons within the larger population of POA-social neurons is responsible for eliciting USV production, we did not technically demonstrate this to be the case in the current dataset. 

      We do find it surprising that so few (only ~20%) of PAG-projecting POA neurons upregulate Fos following female-female interactions marked by high rates of USV production. Even though optogenetic activation of PAG-projecting POA neurons elicits USV production, our finding suggests that the majority of PAG-projecting POA neurons may not play a role in regulating vocalization. In future work, it may be useful to apply an intersectional approach to further understand how the POA regulates USV production (for example, measure or manipulate activity selectively in projection-defined subsets of POA-social neurons).

      (5) Given the considerable prior evidence of POA->PAG circuit in promoting USVs, it is hard to understand why chemogenetic inactivation of POA neurons in males affects mounting but not USV production (Figures 5F-H). Any potential explanation for this discrepancy?

      We have two ideas about this surprising result. First, we examined the TRAPing session social behaviors of female and male POA-social-hM4Di mice. We found that male POA-socialhM4Di mice spent more time than female subjects mounting during the TRAPing sessions, and conversely, males spent less time investigating visitors and tended to produce fewer USVs than female subjects (Fig. S5). Given that our labeling method is activity-dependent, one possibility is that this bias in behavior is reflected in a bias toward labeling of POA neurons related to mounting.  

      Second, each mouse in the TRAP2-based hM4Di datasets received an IP injection of the same amount of 4-OHT (150 nL of 10 mg/mL 4-OHT in filtered corn oil) not adjusted for weight of the mouse. This information was not reported accurately in the Methods, and we have adjusted that section accordingly (line 920). As a result, because male mice typically weigh more than females and would have received a lower effective dosage of 4-OHT, another possibility is that TRAPing in males was less efficient than in females and accounts for the less complete effects on social behaviors. We have added language to the Results to discuss these possibilities (lines 540-560).

      (6) L 472: Typo. "we found that short-term isolation exerts more robust on the effects of male behavior during subsequent interactions with females than during interactions with males."

      Thank you for catching this mistake.

    1. Author response:

      Reviewer #1 (Public review):

      Point 1. The authors postulate a synergistic role for Itgb1 and Itgb3 in the intravasation phenotype, because the single KOs did not replicate the phenotype of the DKO. However, this is not a correct interpretation in the opinion of this reviewer. The roles appear rather to be redundant. Synergistic roles would rather demonstrate a modest effect in the single KO with potentiation in the DKO.

      We agree that the interaction between Itgb1 and Itgb3 appears redundant and we will correct this point in the revised manuscript.

      Point 2. The experiment does not explain how these integrins influence the interaction of the MK with their microenvironment. It is not surprising that attachment will be impacted by the presence or absence of integrins. However, it is unclear how activation of integrins allows the MK to become "architects for their ECM microenvironment" as the authors posit. A transcriptomic analysis of control and DKO MKs may help elucidate these effects.

      We do not currently understand how α5β1 or αvβ3 integrins activation would contribute to ECM remodeling by megakaryocytes. Integrins are well known key regulators of ECM remodelling (https://doi.org/10.1016/j.ceb.2006.08.009). They can transmit traction force that provoques ECM remodelling (https://doi.org/10.1016/j.bpj.2008.10.009). We will discuss our previous study on the observed reduction in RhoA activation in double knockout (DKO) mice (Guinard et al., 2023,  PMID: 37171626), which likely impact the organization of the ECM microenvironment. Alternatively, integrin signalling contribute to gene expression regulation involved in ECM remodelling (ECM proteins, proteases….). We do agree with the reviewer that the transcriptomic analysis could provide strong evidence; however, it is challenging to perform this analysis in vivo. Isolation of native megakaryocytes (MKs) from DKO mice is challenging due to their reduced numbers, requiring too many mice for sufficient RNA and risk of cell contamination. An alternative approach will be to analyze platelets, which are more abundant and easier to isolate, while still mimicking the characteristics of bone marrow MKs. We will use PCR array technology for selected ECM panels and adhesion molecules (from all players currently known to contribute to ECM remodelling), providing a practical way to address the reviewer's suggestions and provide valuable insights.

      Point 3. Integrin DKO have a 50% reduction in platelets counts as reported previously, however laminin α4 deficiency only leads to 20% reduction in counts. This suggests a more nuanced and subtle role of the ECM in platelet growth. To this end, functional assays of the platelets in the KO and wildtype mice may provide more information.

      The difference in platelet counts between integrin DKO and laminin α4 KO mice is not fully understood. Although our study specifically focuses on MK-ECM interactions in the bone marrow, we recognize the importance of providing additional information on platelet functionality. To address this, we will use flow cytometry to examine the levels of P-selectin surface expression and fibrinogen binding under basal conditions and after stimulation with collagen-related peptide and TRAP.

      Point 4. There is insufficient information in the Methods Section to understand the BM isolation approach. Did the authors flush the bone marrow and then image residual bone, or the extruded bone marrow itself as described in PMID: 29104956?

      Additional information on the methodology will be provided to clarify the BM isolation.

      Point 5. The references in the Methods section were very frustrating. The authors reference Eckly et al 2020 (PMID: 32702204) which provides no more detail but references a previous publication (PMID: 24152908), which also offers no information and references a further paper (PMID: 22008103), which, as far as this reviewer can tell, did not describe the methodology of in situ bone marrow imaging.

      To address this confusion, we will add the reference "In Situ Exploration of the Major Steps of Megakaryopoiesis Using Transmission Electron Microscopy" by C. Scandola et al. (PMID: 34570102), which provides a standardized protocol for bone marrow isolation.

      Therefore, this reviewer cannot tell how the preparation was performed and, importantly, how can we be sure that the microarchitecture of the tissue did not get distorted in the process?

      Thank you for pointing this out. While we cannot completely rule out the possibility of distortion, we will clarify the precautions taken to minimize it. We utilized a double fixation process immediately after extruding the bone marrow, followed by embedding it in agarose to preserve its integrity as much as possible. We will address this point in greater detail in Methods section of the revised version.

      Reviewer #2 (Public review):

      Point 1. ECM cage imaging

      a) The value or additional information provided by the staining on nano-sections (A) is not clear, especially considering that the thick vibratome sections already display the entirety of the laminin γ1 cage structure effectively. Further clarification on the unique insights gained from each approach would help justify its inclusion.

      Ultrathin cryosection allow high-resolution imaging (10x fold increased in Z), facilitating the analysis of signal superposition. This study explores the interactions between MKs and their immediate ECM microenvironment, located at a distance of less than one micrometer, making nano-sections optimal for precise analysis of ECM distribution both within and surrounding MKs. This high-resolution approach has revealed the presence of collagen IV, laminin, fibronectin, and fibrinogen near MKs, More importantly, ultrathin cryosection allow us to clearly show with high resolution the presence of activated integrin in contact with laminin an coll IV fibers (see Fig. 3)

      We employed large-volume whole-mount imaging to clarify the overall three-dimensional architecture of the ECM interface, allowing us to identify the cages. Our findings emphasize the role of specific ECM components in facilitating proplatelet passage through the sinusoid barrier, an essential step for platelet production. Further details will be addressed in the revised manuscript.

      b) The sMK shown in Supplementary Figure 1C appears to be linked to two sinusoids, releasing proplatelets to the more distant vessels. Is this observation representative, and if so, can further discussion be provided?

      This observation is not representative; MKs can also be associated with just one sinusoid.

      c) Freshly isolated BM-derived MKs are reported to maintain their laminin γ1 cage. Are the proportions of MKs with/without cages consistent with those observed in microscopy?   

      In the revised manuscript, we will include the quantification of the proportion of BM-derived MKs with/without cages.

      Point 2.  ECM cage formation

      a) The statement "the full assembly of the 3D ECM cage required megakaryocyte interaction with the sinusoidal basement membrane" on page 7 is too strong given the data presented at this stage of the study. Supplemental Figure 1C shows that approximately 10% of pMKs form cages without direct vessel contact, indicating that other factors may also play a role in cage formation.

      The reviewer is correct. We will modify the text to reflect a more cautious interpretation of our results.

      b) The data supporting the statement that "pMK represent a small fraction of the total MK population" (cell number or density) could be shown to help contextualize the 10% of them with a cage.

      New bar graphs will be provided to represent the density of MK in the parenchyma against the total MK in the bone marrow.

      c) How "the full assembly of the 3D ECM cage" is defined at this stage of the study should be clarified, specifically regarding the ECM components and structural features that characterize its completion.

      We recognize that the term ' full assembly' of the 3D ECM cage can be misleading, as it might suggest different stages of cage formation, such as a completed cage, one that is in the process of formation, or an incomplete cage. Since we have not yet studied this concept, we will eliminate the term "full assembly" from the manuscript to avoid any confusion. Instead, we will simply mention the presence of a cage.

      Point 3. Data on MK Circulation and Cage Integrity: Does the cage require full component integrity to prevent MK release in circulation? Are circulating MKs found in Lama4-/- mice? Is the intravasation affected in these mice? Are the ~50% sinusoid associated MK functional?  

      These are very valid points. We will answer all these questions by performing a detailed analysis of MK localization, vessel association and intravascular MK detection using IF and high-resolution EM imaging of Lamα4<sup>-/-</sup> mice. Additionally, we will analyze data from Lamα4-/- bone marrow explants to assess the capacity of MKs to extend proplatelets.

      Point 4. Methodology

      a) Details on fixation time are not provided, which is critical as it can impact antibody binding and staining. Including this information would improve reproducibility and feasibility for other researchers.

      We will added this information in the methods section.

      b) The description of 'random length measuring' is unclear, and the rationale behind choosing random quantification should be explained. Additionally, in the shown image, it appears that only the branching ends were measured, which makes it difficult to discern the randomness in the measurements.

      The random length measurement method uses random sampling to provide unbiased data on laminin/collagen fibers in a 3D cage. Contrary to what the initial image might have suggested, measurements go beyond just the branching ends; they include intervals between various branching points throughout the cage.

      To clarify this process, we will outline these steps: 1) acquire 3D images, 2) project onto 2D planar sections, 3) select random intersection points for measurement, 4) measure intervals using ImageJ software, and 5) repeat the process for a representative dataset. This will better illustrate the randomness of our measurements.

      Point 5.  Figures

      a) Overall, the figures and their corresponding legends would benefit from greater clarity if some panels were split, such as separating images from graph quantifications.

      Following the reviewer’s suggestion, we will fully update all the Figures and separate images from graph quantifications.

      Reviewer #3 (Public review):

      Point 1. The data linking ECM cage formation to MK maturation raises several interesting questions. As the authors mention, MKs have been suggested to mature rapidly at the sinusoids, and both integrin KO and laminin KO MKs appear mislocalized away from the sinusoids. Additionally, average MK distances from the sinusoid may also help separate whether the maturation defects could be in part due to impaired migration towards CXCL12 at the sinusoid. Presumably, MKs could appear mislocalized away from the sinusoid given the data presented suggesting they leaving the BM and entering circulation. Additional data or commentary on intrinsic (ex-vivo) MK maturation phenotypes may help strengthen the author's conclusions and shed light on whether an essential function of the ECM cage is integrin activation at the sinusoid.

      The hypothesis of MK migration towards CXCL12 is interesting, although it has recently been challenged by Stegner et al. (2017), who found that MKs are primarily sessile. However, we cannot exclude this possibility. To address the reviewer's concerns, we will quantify the distance of MKs from the sinusoids. This could help to determine whether the maturation defects are due to impaired migration towards CXCL12 at the sinusoids or other factors, such as the ECM cage.

      We would appreciate some clarification regarding the second point raised by the reviewer. Is the question  specifically addressing whether the ECM cage has an effect on the activation of integrins in the sinusoids? If so, we will use immunofluorescence (IF) to investigate the relationship between the presence of an ECM cage and the activation of integrins on the surface of endothelial cells within the sinusoids. Thank you for your guidance on this matter.

      Point 2. The data demonstrating intact MKs inter circulation is intriguing - can the authors comment or provide evidence as to whether MKs are detectable in blood? A quantitative metric may strengthen these observations.

      We will conduct flow cytometry experiments and prepare blood smears to determine whether intact MKs are detectable in blood.

      Point 3. Supplementary Figure 6 - shows no effect on in vitro MK maturation and proplt, or MK area - But Figures 6B/6C demonstrate an increase in total MK number in MMP-inhibitor treated mice compared to control. Some additional clarification in the text may substantiate the author's conclusions as to either the source of the MMPs or the in vitro environment not fully reflecting the complex and dynamic niche of the BM ECM in vivo.

      This is a valid point. We will revise the text to include further clarification.

      Point 4.  Similarly, one function of the ECM discussed relates to MK maturation but in the B1/3 integrin KO mice, the presence of the ECM cage is reduced but there appears to be no significant impact upon maturation (Supplementary Figure 4). By contrast, MMP inhibition in vivo (but not in vitro) reduces MK maturation. These data could be better clarified in the text, or by the addition of experiments addressing whether the composition and quantity of ECM cage components directly inhibit maturation versus whether effects of MMP-inhibitors perhaps lead to over-activation of the integrins (as with the B4galt KO in the discussion) are responsible for the differences in maturation.

      These are very good questions, but they are difficult to assess in situ. To approach this, we will perform in vitro experiments :

      (1) We will vary collagenIV and laminin411 concentrations in the culture conditions to determine how this affects MK maturation ; and

      (2) We will assess the integrin activation states on cultured MKs treated with MMP inhibitors to determine if MMP inhibitors could influence MK maturation through over-activation of integrins.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      This paper contains what could be described as a "classic" approach towards evaluating a novel taste stimuli in an animal model, including standard behavioral tests (some with nerve transections), taste nerve physiology, and immunocytochemistry of the tongue. The stimulus being tested is ornithine, from a class of stimuli called "kokumi", which are stimuli that enhance other canonical tastes, increasing essentially the hedonic attributes of these other stimuli; the mechanism for ornithine detection is thought to be GPRC6A receptors expressed in taste cells. The authors showed evidence for this in an earlier paper with mice; this paper evaluates ornithine taste in a rat model.

      Strengths:

      The data show the effects of ornithine on taste: in two-bottle and briefer intake tests, adding ornithine results in a higher intake of most, but not all, stimuli tests. Bilateral nerve cuts or the addition of GPRC6A antagonists decrease this effect. Small effects of ornithine are shown in whole-nerve recordings.

      Weaknesses:

      The conclusion seems to be that the authors have found evidence for ornithine acting as a taste modifier through the GPRC6A receptor expressed on the anterior tongue. It is hard to separate their conclusions from the possibility that any effects are additive rather than modulatory. Animals did prefer ornithine to water when presented by itself. Additionally, the authors refer to evidence that ornithine is activating the T1R1-T1R3 amino acid taste receptor, possibly at higher concentrations than they use for most of the study, although this seems speculative. It is striking that the largest effects on taste are found with the other amino acid (umami) stimuli, leading to the possibility that these are largely synergistic effects taking place at the tas1r receptor heterodimer.

      We would like to thank Reviewer #1 for the valuable comments. Our basis for considering ornithine as a taste modifier stems from our observation that a low concentration of ornithine (1 mM), which does not elicit a preference on its own, enhances the preference for umami substances, sucrose, and soybean oil through the activation of the GPRC6A receptor. Notably, this receptor is not typically considered a taste receptor. The reviewer suggested that the enhancement of umami taste might be due to potentiation occurring at the TAS1R receptor heterodimer. However, we propose that a different mechanism may be at play, as an antagonist of GPRC6A almost completely abolished this enhancement. In the revised manuscript, we will endeavor to provide additional information on the role of ornithine as a taste modifier acting through the GPRC6A receptor.

      Reviewer #2 (Public review):

      Summary:

      The authors used rats to determine the receptor for a food-related perception (kokumi) that has been characterized in humans. They employ a combination of behavioral, electrophysiological, and immunohistochemical results to support their conclusion that ornithine-mediated kokumi effects are mediated by the GPRC6A receptor. They complemented the rat data with some human psychophysical data. I find the results intriguing, but believe that the authors overinterpret their data.

      Strengths:

      The authors examined a new and exciting taste enhancer (ornithine). They used a variety of experimental approaches in rats to document the impact of ornithine on taste preference and peripheral taste nerve recordings. Further, they provided evidence pointing to a potential receptor for ornithine.

      Weaknesses:

      The authors have not established that the rat is an appropriate model system for studying kokumi. Their measurements do not provide insight into any of the established effects of kokumi on human flavor perception. The small study on humans is difficult to compare to the rat study because the authors made completely different types of measurements. Thus, I think that the authors need to substantially scale back the scope of their interpretations. These weaknesses diminish the likely impact of the work on the field of flavor perception.

      We would like to thank Reviewer #2 for the valuable comments and suggestions. Regarding the question of whether the rat is an appropriate model system for studying kokumi, we have chosen this species for several reasons: it is readily available as a conventional experimental model for gustatory research; the calcium-sensing receptor (CaSR), known as the kokumi receptor, is expressed in taste bud cells; and prior research has demonstrated the use of rats in kokumi studies involving gamma Glu-Val-Gly (Yamamoto and Mizuta, Chem. Senses, 2022).

      We acknowledge that fundamentally different types of measurements were conducted in the human psychophysical study and the rat study. Kokumi can indeed be assessed and expressed in humans; however, we do not currently have the means to confirm that animals experience kokumi in the same way that humans do. Therefore, human studies are necessary to evaluate kokumi, a conceptual term denoting enhanced flavor, while animal studies are needed to explore the potential underlying mechanisms of kokumi. We believe that a combination of both human and animal studies is essential, as is the case with research on sugars. While sugars are known to elicit sweetness, it is unclear whether animals perceive sweetness identically to humans, even though they exhibit a strong preference for sugars. In the revised manuscript, we will incorporate additional information to address the comments raised by the reviewer. We will also carefully review and revise our previous statements to ensure accuracy and clarity.

      Reviewer #3 (Public review):

      Summary:

      In this study, the authors set out to investigate whether GPRC6A mediates kokumi taste initiated by the amino acid L-ornithine. They used Wistar rats, a standard laboratory strain, as the primary model and also performed an informative taste test in humans, in which miso soup was supplemented with various concentrations of L-ornithine. The findings are valuable and overall the evidence is solid. L-Ornithine should be considered to be a useful test substance in future studies of kokumi taste and the class C G protein-coupled receptor known as GPRC6A (C6A) along with its homolog, the calcium-sensing receptor (CaSR) should be considered candidate mediators of kokumi taste.

      Strengths:

      The overall experimental design is solid based on two bottle preference tests in rats. After determining the optimal concentration for L-Ornithine (1 mM) in the presence of MSG, it was added to various tastants, including inosine 5'-monophosphate; monosodium glutamate (MSG); mono-potassium glutamate (MPG); intralipos (a soybean oil emulsion); sucrose; sodium chloride (NaCl); citric acid and quinine hydrochloride. Robust effects of ornithine were observed in the cases of IMP, MSG, MPG, and sucrose, and little or no effects were observed in the cases of sodium chloride, citric acid, and quinine HCl. The researchers then focused on the preference for Ornithine-containing MSG solutions. The inclusion of the C6A inhibitors Calindol (0.3 mM but not 0.06 mM) or the gallate derivative EGCG (0.1 mM but not 0.03 mM) eliminated the preference for solutions that contained Ornithine in addition to MSG. The researchers next performed transections of the chord tympani nerves (with sham operation controls) in anesthetized rats to identify the role of the chorda tympani branches of the facial nerves (cranial nerve VII) in the preference for Ornithine-containing MSG solutions. This finding implicates the anterior half-two thirds of the tongue in ornithine-induced kokumi taste. They then used electrical recordings from intact chorda tympani nerves in anesthetized rats to demonstrate that ornithine enhanced MSG-induced responses following the application of tastants to the anterior surface of the tongue. They went on to show that this enhanced response was insensitive to amiloride, selected to inhibit 'salt tastant' responses mediated by the epithelial Na+ channel, but eliminated by Calindol. Finally, they performed immunohistochemistry on sections of rat tongue demonstrating C6A positive spindle-shaped cells in fungiform papillae that partially overlapped in its distribution with the IP3 type-3 receptor, used as a marker of Type-II cells, but not with (i) gustducin, the G protein partner of Tas1 receptors (T1Rs), used as a marker of a subset of type-II cells; or (ii) 5-HT (serotonin) and Synaptosome-associated protein 25 kDa (SNAP-25) used as markers of Type-III cells.

      Weaknesses:

      The researchers undertook what turned out to be largely confirmatory studies in rats with respect to their previously published work on Ornithine and C6A in mice (Mizuta et al Nutrients 2021).

      The authors point out that animal models pose some difficulties of interpretation in studies of taste and raise the possibility in the Discussion that umami substances may enhance the taste response to ornithine (Line 271, Page 9). They miss an opportunity to outline the experimental results from the study that favor their preferred interpretation that ornithine is a taste enhancer rather than a tastant.

      At least two other receptors in addition to C6A might mediate taste responses to ornithine: (i) the CaSR, which binds and responds to multiple L-amino acids (Conigrave et al, PNAS 2000), and which has been previously reported to mediate kokumi taste (Ohsu et al., JBC 2010) as well as responses to Ornithine (Shin et al., Cell Signaling 2020); and (ii) T1R1/T1R3 heterodimers which also respond to L-amino acids and exhibit enhanced responses to IMP (Nelson et al., Nature 2001). While the experimental results as a whole favor the authors' interpretation that C6A mediates the Ornithine responses, they do not make clear either the nature of the 'receptor identification problem' in the Introduction or the way in which they approached that problem in the Results and Discussion sections. It would be helpful to show that a specific inhibitor of the CaSR failed to block the ornithine response. In addition, while they showed that C6A-positive cells were clearly distinct from gustducin-positive, and thus T1R-positive cells, they missed an opportunity to clearly differentiate C6A-expressing taste cells and CaSR-expressing taste cells in the rat tongue sections.

      It would have been helpful to include a positive control kokumi substance in the two-bottle preference experiment (e.g., one of the known gamma-glutamyl peptides such as gamma-glu-Val-Gly or glutathione), to compare the relative potencies of the control kokumi compound and Ornithine, and to compare the sensitivities of the two responses to C6A and CaSR inhibitors.

      The results demonstrate that enhancement of the chorda tympani nerve response to MSG occurs at substantially greater Ornithine concentrations (10 and 30 mM) than were required to observe differences in the two bottle preference experiments (1.0 mM; Figure 2). The discrepancy requires careful discussion and if necessary further experiments using the two-bottle preference format.

      We would like to thank Reviewer #3 for the valuable comments and helpful suggestions. We propose that ornithine has two stimulatory actions: one acting on GPRC6A, particularly at lower concentrations, and another on amino acid receptors such as T1R1/T1R3 at higher concentrations. Consequently, ornithine is not preferable at lower concentrations but becomes preferable at higher concentrations. For our study on kokumi, we used a low concentration (1 mM) of ornithine. The possibility mentioned in the Discussion that 'the umami substances may enhance the taste response to ornithine' is entirely speculative. We will reconsider including this description in the revised version. As the reviewer suggested, in addition to GPRC6A, ornithine may bind to CaSR and/or T1R1/T1R3 heterodimers. However, we believe that ornithine mainly binds to GPRC6A, as a specific inhibitor of this receptor almost completely abolished the enhanced response to umami substances, and our immunohistochemical study indicated that GPRC6A-expressing taste cells are distinct from CaSR-expressing taste cells (see Supplemental Fig. 3). We conducted essentially the same experiments using gamma-Glu-Val-Gly in Wistar rats (Yamamoto and Mizuta, Chem. Senses, 2022) and compared the results in the Discussion. The reviewer may have misunderstood the chorda tympani results: we added the same concentration (1 mM) used in the two-bottle preference test to MSG (Fig. 5-B). Fig. 5-A shows nerve responses to five concentrations of plain ornithine. In the revised manuscript, we will strive to provide more precise information reflecting the reviewer’s comments.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) The behavioral effects found with the CPRC6A antagonists are not entirely convincing, as the antagonist is seemingly just mixed up in the solution with the stimuli. There are no control experiments demonstrating that the antagonists do not have a taste themselves.

      We mixed the antagonists into both liquids used in the two-bottle preference test to eliminate any potential taste effects of the antagonists themselves. In the electrophysiological experiments, the antagonist was incorporated into the solution after confirming that it did not elicit any appreciable response in the taste nerve.

      (2) The effects of ornithine found with quinine did not have a satisfying explanation - if there is some taste cell-taste cell modulation that accounts for the taste enhancement, why is the quinine less aversive? Why is it not enhanced like the other compounds?

      The effects of ornithine on quinine responses remain difficult to explain. A previous study (Tokuyama et al., Chem Pharm Bull, 2006) proposed that ornithine prevents bitter substances from binding to bitter receptors, although this hypothesis lacks definitive evidence. In the present study, our findings suggest that the binding of quinine to bitter receptors is essential, as another agonist, gallate, also enhanced the preference for quinine, but this effect was abolished by EGCG, a GPRC6A antagonist (see Supplemental Fig. 2).

      (3) Unless I am missing something, there appears to be no quantitative analysis of the immunocytochemical data, just assertions.

      We have made quantitative analyses in the revised text, and the following sentences have been added: “Approximately 11% of GPRC6A-positive cells overlapped with IP3R3 (9 double-positive cells/80 GPRC6A-positive cells), while approximately 8.3% of IP3R3-positive cells expressed GPRC6A (9 double-positive /109 IP3R3-positive cells). In addition, GPRC6A-positive cells were unlikely to colocalize with a-gustducin, another marker for a subset of type II cells, in single taste cells (0 double-positive cell/93 GPRC6A-positive cells). Regarding type III cell markers, GPRC6A-positive cells were unlikely to colocalize with 5-HT in single taste cells (0 double-positive cell/75 GPRC6A-positive cells).”

      (4) The hallmarks of Kokumi taste include descriptors such as "thickness", and "mouthfeel", which sound like potential somatosensory attributes. Perhaps the authors should consider this possibility for at least some of the effects found.

      The term kokumi, a Japanese word, refers to a phenomenon in which the flavor of complexly composed food is enhanced through certain processes, making them more delicious. To date, kokumi has been described using the representative terms thickness, mouthfulness, and continuity, originally introduced in the first paper on kokumi by Ueda et al. (1990). However, these terms are derived from Japanese and may not fully convey the nuances of the original language when translated into these simple English words. In particular, thickness is often interpreted as referring to physical properties such as viscosity or somatosensory sensations. Since kokumi inherently lacks somatosensory elements, this revised paper adopts alternative terms and explanations for the three components of kokumi to prevent misunderstanding and confusion.

      Therefore, to clarify that kokumi attributes are inherently gustatory, thickness is replaced with intensity of whole complex tastes (rich flavor with complex tastes), emphasizing the synergistic effects of a variety of tastes rather than the mere enhancement of a single flavor. Mouthfulness is clarified as not referring to mouthfeel (the tactile sensation a food gives in the mouth) but rather as spread of taste and flavor throughout the oral cavity, describing how the flavor fills the mouth. Continuity is replaced with persistence of taste (lingering flavor).

      (5) I don't think the human experiment (S1) belongs to the paper, even as a supplementary bit of data. It's only 17 subjects, they are all female, and we don't know anything about how they were selected, even though it states they are all students/staff at Kio. Were any of them lab members? Were they aware of the goals of the experiment? Could simply increasing the amount of solute in the soup make it seem thicker? This (sparse) data seems to have been shoehorned into the paper without enough detail/justification.

      Despite the reviewer’s suggestion, we would like to include the human experiment because the rationale of the present study is to confirm, through a human sensory test, that the kokumi of a complex solution (in this case, miso soup) is enhanced by the addition of ornithine. This is followed by basic animal experiments to investigate the underlying mechanisms. Therefore, this human study serves an important role.

      The total number of participants increased to 22 (19 women and three men) following an additional experiment with 5 new participants. New results have been shown in Supplemental Figure 1 with statistical analyses. The rewritten parts are as follows:

      We recruited 22 participants (19 women and three men, aged 21-28 years) from Kio University who were not affiliated with our laboratory, including students and staff members. All participants passed a screening test based on taste sensitivity. According to the responses obtained from a pre-experimental questionnaire, we confirmed that none of the participants had any sensory abnormalities, eating disorders, or mental disorders, or were taking any medications that may potentially affect their sense of taste. All participants were instructed not to eat or drink anything for 1 hour prior to the start of the experiment. We provided them with a detailed explanation of the experimental procedures, including safety measures and personal data protection, without revealing the specific goals of the study.

      (6) The introduction could be more concise - for example, when describing Kokumi stimuli such as ornithine and its possible receptors, the authors do not need to add the detail about how this stimulus was deduced from adding clams to the soup. Details like this can be reserved for the discussion.

      Thank you for this comment. We have tried to shorten the Introduction.

      (7) Line 86: awkward phrasing - this doesn't need to be a rhetorical question.

      We have deleted the sentence.

      (8) Supplementary Figure 1: The labels on the figure say "Miso soup in 1 mM Orn" when the Orn is dissolved into the soup.

      Thank you for pointing out our mistake. We have changed the description, such as “1 mM Orn in miso soup”.

      Reviewer #2 (Recommendations for the authors):

      Major concerns

      (1) The impact of "kokumi" taste ligands on food perception appears to be profound in humans. This observation is fascinating because it implies that molecules like ornithine impact a variety of flavor perceptions, some of which are non-gustatory in nature (e.g., spread, mouthfulness and harmony). What remains unclear is whether "kokumi" ligands produce analogous sensations in rodents. If they don't, then rodents are an inappropriate model system for studying the impact of kokumi on flavor perceptions. The authors fail to address this key issue, and uncritically assume that kokumi ligands produce sensations like thickness, mouthfulness, and continuity in rodents. For this reason, the authors' reference to GPRC6A as a kokumi receptor is inappropriate.

      Thank you very much for the valuable comments. The term kokumi refers to a phenomenon in which the flavor of complexly composed foods is enhanced through certain processes, making them more delicious. It is an important concept in the field of food science, which studies how to make prepared dishes more enjoyable. Kokumi is also considered a higher-order, profound cognitive function evaluated by humans who experience a wide variety of foods. However, it is unclear whether animals, particularly experimental animals, can perceive kokumi in the same way humans do.

      To date, kokumi has been described using the representative terms thickness, mouthfulness, and continuity, originally introduced in the first paper on kokumi by Ueda et al. (1990). However, these terms are derived from Japanese and may not fully convey the nuances of the original language when translated into these simple English words. In particular, thickness is often interpreted as referring to physical properties such as viscosity or somatosensory sensations. Since kokumi inherently lacks somatosensory elements, this revised paper adopts alternative terms and explanations for the three components of kokumi to prevent misunderstanding and confusion.

      Therefore, to clarify that kokumi attributes are inherently gustatory, thickness is replaced with intensity of whole complex tastes (rich flavor with complex tastes), emphasizing the synergistic effects of a variety of tastes rather than the mere enhancement of a single flavor. Mouthfulness is clarified as not referring to mouthfeel (the tactile sensation a food gives in the mouth) but rather as spread of taste and flavor throughout the oral cavity, describing how the flavor fills the mouth. Continuity is replaced with persistence of taste (lingering flavor).

      Rodents are thought to possess basic taste functions similar to humans, such as the expression of taste receptors, including kokumi receptors, in taste cells. Regardless of whether rodents can perceive kokumi, findings from studies on rodents may provide insights into aspects of the kokumi concept as experienced by humans.

      Indeed, the results of this study indicate that ornithine enhances umami, sweetness, fat taste, and saltiness, leading to the enhancement of complex flavors—referred to as intensity of whole taste. The activation of various taste cells, resulting in the enhancement of multiple tastes, may contribute to the sensation of flavors spreading throughout the oral cavity. Furthermore, the strong enhancement of MSG and MPG suggests that glutamate contributes to the mouthfulness and persistence of taste characteristic of kokumi.

      (2) A related concern is that the authors did not make any measurements that model kokumi sensations documented in the literature. For example, they would need to develop behavioral/electrophysiological measurements that reflect the known effects of kokumi ligands on flavor perception (i.e., increases in intensity, spread, continuity, richness, harmony, and punch). For example, ornithine is thought to produce more "punch" (i.e., a more rapid rise in intensity). This could be manifested as a more rapid rise in peripheral taste response or a more rapid fMRI response in the taste cortex. Alternatively, ornithine is thought to increase "continuity" (i.e., make the taste response more persistent). This response would presumably be manifested as a peripheral taste response that adapts more slowly or a more persistent fMRI response. As it stands, the authors have documented that ornithine increases (i) the preference of rats for some chemical stimuli, but not others; and (ii) the response of the CT nerve to some but not all taste stimuli.

      In animal experiments, it is challenging to examine each attribute of kokumi. The increase of complex tastes can be investigated through behavioral experiments and neural activity recordings. However, phenomena such as spread or harmony, which arise from profound human judgments, are difficult to validate in animal studies.

      While it was possible to examine persistence through neural responses to tastants, all stimuli were rinsed at 30 seconds after onset of stimulation, so the exact duration of persistence was not investigated. However, since the MSG response was enhanced approximately 1.5 times with the addition of ornithine, it is strongly suggested that the duration might also have been prolonged.

      Regarding punch, no differences were observed in the neural responses when ornithine was added, likely because the phasic response already had a rapid onset.

      In the context of fMRI studies, there has been a report that adding glutathione to mixtures of umami and salt solutions increases responses (Goto et al. Chem Senses, 2016). However, research specifically examining the attributes of kokumi has not yet been reported.

      (3) The quality of the SNAP-25 immunohistochemistry is poor (see Figure 7D), with lots of seemingly nonspecific staining in and outside the taste bud.

      The quality of the SNAP-25 is not poor. It is known that SNAP-25 labels not only type III cells but also the dense network of intragemmal nerve fibers (Tizzano et al., Immunohistochemical Analysis of Human Vallate Taste Buds. Chem Senses.40:655-60, 2015). Therefore, lots of seemingly nonspecific staining is due to intense SNAP-25-immunoreactivity of the nerve fibers.

      (4) The authors need to drastically scale back the scope of their conclusions. What they can say is that ornithine appears to enhance the taste responses of rats to a variety of taste stimuli and that this effect appears to be mediated by the GPRC6A receptor. They cannot use their data to address kokumi effects in humans, as they have not attempted to model any of these effects. Given the known problems with pharmacological blocking agents (e.g., nonspecificity), the authors would significantly strengthen their case if they could generate similar results in a GPRC6A knockout mouse.

      Our research approach begins with confirming in humans that the addition of ornithine to complex foods (such as miso soup) induces kokumi. Based on this confirmation, we conduct fundamental studies using animal models to investigate the peripheral taste mechanisms underlying the expression of kokumi.

      It is possible that the key to kokumi expression lies in the enhancement of desirable tastes (particularly umami) and the suppression of unpleasant tastes. Moving forward, we will deepen our fundamental research on the action of ornithine mediated through GPRC6A, including studies using knockout mice.

      (5) The introduction is too long. Much of the discussion of kokumi perception in humans should either be removed or shortened considerably.

      Following the reviewer’s suggestion, the introduction has been shortened.

      (6) I recommend that the authors break up the Methods and Results sections into different experiments. This would enable the authors to provide separate rationales for each procedure. For instance, the authors conducted a variety of different behavioral procedures (e.g., long- and short-term preference tests, and preference tests with and without GPRC6A receptor antagonists).

      Rather than following the reviewer’s suggestion, we have added subheadings to describe the purpose of each experiment. This approach would help readers better understand the experimental flow, as each experiment is relatively straightforward.

      (7) The inclusion of the human data is odd for two reasons. First, the measurements used to assess the impact of ornithine on flavor perception in humans were totally different than those used in rats. This makes it impossible to compare the human and rat datasets. Second, the human study was rather limited in scope, had small effect sizes, and had a lot of individual variation. For these reasons, the human data are not terribly helpful. I recommend that the authors remove the human data from this paper, and publish them as part of a more extensive study on humans.

      Despite the reviewer’s suggestion, we would like to include the human experiment because the rationale of the present study is to confirm, through a human sensory test, that the kokumi of a complex solution (in this case, miso soup) is enhanced by the addition of ornithine. This is followed by basic animal experiments to investigate the underlying mechanisms. Therefore, this human study serves an important role. The considerable variation in the scores suggests that evaluating the three kokumi attributes is challenging and likely influenced by differences in judgment criteria among participants.

      The total number of participants increased to 22 (19 women and three men) following an additional experiment with 5 new participants. New results have been shown in Supplemental Figure 1 with statistical analyses. The rewritten parts are as follows:

      We recruited 22 participants (19 women and three men, aged 21-28 years) from Kio University who were not affiliated with our laboratory, including students and staff members. All participants passed a screening test based on taste sensitivity. According to the responses obtained from a pre-experimental questionnaire, we confirmed that none of the participants had any sensory abnormalities, eating disorders, or mental disorders, or were taking any medications that may potentially affect their sense of taste. All participants were instructed not to eat or drink anything for 1 hour prior to the start of the experiment. We provided them with a detailed explanation of the experimental procedures, including safety measures and personal data protection, without revealing the specific goals of the study.

      (8) While the use of English is generally good, there are many instances where the English is a bit awkward. I recommend that the authors ask a native English speaker to edit the text.

      Thank you for this comment. The text has been edited by a native English speaker.

      Minor concerns

      (1) Lines 13-14: The authors state that "the concept of 'kokumi' has garnered significant attention in gustatory physiology and food science." This is an exaggeration. Kokumi has generated considerable interest in food science but has yet to generate much interest in gustatory physiology.

      We have rewritten this part: “The concept of “kokumi” has generated considerable interest in food science but kokumi has not been well studied in gustatory physiology.”

      (2) Line 20: The use of "specific taste" is unclear in this context. The authors indicate (in Figure 5A) that 1 mM ornithine generates a CT nerve response. They also reveal (in Figure 1A) that rats do not prefer 1 mM ornithine over water. The results from a preference test do not provide insight into whether a solution can be tasted; they merely demonstrate a lack of preference for that solution. Based on these data, the authors cannot infer that 1 mM ornithine cannot be tasted.

      We agree with the reviewer’s comment. Ornithine at 1 mM concentration may have a weak taste because this solution elicited a small neural response (Fig. 5-A). We have rewritten the text: “… at a concentration without preference for this solution.”

      (3) Line 44: Sensory information from foods enters the oral and the nasal cavity.

      The nasal cavity has been added.

      (5) Lines 59: The terms "thickness", "mouthfulness" and "continuity" are not intuitive in English, and may reflect, at least in part, a failure in translation. The word thickness implies a tactile sensation (e.g., owing to high viscosity), but the authors use it to indicate a flavor that is more intense and onsets more quickly. The word mouthfulness is supposed to indicate that a flavor is experienced throughout the oral cavity. The problem here is that this happens with all tastants, independent of the presence of substances like ornithine. Indeed, taste buds occur in a limited portion of the oral epithelium, but we nevertheless experience tastes throughout the oral cavity, owing to a phenomenon called tactile referral (see the following reference: Todrank and Bartoshuk, 1991, A taste illusion: taste sensation localized by touch" Physiology & Behavior 50:1027-1031). The word continuity does not imply that the taste is long-lasting or persistent.

      These three attributes were originally introduced by Ueda et al. (1990), who translated Japanese terms describing the profound characteristics of kokumi, which are deeply rooted in Japanese culinary culture. However, these simply translated terms have caused global misunderstanding and confusion, because they sound like somatosensory rather than gustatory descriptions. Therefore, to clarify that kokumi attributes are inherently gustatory, in the revised version we use the terms “intensity of whole complex tastes (rich flavor with complex tastes)” instead of thickness, “mouthfulness (spread of taste and flavor throughout the oral cavity),” and “persistence of taste (lingering flavor)” instead of continuity.

      The results of this study indicate that ornithine enhances umami, sweetness, fat taste, and saltiness, leading to the enhancement of complex flavors—referred to as intensity of whole taste. The activation of various taste cells, resulting in the enhancement of multiple tastes, may contribute to the sensation of flavors spreading throughout the oral cavity. Furthermore, the strong enhancement of MSG and MPG suggests that glutamate contributes to the mouthfulness and persistence of taste characteristic of kokumi.

      (6) Figure legends: The authors provide results of statistical comparisons in several of the figures. They need to explain what statistical procedures were performed. As it stands, it is impossible to interpret the asterisks provided.

      We have explained statistical procedures in each Figure legend.

      (7) I did not see any reference to the sources of funding or any mention of potential conflicts of interest.

      We have added the following information:

      Funding: JSPS KAKENHI Grant Numbers JP17K00935 (to TY) and JP22K11803(to KU).

      Declaration of interests: The authors declare that they have no competing interests.

      Reviewer #3 (Recommendations for the authors):

      (1) I suggest that the authors increase their level of interest in glutathione and gamma-glutamyl peptides. This might include an appropriate gamma-glutamyl control substance in the two-bottle preference study (see Public Review). It might also include more careful attention to the work that identified glutathione as an activator of the CaSR (Wang et al., JBC 2006) and the nature of its binding site on the CaSR which overlaps with its site for L-amino acids (Broadhead et al., JBC 2011). This latter article also identified S-methyl glutathione, in which the free-SH group is blocked, as a high-potency activator of the CaSR. It would be expected to show comparable potency to gamma-glu-Val-Gly in assays of kokumi taste.

      We have appropriately referenced glutathione and gamma-Glu-Val-Gly, potent agonists of CaSR, where necessary. In our previous study (Yamamoto and Mizuta, Chem Senses, 2022), we examined the additive effects of these substances on basic taste stimuli in rodents, and the results were compared in greater detail with those obtained from the addition of ornithine in the present study. We have also discussed the potential binding of ornithine to other receptors, including CaSR and T1R1/T1R3 heterodimers.

      (2) Figures:

      -None of the figures were labelled with their Figure numbers. I have inferred the Figure numbers from the legends and their positions in the pdf.

      We are sorry for this inconvenience.

      - The labelling of Figure 1 and Figure 2 are problematic. In Figure 1 it should be made clear that the horizontal axes refer to the Ornithine concentration. In Figure 2 it should be made clear that the horizontal axes refer to the tastant concentrations (MSG, IMP, etc) and that the Ornithine concentrations were fixed at either zero or 1.0 mM.

      We are sorry for the lack of information about the horizontal axes. We have explained the horizontal axes in figure legends in Figs. 1 and 2. The labelling of both figures has also been modified to make this clear.

      - Figure 3B: 'Control' should appear at the top of this panel since the panels that follow all refer to it.

      Following the reviewer’s suggestion, we have added ‘Control’ at the top of Figure 3B.

      - Figure 5A. Provide a label for the test substance, presumably Ornithine.

      Yes, we have added ‘Ornithine’.

      - Figure 7 would be strengthened by the inclusion of immunohistochemistry analyses of the CaSR.

      We are sorry that we did not analyze immunohistochemistry for the CaSR because a previous study precisely had analyzed the CaSR expression on taste cells in rats. We have analyzed co-expression of GPRC6A and CaSR (see Supplemental Figure 3).

      (3) Other Matters:

      - Line 38: list the five basic taste modalities here.

      Yes, we have included the five basic taste modalities here.

      - Line 107: 'even if ... kokumi ... is less developed in rodents' - if there is evidence that kokumi is less developed in rodents it should be cited here.

      We cannot cite any references here because no studies have compared the perception of kokumi between humans and rodents.

      - Line 308: 'recently we conducted experiments in rats using gallate ...' - the authors appear to imply that they performed the research in Reference 43, however, I was unable to find an overlap between the two lists of authors.

      We are not doing a similar study as the research in Reference 43 (40 in the revised paper). Following the result that gallate is an agonist of GPRC6A as shown by Reference 43, we were interested in doing similar behavioral experiments using gallate instead of ornithine.

      The sentences have been rewritten to avoid misunderstanding.

      - Line 506: the sections are said to be 20 mm thick - should this read 20 micrometers?

      Thank you. We have changed to 20 micrometers.

    1. Author response:

      Public Reviews: 

      Reviewer #1 (Public review): 

      The Bagnat and Rawls groups' previous published work (Park et al., 2019) described the kinetics and genetic basis of protein absorption in a specialized cell population of young vertebrates termed lysosome-rich enterocytes (LREs). In this study they seek to understand how the presence and composition of the microbiota impacts the protein absorption function of these cells and reciprocally, how diet and intestinal protein absorption function impact the microbiome. 

      Strengths of the study include the functional assays for protein absorption performed in live larval zebrafish, which provides detailed kinetics on protein uptake and degradation with anatomic precision, and the gnotobiotic manipulations. The authors clearly show that the presence of the microbiota or of certain individual bacterial members slows the uptake and degradation of multiple different tester fluorescent proteins. 

      To understand the mechanistic basis for these differences, the authors also provide detailed single-cell transcriptomic analyses of cells isolated based on both an intestinal epithelial cell identity (based on a transgenic marker) and their protein uptake activity. The data generated from these analyses, presented in Figures 3-5, are valuable for expanding knowledge about zebrafish intestinal epithelial cell identities, but of more limited interest to a broader readership. Some of the descriptive analysis in this section is circular because the authors define subsets of LREs (termed anterior and posterior) based on their fabp2 expression levels, but then go on to note transcriptional differences between these cells (for example in fabp2) that are a consequence of this initial subsetting. 

      Inspired by their single-cell profiling and by previous characterization of the genes required for protein uptake and degradation in the LREs, the authors use quantitative hybridization chain reaction RNA-fluorescent in situ hybridization to examine transcript levels of several of these genes along the length of the LRE intestinal region of germ-free versus mono-associated larvae. They provide good evidence for reduced transcript levels of these genes that correlate with the reduced protein uptake in the mono-associated larval groups. 

      The final part of the study (shown in Figure 7) characterized the microbiomes of 30-day-old zebrafish reared from 6-30 days on defined diets of low and high protein and with or without homozygous loss of the cubn gene required for protein uptake. The analysis of these microbiomes notes some significant differences between fish genotypes by diet treatments, but the discussion of these data does not provide strong support for the hypothesis that "LRE activity has reciprocal effects on the gut microbiome". The most striking feature of the MDS plot of Bray Curtis distance between zebrafish samples shown in Figure 7B is the separation by diet independent of host genotype, which is not discussed in the associated text. Additionally, the high protein diet microbiomes have a greater spread than those of the low protein treatment groups, with the high protein diet cubn mutant samples being the most dispersed. This pattern is consistent with the intestinal microbiota under a high protein diet regimen and in the absence of protein absorption machinery being most perturbed in stochastic ways than in hosts competent for protein uptake, consistent with greater beta dispersal associated with more dysbiotic microbiomes (described as the Anna Karenina principle here: https://pubmed.ncbi.nlm.nih.gov/28836573/). It would be useful for the authors to provide statistics on the beta dispersal of each treatment group. 

      Overall, this study provides strong evidence that specific members of the microbiota differentially impact gene expression and cellular activities of enterocyte protein uptake and degradation, findings that have a significant impact on the field of gastrointestinal physiology. The work refines our understanding of intestinal cell types that contribute to protein uptake and their respective transcriptomes. The work also provides some evidence that microbiomes are modulated by enterocyte protein uptake capacity in a diet-dependent manner. These latter findings provide valuable datasets for future related studies. 

      We thank the reviewer for their thorough and kind assessment. We appreciate the suggestion for edits and for pointing out areas that need further clarification.

      One point that clearly needs further explanation is the use fabp6 (referred to as fabp2 by the reviewer) to define anterior LREs and their gene expression pattern. which includes high levels of fabp6. This was deemed by the reviewer as a “circular argument”.  We would like to clarify that the rationale for using fabp6 as anchor is that we had previously reported overlap between fabp6 and LREs (see Fig.6C-E in Wen et al. PMID: 34301599) and thus were able here to define fabp6’s spatial pattern in relation to other LRE markers and the neighboring ileocyte population using transgenic markers and HCR. Thus, far from being a circular argument, using fabp6 allowed us to identify other markers that are differentially expressed between anterior and posterior LREs, which share a core program that we highlight in our study. In the revised manuscript we will clarify this point.

      We will also add the analysis suggested for the 16S rRNA gene sequencing data, include statistics on beta dispersal, and expand the discussion of these data as suggested.

      Reviewer #2 (Public review): 

      Summary: 

      The authors set out to determine how the microbiome and host genotype impact host protein-based nutrition. 

      Strengths: 

      The quantification of protein uptake dynamics is a major strength of this work and the sensitivity of this assay shows that the microbiome and even mono-associated bacterial strains dampen protein uptake in the host by causing down-regulation of genes involved in this process rather than a change in cell type. 

      The use of fluorescent proteins in combination with transcript clustering in the single cell seq analysis deepens our understanding of the cells that participate in protein uptake along the intestine. In addition to the lysozome-rich enterocytes (LRE), subsets of enteroendocrine cells, acinar, and goblet cells also take up protein. Intriguingly, these non-LRE cells did not show lysosomal-based protein degradation; but importantly analysis of the transcripts upregulated in these cells include dab2 and cubn, genes shown previously as being essential to protein uptake. 

      The derivation of zebrafish mono-associated with single strains of microbes paired with HCR to localize and quantify the expression of host protein absorption genes shows that different bacterial strains suppress these genes to variable extents. 

      The analysis of microbiome composition, when host protein absorption is compromised in cubn-/- larvae or by reducing protein in the food, demonstrates that changes to host uptake can alter the abundance of specific microbial taxa like Aeramonas. 

      Weaknesses: 

      The finding that neurons are positive for protein uptake in the single-cell data set is not adequately discussed. It is curious because the cldn:GFP line used for sorting does not mark neurons and if the neurons are taking up mCherry via trans-synaptic uptake from EECs, those neurons should be mCherry+/GFP-; yet methods indicate GFP+ and GFP+/mCherry+ cells were the ones collected and analyzed. 

      We thank the Reviewer for the kind and positive assessment of our work, for suggestions to improve the accessibility and clarity of the manuscript, and for pointing out an issue related to a neuronal population that needs further clarification.

      We confirm that there is a population of neurons that express cldn15la (and cldn15la:GFP). They are not easily visualized by microscopy because IECs express this gene at a relatively much higher level. However, the endogenous cldn15la transcript can be found in a recently published dataset (PMID: 35108531) as well as in ours. We will add a Discussion point to clarify this issue.

      Reviewer #3 (Public review): 

      Summary: 

      Childers et al. address a fundamental question about the complex relationship within the gut: the link between nutrient absorption, microbial presence, and intestinal physiology. They focus on the role of lysosome-rich enterocytes (LREs) and the microbiota in protein absorption within the intestinal epithelium. By using germ-free and conventional zebrafishes, they demonstrate that microbial association leads to a reduction in protein uptake by LREs. Through impressive in vivo imaging of gavaged fluorescent proteins, they detail the degradation rate within the LRE region, positioning these cells as key players in the process. Additionally, the authors map protein absorption in the gut using single-cell sequencing analysis, extensively describing LRE subpopulations in terms of clustering and transcriptomic patterns. They further explore the monoassociation of ex-germ-free animals with specific bacterial strains, revealing that the reduction in protein absorption in the LRE region is strain-specific. 

      Strengths: 

      The authors employ state-of-the-art imaging to provide clear evidence of the protein absorption rate phenotype, focusing on a specific intestinal region. This innovative method of fluorescent protein tracing expands the field of in vivo gut physiology. 

      Using both conventional and germ-free animals for single-cell sequencing analysis, they offer valuable epithelial datasets for researchers studying host-microbe interactions. By capitalizing on fluorescently labelled proteins in vivo, they create a new and specific atlas of cells involved in protein absorption, along with a detailed LRE single-cell transcriptomic dataset. 

      Weaknesses: 

      While the authors present tangible hypotheses, the data are primarily correlative, and the statistical methods are inadequate. They examine protein absorption in a specific, normalized intestinal region but do not address confounding factors between germ-free and conventional animals, such as size differences, transit time, and oral gavage, which may impact their in vivo observations. This oversight can lead to bold conclusions, where the data appear valuable but require more nuance. 

      The sections of the study describing the microbiota or attempting functional analysis are elusive, with related data being overinterpreted. The microbiome field has long used 16S sequencing to characterize the microbiota, but its variability due to experimental parameters limits the ability to draw causative conclusions about the link between LRE activity, dietary protein, and microbial composition. Additionally, the complex networks involved in dopamine synthesis and signalling cannot be fully represented by RNA levels alone. The authors' conclusions on this biological phenomenon based on single-cell data need support from functional and in vivo experiments. 

      We thank the reviewer for their assessment and for pointing out some areas that need to be explained better and/or discussed further.

      The reviewer mentions some potential confounding factors (ie., size differences, transit time, oral gavage) in the gnotobiotic experiments. We would like to convey that these aspects have been addressed in our experimental design and will be clarified in our full in the revised manuscript by adding information to Methods or by adding data statements. Briefly: 1-larval sizes were recorded and found to be similar between GF and monoassociated larvae. A statement will be added to text.; 2-while intestinal transit time has been reported to be affected by microbes in larval zebrafish (PMIDs: 16781702, 28207737, 33352109) and is a topic of interest, it does not represent a confounding factor for our experiments. In our assay, luminal cargo is present at high concentrations throughout the gut and is not limiting at any point during the assay; 3-gavage, which is necessary for quantitative assays, is indeed an experimental manipulation that may somehow alter the subjects (the same is true for microscopy and virtually any research method). However, any potential effects of gavage manipulation would not explain differences between GF and CV animals or alter our conclusions about microbial or dietary effects. We will elaborate on this in the revised Discussion.

      We acknowledge that microbiota composition is prone to relatively high degrees of interindividual and interexperimental variation, and that measuring microbiota composition using 16S rRNA gene sequencing is accompanied by inherent technical limitations such as limited taxonomic resolution, primer bias, etc.  It is important to note that comparable assays such as shotgun metagenomic DNA sequencing are not currently suitable for samples such as larval zebrafish or their dissected digestive tracts where the relative superabundance of host DNA prevents adequate coverage of microbial DNA. However, 16S rRNA gene sequencing remains a mainstream assay in the larger microbial ecology field, has proven effective at revealing important impacts of environmental factors on the gut microbiota (PMIDs: 21346791, 31409661, 31324413). Our results here also illustrate how 16S rRNA gene sequencing can be a useful method to detect perturbations to the zebrafish gut microbiome. Reproducing previous findings, we detected in our samples many of the core zebrafish microbiota taxa that have been identified by other studies (PMIDs: 26339860, 21472014, 17055441). To increase the robustness of our results, we included several biological replicates for each condition, co-housed genotypes and included large sample sizes to minimize environmental variation between groups. Importantly, replicates housed in different tanks showed similar results. We will emphasize these points in the revised Discussion. To further underscore this in the revised manuscript, we will add a beta diversity plot and statistical analysis showing that the microbiome was not significantly affected by our experimental replicates.

      Regarding dopamine pathways, we thank the reviewer for pointing out that the language we used in our interpretation of this and other pathways enriched in our scRNAseq data was too strong. In the revised manuscript, we will soften those conclusions, and instead indicate that these may be areas worthy of future dedicated investigation.

      Finally, the reviewer mentions the use of inadequate statistical methods for some analyses but without specifying or indicating alternative analyses. Only the need to justify the use of two-way ANOVA was made explicit. In this point, we respectfully disagree and would like to emphasize that we use statistical methods that are standards in the field. We will nevertheless add a justification for the use of two-way ANOVA where appropriate. Briefly, the two-way ANOVA test was used to compare fluorescence profiles of gavages cargoes or HCR probes at each level along the length of the LRE region. This test accounts for differences in fluorescence between experimental conditions at each level (binned 30 μm areas) along the LRE region (~300 μm). This test allows us to capture differences in fluorescence between experimental conditions while accounting for heterogeneity in the LRE region.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      The model of phosphotransfer from Y169 IKK to S32 IkBa is compelling and an important new contribution to the field. In fact, this model will not be without controversy, and publishing the work will catalyze follow-up studies for this kinase and others as well. As such, I am supportive of this paper, though I do also suggest some shortening and modification.

      We appreciate the reviewers candid response on the difficulty of this study and the requirement of follow-up studies to confirm a direct transfer of the phosphate. We also have edited the manuscript to make it shorter.

      Generally, the paper is well written, but several figures should be quantified, and experimental reproducibility is not always clear. The first 4 figures are slow-going and could be condensed to show the key points, so that the reader gets to Figures 6 and 7 which contain the "meat" of the paper.

      We have indicated the experimental reproducibility in the methodology section against each assay. We have shortened the manuscript corresponding to sections describing figures 1-4. However, when we talked to some of our colleagues whose expertise do not align with kinases and IKK, we realized that some description were necessary to introduce them to the next figures. Additionally, we added Fig. S6 indicating that the radiolabelled phospho-IKK2 Y169F is unable to transfer its own phosphate group(s) to the substrate IkBa.

      Reviewer #2 (Public Review):

      Phosphorylation of IκBα is observed after ATP removal, although there are ambiguous requirements for ADP.

      We agree with the reviewer that this observation is puzzling. We hypothesize that ADP is simultaneously regulating the transfer process likely through binding to the active site.

      It seems that the analysis hinges on the fidelity of pan-specific phosphotyrosine antibodies.

      We agree with the reviewer. To bolster our conclusion, we used antibodies from two different sources. These were Monoclonal mouse anti-Phospho-Tyrosine (catalogue number: 610000) was from BD Biosciences or from EMD Millipore (catalogue no. 05-321X).

      The analysis often returns to the notion that tyrosine phosphorylation(s) (and critical active site Lys44) dictate IKK2 substrate specificity, but evidence for this seems diffuse and indirect. This is an especially difficult claim to make with in vitro assays, omitting the context of other cellular specificity determinants (e.g., localization, scaffolding, phosphatases).

      We agree with the concerns that the specificity could be dependent on other cellular specificity determinants and toned down our claims where necessary. However, we would like to point out that the specificity of IKK2 towards S32 and S36 of IkBa in cells in response to specific stimuli is well-established. It is also well-established that its non-catalytic scaffolding partner NEMO is critical in selectively bringing IkBa to IKK from a large pool of proteins. The exact mechanism of how IKK2 choose the two serines amongst many others in the substrate is not clear.

      Multiple phosphorylated tyrosines in IKK2 were apparently identified by mass spectrometric analyses, but the data and methods are not described. It is common to find non-physiological post-translational modifications in over-expressed proteins from recombinant sources. Are these IKK2 phosphotyrosines evident by MS in IKK2 immunoprecipitated from TNFa-stimulated cells? Identifying IKK2 phosphotyrosine sites from cells would be especially helpful in supporting the proposed model.

      Mass spectrometric data for identification of phosphotyrosines from purified IKK2 is now incorporated (Figure S3A). Although we have not analyzed IKK2 from TNF-a treated cells in this study, a different study of phospho-status of cellular IKK2 indicated tyrosine phosphorylation (Meyer et al 2013).

      Reviewer #3 (Public Review):

      The identity and purity of the used proteins is not clear. Since the findings are so unexpected and potentially of wide-reaching interest - this is a weakness. Similar specific detection of phospho-Ser/Thr vs phospho-Tyr relies largely on antibodies which can have varying degrees of specificity.

      We followed a stringent purification protocol of several steps (optimized for the successful crystallization of the IKK2) that removed most impurities (PMID: 23776406, PMID: 39227404). The samples analysed with ESI MS did not show any significant contaminating kinase from the Sf9 cells.

      Sequence specific phospho-antibodies used in this study are very well characterized and have been used in the field for years (Basak et al 2007, PMID: 17254973). We agree on the reviewer’s concerns on the pan-specific phospho-antibodies. Since phospho-tyrosine detection is the crucial aspect of this study, we minimized such bias by using pan-specific phosphotyrosine antibodies from two independent sources.

      Reviewer #1 (Recommendations For The Authors):

      I understand that Figure 3 shows that K44M abolishes both S32/26 phosphorylation and tyrosine phosphorylation, but not PEST region phosphorylation. This suggests that autophosphorylation is reflective of its known specific biological role in signal transduction. But I do not understand why "these results strongly suggest that IKK2-autophosphorylation is critical for its substrate specificity". That statement would be supported by a mutant that no longer autophosphorylates, and as a result shows a loss of substrate specificity, i.e. phosphorylates non-specific residues more strongly. Is that the case? Maybe Darwech et al 2010 or Meyer et al 2013 showed this.

      Later figures seem to address this point, so maybe this conclusion should be stated later in the paper.

      We have now clarified this in the manuscript and moved the comment to the next section. We have consolidated the results in Figure 3 and 4 in the previous version into a single figure in Figure. The text has also been modified accordingly.

      Page 10: mentions DFG+1 without a proper introduction. The Chen et al 2014 paper appears to inform the author's interest in Y169 phosphorylation, or is it just an additional interesting finding? Does this publication belong in the Introduction or the Discussion?

      The position of Y169 at the DFG+1 was intriguing and the 2014 article Chen et al further bolstered our interest in this residue to be investigated. We think this publication is important in both sections. 

      To understand the significance of Figure 4D, we need a WT IKK2 control: or is there prior literature to cite? This is relevant to the conclusion that Y169 phosphorylation is particularly important for S32 phosphorylation.

      We have now added a new supplementary figure where activities of WT and Y169F IKK2 towards WT and S32/S36 mutants are compared (Figure S3F). At a similar concentration, the activity of WT-IKK2 is many fold higher than that of YtoF mutants (Fig. 4C). The experiments were performed simultaneously, although samples were loaded on different gels but otherwise processed in a similar way. The corresponding data is now included in the manuscript as Figure S3F.

      The cold ATP quenching experiment is nice for testing the model that Y169 functions as a phospho sink that allows for a transfer reaction. However, there is only a single timepoint and condition, which does not allow for a quantitative analysis. Furthermore, a positive control would make this experiment more compelling, and Y169F mutant should show that cold ATP quenching reduces the phosphorylation of IkBa.

      We thank the reviewer for appreciating our experimental design, and pointing out the concerns. We kept the ATP-time point as the maximum of the non-competition experiment. Also, we took 50mM ATP to compare its competition with highest concentration of ADP used. The idea behind using the maximum time and ATP (comparable to ADP) was to capture the effect of competitive-effect of ATP, if any, that would be maximal in the given assay condition in comparison with the phospho-transfer set up in absence of cold ATP. We agree that finer ranges of ATP concentration and time points would have enabled more quantitative analyses. We have now included data where different time intervals are tested (Figure S5D).

      Why is the EE mutant recognized by anti-phospho-serine antibodies? In Figure 2F.

      We anticipate Serine residues besides those in the activation loop to be phosphorylated when IKK2 is overexpressed and purified from the Sf9 cells. Since Glu (E) mimics phospho-Ser, the said antibody cross reacts with the IKK2-EE that mimics IKK2 phosphorylated at Ser177 and 181.

      Figure 7B is clear, but 7C does not add much.

      We have now removed the Fig. 7C in the current version. Figure 7 is now renumbered as Figure 6 that does not contain the said cartoon.  

      Reviewer #2 (Recommendations For The Authors):

      Regarding the specificity arguments (see above in public review), the authors note that NEMO is very important in IKK specificity, and - if I'm understanding correctly - most of these assays were performed without NEMO. Would the IKK2-NEMO complex change these conclusions?

      NEMO is a scaffolding protein whose action goes beyond the activation of the IKK-complex. In cells, NEMO brings IkBa from a pool of thousands of proteins to its bonafide kinase when the cells encounter specific signals. In other words, NEMO channels IKK-activity towards its bonafide substrate IkBa at that moment. Though direct proof is lacking, it is likely that NEMO present IkBa in the correct pose to IKK such that the S32/S36 region of IkBa is poised for phosphorylation. The proposed mechanism in the current study further ensures the specificity and fidelity of that phosphorylation event. We believe this mechanism will be preserved in the IKK-NEMO complex unless proven otherwise. As shown below, IKK2 undergoes tyrosine autophosphorylation in presence of NEMO.

      Author response image 1.

      The work primarily focuses on Y169 as a candidate target for IKK autophosphorylation. This seems reasonable given the proximity to the ATP gamma phosphate. However, Y188F more potently disrupted IκBα phosphorylation. The authors note that this could be due to folding perturbations, but this caveat would also apply to Y169F. A test for global fold perturbations for both Tyr mutants would be helpful.

      Y188 is conserved in S/T kinases and that in PKA (Y204) has been studied extensively using structural, biochemical and biophysical tools. It was found in case of PKA that Y204 participates in packing of the hydrophobic core of the large lobe. Disruption of this core structure by mutation allosterically affect the activity of the kinase. We also observed similar engagement of Y188 in IKK2’s large lobe, and speculated folding perturbations in analogy with the experimental evidence observed in PKA. What we meant was mutation of Y188 would allosterically affect the kinase activity. Y169 on the other hand is unique at that position, an no experimental evidence on the effect of phospho-ablative mutation of this residue exist in the literature. Hence, we refrained from speculating its effect on the folding or conformational allostery, however, such a possibility cannot be ruled out. 

      I struggled to follow the rationalization of the results of Figure 4D, the series of phosphorylation tests of Y169F against IκBα with combinations of phosphoablative or phosphomimetic variants at Ser32 and Ser36. This experiment is hard to interpret without a direct comparison to WT IKK2.

      We agree with the reviewer’s concerns. Through this experiment we wanted to inform about the importance of Tyr-phosphorylation of IKK2 in phosphorylating S32 of IκBα which is of vital importance in NF-kB signaling. We have now provided a comparison with WT-IKK2 in the supplementary Figure S3F. We hope this will help bring more clarity to the issue.

      MD simulations were performed to compare structures of unphosphorylated vs. Ser-phosphorylated (p-IKK2) vs. Ser+Tyr-phosphorylated (P-IKK2) forms of IKK2. These simulations were performed without ATP bound, and then a representative pose was subject to ADP or ATP docking. The authors note distortions in the simulated P-IKK2 kinase fold and clashes with ATP docking. Given the high cellular concentration of ATP, it seems more logical to approach the MD with the assumption of nucleotide availability. Most kinase domains are highly dynamic in the absence of substrate. Is it possible that the P-IKK2 poses are a result of simulation in a non-physiological absence of bound ATP? Ultimately, this MD observation is linked to the proposed model where ADP-binding is required for efficient phospho-relay to IκBα. Therefore, this observation warrants scrutiny. Perhaps the authors could follow up with binding experiments to directly test whether P-IKK2 binds ADP and fails to bind ATP.

      We thank that reviewer for bringing up this issue. This is an important issue and we must agree that we don’t fully understand it yet. We took more rigorous approach this time where we used three docking programs: ATP and ADP were docked to the kinase structures using LeDock and GOLD followed by rescoring with AutoDock Vina. We found that ATP is highly unfavourable to P-IKK2 compared to ADP. To further address these issues, we performed detailed MM-PBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) analyses after MD-simulation to estimate binding free energies and affinities of ADP and ATP for each of the three differently phosphorylated states of IKK2. These analyses (Figure S4 E and F) clearly indicate that phosphorylated IKK2 have much higher preference for ADP over ATP. However, it does not negate ATP-binding by P-IKK2 in a different pose that may not support kinase activity.

      We could not perform any binding experiment because of the following reason. We incubated FL IKK2 WT with or without cold ATP for 30mins, and then incubated these samples with <sup>32</sup>P-ATP and analysed the samples by autoradiography after resolving them on a 10% SDS-PAGE. We found that even after pre-incubation of the kinase with excess cold ATP it still underwent autophosphorylation when radioactive ATP was added as shown below. This prevented us from doing direct binding experiment with ATP as it would not represent true binding event. We also noticed that after removal of bulk ATP post autophosphorylation, phosphorylated IKK2 is capable of further autophosphorylation when freshly incubated with ATP. We have not been able to come up with a condition that would only account for binding of ATP and not hydrolysis. 

      Author response image 2.

      The authors could comment on whether robust phosphorylation of NEMO was expected (Figure 1D). On a related note, why is NEMO a single band in the 1D left panel and double bands on the right?

      No, we did not expect robust phosphorylation of NEMO. However, robust phosphorylation of NEMO is observed only in the absence of IκBα. In presence of IκBα, phosphorylation of NEMO goes down drastically. These were two different preparations of NEMO. When TEV-digestion to remove His-tag is incomplete it gives two bands as the tagged and untagged versions cannot be separated in size exclusion chromatography which is the final step.

      Page 14, line 360. "...observed phosphorylation of tyrosine residue(s) only upon fresh ATP-treatment..." I'm not sure I understand the wording here (or the relevance of the citation). Is this a comment on unreported data demonstrating the rapid hydrolysis of the putative phosphotyrosine(s)? If so, that would be helpful to clarify and report in the supporting information.

      In our X-ray crystallographic studies with phosphorylated IKK2 we failed to observe any density of phosphate moiety. Furthermore, this IKK2 showed further autophosphorylation when incubated with fresh ATP. These two observations lead us to believe that some of the autophosphorylation are transient in nature. However, quantitative kinetic analyses of this dephosphorylation have not been performed.

      Figure S3 middle panel: The PKA substrate overlaid on the IKK2 seems sterically implausible for protein substrate docking. Is that just a consequence of the viewing angle? On a related note, Figure S3 may be mislabeled as S4 in the main text).

      It is a consequence of the viewing angle. Also, we apologize for this inadvertent mislabelling. It has been corrected in the current version.

      Reviewer #3 (Recommendations For The Authors):

      The detection of phosphorylated amino acids relies largely on antibodies which can have a varying degree of specificity. An alternative detection mode of the phospho-amino acids for example by MS would strengthen the evidence.

      We agree with the concern of specificity bias of antibodies. We tried to minimize such bias by using two different p-Tyr antibodies as noted previously and also in the methodology section. We were also able to detect phospho-tyrosine residues by MS/MS analyses, representative spectra are now added (Figure S3A).

      IKK2 purity - protocol states "desired purity". What was the actual purity and how was it checked? MS would be useful to check for the presence of other kinases.

      Purity of the recombinantly purified IKK2s are routinely checked by silver staining. A representative silver stained SDS-PAGE is shown (Figure S1C). It may be noted that, there’s a direct correlation of expression level and solubility, and hence purification yield and quality with the activity of the kinase. Active IKK2s express at much higher level and yields cleaner prep. In our experience, inactive IKKs like K44M give rise to poor yield and purity. We analysed K44M by LC MS/MS to identify other proteins present in the sample. We did not find any significant contaminant kinase the sample (Figure S1D). The MS/MS result is attached.

      Figure 1C&D: where are the Mw markers? What is the size of the band? What is the MS evidence for tyrosine phosphorylation?

      We have now indicated MW marker positions on these figures.

      MS/MS scan data for the two peptides containing pTyr169 and pTyr188 are shown separately (Figure S3A).

      Figure 2F: Why is fresh ATP necessary? Why was Tyr not already phosphorylated? The kinetics of this process appear to be unusual when the reaction runs to completion within 5 minutes ?

      As stated earlier, we believe some of the autophosphorylation are transient in nature. We think the Tyr-phosphorylation are lost due to the action of cellular phosphatases. We agree with the concern of the reviewer that, the reaction appears to reach completion within 5 minutes in Fig 2F. We believe it is probably due to the fact that the amount of kinase used in this study exceeds the linear portion of the dynamic range of the antibody used. Lower concentration of the kinase do show that reaction does not reach completion until 60mins as shown in Fig. 2A.

      Figure 3: Can the authors exclude contamination with a Tyr kinase in the IKK2-K44M prep? The LC/MS/MS data should be included.

      We have reanalysed the sample on orbitrap to check if there’s any Tyr-kinase or any other kinase contamination. We used Spodoptera frugiperda proteome available on the Uniprot website for this analysis. These analyses confirmed that there’s no significant kinase contaminant present in the fraction (Figure S1D).

      What is the specificity of IKK-2 Inhibitor VII? Could it inhibit a contaminant kinase?

      This inhibitor is highly potent against IKK2 and the IKK-complex, and to a lesser extent to IKK1. No literature is available on its activity on other kinases. In an unrelated study, this compound was used alongside MAPK inhibitor SB202190 wherein they observed completely different outcomes of these two inhibitors (Matou-Nasri S, Najdi M, AlSaud NA, Alhaidan Y, Al-Eidi H, Alatar G, et al. (2022) Blockade of p38 MAPK overcomes AML stem cell line KG1a resistance to 5-Fluorouridine and the impact on miRNA profiling. PLoS ONE 17(5):e0267855. https://doi.org/10.1371/journal.pone.0267855). This study indirectly proves that IKK inhibitor VII does not fiddle with the MAPK pathways. We have not found any literature on the non-specific activity of this inhibitor.

      Figure 6B: the band corresponding to "p-IkBa" appears to be similar in the presence of ADP (lanes 4-7) or in the absence of ADP but the presence of ATP (lane 8).

      Radioactive p-IκBα level is more when ADP is added than in absence of ADP. In presence of cold ATP, radioactive p-IκBα level remains unchanged. This result strongly indicate that the addition of phosphate group to IκBα happens directly from the radioactively labelled kinase that is not competed out by the cold ATP.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this work, Harpring et al. investigated divisome assembly in Chlamydia trachomatis serovar L2 (Ct), an obligate intracellular bacterium that lacks FtsZ, the canonical master regulator of bacterial cell division. They find that divisome assembly is initiated by the protein FtsK in Ct by showing that it forms discrete foci at the septum and future division sites. Additionally, knocking down ftsK prevents divisome assembly and inhibits cell division, further supporting their hypothesis that FtsK regulates divisome assembly. Finally, they show that MreB is one of the last chlamydial divisome proteins to arrive at the site of division and is necessary for the formation of septal peptidoglycan rings but does not act as a scaffold for division assembly as previously proposed.

      Strengths:

      The authors use microscopy to clearly show that FtsK forms foci both at the septum as well as at the base of the progenitor cell where the next septum will form. They also show that the Ct proteins PBP2, PBP3, MreC, and MreB localize to these same sites suggesting they are involved in the divisome complex.

      Using CRISPRi the authors knock down ftsK and find that most cells are no longer able to divide and that PBP2 and PBP3 no longer localized to sites of division suggesting that FtsK is responsible for initiating divisome assembly. They also performed a knockdown of pbp2 using the same approach and found that this also mostly inhibited cell division. Additionally, FtsK was still able to localize in this strain, however PBP3 did not, suggesting that FtsK acts upstream of PBP2 in the divisome assembly process while PBP2 is responsible for the localization of PBP3.

      The authors also find that performing a knockdown of ftsK also prevents new PG synthesis further supporting the idea that FtsK regulates divisome assembly. They also find that inhibiting MreB filament formation using A22 results in diffuse PG, suggesting that MreB filament formation is necessary for proper PG synthesis to drive cell division.

      Overall the authors propose a new hypothesis for divisome assembly in an organism that lacks FtsZ and use a combination of microscopy and genetics to support their model that is rigorous and convincing. The finding that FtsK, rather than a cytoskeletal or "scaffolding" protein is the first division protein to localize to the incipient division site is unexpected and opens up a host of questions about its regulation. The findings will progress our understanding of how cell division is accomplished in bacteria with non-canonical cell wall structure and/or that lack FtsZ.

      Weaknesses:

      No major weaknesses were noted in the data supporting the main conclusions. However, there was a claim of novelty in showing that multiple divisome complexes can drive cell wall synthesis simultaneously that was not well-supported (i.e. this has been shown previously in other organisms). In addition, there were minor weaknesses in data presentation that do not substantially impact interpretation (e.g. presenting the number of cells rather than the percentage of the population when quantifying phenotypes and showing partial western blots instead of total western blots).

      We agree with the weaknesses identified by the reviewer. We removed the statements in the Results and Discussion that multiple independent divisome complexes can simultaneously direct PG synthesis. We presented the data in Figs. 3-5 as % of the cells in the population, and complete western blots are shown in Supp. Fig. S1.

      Reviewer #2 (Public review):

      Summary:

      Chlamydial cell division is a peculiar event, whose mechanism was mysterious for many years. C. trachomatis division was shown to be polar and involve a minimal divisome machinery composed of both homologues of divisome and elongasome components, in the absence of an homologue of the classical division organizer FtsZ. In this paper, Harpring et al., show that FtsK is required at an early stage of the chlamydial divisome formation.

      Strengths:

      The manuscript is well-written and the results are convincing. Quantification of divisome component localization is well performed, number of replicas and number of cells assessed are sufficient to get convincing data. The use of a CRISPRi approach to knock down some divisome components is an asset and allows a mechanistic understanding of the hierarchy of divisome components.

      Weaknesses:

      The authors did not analyse the role of all potential chlamydial divisome components and did not show how FtsK may initiate the positioning of the divisome. Their conclusion that FtsK initiates the assembly of the divisome is an overinterpretation and is not backed by the data. However, data show convincingly that FtsK, if perhaps not the initiator of chlamydial division, is definitely an early and essential component of the chlamydial divisome.

      The following statement has been included in the Discussion (pg. 16 of the revised manuscript)  “Although we focused our study on a subset of the divisome and elongasome proteins that Chlamydia expresses (bolded in Fig. 6G), our results support our conclusion that chlamydial budding is dependent upon a hybrid divisome complex and that FtsK is required for the assembly of this hybrid divisome. At this time, we cannot rule out that other proteins act upstream of FtsK to initiate divisome assembly in this obligate intracellular bacterial pathogen.”

      We will soon be submitting another manuscript that addresses how FtsK specifies the site of divisome assembly. This work is too extensive to be included in this manuscript.

      Reviewer #3 (Public review):

      Summary:

      The obligate intracellular bacterium Chlamydia trachomatis (Ct) divides by binary fission. It lacks FtsZ, but still has many other proteins that regulate the synthesis of septal peptidoglycan, including FtsW and FtsI (PBP3) as well as divisome proteins that recruit and activate them, such as FtsK and FtsQLB. Interestingly, MreB is also required for the division of Ct cells, perhaps by polymerizing to form an FtsZ-like scaffold. Here, Harpring et al. show that MreB does not act early in division and instead is recruited to a protein complex that includes FtsK and PBP2/PBP3. This indicates that Ct cell division is organized by a chimera between conserved divisome and elongasome proteins. Their work also shows convincingly that FtsK is the earliest known step of divisome activity, potentially nucleating the divisome as a single protein complex at the future division site. This is reminiscent of the activity of FtsZ, yet fundamentally different.

      Strengths:

      The study is very well written and presented, and the data are convincing and rigorous. The data underlying the proposed localization dependency order of the various proteins for cell division is well justified by several different approaches using small molecule inhibitors, knockdowns, and fluorescent protein fusions. The proposed dependency pathway of divisome assembly is consistent with the data and with a novel mechanism for MreB in septum synthesis in Ct.

      Weaknesses:

      The paper could be improved by including more information about FtsK, the "focus" of this study. For example, if FtsK really is the FtsZ-like nucleator of the Ct divisome, how is the Ct FtsK different sequence-wise or structurally from FtsK of, e.g. E. coli? Is the N-terminal part of FtsK sufficient for cell division in Ct like it is in E. coli, or is the DNA translocase also involved in focus formation or localization? Addressing those questions would put the proposed initiator role of FtsK in Ct in a better context and make the conclusions more attractive to a wider readership.

      We will be submitting another manuscript soon that details the conserved domain organization of FtsK from different bacteria, and the role of the various domains of chlamydial FtsK (including the N-terminus and the C-terminal translocase domain) in directing its localization in dividing Chlamydia. We have added text to the discussion (pg. 16 of the revised manuscript) that describes the sequence homology of chlamydial FtsK to FtsK from E. coli.

      Another weakness is that the title of the paper implies that FtsK alone initiates divisome assembly. However, the data indicate only that FtsK is important at an early stage of divisome assembly, not that it is THE initiator. I suggest modifying the title to account for this--perhaps "FtsK is required to initiate....".

      We agree with the reviewer and modified the title to “FtsK is Critical for the Assembly of the Unique Divisome Complex of the FtsZ-less Chlamydia trachomatis”. We have also modified the text throughout to indicate that FtsK is required for the assembly of the hybrid divisome of Chlamydia

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Suggestions for improvement (mostly minor):

      (1) For several of the graphs, the authors plot the number of cells with a given phenotype on the y-axis, but then describe percentages of cells in the text. It would make it clearer if all the graphs had the percentage of cells on the y-axis instead.

      We have modified the figures to indicate the percentage of cells on the y-axis with a given phenotype.

      (2) In Figures 3, 4, and 5 the authors show separate graphs for plus/minus drug or inducer. These should be on the same graph as they are directly comparing these two different conditions. Having them on separate graphs makes it less clear whether these differences are significant between the two conditions

      We modified Fig. 4 to show +/- inducer in ftsk and pbp2 knockdown strains in the same graph.  Regarding Figures 3 and 5, we believe the figures in the original submission effectively demonstrate the +/- drug conditions, so these figures remain unchanged in the revised manuscript.

      (3) In Figure 2 the authors show microscopy of the colocalization of FtsK with several other divisome proteins from Ct. Quantification of the colocalization of FtsK with these other proteins would provide a more holistic understanding of their colocalization and help further support their argument that FtsK initiates the assembly of the divisome.

      Supp. Fig. S4A of the revised manuscript contains images showing the colocalization of FtsK with the fusions at the septum and the base of dividing cells, and the colocalization of FtsK with the fusions that are only at the base of dividing cells. Supp. Fig. S4B quantified the percentage of dividing cells where FtsK overlaps the localization of each of the fusions at the septum, at the septum and the base, and at the base alone.

      (4) In Figure 6 the authors mention that the PG ring was at a slight angle relative to the MOMP-stained septum. What is the significance of this? The authors mention it several times but do not explain its relevance to divisome assembly. It is not really evident in the images presented.

      We mention in the discussion pgs. 17-18 of the revised manuscript that “The relevance of the angled orientation of PG and MreC rings relative to the MOMP-stained septum in division intermediates is unclear. However, it appears to be a conserved feature of the cell division process and may arise because the divisome proteins are often positioned slightly above or below the plane of the MOMP-stained septum. The positioning of divisome proteins above or below the septum is indicated in Figs. 1 and 2.

      We included cartoons in Fig. 6C of the revised manuscript to assist the reader in visualizing the angled orientation of the PG ring relative to the MOMP-stained septum.

      (5) In line 270 the authors claim that "these are the first data in any system to suggest that septal PG synthesis/modification is simultaneously directed by multiple independent divisome complexes." However, their experiments do not demonstrate that multiple divisome complexes are active at the same time. They show that multiple foci of FtsK etc. are present at sites where PG synthesis has occurred, but that does not necessarily mean that each focus/complex was actively synthesizing PG at the same time. Moreover, similar approaches were used to support a claim that septal PG synthesis is directed by multiple discrete divisome complexes previously (e.g. in Figure 1 of Bisson-Filho et al. 2017 (PMID: 28209898) in Bacillus subtilis and in Perez et al 2021 (PMID: 33269494) in Streptococcus pneumoniae). This claim is not central to the main conclusions of the study and could just be removed.

      This statement has been removed from the Results and the Discussion.

      (6) In Figure 6B the authors see three distinct FtsK foci. Why is this the only place in the manuscript where they see three foci? They mentioned previously that they saw foci at the septum and at the base of the progenitor mother cell, but why are there three foci here?

      The vast majority of dividing cells displayed one foci at the septum and/or the base.  Representative images were chosen that reflected the localization profiles observed in the majority of cells. While we observed cells with  multiple foci, as shown in Figure 6C, these cells were relatively rare   (~2% of cells for all the divisome proteins in 3 independent experiments).  Since  the number of cells with multiple foci were relatively rare, we chose to group these cells with the cells that had single foci at the septum, the septum and base, or base alone categories in the quantification shown in Fig. 2C. This is stated in the legend of Fig. 2 of the revised manuscript.

      (7) The Discussion section is lacking a couple of things that would put the data in a broader context. Can the authors speculate on how FtsK knows how to find the division site? I.e. what might be upstream of FtsK localization? Additionally, the authors do not talk about the FtsK sequence or domains at any point in the paper. Does Ct FtsK have a similar sequence/structure to FtsKs from other bacteria? Are there any differences in sequence/structure that might tell us about its function in Ct?

      We will be submitting another manuscript soon that examines how the site of assembly of the divisome is defined in dividing Chlamydia. This manuscript will also define the localization of the different sub-domains of chlamydial FtsK during cell division.  For this manuscript, we added a paragraph in the Discussion (pg. 16 of the revised manuscript) that states the domain organization is conserved in FtsK proteins from different bacteria. This paragraph includes information regarding the % sequence identity of the C-terminus and the N-terminus of chlamydial FtsK when compared to E. coli FtsK.

      (8) For Supplementary Figure S1B-C. The authors should show the full blots rather than just the single band of the protein of interest to show that the antibodies are specific. Additionally, the authors should include a loading control to show that they loaded the same amount of protein for each sample.

      We have included the full blots in Supp. Fig. S1 of the revised manuscript. We do not see the need for including a loading control for these blots because we are not making arguments about the relative level of the proteins that were assayed. We only use the blots to show that the fusion proteins are primarily a single species of the predicted molecular mass.

      (9) In Supplementary Figure S4A the authors use RT-qPCR to measure ftsK and pbp2 transcript levels. Since they have antibodies against these proteins, they should also include Western blots to show that the proteins are not being produced when targeted using CRISPRi.

      We have included data in Supp. Fig. S5E of the resubmission that indicates foci of FtsK and PBP2 could not be detected following the knockdown of ftsk and pbp2. We feel that these data support our conclusion that the induced expression of dCas12 in the the ftsk and pbp2 knockdown strains results in the downregulation of the endogenous FtsK and PBP2 polypeptides.

      (10) In lines 261-262 the authors say that "PG organization was the same or differed at the septum." What is the PG organization being compared to? Same or different from what?

      We agree with the reviewer that the text in lines 261-262 in the original submission was confusing.  The text has been modified.

      (11) Lines 201-215 the authors refer to Supplementary Figure S3 throughout this section, but they should refer to Supplementary Figure S4.

      This has been corrected.

      Reviewer #2 (Recommendations for the authors):

      I am not convinced that this paper shows that FtsK initiates the assembly of the divisome since the authors did not analyse the role and localization of all other chlamydial divisome components. Out of the ten homologues of divisome and elongasome components encoded by C. trachomatis genome, only five are investigated in this study. There is no explanation about how these five were chosen.

      We state on pg. 16 of the revised manuscript that “Although we focused our study on a subset of the divisome and elongasome proteins that Chlamydia expresses (bolded in Fig. 6G), our results support our conclusion that chlamydial budding is dependent upon a hybrid divisome complex and that FtsK is required for the assembly of this hybrid divisome. At this time, we cannot rule out that other proteins act upstream of FtsK to initiate divisome assembly in this obligate intracellular bacterial pathogen.

      Results convincingly indicate that FtsK is an early divisome component, but proofs are lacking to indicate that it initiates the divisome formation. Indeed, the authors do not show how FtsK would be the first protein to selectively accumulate at a given location to initiate the divisome formation. For this reason, the model they propose at the end of their study is not backed by sufficient data, to my opinion.

      We agree with the reviewer that our data does not show that FtsK initiates divisome assembly. The title of the manuscript has been modified to “FtsK is Critical for the Assembly of the Unique Divisome Complex of the FtsZ-less Chlamydia trachomatis” and the text throughout has been modified to indicate that FtsK is the first protein we assayed that associates with nascent divisomes at the base of dividing cells. We will soon be submitting another manuscript that details how FtsK is recruited to a specific site to initiate nascent divisome assembly, This work is too extensive to be included in this manuscript.

      There are also discrepancies in the number of cells analysed to quantify the localization of divisome components, ranging from 50 to 250 cells. The authors could better explain why there are such variations.

      There were differences in the number of cells analyzed in the various experiments, but in every instance the effect of inhibitors (A22 and mecillinam) or ftsk and pbp2 knockdown on divisome assembly was statistically significant.

      There are a few mistakes in the text regarding figure numbering (Figure S4 is mentioned as S3 in the text). Figures 5B and D are not specifically cited.

      These mistakes have been corrected in the revised manuscript.

      Line 261-262: the sentence starting "Our imaging analysis.." is not clear to me.

      We agree with the reviewer that the text in lines 261-262 was confusing.  The text has been modified (pg. 14 of the revised manuscript).

      Line 270-271: there are insufficient proofs to say that there are multiple independent divisome complexes. This is in my opinion an overinterpretation of the data, since there is no proof that these complexes are independent.

      This statement has been removed from the text.

      A few details are lacking in the figure legends:

      Figure 2C: when was the expression of the different mCherry and 6xHis constructs induced?

      The onset and length of the induction of the fusions have been included in the legend of Fig. 2.

      Bars are sometimes mentioned as uM and should be um. Bars sizes, number of replicates, and/or meaning of the error bars are lacking in legends of Figures S2, S3, and S4

      This has been corrected in the revised manuscript.

      The consistency of Figures could be improved between Figures 3A, 4A, B, and 5A. The results of treated cells could be always shown as dark grey. It would help the reader.

      We have used consistent coloring in Figs. 3-5 to indicate the treated cells.

      Reviewer #3 (Recommendations for the authors):

      (1) Lines 113-118: do Ct cells increase in size as they get closer to starting division? If so, could a pseudo-time course (demograph) be done to bolster the evidence that the base foci formed mainly in predivisional cells and not newborn cells? This evidence might be more convincing than the data in Figures 1F and G.

      Chlamydial cells in the population were heterogeneous in size at the timepoint we are studying. This observation is consistent with previous reports in the literature (Liechti et al.,2021). While we agree that a pseudo-time course could potentially bolster the evidence about when FtsK foci appear, we believe our current analysis sufficiently demonstrates that basal foci of FtsK appear prior to the appearance of new buds at the base of dividing cells.

      (2) Figure 3E: It looks like MreC localization to foci doesn't strictly require MreB polymerization. Is this known for E. coli or other species?

      To our knowledge, MreC assembly into a filament has not been shown to be dependent upon MreB in other bacteria.  In Caulobacter crescentus, MreC forms a helical structure that is not dependent upon MreB or MreB filament formation (Dye et al., 2005. PNAS; Divakaruni et al., 2005. PNAS).

      (3) Figure 5E: why is nearly half of PBP2 and PBP3 still localized to foci at the membrane even after treatment with mecillinam? This suggests, as the authors mention, that mecillinam reduces the efficiency of localization to the divisome but does not eliminate it. Any ideas why?

      At this time, we do not know why inhibiting the catalytic activity of PBP2 with mecillinam does not fully prevent the association of PBP2 with the chlamydial divisome. We have included a statement in the Results (pg. 13 of the revised manuscript) that inhibiting the catalytic activity of PBP2 prevents it from efficiently associating with or maintaining its association with polarized divisome complexes.

      (4) Line 262-263: This sentence is confusing-please rephrase. The same as what? Differed from what?

      We agree with the reviewer. The wording in lines 262-263 of the original submission has been modified.  

      (5) Lines 265-267 and Figure 6: Adding cartoon schematics might help readers visualize cell orientations in Fig. 6 (especially 6B).

      Cartoons have been added to Fig. 6C (Fig. 6B in the original submission) to orient the reader.

      (6) Line 294-298: Do the authors think that the residual 5-10% of PG foci after FtsK knockdown is due to the ability of residual FtsK to organize divisomes?

      We show that knockdown of FtsK is not complete, and while we cannot be certain, it is likely, that the PG foci detected in FtsK knockdown cells is due to the ability of the residual FtsK to organize divisomes that direct PG synthesis.

      (7) Do the authors have any evidence that FtsK foci are mobile like treadmilling FtsZ?

      We have not performed real-time imaging studies, and we currently have no evidence that FtsK foci are mobile.

      (8) FtsK foci here are reminiscent of mobile foci formed by the FtsK-like SpoIIIE at the Bacillus subtilis sporulation septum. This might be a good idea to mention in the Discussion. Is it possible that Ct FtsK is also involved in coordinating chromosome partitioning through the developing septum? (That is another reason why it would be useful to know if the translocase domain was dispensable for localization/activity).

      We are currently preparing another manuscript that documents the contribution of the various domains of FtsK to its localization profile and whether the division defect in ftsk knockdown cells can be suppressed by specific subdomains of FtsK. This manuscript not only will include these data, it will also include experiments that address how the site of polarized budding is defined. In the revised manuscript, we have included a description of how the domain organization of chlamydial FtsK is similar to E. coli FtsK (pg. 16 of revision). Chlamydial FtsK also has a similar domain organization as SpoIIIE from B. subtilis. The C-terminal catalytic domain of SpoIIIE is 45% identical to chlamydial FtsK. The N-terminus of SpoIIIE is predicted to encode 4 transmembrane spanning helices, like chlamydial FtsK. However, the N-terminus of SpoIIIE shares no sequence homology with the N-terminus of chlamydial FtsK.  We have not included the similar domain organization of SpoIIIE and chlamydial FtsK in the revised manuscript.

      (9) It seems that FtsK foci localize to a particular spot opposite from the active septum, although how this spot is specified is not clear. Is there any geometric clue for FtsK's localization like there is for Min-specified FtsZ localization?

      As mentioned above, we are currently preparing another manuscript that documents our efforts to understand how the site of polarized budding is defined.  This analysis is too extensive to include in this study.

      (10) As mentioned in the Summary, do the authors know whether the N-terminal membrane binding part of FtsK (FtsKn) sufficient for localization/divisome assembly in Ct as it is in other species? Oullette et al. 2012 showed that FtsKn could interact with MreB in BACTH.

      We are currently preparing another manuscript that documents the contribution of the various domains of FtsK to its localization profile.

      (11) The previous BACTH result with MreB and FtsKn implies that this interaction is direct, yet the current data suggest that this is not the case. Can the authors comment on this? Is this due to bridging effects inherent in the BACTH system?

      We have not presented any data to indicate that FtsK and MreB do not interact. We have only shown that FtsK localization is not dependent upon MreB filament formation (Fig. 3).

      (12) The FtsZ-independent role of FtsK in nucleating the divisome suggests that Ct FtsK may differ from other FtsKs structurally - can this be explored, perhaps with AlphaFold 3?

      As mentioned above, we have included a paragraph in the discussion of the revised manuscript (pg. 16 of the revised manuscript) that states the domain organization of chlamydial FtsK is similar to E.coli FtsK. This conserved domain organization is evident when we view the structures of the proteins using Alphafold.

      (13) Typo on line 559: should be HeLa.

      This has been corrected.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      The Bagnat and Rawls groups' previous published work (Park et al., 2019) described the kinetics and genetic basis of protein absorption in a specialized cell population of young vertebrates termed lysosome-rich enterocytes (LREs). In this study they seek to understand how the presence and composition of the microbiota impacts the protein absorption function of these cells and reciprocally, how diet and intestinal protein absorption function impact the microbiome.

      Strengths of the study include the functional assays for protein absorption performed in live larval zebrafish, which provides detailed kinetics on protein uptake and degradation with anatomic precision, and the gnotobiotic manipulations. The authors clearly show that the presence of the microbiota or of certain individual bacterial members slows the uptake and degradation of multiple different tester fluorescent proteins.

      To understand the mechanistic basis for these differences, the authors also provide detailed single-cell transcriptomic analyses of cells isolated based on both an intestinal epithelial cell identity (based on a transgenic marker) and their protein uptake activity. The data generated from these analyses, presented in Figures 3-5, are valuable for expanding knowledge about zebrafish intestinal epithelial cell identities, but of more limited interest to a broader readership. Some of the descriptive analysis in this section is circular because the authors define subsets of LREs (termed anterior and posterior) based on their fabp2 expression levels, but then go on to note transcriptional differences between these cells (for example in fabp2) that are a consequence of this initial subsetting.

      Inspired by their single-cell profiling and by previous characterization of the genes required for protein uptake and degradation in the LREs, the authors use quantitative hybridization chain reaction RNA-fluorescent in situ hybridization to examine transcript levels of several of these genes along the length of the LRE intestinal region of germ-free versus mono-associated larvae. They provide good evidence for reduced transcript levels of these genes that correlate with the reduced protein uptake in the mono-associated larval groups.

      The final part of the study (shown in Figure 7) characterized the microbiomes of 30-day-old zebrafish reared from 6-30 days on defined diets of low and high protein and with or without homozygous loss of the cubn gene required for protein uptake. The analysis of these microbiomes notes some significant differences between fish genotypes by diet treatments, but the discussion of these data does not provide strong support for the hypothesis that "LRE activity has reciprocal effects on the gut microbiome". The most striking feature of the MDS plot of Bray Curtis distance between zebrafish samples shown in Figure 7B is the separation by diet independent of host genotype, which is not discussed in the associated text. Additionally, the high protein diet microbiomes have a greater spread than those of the low protein treatment groups, with the high protein diet cubn mutant samples being the most dispersed. This pattern is consistent with the intestinal microbiota under a high protein diet regimen and in the absence of protein absorption machinery being most perturbed in stochastic ways than in hosts competent for protein uptake, consistent with greater beta dispersal associated with more dysbiotic microbiomes (described as the Anna Karenina principle here: https://pubmed.ncbi.nlm.nih.gov/28836573/). It would be useful for the authors to provide statistics on the beta dispersal of each treatment group.

      Overall, this study provides strong evidence that specific members of the microbiota differentially impact gene expression and cellular activities of enterocyte protein uptake and degradation, findings that have a significant impact on the field of gastrointestinal physiology. The work refines our understanding of intestinal cell types that contribute to protein uptake and their respective transcriptomes. The work also provides some evidence that microbiomes are modulated by enterocyte protein uptake capacity in a diet-dependent manner. These latter findings provide valuable datasets for future related studies.

      We thank the Reviewer for their thorough and kind assessment. We appreciate the suggestion for edits and for pointing out areas that needed further clarification.

      One point in need of further explanation is the use fabp6 (referred to as fabp2 by the reviewer) to define anterior LREs and their gene expression pattern, which includes high levels of fabp6, something that was deemed a “circular argument” by the reviewer.  The rationale for using fabp6 as a reference is that we were able to define its spatial pattern in relation to other LRE markers and the neighboring ileocyte population using transgenic markers (Lickwar et al., 2017; Wen et al., 2021). Thus, far from being a circular argument, using fabp6 allowed us to identify other markers that are differentially expressed between anterior and posterior LREs, which share a core program that we highlight in our study. In the revised manuscript, we clarified this point (lines 166 – 169).

      We followed the Reviewer’s suggestion to test if LRE activity and dietary protein affected beta dispersal. Our analyses revealed that beta dispersion was not significantly different between our experimental conditions. We added details about this analysis (lines 384 – 386) and a new supplemental figure panel (Figure S7C).

      Reviewer #2 (Public review):

      Summary:

      The authors set out to determine how the microbiome and host genotype impact host protein-based nutrition.

      Strengths:

      The quantification of protein uptake dynamics is a major strength of this work and the sensitivity of this assay shows that the microbiome and even mono-associated bacterial strains dampen protein uptake in the host by causing down-regulation of genes involved in this process rather than a change in cell type.

      The use of fluorescent proteins in combination with transcript clustering in the single cell seq analysis deepens our understanding of the cells that participate in protein uptake along the intestine. In addition to the lysozome-rich enterocytes (LRE), subsets of enteroendocrine cells, acinar, and goblet cells also take up protein. Intriguingly, these non-LRE cells did not show lysosomal-based protein degradation; but importantly analysis of the transcripts upregulated in these cells include dab2 and cubn, genes shown previously as being essential to protein uptake.

      The derivation of zebrafish mono-associated with single strains of microbes paired with HCR to localize and quantify the expression of host protein absorption genes shows that different bacterial strains suppress these genes to variable extents.

      The analysis of microbiome composition, when host protein absorption is compromised in cubn-/- larvae or by reducing protein in the food, demonstrates that changes to host uptake can alter the abundance of specific microbial taxa like Aeramonas.

      Weaknesses:

      The finding that neurons are positive for protein uptake in the single-cell data set is not adequately discussed. It is curious because the cldn:GFP line used for sorting does not mark neurons and if the neurons are taking up mCherry via trans-synaptic uptake from EECs, those neurons should be mCherry+/GFP-; yet methods indicate GFP+ and GFP+/mCherry+ cells were the ones collected and analyzed.

      We thank the Reviewer for the kind and positive assessment of our work, for suggestions to improve the accessibility and clarity of the manuscript, and for pointing out an issue related to a neuronal population that needed further clarification.

      It turns out that there is a population of neurons that express cldn15la. They are not easily visualized by microscopy because IECs express this gene much more highly. However, the endogenous cldn15la transcripts can be found in neurons as shown in a recently published dataset (PMID: 35108531) as well as in this study We added a discussion point to clarify this issue (lines 463 – 465).

      Reviewer #3 (Public review):

      Summary:

      Childers et al. address a fundamental question about the complex relationship within the gut: the link between nutrient absorption, microbial presence, and intestinal physiology. They focus on the role of lysosome-rich enterocytes (LREs) and the microbiota in protein absorption within the intestinal epithelium. By using germ-free and conventional zebrafishes, they demonstrate that microbial association leads to a reduction in protein uptake by LREs. Through impressive in vivo imaging of gavaged fluorescent proteins, they detail the degradation rate within the LRE region, positioning these cells as key players in the process. Additionally, the authors map protein absorption in the gut using single-cell sequencing analysis, extensively describing LRE subpopulations in terms of clustering and transcriptomic patterns. They further explore the monoassociation of ex-germ-free animals with specific bacterial strains, revealing that the reduction in protein absorption in the LRE region is strain-specific.

      Strengths:

      The authors employ state-of-the-art imaging to provide clear evidence of the protein absorption rate phenotype, focusing on a specific intestinal region. This innovative method of fluorescent protein tracing expands the field of in vivo gut physiology.

      Using both conventional and germ-free animals for single-cell sequencing analysis, they offer valuable epithelial datasets for researchers studying host-microbe interactions. By capitalizing on fluorescently labelled proteins in vivo, they create a new and specific atlas of cells involved in protein absorption, along with a detailed LRE single-cell transcriptomic dataset.

      Weaknesses:

      While the authors present tangible hypotheses, the data are primarily correlative, and the statistical methods are inadequate. They examine protein absorption in a specific, normalized intestinal region but do not address confounding factors between germ-free and conventional animals, such as size differences, transit time, and oral gavage, which may impact their in vivo observations. This oversight can lead to bold conclusions, where the data appear valuable but require more nuance.

      The sections of the study describing the microbiota or attempting functional analysis are elusive, with related data being overinterpreted. The microbiome field has long used 16S sequencing to characterize the microbiota, but its variability due to experimental parameters limits the ability to draw causative conclusions about the link between LRE activity, dietary protein, and microbial composition. Additionally, the complex networks involved in dopamine synthesis and signalling cannot be fully represented by RNA levels alone. The authors' conclusions on this biological phenomenon based on single-cell data need support from functional and in vivo experiments.

      We thank the Reviewer for their assessment and for pointing out some areas that needed to be explained better and/or discussed.

      The Reviewer mentions some potential confounding factors (ie., size differences, transit time, oral gavage) in the gnotobiology experiments. We would like to convey that these aspects have been addressed in our experimental design and are now clarified in the revised manuscript: 1- larval sizes were recorded and found to be similar between GF and monoassociated larvae (Figure S6A); 2- while intestinal transit time may be affected by microbes and is a topic of interest, in our assay luminal mCherry cargo is present at high levels throughout the gut and is not limiting at any point during the experiment; 3- gavage, which is necessary for quantitative assays, is indeed an experimental manipulation that may somehow alter the subjects (the same is true for microscopy and virtually any research method). However, it cannot explain differences between GF and CV or alter our conclusions via microbial or dietary effects. We now elaborate the former point in the revised discussion (line 426). A new panel has been added for Fig.S6 to show that standard length was similar in GF and monoassociated larvae (Figure S6A).

      We are aware that microbial community composition is often highly variable between experiments and this necessitates adequately high biological replication and inclusion of internal controls to allow conclusions to be drawn. Nevertheless, studies evaluating the utility of 16S rRNA gene sequencing have found that this analysis reveals important impacts of environmental factors on the gut microbiome (PMIDs: 21346791, 31409661, 31324413). Our results provide further evidence that 16S rRNA gene sequencing remains a useful method to detect perturbations to the zebrafish gut microbiome. Reproducing previous findings, we detected many of the core zebrafish microbiota strains in our samples that have been identified by other studies (PMIDs: 26339860, 21472014, 17055441). To ensure the robustness of our results, we included several biological replicates for each condition, co-housed genotypes and included large sample sizes to minimize environmental variability between groups. In response to this reviewer concern, we have added a supplemental beta diversity plot and statistical analyses showing that the microbiomes in our larvae were significantly different from the diets or tank water (Figure S7A). This analysis shows that the host environment influenced microbial community composition (lines 376 – 378). We also added an additional supplemental panel and performed analysis showing that the experimental replicates (i.e., different tanks) were not a significant source of variation in this study (lines 378 – 380) (Figure S7B). This result underscores that the microbiota in these larvae were influenced by both the host and diet.

      Regarding dopamine pathways, we acknowledge that it involves complex biology that will require dedicated studies. In this work, we simply point out gene expression patterns we find interesting as they may inform future studies.

      Finally, the Reviewer mentions the use of inadequate statistical methods for some analyses without specifying or indicating alternative analyses, only the need to justify the use of two-way ANOVA is made explicit. In this point, we respectfully disagree and would like to emphasize that we use statistical methods that are standard in the field (PMID: 37707499). We nevertheless added a justification for the use of two-way ANOVA where appropriate (lines 635-637, 653-654, 773-776). The two-way ANOVA test was to compare fluorescence profiles of gavages cargoes or HCR probes along the length of the LRE region. This test accounts for differences in fluorescence between experimental conditions in segments (30 μm) along the LRE region (~300 μm). This allows us to capture differences in fluorescence between experimental conditions while accounting for heterogeneity in the LRE region. Please see our comment below for more information about our use of the 2-way ANOVA.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Please provide in the materials and methods the strain identifiers and sources of the bacteria used in the study.

      Thank you for the suggestions. Strain identifiers and source information were added to the methods (lines 576-579).

      Reviewer #2 (Recommendations for the authors):

      (1) This is a very satisfying and thorough analysis of the reciprocal influence of diet, microbiome, and host genotype on protein absorption by the host. Below I make suggestions that mainly relate to making the paper more accessible to a broader audience.

      (2) Line 233 Starts a section that reports the findings of the scRNA dataset. The writing is inconsistent with respect to how the genes are listed: whether abbreviation only or spelled out followed by abbreviation. I prefer the latter. For example, slc10a2 is a bile acid Na cotransporter but for those not in the know, they would have to look this up. Perhaps adding a supplementary table that provides a gene list of those discussed in the text with abbreviation/spelled-out, and KEGG terms.

      Thank you for pointing out inconsistent gene labeling. We have revised the text with spelled out gene names followed by abbreviations.

      (3) Line 461 Where did the neurons come from when you were sorting cldn+ cells?

      Neuronal expression of cldn15la was detected in our data and other published datasets (PMID: 37995681, 35108531). We added a note to the text clarifying that neuronal cells can express cldn15la (lines 463-465).

      (4) Line 561 1x tricaine should be converted to percentage in solution or concentration throughout.

      The tricaine concentration was 0.2 mg/mL. We added this detail to the methods (line 596).

      (5) Line 612 Please clarify how normalizations are carried out: is it to the peak value in the germ-free condition? CV never reaches 1.

      AUC values were normalized to the peak value in the GF condition at 60 minutes PG. We clarified this step in the methods (lines 618-619).

      (6) Line 654-663 I think mCherry here should be mTourquoise?

      Thank you for catching this typo. We corrected it in the text.

      (7) In Figure 1 Please consider adding a color so that magenta does not represent BOTH germ-free AND mCherry.

      Due to the many colors of fluorescent proteins and HCR probes in this paper, we were not able to find an alternative plot line color to represent GF.

      (8) In Figure 2 I suggest consistency with respect to the order you present GF/CV

      Figure 1 GF->CV

      Figure 2 CV->GF

      My preference is GF->CV

      Images in Figure 2 were re-ordered following reviewer’s recommendation.

      Here, 20 minute time point also appears qualitatively different between GF and CV.

      There can be slight differences in LREs between individuals. These images were selected because they represented the average differences in the amount of mTurquoise degradation activity that occurred between 20 – 60 minutes post-flushing in the GF and CV conditions.

      In Figure 3E Figure legend refers to being able to see BSA in vacuoles. The image should be modified to show this- currently too small.

      In response, we enlarged the confocal microscopy images showing DQ red BSA in the LRE region (Figure 3E). We added a panel with confocal microscopy images of the LREs in 6 dpf larva gavaged with DQ red BSA (Figure S3F). These images show that DQ red BSA fluorescence was localized to the LRE lysosomal vacuole.

      In Figure 5D, Posterior LRE should be pink not green in the key to the right of the heatmap.

      Thank you for catching this error. We have corrected the colors (Figure 5D).

      Reviewer #3 (Recommendations for the authors):

      (1) Introduction and context:

      Expand the introduction to include more background on microbial-mediated protein absorption, with references to relevant findings in Drosophila. This will provide a stronger foundation for the study's contributions to the field.

      Thank you for this suggestion. We added information about microbe-mediated amino acid harvest in Drosophila to the introduction (lines 49-53).

      (12) Methodological suggestions:

      Measure and report differences between germ-free (GF) and conventional (CV) animals, such as transit time, to account for potential confounding factors in protein absorption dynamics.

      We respectfully assert that a transit assay is not required for this study and could actually create confusion as an effect in transit time could be interpreted as a contributing factor when it is in fact not the case due to the experimental design. This is because the concentration of luminal protein was equivalent in GF and CV larvae (Figure S1E), so the LREs had equal saturating access to those proteins in both conditions. Furthermore, we showed the microbiota did not degrade fluorescent protein (Figure S1F). Therefore, we feel confident that there was lower protein uptake in the LREs of CV larvae because the microbiome exerted regulatory effects on LRE activity.

      Provide detailed information on the gating strategy used for single-cell sorting to enhance the dataset's utility and support claims about cell changes.

      The methods we used for sorting cells were previously described (PMID: 31474562). In this manuscript, we describe them under the heading “Fluorescence activated cell sorting for single cell RNA-sequencing.”

      Explain the "GeneRatio" metric in figure legends for clarity.

      The GeneRatio is the ratio of genes associated with each individual GO term to the number of genes associated with the domain. An explanation was added to the caption (Figure S3C).

      (13) Visual and statistical improvements:

      Include images of labeled peptidases within lysosome-rich enterocytes (LREs) to reinforce findings.

      Thank you for the suggestion. We added images of labeled peptidases in the LRE region (Figure S6E-D).

      For Panels 4-F and 5-D, consider using violin plots of selected genes to improve clarity and emphasize major ideas.

      In Figure 4F, the heatmap shows multiple genes were upregulated in mCherry-positive cells. We tried the plotting suggested by the reviewer and felt that violin plots could not convey this message as clearly. Likewise, the heatmap in Figure 5D effectively shows the gradient of expression between ileocytes, anterior and posterior LREs.

      Strengthen statistical analysis by employing more rigorous methods and justifying their selection, such as using two-way ANOVA where appropriate.

      The two-way ANOVA was used to quantify protein uptake or HCR probe fluorescence along the length of the LRE region. This statistical test allowed us to compare differences in fluorescence between experimental conditions in multiple LRE segments (see Authoer response image 1 below for example). As our assays show, the LRE region is heterogenous with segments showing different levels of activity and gene expression. The two-way ANOVA is appropriate because it allows us to account for this heterogeneity by comparing fluorescence across multiple segments.

      Author response image 1.

      Our figures display these fluorescent levels in line plots (above, left) rather than bar plots (above, right). The results are easier to visualize interpret in line plots, and they display the fluorescence profiles in greater detail.

      (14) Technical corrections:

      Correct figure references: Figure 5 about tryptophan metabolism should be 5A, S5G-S5H.

      We corrected the figure references.

      Line 518: Spell out "heterozygotes" instead of using "gets".

      We changed the term from “hets” to “heterozygotes.”

      (15) Revise Figure S2 citation to match the actual figure labeling.

      We corrected the text to indicate “Figure S2” rather than “Figure S2A.”

      Additional manuscript modification

      · Figure panels 3B-C, S3A-B, 4A-C: Two cluster were relabeled with improved descriptors based on our updated annotations. The clusters “Pharynx-esophagus-cloaca 1” (PEC1) and PEC2 were relabeled as “Pharynx-cloaca 1” and “Pharynx-cloaca 2.”

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this study, Basha and colleagues aim to test whether the thalamic nucleus reuniens can facilitate the hippocampus/prefrontal cortex coupling during sleep. Considering the importance of sleep in memory consolidation, this study is important to understand the functional interaction between these three majorly involved regions. This work suggests that the thalamic nucleus reuniens has a functional role in synchronizing the hippocampus and prefrontal cortex.

      Strengths:

      The authors performed recordings in naturally sleeping cats, and analysed the correlation between the main slow wave sleep oscillatory hallmarks: slow waves, spindles, and hippocampal ripples, and with reuniens' neurons firing. They also associated intracellular recordings to assess the reuniens-prefrontal connectivity, and computational models of large networks in which they determined that the coupling of oscillations is modulated by the strength of hippocampal-thalamic connections.

      Thank you for your positive evaluation.

      Weaknesses:

      The authors' main claim is made on slow waves and spindle coupling, which are recorded both in the prefrontal cortex and surprisingly in reuniens. Known to be generated in the cortex by cortico-thalamic mechanisms, the slow waves and spindles recorded in reuniens show no evidence of local generation in the reuniens, which is not anatomically equipped to generate such activities. Until shown differently, these oscillations recorded in reuniens are most likely volume-conducted from nearby cortices. Therefore, such a caveat is a major obstacle to analysing their correlation (in time or frequency domains) with oscillations in other regions.

      (1) We fully agree with the reviewer that reuniens likely does not generate neither slow waves nor spindles. We do not make such claim, which we clearly stated in the discussion (lines 319-324). We propose that Reuniens neurons mediate different forms of activity. In the model, we introduced MD nucleus only because without MD we were unable to generate spindles. While the slow waves and spindles are generated in other thalamocortical regions, the REU neurons show these rhythms due to long-range projections from these regions to REU as has been shown in the model.

      (2) Definitely, we cannot exclude some influence of volume conductance on obtained LFP recordings in REU nucleus. However, we show modulation of spiking activity within REU by spindles. Spike modulation cannot be explained by volume conductance but can be explained by either synaptic drive (likely the case here) or some intrinsic neuronal processes (like T-current).

      (3) In our REU recordings for spike identification we used tetrode recordings. If slow waves and spindles are volume conducted, then slow waves and spindles recorded with tetrodes should have identical shape. Following reviewer comment, we took these recordings and subtracted one channel from another. The difference in signal during slow waves is in the order 0.1 mV. Considering that the distance between electrodes is in the order of 20 um, such a difference in voltage is major and can only be explained by local extracellular currents, likely due to synaptic activities originating in afferent structures.

      Finally, the choice of the animal model (cats) is the best suited one, as too few data, particularly anatomical ones regarding reuniens connectivity, are available to support functional results.

      (1) Thalamus of majority of mammals (definitely primates and carnivores, including cats) contain local circuit interneurons (about 30 % of all neurons). A vast majority of studies in rodents (except LGN nucleus) report either absence or extremally low (i.e. Jager P, Moore G, Calpin P, et al. Dual midbrain and forebrain origins of thalamic inhibitory interneurons. eLife. 2021; 10: e59272.) number of thalamic interneurons. Therefore, studies on other species than rodents are necessary, and bring new information, which is impossible to obtain in rodents.

      (2) Cats’ brain is much larger than the brain of mice or rats, therefore, the effects of volume conductance from cortex to REU are much smaller, if not negligible. The distance between REU and closest cortical structure (ectosylvian gyrus) in cats is about 15 mm.

      (3) Indeed, there is much less anatomical data on cats as opposed to rodents. This is why, we performed experiments shown in the figure 1. This figure contains functional anatomy data. Antidromic responses show that recorded structure projects to stimulated structure. Orthodromic responses show that stimulated structure projects to recorded structure.

      Reviewer #2 (Public Review):

      Summary:

      The interplay between the medial prefrontal cortex and ventral hippocampal system is critical for many cognitive processes, including memory and its consolidation over time. A prominent idea in recent research is that this relationship is mediated at least in part by the midline nucleus reuniens with respect to consolidation in particular. Whereas the bulk of evidence has focused on neuroanatomy and the effects of temproary or permanent lesions of the nucleus reuniens, the current work examined the electrophysiology of these three structures and how they inter-relate, especially during sleep, which is anticipated to be critical for consolidation. They provide evidence from intercellular recordings of the bi-directional functional connectivity among these structures. There is an emphasis on the interactions between these regions during sleep, especially slow-wave sleep. They provide evidence, in cats, that cortical slow waves precede reuniens slow waves and hippocampal sharp-wave ripples, which may reflect prefrontal control of the timing of thalamic and hippocampal events, They also find evidence that hippocampal sharp wave ripples trigger thalamic firing and precede the onset of reuniens and medial prefrontal cortex spindles. The authors suggest that the effectiveness of bidirectional connections between the reuniens and the (ventral) CA1 is particularly strong during non-rapid eye movement sleep in the cat. This is a very interesting, complex study on a highly topical subject.

      Strengths:

      An excellent array of different electrophysiological techniques and analyses are conducted. The temporal relationships described are novel findings that suggest mechanisms behind the interactions between the key regions of interest. These may be of value for future experimental studies to test more directly their association with memory consolidation.

      We thank this reviewer for very positive evaluation of our study.

      Weaknesses:

      Given the complexity and number of findings provided, clearer explanation(s) and organisation that directed the specific value and importance of different findings would improve the paper. Most readers may then find it easier to follow the specific relevance of key approaches and findings and their emphasis. For example, the fact that bidirectional connections exist in the model system is not new per se. How and why the specific findings add to existing literature would have more impact if this information was addressed more directly in the written text and in the figure legends.

      Thank you for this comment. In the revised version, we will do our best to simplify presentation and more clearly explain our findings.

      Reviewing Editor (Recommendations for Authors):

      Please discuss the ability of reuniens to generate spindles?

      We briefly discussed this in previous version. We now extended the discussion (p. 18).

      For population data, how many cats were used in acute and chronic experiments, where does the population data originate in Fig. 2? How repeatable were the findings across animals? Was histology verified in each animal?

      As previously stated in the beginning of method section we totally used 20 cats: 16 anesthetized (or acute) and 4 non-anesthetized (or chronic). We added number of cats in appropriate places in the result section. Population data in figure 2 comes from 48, 49 or 52 recording sessions (depending on the type of analysis, and indicated in the figure legend) from 4 chronic cats; we clarified this information in the legend. Results were highly repeatable across animals. Histology was verified in all chronic and acute animals, we added a sentence in the method section.

      Explanation of figures is very poor, values in figures should be reported in results so they can be compared in the context of the description.

      In this revised version, we report most numbers present in figures and their legend to the main text (result section).

      The depth of the recording tungsten electrodes are meaningless without the AP and ML coordinates given how heterogenous mPFC is. What is the ventromedial wall of the mPFC in the cat?

      We added the ML and AP coordinates in the method section. We corrected ventromedial wall for ventroposterior part of the mPFC.

      What are the two vertical lines in 1F?

      This was an error while preparing the figure. The panel was corrected.

      Line 90 mean +-SD of what? There are no numbers.

      Thanks, we now indicate the values.

      Panel 2L does not show increased spindling in reuniens prior to PFC as indicated in the results, please explain. It does show SWR in the hippocampus prior to spindles, what is the meaning of such a time relationship?

      Panel 2L did show an increased spindling reuniens prior to mPFC, but indeed at the time scale shown, it was not very clear. In this revised manuscript, we added an inset zooming around time zero to make this point clearer.

      Panel 2L indeed show an increase in SWR prior to the increase in spindle in both Reuniens and mPFC.

      As stated in the discussion, ‘We found that hippocampal SWRs trigger thalamic firing and precede the onset of reuniens and mPFC spindles, which points to SWRs as one of candidate events for spindle initiation.’

      It is unclear what the slow waves of PFC mean, these represent filtered PFC lfp, but is this a particular oscillation? They continue to occur during the spindle, while the slow waves supposedly trigger the spindle. Please explain and clarify.

      We recently published a review article involving several scientists studying both human and animal sleep that has inserted Box. 1 (Timofeev I, Schoch S, LeBourgeois M, Huber R, Riedner B, Kurth S. Spatio-temporal properties of sleep slow waves and implications for development. Current Opinion in Physiology. 2020; 15: 172–182). In this box among other terms, we provide current definition of slow waves vs slow oscillation. Briefly, if slow waves are repeated with a given rhythm, they typically form slow oscillation. However, if they occur in isolation or are not rhythmic, they remain slow waves, but cannot be called slow oscillation.

      Regarding relation of spindles and slow oscillation. We are currently systematically analyzing data on spindles and slow waves obtained from head-restrained and freely behaving cats. One of the main findings is that a majority of ‘cortical’ spindles are local. Local to the extent that spindles can occur in alternation in two neighboring cortical cells. Largely, LFP sleep spindles occur more or less synchronously within suprasylvian gyrus of cats where indeed a large majority of them was triggered by slow waves. The synchrony between LFP spindles in suprasylvian vs other other cortical areas is much less clear. So, it is not surprizing that spindles in one bran region can occur when there is a slow wave present in some other brain region. Something of a kind was also shown in human (Mölle M, Bergmann TO, Marshall L, Born J. Fast and slow spindles during the sleep slow oscillation: disparate coalescence and engagement in memory processing. Sleep. 2011; 34 (10): 1411-1421).

      In this regard, we are not ready to include modifications in the manuscript.

      Line 134, where is spindle amplitude shown? Plots report power within the spindle frequency band, which obviously captures more than just spindles.

      No, plots of figure 3 B, C show the phase-amplitude coupling (PAC) strength. These were calculated with detected spindles, therefore, while we cannot exclude some false spindle detections, we are confident that the false spindle detections are at a negligible level. We modified text and instead of spindle amplitude, we describe SW-spindle amplitude coupling. This reflects our analysis with exactitude.

      The discussion must include the medio dorsal nucleus which is the largest thalamic input to the prefrontal cortex and also receives input from the hippocampus. In particular, the case must be made for why reuniens would play a more important or different role than MD? (For example: Occurrence of Hippocampal Ripples is Associated with Activity Suppression in the Mediodorsal Thalamic Nucleus - PMC (nih.gov)).

      We cited the suggested study. We cannot say whether reuniens plays a more or less important role. What is clear is that hippocampal ripples at the onset of spindles trigger increased firing in both MD and reuniens. Our extracellular recordings (Fig. 4, K) suggest that the increased firing is associated with spike-bursts. We also have a parallel unpublished study done on anesthetized mice showing SWR triggered inhibitory potentials in both reuniens and MD that reverses around -65mV - -70 mV. Because the majority of SWR occurred at the onset of cortical up state, a relative role of cortico-thalamic vs hippocampo-thalamic drive is not easy to separate. We hope, we will convincingly do this in our forthcoming study, with the limitation that it was done on anesthetized mice.

      Reviewer #1 (Recommendations For The Authors):

      I strongly encourage the authors to perform current source density analyses on the LFP signals recorded in the nucleus reuniens to make sure that the observed oscillations are indeed locally generated. So far, the anatomical organisation in reuniens cannot support the local generation of oscillations, such as spindles and slow wave. At least in rodents (the cat reuniens does not seem too different, until shown differently), there were no oscillators found in reuniens, and at least not arranged like in cortical areas, allowing the summation in time, and particularly space, of rhythmic input currents. Bipolar recordings with pairs of twisted electrodes might also be useful to assess the local existence of spindles and slow waves.

      Current source density calculation is possible when one knows the exact distance between recording sites. As we used tetrodes made with 4 twisted platinum-iridium wires, we know more or less the range of distance between recording sites, but not the exact distance between any given pair of electrodes.

      Then, the physical distance between the reuniens and any cortical structure is about 8-9 mm. Therefore, with such distances, volume conductance is expected to be negligible. If slow waves and spindles are volume conducted, then slow waves and spindles recorded with tetrodes should have identical shape. Following reviewer comment, we took these recordings and subtracted one channel from another. The difference in signal during slow waves is in the order 0.1 mV. Considering that the distance between electrodes is in the order of 20 um, such a difference in voltage is major and can only be explained by local extracellular currents, likely due to synaptic activities originating in afferent structures.

      Below, we plotted the voltage of one channel of the tetrode versus another channel of the same tetrode. If the signal was simply volume conducted, one would expect to see the vast majority of points on the x=y line (red).

      Author response image 1.

      Below is a segment of mPFC LFP recording (upper black trace), mPFC LFP filtered for spindle frequency (7-15 Hz) and the spindle detected (black lines above the filtered trace. Then two LFP traces from a tetrode in the Reuniens (orange and light blue) are overlayed. The second trace (Blue) from bottom represents the substraction of Reuniens 1 minus Reuniens 2 channel, and just below (lower Blue trace) is this susbtraction trace filtered for spindle frequency (7-15 Hz) showing clear voltage difference in the spindle range between the two electrodes. Note also that around time 179-179.5 s, there is clear spindle oscillation in the mPFC recording which is not present in the Reuniens recordings.

      Author response image 2.

      Therefore, we are convinced that in our recordings, volume conductance did not play any significant role.

      Another concern regarding delays between events, like slow waves, measured between two regions (as exemplified by Figure 3). It appears that the delays were calculated from the filtered signal. Figure 3G shows a delay between the peak of the mPFC slow wave between the raw and the filtered signal, which might be artifactual of the processing. It is though not (or less) visible for the reuniens recording. Such mismatch might explain the observed differences in delays.

      Thanks for this comment. We recomputed the analysis using the original signal (smoothed) and obtained very similar results. Panels H and I of figure 3 were updated using the new analysis performed on original signal.

      The overall analyses of LFP-triggered reuniens MUA activity lack of statistics (at least z-scored firing to normalise the firings).

      Fig. 2 H and I are representative examples for histograms; statistical data are shown in circular plots as explained in the legend. Fig. 2 L, shows populational data and we provide now standard error. Fig. 4 C and D show individual example. Fig. 4 E shows histograms of activity of all identified putative single units. Units that show significant modulation are displayed above white line. Fig. 4 F shows populational data for significantly modified units.  

      A last point of detail in the model, which surprisingly shows reuniens to excitatory hippocampal cells' connectivity. Recent literature reports that reuniens only connect hippocampal interneurons, and not principal cells (at least in rodents, I could not find any report in cats). I wonder how changing this parameter would affect the results of the computational investigation, particularly the results shown in Figure 6.

      There are several studies in the literature showing a direct excitation from the Reuniens to pyramidal cells in the CA1, here are three of them:

      Goswamee, P., et al. (2021). "Nucleus Reuniens Afferents in Hippocampus Modulate CA1 Network Function via Monosynaptic Excitation and Polysynaptic Inhibition." Frontiers in Cellular Neuroscience 15.

      Dolleman-Van der Weel MJ, Lopes da Silva FH, Witter MP (1997) Nucleus Reuniens Thalami Modulates Activity in Hippocampal Field CA1 through Excitatory and Inhibitory Mechanisms. The Journal of Neuroscience 17:5640.

      Dolleman-van der Weel MJ, Lopes da Silva FH, Witter MP (2017) Interaction of nucleus reuniens and entorhinal cortex projections in hippocampal field CA1 of the rat. Brain Structure and Function 222:2421-2438.

      Because this is not a review paper, we opted to not cite all the papers describing connectivity between mPFC, hippocampus and thalamus.

      Reviewer #2 (Recommendations For The Authors):

      I respectively suggest that the earlier (public) comments listed above should be addressed. In addition, it would be useful to make it clearer when non-rapid eye movement sleep was being addressed and when rapid eye movement was being addressed. Is it of value to use a single term instead of adding "slow wave sleep" or else clarify when either term is used? The addition of more subheadings might help. Moreover, the relative contribution/value of evidence from these two sleep states was not addressed or was not very clear.

      We tried to make it clearer when NREM and when REM was analysed.

      We replaced slow-wave sleep with NREM sleep in the figure 5 title.

      We added several subheadings in the discussion.

      Relative contribution of NREM vs REM sleep was not addressed? Sorry but we do not clearly understand your question. Figs. 2 and 3 deal mainly with NREM sleep (Fig 2.B has an example of REM sleep). Fig. 4 essentially describes results obtained during REM sleep.

      I was not sure if the Abstract summarised the key take-home messages from the large amount of evidence provided. Some choices are needed, of course, but "evidence of bidirectional connectivity" struck me as less novel than other evidence provided. Given the huge amount of findings provided, which is commendable, it is still useful to present it perhaps in a more digestible fashion. For example, the headings or the first sentence(s) below headings could indicate the aim or the outcome of the specific method/analysis/findings.

      We rewrote abstract and we also added some conclusion to highlight major findings and their meaning.

      It is more common to use NRe or Re, rather than REU.

      We avoided using RE as, for decades, we used RE to abbreviate the thalamic reticular nucleus in several publications. In this revised version, we spell at full - Reuniens.

      Line 49 mentions "short-term" memory. Please specify this more clearly as it is otherwise ambiguous. Also, line 303.

      We rephrased the sentence: In particular, the hierarchical coupling of slow waves, spindles and SWRs is thought to play a key role in memory consolidation.

      Line 303 was likely about the ventromedial wall: we corrected that sentence.

      Line 62: the word, "required" (for memory function) is too strong because there is evidence that it is not always required.

      We modified the sentence for plays a major role.

      The focus within the medial prefrontal cortex could be specified more clearly / earlier.

      The mPFC is mentioned in the second sentence of the abstract and in the first sentence of the introduction.

      Line 134: The heading states "determine" and then mentions modulation. These terms may not be interchangeable or they need clarification.

      We changed it to slow wave-spindle amplitude coupling. This represents exactly our analysis.

      Line 204: Does "cortical network" mean prefrontal cortex network"?

      Yes, as described in lines 192-193, the two cortical networks (N1 and N2) of the model represent the mPFC layer 5 and 6 respectively.

      Lines 283 to 289: These were not very clear to me.

      These lines described the potential mechanisms for the responses to hippocampal and reuniens stimulation recorded intracellularly (results in figure 1). We modified this paragraph for clarity.

      Line 296: Specify the "claim".

      We modified the sentence for “[…] provides supporting evidence for this claim that nucleus Reuniens might synchronize the activity of ventral hippocampus and mPFC.”

      The discussion naturally focuses on the thalamic nucleus reuniens, but also occasionally mentions the thalamic mediodorsal nucleus. The distinction, assuming this is highly relevant, could be expressed more clearly (direct comparison with their previous papers).

      We never published a study on the mediodorsal nucleus. We do have some unpublished results from recordings in the MD nucleus and they reveal the presence of an inhibitory component at the beginning of cortical active states, therefore behaving in a similar way to first order nuclei. It is then possible that spindles recorded in the reuniens are actually generated in the MD nucleus and then transmitted to Reuniens through the thalamic reticular nucleus, as both MD and reuniens are connected to the rostral thalamic reticular nucleus. We added some discussion about this.

      Figure 1B: Do the authors have any additional evidence of the placements in the reuniens, because the photo provided suggests a large area beyond the reuniens boundary. Also, please confirm is the CEM between Rh and Re in the cat (I think the Rh and Re are adjacent in the rat).

      Figure 1B is from an electrolytic lesion, which is necessarily bigger than the tip of the electrode. Therefore the center of the electrolytic lesion indicates where the electrode tip was located which is well within the reuniens nucleus.

      Also, yes CE (Nucleus centralis thalami, pars medialis) is located between the reuniens and rhomboid in cats. This can be found in two cat atlas:  

      Reinoso-Suárez, F. (1961). Topographischer Hirnatlas der Katze für experimental-physiologische Untersuchungen (Merck).

      Berman AL, Jones EG (1982) The Thalamus and Basal Telencephalon of the Cat: A Cytoarchitectonic Atlas with Stereotaxic Coordinates: University of Wisconsin Press.

      The first mention of hippocampus in the figure legends should remind the reader by stating "ventral hippocampus".

      In this revised version, we added “ventral” in several instances both in the main text and in figure legend.

      Figure 2: It seems unusual to mention "unusually short NREM". Presumably, things are the same otherwise - if so, perhaps mention that, especially if some of the effects reflect an "unusual" episode.

      We display this particular segment because we want to show continuous recording in which still individual elements characterizing specific states are still visible.

      Some effects look like they are strong and others perhaps weaker. If so, how do these impact the final conclusions?

      Sorry, we did not understand clearly what is meant here by the reviewer. In general, if any effect has statistically significant difference (old fashion 0.05) we consider it as significant. Any other cases are described on individual basis.

      Perhaps "MAD" should be in full on the first occasion, if not already.

      It was spelled out at line 659, but we now spell it out also in the results section and in figure 2 legend.

      Methods: the key question is the use of rodent recordings to classify cat recordings. It would be good to have a reference indicating that this can be directly used for cats, which may have different sleep cycles and patterns compared to rats.

      We did not use rodent recordings to classify cat recordings, however we did used a state detection script that was developed with rodent recordings. As mentioned in the method section, we adapted the script to cat mPFC recordings and then manual corrections were made to correctly detect REM episodes. Respectfully, our lab investigates sleep-wake in non-anesthetized animals for a few decades; we developed state detection algorithm in mice, cats, marmosets when needed (to analyse months of recordings), and we have an extensive expertise in identifying states of vigilance from electrophysiological recordings.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This manuscript presents a comprehensive exploration of the role of liver-specific Survival Motor Neuron (SMN) depletion in peripheral and central nervous system tissue pathology through a well-constructed mouse model. This study is pioneering in its approach, focusing on the broader physiological implications of SMN, which has traditionally been associated predominantly with spinal muscular atrophy (SMA).

      Strengths:

      (1) Novelty and Relevance: The study addresses a significant gap in understanding the role of liver-specific SMN depletion in the context of SMA. This is a novel approach that adds valuable insights into the multi-organ impact of SMN deficiency.

      (2) Comprehensive Methodology: The use of a well-characterized mouse model with liver-specific SMN depletion is a strength. The study employs a robust set of techniques, including genetic engineering, histological analysis, and various biochemical assays.

      (3) Detailed Analysis: The manuscript provides a thorough analysis of liver pathology and its potential systemic effects, particularly on the pancreas and glucose metabolism.

      (4) Clear Presentation: The manuscript is well written. The results are presented clearly with well-designed figures and detailed legends.

      We thank the reviewer for their positive comments. They had some concerns for us to consider (see below). We provide a point-by-point response to their comments.

      Weaknesses:

      (1) Limited Time Points: The study primarily focuses on a single time point (P19). This limits the understanding of the temporal progression of liver and pancreatic pathology in the context of SMN depletion. Longitudinal studies would provide a better understanding of disease progression.

      We thank the reviewer for the suggestion. We extended our analysis to include P60 mice and performed both liver and pancreatic analyses at this time point to address this suggestion.

      (2) Incomplete Recombination: The mosaic pattern of Cre-mediated excision leads to variability in SMN depletion, which complicates the interpretation of some results. Ensuring more consistent recombination across samples would strengthen the conclusions.

      The variability in Cre-mediated excision is inherently stochastic, influenced by factors such as Cre expression levels, timing of recombination, and the accessibility of the target locus in individual cells. Achieving complete consistency across samples is particularly challenging, especially given the complexity of our breeding scheme, which occasionally results in litters without any animals of the desired genotype. Importantly, our study not only demonstrates that liver-specific SMN depletion results in liver alterations and pancreatic dysfunction but also highlights the limitations and challenges associated with this mouse model. By doing so, we aim to provide valuable insights for other researchers considering similar approaches in future studies.

      Reviewer #2 (Public review):

      Summary:

      Marylin Alves de Almeida et al. developed a novel mouse cross via conditionally depleting functional SMN protein in the liver (AlbCre/+;Smn2B/F7). This mouse model retains a proportion of SMN in the liver, which better recapitulates SMN deficiency observed in SMA patients and allows further investigation into liver-specific SMN deficiency and its systemic impact. They show that AlbCre/+;Smn2B/F7 mice do not develop an apparent SMA phenotype as mice did not develop motor neuron death, neuromuscular pathology or muscle atrophy, which is observed in the Smn2B/- controls. Nonetheless, at P19, these mice develop mild liver steatosis, and interestingly, this conditional depletion of SMN in the liver impacts cells in the pancreas.

      Strengths:

      The current model has clearly delineated the apparent metabolic perturbations which involve a significantly increased lipid accumulation in the liver and pancreatic cell defects in AlbCre/+;Smn2B/F7 mice at P19. Standard methods like H&E and Oil Red-O staining show that in AlbCre/+;Smn2B/F7 mice, their livers closely mimic the livers of Smn2B/- mice, which have the full body knockout of SMN protein. Unlike previous work, this liver-specific conditional depletion of SMN is superior in that it is not lethal to the mouse, which allows an opportunity to investigate the long-term effects of liver-specific SMN on the pathology of SMA.

      We thank the reviewer for their positive comments. They had some concerns for us to consider (see below). We provide a point-by-point response to their comments (review comments in black, our response in red).

      Weaknesses:

      Given that SMA often involves fatty liver, dyslipidemia and insulin resistance, using the current mouse model, the authors could have explored the long-term effects of liver-specific depletion of SMN on metabolic phenotypes beyond P19, as well as systemic effects like glucose homeostasis. Given that the authors also report pancreatic cell defects, the long-term effect on insulin secretion and resistance could be further explored. The mechanistic link between a liver-specific SMN depletion and apparent pancreatic cell defects is also unclear.

      We extended our analysis to include P60 mice and performed both liver and pancreatic analyses at this time point to address this suggestion. In addition, we discussed the liver-pancreas axis in the Discussion.

      Discussion:

      This current work explores a novel mouse cross in order to specifically deplete liver SMN using an Albumin-Cre driver line. This provides insight into the contribution of liver-specific SMN protein to the pathology of SMA, which is relevant for understanding metabolic perturbations in SMA patients. Nonetheless, given that SMA in patients involve a systemic deletion or mutation of the SMN gene, the authors could emphasize the utility of this liver-specific mouse model, as opposed to using in vitro models, which have been recently reported (Leow et al, 2024, JCI). Authors should also discuss why a mild metabolic phenotype is observed in this current mouse model, as opposed to other SMA mouse models described in literature.

      We appreciate the reviewer’s insightful comment. We have thoroughly addressed this suggestion in the Discussion section, particularly in lines 284-298; 309-322 and 334-359.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Longitudinal Studies: Conducting studies at maybe one more time points postnatally to provide a clearer picture of how liver-specific SMN depletion affects tissue pathology over time.

      We extended our analysis to include P60 mice and performed both liver and pancreatic analyses at this time point to address this suggestion.

      (2) Functional Assays: Incorporate glucose tolerance tests, insulin sensitivity tests, and more detailed metabolic profiling to better understand the physiological consequences of liver-specific SMN depletion on glucose metabolism and pancreatic function.

      We sincerely thank the reviewer for this suggestion. We have included a full panel of metabolic hormones associated with glucose metabolism from animals at P19 and P60. These new data, along with additional figures, have now been provided in our revised manuscript.

      (3) Mechanism: Discuss the molecular pathways affected by SMN depletion in the liver and pancreas. Mechanistic studies including transcriptomic or proteomic analyses to identify dysregulated pathways will help.

      We appreciate the reviewer’s insightful comment. We have thoroughly addressed this suggestion in the Discussion section, particularly in lines 284-298 and 334-359.

      (4) Typos in the abstract: beta cells secret insulin and alpha cells produce gulcagon. 

      Thank you for catching this error. It has been corrected to reflect that insulin is produced by beta cells and glucagon by alpha cells.

      (5) Efficiency and specificity of the Alb-Cre: if possible, cross the Alb-Cre with the Rosa26 reporter line to test the efficiency and specificity of the Alb-Cre.

      We agree that this would provide valuable insights. However, initiating a new breeding program to generate the required genotypes would take over a year and is beyond the scope of this study. To address this in part, we performed Cre immunostaining of the liver, pancreas, and spinal cord at P19, as well as the liver at P60. These results, now included in Supplemental Figure 1, demonstrate liver-specific expression and variability across hepatocytes.

      Reviewer #2 (Recommendations for the authors):

      The title of this manuscript is potentially misleading. The manuscript largely investigates the involvement of SMN protein on peripheral organs such as the liver, muscles, neuromuscular junction, and the pancreas. Yet, the title could be interpreted that the peripheral nervous system or central nervous system is the main focus. The title should be edited to indicate key terms such as "motor neuron and peripheral tissue pathology".

      Thank you for pointing this out. We have revised the title to better represent the study’s focus. It is now “Impact of liver-specific survival motor neuron (SMN) depletion on central nervous system and peripheral tissue pathology”.

      Suggestions:

      Please clarify and explain clearly the various mouse lines (Smn2B/+, Smn2B/- and +/+; Smn2B/F7 ) used as controls as the nomenclature used is confusing. In addition, authors could consider the use of a wild-type mouse line to be used as a control to validate changes in AlbCre/+;Smn2B/F7 mice.

      We have now provided clarification on mouse line nomenclature in the Results section (lines 104–124). Full-body heterozygous mice (_Smn_2B/+) are used as controls due to their slightly reduced SMN protein levels and absence of phenotypic changes compared to wild-type mice.

      Given that the main phenotype implicated by the liver-specific depletion of SMN protein in AlbCre/+;Smn2B/F7 mice is pancreatic abnormalities (changes in alpha- and beta- cell numbers and blood glucose levels), authors should expand further on the pancreatic phenotype.  

      We added a full panel of metabolic hormones related to glucose metabolism in animals at P19 and P60. Furthermore, this has been discussed in detail in lines 284-298 and 334-344 of the Discussion.

      A pancreas-specific depletion of SMN would provide this current manuscript with a better understanding of the role of SMN in regulating SMA pathology and provide more definitive conclusions on the contribution of liver-specific SMN depletion on normal pancreatic function.

      We agree that this would be very informative. However, to do this would require initiation of a new breeding program that will take more than a year to arrive at the right genotypes. Although valuable, it is beyond the scope of the present study.

      The authors should also delineate the role of hepatic SMN in pancreatic function, and how the intrinsic liver-specific loss of SMN directly impacts the pancreas. Currently, literature demonstrates that the fatty liver phenotype in SMA patients is a primary SMN-dependent hepatocyte-intrinsic liver defect associated with mitochondrial and other hepatic metabolism implications (see Leow et al, 2024 J Clin Invest). Given that the authors describe that SMN protein levels are not altered in the pancreas of AlbCre/+;Smn2B/F7 mice at P19, the authors ought to clarify how pancreas development and function is impacted in this mouse model, whether in-utero or postnatally. This could potentially underscore the cross-talk between liver SMN and pancreas function.

      We have discussed the relationship between hepatic SMN and pancreatic function in the Discussion at lines 284-298 and 334-359.

      Authors should also perform some metabolic tolerance tests to both oral glucose and insulin at an older age (e.g. P60) to study their homeostasis in these mice. These would help to substantiate the authors' conclusion and provide the paper with a greater level of novelty.

      We thank the reviewer for this suggestion. A full panel of metabolic hormones related to glucose metabolism at P19 and P60 has been included, supported by additional figures that enhance the manuscript's novelty and depth.

      Authors mentioned in the Discussion in lines 238 to 240: "Altogether, our findings underscore the necessity of conducting further investigations at later time points to unveil potential modifications in other pathways and their repercussions on liver physiology". Please elucidate the effects of longer term liver-specific depletion of SMN beyond P19, such as the onset of NAFLD or a diabetic phenotype due to pancreatic dysfunctions.

      We extended our data to include P60 mice and performed liver and pancreatic analyses at these time points. The observed effects were transient, possibly due to the stochastic nature of Cre expression.

      In addition, while AlbCre/+;Smn2B/F7 mice had similar weight gain trends as controls, it does appear that AlbCre/+;Smn2B/F7 mice weigh more than their controls by P60 (Figure 9C). This data would provide more convincing evidence of the metabolic defects observed in these mice.

      As per the reviewer’s suggestion, we included new data (Figure 9D) showing % weight gain at P60 normalized to basal weight at P7. However, no statistically significant differences were detected.

      Other than protein quantification, authors should perform immunohistochemistry or in-situ hybridization of SMN and imaging of AlbCre/+;Smn2B/F7 organs to validate the loss of liver-specific SMN. It is unclear from western blots that the expression of SMN is only in hepatocytes.

      We thank the reviewer for the suggestion. Unfortunately, SMN antibodies have not produced reliable tissue immunostaining. To address this, we performed Cre immunostaining of the liver, pancreas, and spinal cord at P19, and the liver at P60, which demonstrated liver-specific expression. These results are now included in Supplemental Figure 1.

      Authors should consider re-wording lines 228 through 231: "While our current analysis did not reveal significant differences in AlbCre/+;Smn2B/F7 mice, the observed upward trend in transferrin and HO levels suggests ongoing changes in iron metabolism, which may not be fully manifested at P19". Alternatively, a higher number of mouse samples would allow them to qualify this statement. Authors should also consider comparing levels of liver biomarkers such as ALT and AST, to check for liver homeostatic function.

      We have removed speculative statements to avoid unsupported claims.

      Recommendations:

      The methods and additional details to generate the AlbCre/+;Smn2B/F7 should be explained better in section 2.1 of the Results. It is potentially confusing as to why these mice had to carry both 2B and F7 alleles. Additionally, the role of the F7 allele is not deliberately clear in the Introduction.

      Additional details are now included in the Introduction (lines 87-90) and the Results section (lines 104-124).

      Authors should refer to Leow et al 2024 (J Clin Invest) and discuss how their current findings compare with their hepatocyte-intrinsic SMN deficiency IPSCs model.<br /> We note a previous publication (Deguise et al 2021 Cell Mol Gastroenterol Hepatol) by the authors which characterized the Smn2B/- mouse model and its NAFLD/NASH features. From our understanding, the Smn2B/- mouse model appears to recapitulate SMA phenotype well, such as the early onset of hepatic steatosis and neurological conditions. As a follow-up to this publication, authors should discuss why this current study of a liver-specific SMN depletion is important and relevant to the study of SMA pathology.

      We thank the reviewer for the insightful suggestions. We have included a discussion of these findings and their relevance to the study of SMA pathology in lines 284-298 and 309-322.

      Minor corrections:

      Abstract (line 32) reads: "a decrease in insulin producing alpha-cells and an increase in glucagon producing beta-cells". The authors should clarify and correct as insulin producing beta-cells and glucagon producing alpha-cells.

      Thank you for catching the error. We corrected the description of insulin- and glucagon-producing cells.

      Please clarify the number and gender of mice used for weight tracking and motor function experiments up to P60 (Figure 9C). It would be inappropriate if male and female mice were plotted together. If so, authors should stratify data by gender.

      We thank the reviewer for the suggestion. Unfortunately, we did not stratify the animals by sex due to the unequal and insufficient number of males and females in our study. To address this, we normalized weight gain to each animal’s starting weight, and no significant differences were observed (now shown in Figure 9D).

      The number of figures should be reduced. We recommend merging Figures 1 and 2 (generation of AlbCre/+;Smn2B/F7 mouse line and validation) and Figures 3 and 4 (liver function). Figures 5 through 9 may be supplemental figures instead.

      We thank the reviewer for the suggestions. We merged Figures 1 and 2, and Figures 3 and 4, as requested. However, we would prefer to keep the other figures within the main results as they assess the impact of liver-specific depletion of SMN on other pathologies within the mouse model.

      Standardize the use of asterisks and reporting p-values in Figure 2. All other figures in the manuscript utilize asterisks, but Figures 2C', 2D' and 2E' use p-values across comparisons.

      P-values were included only when they approached statistical significance, providing additional clarity to the results.

      It is unclear what the white arrow in Figure 7A indicates.

      It is meant to point out the absence of an innervating axon. Please see Figure 5 legend, lines 801-802.

      Note spelling errors in Figures 8B and 8C: 'Muscle flber'.

      Thank you for catching this. We have corrected the typo to indicate muscle fiber instead.

      Please clarify if muscle fiber size should be indicated as µm2 instead of µ2 in Figures 8B and 8C, as written in Materials and Methods under line 394.

      Thank you for catching this. We corrected the typo to indicate µm2 instead.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer 1:

      (1) The overall conclusion, as summarized in the abstract as "Together, our study documents the diversification of locomotor and oculomotor adaptations among hunting teleost larvae" is not that compelling. What would be much more interesting would be to directly relate these differences to different ecological niches (e.g. different types of natural prey, visual scene conditions, height in water column etc), and/or differences in neural circuit mechanisms. While I appreciate that this paper provides a first step on this path, by itself it seems on the verge of stamp collecting, i.e. collecting and cataloging observations without a clear, overarching hypothesis or theoretical framework.

      There are limited studies on the prey capture behaviors of larval fishes, and ours is the first to compare multiple species systematically using a common analysis framework. Our analysis approach could have uncovered a common set of swim kinematics and capture strategies shared by all species; but instead, we found that medaka used a monocular strategy rather than the binocular strategy of cichlids and zebrafish. Our analysis similarly could have revealed first-feeding larvae of all species go through a “bout” stage, which was previously proposed as important for sensorimotor decision making (Bahl et al., 2019), but instead we found that medaka and some cichlids have more continuous swimming from an early life stage. Finally, the rate at which prey capture kinematics evolves is not known. Our approach could have revealed rapid diversification of feeding strategies in cichlids (similarly to how adult feeding behavior evolves), but instead we found smaller differences within cichlids than between cichlids and medaka.

      (2) The data to support some of the claims is either weak or lacking entirely.

      Highlighted timestamps in videos, new stats in fig 1H and fig 2, updated supplementary figures now provide additional support for claims.

      - It would be helpful to include previously published data from zebrafish for comparison.

      We appreciate the suggestion. Mearns et al. (2020) provided a comprehensive account of prey capture in zebrafish larvae in an almost identical setup with similar analyses. We do not feel it is necessary to recount all the findings in that paper here. There are many studies on prey capture in zebrafish from the past 20 years, and reproducing these here would not add anything to that extensive pre-existing literature.

      - Justification is required for why it is meaningful to compare hunting strategies when both fish species and prey species are being varied. For instance, artemia and paramecia are different sizes and have different movement statistics.

      We added text explaining why different food was chosen for medaka/cichlids. There is no easy way to stage match fishes as evolutionarily diverged as cichlids, medaka, and zebrafish. Size is a reasonable metric within a species, but there is no guarantee that sizematched larvae of two different species are at the same level of maturity. Therefore, we thought the most appropriate stage to address is when larvae first start feeding, as this enables us to study innate prey capture behavior before any learning or experience-dependent changes have taken place. Given that zebrafish, medaka and cichlid larvae are different sizes when they first start feeding, it was necessary to study their hunting behavior to different prey items.

      - It would be helpful in Figure 1A to add the abbreviations used elsewhere in the paper. I found it slightly distracting that the authors switch back and forth in the paper between using "OL" and "medaka" to refer to the same species: please pick one and then remain consistent.

      Medaka is the common name for the japanese rice fish, O. latipes. Cichlilds do not have common names are only referred to by their scientific names. Since readers are more likely to be familiar with the common name, medaka, we now use medaka (OL) throughout the manuscript, which we hope makes the text clearer.

      - The conceptual meaning of behavioral segmentation is somewhat unclear. For zebrafish, the bouts already come temporally segmented. However in medaka for instance, swimming is more continuous, and the segmentation is presumably more in terms of "behavioral syllables" as have been discussed for example mouse or drosophila behavior (in the last row of Figure S1 it is not at all obvious why some of the boundaries were placed at their specific locations). It's not clear whether it's meaningful to make an equivalence between syllables and bouts, and so whether for instance Figure 1H is making an apples-to-apples comparison.

      We clarified the text to say we are comparing syllables, rather than bouts.

      - The interpretation of 1H is that "medaka exhibited significantly longer swims than cichlids"; however this is not supported by the appropriate statistical test. The KS test only says that two probability distributions are different; to say that one quantity is larger than another requires a comparison of means.

      Updated Fig 1H; boostrap test (difference of medians) and re plotted data as violin plots.

      (2) The data to support some of the claims is either weak or lacking entirely.

      Highlighted timestamps in videos, new stats in fig 1H and fig 2, updated supplementary figures now provide additional support for claims.

      - I think the evidence that there are qualitatively different patterns of eye convergence between species is weak. In Figure 2A I admire the authors addressing this using BIC, and the distributions are clearly separated in LA (the Hartigan dip test could be a useful additional test here). However for LO, NM, and AB the distributions only have one peak, and it's therefore unclear why it's better to fit them with two Gaussians rather than e.g. a gamma distribution. Indeed the latter has fewer parameters than a two-gaussian model, so it would be worthwhile to use BIC to make that comparison. The positions of the two Gaussians for LO, NM, and AB are separated by only a handful of degrees (cf LA, where the separation is ~20 degrees), which further supports the idea that there aren't really two qualitatively different convergence states here.

      Added explanation to text.

      - Figure S2 is unfortunately misleading in this regard. I don't claim the authors aimed to mislead, but they have made the well-known error of using colors with very different luminances in a plot where size matters (see e.g.

      https://nam12.safelinks.protection.outlook.com/?url=https%3A %2F%2Fwww.r-project.org%2Fconferences%2FDSC2003%2FProceedings%2FIhaka.pdf&data=05%7C02%7Cdme arns%40princeton.edu%7C17ae2b44f0f246f15ddd08dc9b8e2 01c%7C2ff601167431425db5af077d7791bda4%7C0%7C0%7

      C638556282750568814%7CUnknown%7CTWFpbGZsb3d8ey

      JWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJ XVCI6Mn0%3D%7C0%7C%7C%7C&sdata=Ll4J4Xo39JEtKb %2FNnRWNoyedZAu5aAOMq0lHJCwsfXI%3D&reserved=0).

      Thus, to the eye, it appears there's a big valley between the red and blue regions, but actually, that valley is full of points: it's really just one big continuous blob.

      Kernel density estimation of eye convergence angles were added to Figure S2. The point we wish to make is that there is higher density when both eyes are rotated invwards (converged) in cichlids, but not medaka (O. latipes). The valley between converged and unconverged states being full of points is due to (1) slight variation with placement of key points in SLEAP, which blurs the boundary between states and (2) the eye convergence angle must pass through the valley in order to become converged, so necessarily there are points in between the two extremes of eye convergence.

      - In Figure 2D please could the authors double-check the significance of the difference between LO and NM: they certainly don't look different in the plot.

      Thank for for flagging this. We realize the way we previously reported the stats was open to misinterpretation. We have updated figure 2C, D and F to use letters to indicate statistical groupings, which hopefully makes it clearer which species are statistically different from each other.

      - In Figure 2G it's not clear why AB is not included. It is mentioned that the artemia was hard to track in the AB videos, but the supplementary videos provided do not support this.

      The contrast of the artemia in the AB videos is sufficiently different from the other cichlid videos that our pre-trained YOLO model fails. Retraining the model would be a lot of extra work and we feel like a comparison of three species is sufficient to address the sensorimotor transformations that occur over the course of prey capture in cichlids.

      - The statement "Zebrafish larvae have a unique swim repertoire during prey capture, which is distinct from exploratory swim bouts" is not supported by the work of others or indeed the authors' own work. In Figure 4F all types of bouts can occur at any time, it's just the probability at which they occur that varies during prey capture versus other times (see also Mearns et al (2020) Figure S4B).

      The point is well taken that there probably is not a hard separation between spontaneous and prey capture swims based on tail kinematics alone, which is also shown in Marques et al. (2018). However, we think that figure 2I of Mearns et al., which plots the probability of swims being drawn from different parts of the behavior space during prey capture (eyes converged) or not (eyes unconverged), shows that the repertoire of swims during the two states is substantially different. Points are blue or red; there are very few pale blue/pale red points in that figure panel. Figure S4B is showing clustered data, and clustering is a notoriously challenging problem for which there exists no perfect solution (Kleinberg, 2002). The clusters in Mearns et al. incorporated information about transition structure, as this was necessary for obtaining interpretable clusters for subsequent analyses. However, a different clustering approach could have yielded different boundaries, which may have shown more (or less) separation of bout types during prey capture/exploratory swimming. Therefore, we have updated the text to say that zebrafish perferentially perform different swim types during prey capture and exploration, and re-interpreted the behavior of cichlids similarly.

      - More discussion is warranted of the large variation in the number of behavioral clusters found between species (11-32). First, how much is this variation really to be trusted? I appreciate the affinity propogation parameters were the same in all cases, but what parameters "make sense" is somewhat dependent on the particular data set. Second, if one does believe this represents real variation, then why? This is really the key question, and it's unsatisfying to merely document it without trying to interpret it.

      Extended paragraph with more interpretation.

      - What is the purpose of "hovers"? Why not stay motionless? Could it be a way of reducing the latency of a subsequent movement? Is this an example of the scallop theorem?

      Added a couple of sentences speculating on function.

      - I'm not sure "spring-loaded" is a good term here: the tension force of a coiled tail is fairly negligible since there's little internal force actively trying to straighten it.

      Rewrote this part to highlight that fish spring toward the prey, without the implication that tension forces in the tail are responible for the movement. However, we are not aware of any literature measuring passive forces within the tail of fishes. Presumably the notochord is relatively stiff and may provide an internal force trying to straighten the tail.

      - There are now several statements for which no direct evidence is presented. We shouldn't have to rely on the author's qualitative impressions of what they observed: show us quantitative analysis.

      * "often hover"

      * "cichlids often alternate between approaches and hover swims"

      * "over many hundreds of milliseconds"

      * "we have also observed suction captures and ram-like attacks"

      * "may swim backwards"

      * "may expel prey from their mouth"

      * "cichlid captures often occur in two phases"

      Added references to supplementary videos with timestamps to highlight these behaviors.

      - I don't find it plausible that sated fish continue hunting prey that they know they're not going to eat just for the practice.

      Removed the speculation.

      - In Figure 3 is it not possible to include medaka, based on the hand-tracked paramecia?

      The videos are recorded at high frame rate, so it would be a lot of additional work to track these manually. Furthermore, earlier in prey capture it is very difficult to tell by watching videos which prey the medaka are tracking, especially as single paramecia can drift in and out of focus in the videos. Since there is no eye convergence, it is very difficult to ascertain for certain when tracking a given prey begins. In Fig 4, it was only possible to track paramecia by hand since it is immediately prior to the strike and from the video it is possible to see which paramecium the fish targeted. Our analyses of heading changes was performed over the 200 ms prior to a strike, which we think is a conservative enough cutoff to say that fish were probably pursuing prey in this window (it is shorter than the average behavioral syllable duration in medaka).

      - Figure 3 (particularly 3D) suggests the interesting finding that LA essentially only hunt prey that is directly in front of them (unlike LO and NM, the distribution of prey azimuth actually seems to broaden slightly over the duration of hunting events).

      This is worthy of discussion.

      We offer a suggestion for the many instances of prey capture being initiated in the central visual field in LA later in the manuscript when we discuss spitting behavior. We have added text to make this point earlier in the manuscript. The increase in azimuthal range at the end of prey capture may be due to abort swims (e.g. supp. vid. 1, 00:21). The widening of azimuthal angles is present in LO and NM also and is not unique to LA.

      - The reference Ding et al (2016) is not in the reference list.

      Wrong paper was referenced. Should be Ding 2019, which has been added to bibliography.

      - I am not convinced that medaka exhibit a unique side-swing behavior. I agree there is this tendency in the example movie, however, the results of the quantification (Figure 4) are underwhelming. First, cluster 5 in 4K appears to include a proportion of cases from LA and AB. These proportions may be small, but anything above zero means this is not unique to medaka. Second, the heading angle (4N) starts at 4 degrees for LA and 8 degrees for medaka. This difference is genuine but very small, much smaller than what's drawn in the schematic (4M). I'm not sure it's justifiable to call a difference of 4 degrees a qualitatively different strategy.

      We have changed the text to highlight that side swing is highly enriched in medaka. Comparing 4J to 3B we would argue that there is a qualitative difference in the strategy used to capture prey in the cichlid larvae we study here and medaka. We agree that further work is required to understand distance estimation behaviors in different species. In this manuscript, we use heading angle as a proxy for how prey position might change on the retina over a hunting sequence. But as the heading and distance are changing over time, the actual change in angle on the retina for prey may be much larger than the ~8 degree shift reported here. The actual position of the prey is also important here, which, for reasons mentioned above, we could not track. Given the final location of prey in the visual field prior to the strike (Fig 4J), the most parsimonious explanation of the data is that the prey is always in the monocular visual field. In cichlids, the prey is more-or-less centered in the 200 ms preceding the strike. While it is true theat the absolute difference in heading is 4 degrees, when converted to an angular velocity (4N, right), the medaka (OL) effectively rotate twice as fast as LA (20 deg/s vs 40 deg/s), which we think is a substantial difference and evidence of a different targeting strategy.

      - 4K: This is referred to in the caption as a confusion matrix, which it's not.

      Fixed.

      - 4N right panel: how many fish contributed to the points shown?

      Added to figure legend (n=113, LA; n=36, OL). Same data in left and right panels.

      - In the Discussion it is hypothesized that medaka use their lateral line in hunting more than in other species. Testing this hypothesis (even just compared to one other species) would be fairly straightforward, and would add significant interest to the paper overall.

      We agree that this is an interesting experiment for follow up studies, but it is beyond the scope of the current manuscript as we do not have the appropriate animal license for this experiment.

      Reviewer 2:

      The paper is rather descriptive in nature, although more context is provided in the discussion. Most figures are great, but I think the authors could add a couple of visual aids in certain places to explain how certain components were measured.

      Added new supplemental figure (Supp Fig 2)

      Figure 1B- it could be useful to add zebrafish and medaka to the scientific names (I realize it's already in Figure A but I found myself going back and forth a couple of times, mostly trying to confirm that O. latipes is medaka).

      Added common names to 1B, sprinkled reminders of OL/medaka throughout text.

      Figure 1G. I wasn't sure how to interpret the eye angle relative to the midline. Can they rotate their eyes or is this due to curvature in the 'upper' body of the fish? Adding a schematic figure or something like that could help a reader who is not familiar with these methods. Related to this, I was a bit confused by Figure 2A. After reading the methods section, I think I understand - but I little cartoon to describe this would help. It also reminds the reader (especially if they don't work with fish) that fish eyes can rotate. I also wanted to note that initially, I thought convergence was a measure of how the two eyes were positioned relative to the prey given the emphasis given on binocular vision, and only after reading certain sections again did I realize convergence was a measure of eye rotation/movement.

      New supplemental figure explaining how eye tracking is performed

      Figure 3. It was not immediately clear to me what onset, middle, and end represented - although it is explained in the caption. I think what tripped me up is the 'eye convergence' title in the top right corner of Figure 3A.

      Updated figure with schematic illustrating that time is measured relative to eye convergence onset and end.

      The result section about attack swim, S-strike, capture spring, etc. was a bit confusing to read and could benefit from a couple of concise descriptions of these behaviors. For example, I am not familiar with the S strike but a couple of paragraphs into this section, the reader learns more about the difference between S strike vs. attack swim. This can be mentioned in the first paragraph when these distinct behaviors are mentioned.

      Added description of behavior earlier in text.

      Figure 4. Presents lots of interesting data! I wonder if using Figure 1E could help the reader better understand how these measurements were taken.

      New supplemental figure added, explaining how tail tracking is performed.

      I probably overlooked this, but I wonder why so many panels are just focused on one species.

      Added explanation to the text.

      Is the S-shaped capture strategy the same as an S strike?

      Clarified in text to say "S-strike-like". This is a description of prey capture from adult largemouth bass in New et al. (2002). From the still frames shown in that paper, the kinematics looks similar to an S-strike or capture spring. The important point we wish to make is that tail is coiled in an S-shape prior to a strike, which indicates this that a kinematically similar behavior exists fishes beyond just larval cichlids and zebrafish.

      At the end of the page, when continuous swimming versus interrupted swimming is discussed, please remind the reader that medaka shows more continuous swimming (longer bouts).

      Added "while medaka swim continuously with longer bouts ("gliding")".

      After reading the discussion, it looks like many findings are unique. For example, given that medaka is such a popular model species in biology, it strikes me that nobody has ever looked into their hunting movements before. If their findings are novel, perhaps they should state so it is clear that the authors are not ignoring the literature.

      We have highlighted what we believe to be the novelty of our findings (first description of prey capture in larval cichlids and medaka). To our knowledge, we are first to describe hunting in medaka; but there is an extensive literature on medaka dating back to the early 20th century, some of which is only published in Japanese. We have done our best to review the literature, but we cannot rule out that there are papers that we missed. No English language article or review we found mentions literature on hunting behavior in medaka larvae.

      Reviewer 3:

      More evidence is needed to assess the types of visual monocular depth cues used by medaka fish to estimate prey location, but that is beyond the scope of this compelling paper. For example, medaka may estimate depth through knowledge of expected prey size, accommodation, defocus blur, ocular parallax, and/or other possible algorithms to complement cues from motion parallax.

      Added sentence to discussion highlighting that other cues may also contribute to distance estimation in cichlids and medakas. Follow-up studies will require new animal license.

      None. It's quite nice, timely, and thorough work! For future work, one could use 3D pose estimation of eye and prey kinematics to assess the dynamics of the 2D image (prey and background) cast onto the retina. This sort of representation could be useful to infer which monocular depth cues may be used by medaka during hunting.

      Great suggestion for follow up studies. Bolton et al. and Mearns et al. both find changes in z associated with prey capture, and it would be interesting to see how other fish species use the full 3-dimensional water column during prey capture, especially considering the diversity of hunting strategies in adult cichlids (ranging from piscivorous species, like LA, to algar grazers).

      In Figure 4N, you use "change in heading leading up to a strike as a proxy for the change in visual angle of the prey for cichlids and medaka." This proxy makes sense, but you also have the eye angles and (in some cases) the prey positions. One could estimate the actual change in visual angle from this information, which would also allow one to measure whether the fish are trying to stabilize the position of the prey on a high-acuity patch of the retina during the final moments of the hunt. This information may also shed light on which monocular depth cues are used.

      As addressed in comment to reviewer 1, this would require actually manually tracking individual paramecia over hundreds of frames. It is not possible to determine exactly when hunting begins in medaka, and it is prone to errors if medaka switch between targets over the course of a hunting episode. This question is better addressed with psychophysics experiments in embedded animals where it is possible to precisely control the stimulus, but this requires new animal licenses and is beyond the scope of this paper.

      In Figure 5, you could place the prey object a little farther from the D. rerio fish for the S-strike diagram.

      Fixed.

      Figure 4F legend should read "...at the peak of each bout."

      Fixed.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Thank you for your constructive feedback and recognition of our work. We followed your suggestion and improved the accuracy of the language used to interpret some of our findings. 

      Summary:

      The present study by Mikati et al demonstrates an improved method for in-vivo detection of enkephalin release and studies the impact of stress on the activation of enkephalin neurons and enkephalin release in the nucleus accumbens (NAc). The authors refine their pipeline to measure met and leu enkephalin using liquid chromatography and mass spectrometry. The authors subsequently measured met and leu enkephalin in the NAc during stress induced by handling, and fox urine, in addition to calcium activity of enkephalinergic cells using fiber photometry. The authors conclude that this improved tool for measuring enkephalin reveals experimenter handling stress-induced enkephalin release in the NAc that habituates and is dissociable from the calcium activity of these cells, whose activity doesn't habituate. The authors subsequently show that NAc enkephalin neuron calcium activity does habituate to fox urine exposure, is activated by a novel weigh boat, and that fox urine acutely causes increases in met-enk levels, in some animals, as assessed by microdialysis.

      Strengths:

      A new approach to monitoring two distinct enkephalins and a more robust analytical approach for more sensitive detection of neuropeptides. A pipeline that potentially could help for the detection of other neuropeptides.

      Weaknesses:

      Some of the interpretations are not fully supported by the existing data or would require further testing to draw those conclusions. This can be addressed by appropriately tampering down interpretations and acknowledging other limitations the authors did not cover brought by procedural differences between experiments.

      We have taken time to go through the manuscript ensuring we are more detailed and precise with our interpretations as well as appropriately acknowledging limitations. 

      Reviewer #2 (Public Review):

      Thank you for your constructive and thorough assessment of our work. In our revised manuscript, we adjusted the text to reflect the references you mentioned regarding the methionine oxidation procedure. Additionally, we expanded the methods section to include the key details of the statistical tests and procedures that you outlined. 

      Summary:

      The authors aimed to improve the detection of enkephalins, opioid peptides involved in pain modulation, reward, and stress. They used optogenetics, microdialysis, and mass spectrometry to measure enkephalin release during acute stress in freely moving rodents. Their study provided better detection of enkephalins due to the implementation of previously reported derivatization reaction combined with improved sample collection and offered insights into the dynamics and relationship between Met- and Leu-Enkephalin in the Nucleus Accumbens shell during stress.

      Strengths:

      A strength of this work is the enhanced opioid peptide detection resulting from an improved microdialysis technique coupled with an established derivatization approach and sensitive and quantitative nLC-MS measurements. These improvements allowed basal and stimulated peptide release with higher temporal resolution, lower detection thresholds, and native-state endogenous peptide measurement.

      Weaknesses:

      The draft incorrectly credits itself for the development of an oxidation method for the stabilization of Met- and Leu-Enk peptides. The use of hydrogen peroxide reaction for the oxidation of Met-Enk in various biological samples, including brain regions, has been reported previously, although the protocols may slightly vary. Specifically, the manuscript writes about "a critical discovery in the stabilization of enkephalin detection" and that they have "developed a method of methionine stabilization." Those statements are incorrect and the preceding papers that relied on hydrogen peroxide reaction for oxidation of Met-Enk and HPLC for quantification of oxidized Enk forms should be cited. One suggested example is Finn A, Agren G, Bjellerup P, Vedin I, Lundeberg T. Production and characterization of antibodies for the specific determination of the opioid peptide Met5-Enkephalin-Arg6-Phe7. Scand J Clin Lab Invest. 2004;64(1):49-56. doi: 10.1080/00365510410004119. PMID: 15025428.

      Thank you for highlighting this. It was not our intention to imply that we developed the oxidation method, rather that we were able improve the detection of metenkephalin by oxidation of the methionine without compromising the detection resolution of leu-enkephalin, enabling the simultaneous detection of both peptides. We have addressed this is the manuscript and included the suggested citation. 

      Another suggestion for this draft is to make the method section more comprehensive by adding information on specific tools and parameters used for statistical analysis:

      (1) Need to define "proteomics data" and explain whether calculations were performed on EIC for each m/z corresponding to specific peptides or as a batch processing for all detected peptides, from which only select findings are reported here. What type of data normalization was used, and other relevant details of data handling? Explain how Met- and Leu-Enk were identified from DIA data, and what tools were used.

      Thank you for pointing out this source of confusion. We believe it is because we use a different DIA method than is typically used in other literature. Briefly, we use a DIA method with the targeted inclusion list to ensure MS2 triggering as opposed to using large isolation widths to capture all precursors for fragmentation, as is typically done with MS1 features. For our method, MS2 is triggered based on the 4 selected m/z values (heavy and light versions of Leu and Met-Enkephalin peptides) at specific retention time windows with isolation width of 2 Da; regardless of the intensity of MS1 of the peptides. 

      (2) Simple Linear Regression Analysis: The text mentions that simple linear regression analysis was performed on forward and reverse curves, and line equations were reported, but it lacks details such as the specific variables being regressed (although figures have labels) and any associated statistical parameters (e.g., R-squared values). 

      Additional detail about the linear regression process was added to the methods section, please see lines 614-618. The R squared values are also now shown on the figure. 

      ‘For the forward curves, the regression was applied to the measured concentration of the light standard as the theoretical concentration was increased. For plotting purposes, we show the measured peak area ratios for the light standards in the forward curves. For the reverse curves, the regression was applied to the measured concentration of the heavy standard, as the theoretical concentration was varied.’

      (3) Violin Plots: The proteomics data is represented as violin plots with quartiles and median lines. This visual representation is mentioned, but there is no detail regarding the software/tools used for creating these plots.

      We used Graphpad Prism to create these plots. This detail has been added to the statistical analysis section. See line 630.

      (4) Log Transformation: The text states that the data was log-transformed to reduce skewness, which is a common data preprocessing step. However, it does not specify the base of the logarithm used or any information about the distribution before and after transformation.

      We have added the requested details about the log transformation, and how the data looked before and after, into the statistical analysis section. We followed convention that the use of log is generally base 10 unless otherwise specified as natural log (base 2) or a different base. See lines 622-625

      ‘The data was log10 transformed to reduce the skewness of the dataset caused by the variable range of concentrations measured across experiments/animals. Prior to log transformation, the measurements failed normality testing for a Gaussian distribution. After the log transformation, the data passed normality testing, which provided the rationale for the use of statistical analyses that assume normality.’

      (5) Two-Way ANOVA: Two-way ANOVA was conducted with peptide and treatment as independent variables. This analysis is described, but there is no information regarding the software or statistical tests used, p-values, post-hoc tests, or any results of this analysis.

      Information about the two-way ANOVA analysis has been added to the statistical analysis section. Additionally, more detailed information has been added to the figure legends about the statistical results. Please see lines 625-628.

      ‘Two-way ANOVA testing with peptide (Met-Enk or Leu-Enk) and treatment (buffer or stress for example) as the two independent variables. Post-hoc testing was done using Šídák's multiple comparisons test and the p values for each of these analyses are shown in the figures (Figs. 1F, 2A).’ 

      (6) Paired T-Test: A paired t-test was performed on predator odor proteomic data before and after treatment. This step is mentioned, but specific details like sample sizes, and the hypothesis being tested are not provided.

      The sample size is included in the figure legend to which we have included a reference. We have also included the following text to highlight the purpose of this test. See lines 628-630

      A paired t-test was performed on the predator odor proteomic data before and after odor exposure to test that hypothesis that Met-Enk increases following exposure to predator odor  (Fig. 3F). These analyses were conducted using Graphpad Prism.

      (7) Correlation Analysis: The text mentions a simple linear regression analysis to correlate the levels of Met-Enk and Leu-Enk and reports the slopes. However, details such as correlation coefficients, and p-values are missing.

      We apologize for the use of the word correlation as we think it may have caused some confusion and have adjusted the language accordingly. Since this was a linear regression analysis, there is no correlation coefficient. The slope of the fitted line is reported on the figures to show the fitted values of Met-Enk to Leu-Enk. 

      (8) Fiber Photometry Data: Z-scores were calculated for fiber photometry data, and a reference to a cited source is provided. This section lacks details about the calculation of zscores, and their use in the analysis. 

      These details have been added to the statistical analysis section. See lines 634-637

      ‘For the fiber photometry data, the z-scores were calculated as described in using GuPPy which is an open-source python toolbox for fiber photometry analysis. The z-score equation used in GuPPy is z=(DF/F-(mean of DF/F)/standard deviation of DF/F) where F refers to fluorescence of the GCaMP6s signal.’

      (9) Averaged Plots: Z-scores from individual animals were averaged and represented with SEM. It is briefly described, but more details about the number of animals, the purpose of averaging, and the significance of SEM are needed.

      We have added additional information about the averaging process in the statistical analysis section. See lines 639-643.

      ‘The purpose of the averaged traces is to show the extent of concordance of the response to experimenter handling and predator odor stress among animals with the SEM demonstrating that variability. The heatmaps depict the individual responses of each animal. The heatmaps were plotted using Seaborn in Python and mean traces were plotted using Matplotlib in Python.’

      A more comprehensive and objective interpretation of results could enhance the overall quality of the paper.

      We have taken this opportunity to improve our manuscript following comments from all the reviewers that we hope has resulted in a manuscript with a more objective interpretation of results. 

      Reviewer #3 (Public Review):

      Thank you for your thoughtful review of our work. To clarify some of the points you raised, we revised the manuscript to include more detail on how we distinguish between the oxidized endogenous and standard signal, as well as refine the language concerning the spatial resolution. We also edited the manuscript regarding the concentration measurements. We conducted technical replicates, so we appreciate you raising this point and clarify that in the main text. 

      Summary:

      This important paper describes improvements to the measurement of enkephalins in vivo using microdialysis and LC-MS. The key improvement is the oxidation of met- to prevent having a mix of reduced and oxidized methionine in the sample which makes quantification more difficult. It then shows measurements of enkephalins in the nucleus accumbens in two different stress situations - handling and exposure to predator odor. It also reports the ratio of released met- and leu-enkephalin matching what is expected from the digestion of proenkephalin. Measurements are also made by photometry of Ca2+ changes for the fox odor stressor. Some key takeaways are the reliable measurement of met-enkephalin, the significance of directly measuring peptides as opposed to proxy measurements, and the opening of a new avenue into the research of enkephalins due to stress based on these direct measurements.

      Strengths:

      -Improved methods for measurement of enkephalins in vivo.

      -Compelling examples of using this method.

      -Opening a new area of looking at stress responses through the lens of enkephalin concentrations.

      Weaknesses:

      (1) It is not clear if oxidized met-enk is endogenous or not and this method eliminates being able to discern that.

      We clarified our wording in the text copied below to provide an explanation on how we distinguish between the two. Even after oxidation, the standard signal has a higher m/z ratio due to the presence of the Carbon and Nitrogen isotopes as described in the Chemicals section of the methods ‘For Met Enkephalin, a fully labeled L-Phenylalanine (<sup>13</sup>C<sub>9</sub>, <sup>15</sup>N) was added (YGGFM). The resulting mass shift between the endogenous (light) and heavy isotope-labeled peptide are 7Da and 10Da, respectively.’, so they can still be differentiated from the endogenous signal. We have clarified the language in the results section. See lines 82-87. 

      ‘After each sample collection, we add a consistent known concentration of isotopically labeled internal standard of Met-Enk and Leu-Enk of 40 amol/sample to the collected ISF for the accurate identification and quantification of endogenous peptide. These internal standards have a different mass/charge (m/z) ratio than endogenous Met- and Leu-Enk. Thus, we can identify true endogenous signal for Met-Enk and Leu-Enk (Suppl Fig. 1A,C) versus noise, interfering signals, and standard signal (Suppl. Fig. 1B,D).’

      (2) It is not clear if the spatial resolution is really better as claimed since other probes of similar dimensions have been used.

      Apologies for any confusion here. To clarify we primarily state that our approach improves temporal resolution and in a few cases refer to improved spatiotemporal resolution, which we believe we show. The dimensions of the microdialysis probe used in these experiments allow us to target the nucleus accumbens shell and as well as being smaller – especially at the membrane level - than a fiber photometry probe. 

      (3) Claims of having the first concentration measurement are not quite accurate.

      Thank you for your feedback. To clarify, we do not claim that we have the first concentration measurements, rather we are the first to quantify the ratio of Met-Enk to Leu-Enk in vivo in freely behaving animals in the NAcSh. 

      (4) Without a report of technical replicates, the reliability of the method is not as wellevaluated as might be expected.

      We have added these details in the methods section, please see lines 521-530. 

      ‘Each sample was run in two technical replicates and the peak area ratio was averaged before concentration calculations of the peptides were conducted. Several quality control steps were conducted prior to running the in vivo samples. 1) Two technical replicates of a known concentration were injected and analyzed – an example table from 4 random experiments included in this manuscript is shown below. 2) The buffers used on the day of the experiment (aCSF and high K+ buffer) were also tested for any contaminating Met-Enk or Leu-Enk signals by injecting two technical replicates for each buffer. Once these two criteria were met, the experiment was analyzed through the system. If either step failed, which happened a few times, the samples were frozen and the machines were cleaned and restarted until the quality control measures were met.’

      Recommendations For The Authors:

      Reviewer #1 (Recommendations For The Authors):

      • The authors should provide appropriate citations of a study that has validated the Enkephalin-Cre mouse line in the nucleus accumbens or provide verification experiments if they have any available.

      Thank you for your comment. We have added a reference validating the Enk-Cre mouse line in the nucleus accumbens to the methods section and is copied here. 

      D.C. Castro, C.S. Oswell, E.T. Zhang, C.E. Pedersen, S.C. Piantadosi, M.A. Rossi, A.C. Hunker, A. Guglin, J.A. Morón, L.S. Zweifel, G.D. Stuber, M.R. Bruchas, An endogenous opioid circuit determines state-dependent reward consumption, Nature 2021 598:7882 598 (2021) 646–651. https://doi.org/10.1038/s41586-02104013-0.

      • Better definition of the labels y1,y2,b3 in Figures 1 and S1 would be useful. I may have missed it but it wasn't described in methods, results, or legends.

      Thank you for this comment. We have added this information to Fig.1 legend ‘Y1, y2, b3 refer to the different elution fragments resulting from Met-Enk during LC-MS.

      • It is interesting that the ratio of KCl-evoked release is what changes differentially for Met- vs Leu. Leu enk increases to the range of met-enk. There is non-detectable or approaching being non-detectable leu-enk (below the 40 amol / sample limit of quantification) in most of the subjects that become apparent and approach basal levels of met-enkephalin. This suggests that the K+ evoked response may be more pronounced for leu-enk. This is something that should be considered for further analysis and should be discussed.

      Thank you for this astute observation, and you make a great point. We have added some discussion of this finding in the results and discussion sections see lines 111112 and lines 253-257. 

      ‘Interestingly, Leu-Enk showed a greater fold change compared to baseline than did Met-Enk with the fold changes being 28 and 7 respectively based on the data in Fig.1F.’

      ‘We also noted that Leu-Enk showed a greater fold increase relative to baseline after depolarization with high K+ buffer as compared to Met-Enk. This may be due to increased Leu-Enk packaging in dense core vesicles compared to Met-Enk or due to the fact that there are two distinct precursor sources for Leu-Enk, namely both proenkephalin and prodynorphin while Met-Enk is mostly cleaved from proenkephalin (see Table 1 [48]).’

      • For example in 2E, it would be helpful to label in the graph axis what samples correspond to the manipulation and also in the text provide the reader with the sample numbers. The authors interpret the relationship between the last two samples of baseline and posthandling stress as the following in the figure legend "the concentration released in later samples is affected; such influence suggests that there is regulation of the maximum amount of peptide to be released in NAcSh. E. The negative correlation in panel d is reversed by using a high K+ buffer to evoke Met-Enk release, suggesting that the limited release observed in D is due to modulation of peptide release rather than depletion of reserves." However, the correlations are similar between 2D and E and it appears that two mice are mediating the difference between the two groups. The appropriate statistical analysis would be to compare the regressions of the two groups. Statistics for the high K+ (and all other graphs where appropriate) need to be reported, including the r2 and p-value.

      Thank you for your constructive critique. To elucidate the effect of high K+, we have plotted the regression line and reported the slope for Fig. 2E. Notably, the slope is reduced by a factor of 2 and appears to be driven by a large subset of the animals. The statistics for the high K+ graph are shown on the figure (Fig 1F) which test the hypothesis of whether high K+ leads to the release of Leu-Enk and Met-Enk respectively compared to baseline with aCSF. We have added the test statistics to the figure legend for additional clarity. Fig. 1G has no statistics because it is only there to elucidate the ratio between Met-Enk and Leu-Enk in the same samples. We did not test any hypotheses related to whether there are differences between their levels as that is not relevant to our question. The correlation on the same data is depicted in Fig. 1H, and we have added the R<sup>2</sup> value per your request. 

      • The interpretation that handling stress induces enkephalin release from microdialysis experiments is also confounded by other factors. For instance, from the methods, it appears that mice were connected and sample collection started 30 min after surgery, therefore recovery from anesthesia is also a confounding variable, among other technical aspects, such as equilibration of the interstitial fluid to the aCSF running through the probe that is acting as a transmitter and extracellular molecule "sink". Did the authors try to handle the mice post hookup similar to what was done with photometry to have a more direct comparison to photometry experiments? This procedural difference, recording from recently surgerized animals (microdialysis) vs well-recovered animals with photometry should be mentioned in addition to the other caveats the authors mention.

      Thank you for your comment. We are aware of this technical limitation, and it is largely why we sought to conduct the fiber photometry experiments to get at the same question. As you requested, we have included additional language in the discussion to acknowledge this limitation and how we chose to address it by measuring calcium activity in the enkephalinergic neurons, which would presumably be the same cell population whose release we are quantifying using microdialysis. See lines 262-273.  

      ‘Our findings showed a robust increase in peptide release at the beginning of experiments, which we interpreted as due to experimenter handling stress that directly precedes microdialysis collections. However, there are other technical limitations to consider such as the fact that we were collecting samples from mice that were recently operated on. Another consideration is that the circulation of aCSF through the probe may cause a sudden shift in oncotic and hydrostatic forces, leading to increased peptide release to the extracellular space. As such, we wanted to examine our findings using a different technique, so we chose to record calcium activity from enkephalinergic neurons - the same cell population leading to peptide release. Using fiber photometry, we showed that enkephalinergic neurons are activated by stress exposure, both experimenter handling and fox odor, thereby adding more evidence to suggest that enkephalinergic neurons are activated by stress exposure which could explain the heightened peptide levels at the beginning of microdialysis experiments.’

      • The authors should provide more details on handling stress manipulation during photometry. For photometry what was the duration of the handling bout, what was the interval between handling events, and can the authors provide a description of what handling entailed? Were mice habituated to handling days before doing photometry recording experiments?

      Thank you for your suggestion. We have addressed all of your points in the methods section. See lines 564-570. 

      ‘The handling bout which mimicked traditional scruffing lasted about 3-5 seconds. The mouse was then let go and the handling was repeated another two times in a single session with a minimum of 1-2 minutes between handling bouts. Mice were habituated to this manipulation by being attached to the fiber photometry rig, for 3-5 consecutive days prior to the experimental recording. Additionally, the same maneuver was employed when attaching/detaching the fiber photometry cord, so the mice were subjected to the same process several times.’

      • For the novel weigh boat experiments, the authors should explicitly state when these experiments were done in relation to the fox urine, was it a different session or the same session? Were they the same animals? Statements like the following (line 251) imply it was done in the same animals in the same session but it should be clarified in the methods "We also showed using fiber photometry that the novelty of the introduction of a foreign object to the cage, before adding fox odor, was sufficient to activate enkephalinergic neurons."

      As shown in supplementary figure 4, individual animal data is shown for both water and fox urine exposure (overlaid) to depict whether there were differences in their responses to each manipulation – in the same animal. And yes, you are correct, the animals were first exposed to water 3 times in the recording session and then exposed to fox urine 3 times in the same session. We have added that to the methods section describing in vivo fiber photometry. See lines 575-576.  

      • Statistical testing would be needed to affirm the conclusions the authors draw from the fox urine and novel weigh boat experiments. For example, it shows stats that the response attenuates, that it is not different between fox urine and novel (it looks like the response is stronger to the fox urine when looking at the individual animals), etc. These data look clear but stats are formally needed. Formal statistics are also missing in other parts of the manuscript where conclusions are drawn from the data but direct statistical comparisons are not included (e.g. Fig 2.G-I).

      The photometry data is shown as z-scores which is a formal statistical analysis. ANOVA would be inappropriate to run to compare z-scores. We understand that this is erroneously done in fiber photometry literature, however, it remains incorrect. The z-scores alone provide all the information needed about the deviation from baseline. We understand that this is not immediately clear to readers, and we thank you for allowing us to explain why this is the case. We have added test statistics to figure legends where hypothesis testing was done and p-values were reported. 

      • Did the authors try to present the animals with repeated fox urine exposure to see if this habituates like the photometry?

      No, we did not do that experiment due to the constrained timing within which we had to run our microdialysis/LC-MS timeline, but it is a great point for future exploration. 

      • It would be useful to present the time course of the odor experiment for the microdialysis experiment.

      The timeline is shown in Fig.1a and Fig.3e. To reiterate, each sample is 13 minutes long.

      • Can the authors determine if differences in behavior (e.g. excessive avoidance in animals with with one type of response) or microdialysis probe location dictate whether animals fall into categories of increased release, no release, or no-detection? From the breakdown, it looks like it is almost equally split into three parts but the authors' descriptions of this split are somewhat misleading (line 210). " The response to predator odor varies appreciably: although most animals show increased Met-Enk release after fox odor exposure, some show continued release with no elevation in Met-Enk levels, and a minority show no detectable release".

      Thank you for your constructive feedback. We do not believe the difference in behavior is correlated with probe placement. The hit map can be found in suppl. Fig 3 and shows that all mice included in the manuscript had probes in the NAcSh. We purposely did not distinguish between dorsal and ventral because of our 1 mm membrane would make it hard to presume exclusive sampling from one subregion. That is a great point though, and we have thought about it extensively for future studies. We have edited the language to reflect the almost even split of responses for Met-Enk and appreciate you pointing that out. 

      • Overall, given the inconsistencies in experimental design and overall caveats associated, I think the authors are unable to draw reasonable conclusions from the repeated stressor experiments and something they should either consider is not trying to draw strong conclusions from these observations or perform additional experiments that provide the grounds to derive those conclusions.

      We have included additional language on the caveats of our study, and our use of a dual approach using fiber photometry and microdialysis was largely driven by a

      desire to offer additional support of our conclusions. We expected pushback about our conclusions, so we wanted to offer a secondary analysis using a different technique to test our hypothesis. To be honest the tone of this comment and content is not particularly constructive (especially for trainees) nor does it offer a space to realistically address anything. This work took multiple years to optimize, it was led by a graduate student, and required a multidisciplinary team. As highlighted, we believe it offers an important contribution to the literature and pushes the field of peptide detection forward.  

      Reviewer #2 (Recommendations For The Authors):

      A more comprehensive and objective interpretation of results could enhance the overall quality of the paper. The manuscript contains statements like "we are the first to confirm," which can be challenging to substantiate and may not significantly enhance the paper. It's essential to ensure that novelty statements are well-founded. For example, the release of enkephalins from other brain regions after stress exposure is well-documented but not addressed in the paper. Similarly, the role of the NA shell in stress has been extensively studied but lacks coverage in this manuscript.

      We have edited the language to reflect your feedback. We have also included relevant literature expanding on the demonstrated roles of enkephalins in the literature. We would like to note that most studies have focused on chronic stress, and we were particularly interested in acute stress. See lines 129-134.

      ‘These studies have included regions such as the locus coeruleus, the ventral medulla, the basolateral nucleus of the amygdala, and the nucleus accumbens core and shell. Studies using global knockout of enkephalins have shown varying responses to chronic stress interventions where male knockout mice showed resistance to chronic mild stress in one study, while another study showed that enkephalin-knockout mice showed delayed termination of corticosteroid release. [33,34]’ 

      Finally, not a weakness but a clarification suggestion: the method description mentions the use of 1% FA in the sample reconstitution solution and LC solvents, which is an unusually high concentration of acid. If this concentration is intentional for maintaining the peptides' oxidation state, it would be beneficial to mention this in the text to assist readers who might want to replicate the method.

      This is correct and has been clarified in the methods section

      Reviewer #3 (Recommendations For The Authors):

      -The Abstract should state the critical improvements that are made. Also, quantify the improvements in spatiotemporal resolution.

      Thank you for your comment. We have edited the abstract to reflect this. 

      - The use of "amol/sample" as concentration is less informative than an SI units (e.g., pM concentration) and should be changed. Especially since the volume used was the same for in vivo sampling experiments.

      Thank you for your comment. We chose to report amol/sample because we are measuring such a small concentration and wanted to account for any slight errors in volume that can make drastic differences on reported concentrations especially since samples are dried and resuspended.  

      -Please check this sentence: "After each collection, the samples were spiked with 2 µL of 12.5 fM isotopically labeled Met-Enkephalin and Leu-Enkephalin" This dilution would yield a concentration of ~2 fM. In a 12 uL sample, that would be ~0.02 amol, well below the detection limit. (note that fM would femtomolar concentration and fmol would be femtomoles added).

      -"liquid chromatography/mass spectrometry (LC-MS) [9-12]"... Reference 9 is a RIA analysis paper, not LC-MS as stated.

      Thank you for catching these. We have corrected the unit and citation. 

      -Given that improvements in temporal resolution are claimed, the lack of time course data with a time axis is surprising. Rather, data for baseline and during treatment appear to be combined in different plots. Time course plots of individuals and group averages would be informative.

      Due to the expected variability between individual animal time course data, where for example, we measure detectable levels in one sample followed by no detection, it was very difficult to combine data across time. Therefore, to maximize data inclusion from all animals that showed baseline measurements and responses to individual manipulations, we opted to report snapshot data. Our improvement in temporal resolution refers to the duration of each sample rather than continuous sampling, so those two are unrelated. Thank you for your feedback and allowing us to clarify this.

      - I do not understand this claim "We use custom-made microdialysis probes, intentionally modified so they are similar in size to commonly used fiber photometry probes to avoid extensive tissue damage caused by traditional microdialysis probes (Fig. 1B)." The probes used are 320 um OD and 1 mm long. This is not an uncommon size of microdialysis probes and indeed many are smaller, so is their probe really causing less damage than traditional probes?

      Thank you for your comment. We are only trying to make the point that the tissue damage from these probes is comparable to commonly used fiber photometry probes. We only point that out because tissue damage is used as a point to dissuade the usage of microdialysis in some literature, and we just wanted to disambiguate that. We have clarified the statement you pointed out.  

      -The oxidation procedure is a good idea, as mentioned above. It would be interesting to compare met-enk with and without the oxidation procedure to see how much it affects the result (I would not say this is necessary though). It is not uncommon to add antioxidants to avoid losses like this. Also, it should be acknowledged that the treatment does prevent the detection of any in vivo oxidation, perhaps that is important in met-enk metabolism?

      The comparison between oxidized and unoxidized Met-Enk detection is in figure 1C. 

      -It would be a best practice to report the standard deviation of signal for technical replicates (say near in vivo concentrations) of standards and repeated analysis of a dialysate sample to be able to understand the variability associated with this method. Similarly, an averaged basal concentration from all rats.

      Thank you for your comment. We have included a table showing example quality control standard injections from 4 randomly selected experiments included in the manuscript that were run before and after each experiment and descriptive statistics associated with these technical replicates. We also added some detail to the methods section to describe how quality control is done. See lines 521-530. 

      ‘Each sample was run in two technical replicates and the peak area ratio was averaged before concentration calculations of the peptides were conducted. Several quality control steps were conducted prior to running the in vivo samples. 1) Two technical replicates of a known concentration were injected and analyzed – an example table from 4 random experiments included in this manuscript is shown below. 2) The buffers used on the day of the experiment (aCSF and high K+ buffer) were also tested for any contaminating Met-Enk or Leu-Enk signals by injecting two technical replicates for each buffer. Once these two criteria were met, the experiment was analyzed through the system. If either step failed, which happened a few times, the samples were frozen and the machines were cleaned and restarted until the quality control measures were met.’

      EDITORS NOTE

      Should you choose to revise your manuscript, please include full statistical reporting including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05.

      Thank you for your suggestion. We have included more detail about statistical analysis in the figure legends per this comment and reviewer comments.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      The propagation of electrical signals within neuronal circuits is tightly regulated by the physical and molecular properties of neurons. Since neurons vary in size across species, the question arises whether propagation speed also varies to compensate for it. The present article compares numerous speed-related properties in human and rat neurons. They found that the larger size of human neurons seems to be compensated by a faster propagation within dendrites but not the axons of these neurons. The faster dendritic signal propagation was found to arise from wider dendritic diameters and greater conductance load in human neurons. In addition, the article provides a careful characterization of human dendrites and axons, as the field has only recently begun to characterize post-operative human cells. There are only a few studies reporting dendritic properties and these are not all consistent, hence there is the added value of reporting these findings, particularly given that the characterization is condensed in a compartmental model.

      Strengths:

      The study was performed with great care using standard techniques in slice electrophysiology (pharmacological manipulation with somatic patch-clamp) as well as some challenging ones (axonal and dendritic patch-clamp). Modeling was used to parse out the role of different features in regulating dendritic propagation speed. The finding that propagation speed varies across species is novel as previous studies did not find a large change in membrane time constant or axonal diameters (a significant parameter affecting speed). A number of possible, yet less likely factors were carefully tested (Ih, membrane capacitance). The main features outlined here are well-known to regulate speed in neuronal processes. The modeling was also carefully done to verify that the magnitude of the effects is consistent with the difference in biophysical properties. Hence, the findings appear very solid to me.

      Weaknesses:

      The role of diameter in regulating propagation speed is well-known in the axon literature.

      We thank the reviewer for this comment. This is indeed true. The paper does not claim that this is new – we just refereed to Waxman’s book to acknowledge this established effect. Our main emphasize is on the impact of dendritic (rather than axonal) diameter – highlighting the faster EPSP speed near the input synapse and converging to steady-state value further away from the soma and using this to explore the impact of differences in dendritic diameter of rat vs. human on EPSP latency and velocity. We now made this point clearer in the revised text.

      Reviewer #2 (Public Review):

      Summary:

      In this paper, Oláh and colleagues introduce new research data on the cellular and biophysical elements involved in transmission within the pyramidal circuits of the human neocortex. They gathered a comprehensive set of patch-clamp recordings from human and rat pyramidal neurons to compare how the temporal aspect of neuronal processing is maintained in the larger human neocortex. A broad range of experimental, theoretical, and computational methods are used, including two-photon guided dual whole-cell recordings, electron microscopy, and computational simulations of reconstructed neurons.

      Recordings from synaptically connected pyramidal neurons revealed longer intercellular path lengths within the human neocortex. Further, by using dual whole-cell recordings from somadendrite and soma-axon locations, they found that short latencies from soma to soma can be partly attributed to an increased propagation speed for synaptic potentials, but not for the propagation of action potentials along the axon.

      Next, in a series of extensive computational modeling studies focusing on the synaptic potentials, the authors observe that the short-latency within large human pyramidal neural circuits may have a passive origin. For a wide array of local synaptic input sites, the authors show that the conductance load of the dendrites, electrically coupled to a large diameter apical dendrite, affects the cable properties. The result is a relatively faster propagation of EPSPs in the human neuron.

      The manuscript is well-written and the physiological experiments and biophysical arguments are very well explained. I appreciated the in-depth theoretical steps for the simulations. That passive cable properties of the dendrites are causing a higher velocity in human dendrites is interesting but there is a disconnect between the experimental findings and the model simulations. Based on the present data the contribution of active membrane properties cannot be dismissed and deserves further experiments.

      See our response below

      Strengths:

      The authors present state-of-the-art 2P-guided dual whole-cell recordings in human neurons. In combination with detailed reconstructions, these approaches represent the next steps in unravelling the information processing in human circuits.

      The computational modeling based on cable theory and experimentally constrained simulations provides an excellent integrated view of the passive membrane properties.

      Weaknesses:

      There are smaller and larger issues with the statistical analyses of the experimental data which muddles the interim conclusions.

      That the cable properties alone are the main explanation for speeding the electrical signaling in human pyramidal neurons appears inconsistent with the experimental data.

      This is an excellent point – we indeed performed analysis on only passive cases – highlighting (and now also ranking) the impact of the various morpho-electrical properties of the neurons on the differences in signal latency in human vs. rats. We did explored (not shown) the effect of active channels in the dendrites (including the h-current); as expected the results strongly depend on channel density and their spatial distribution over the dendritic tree. As we do not know these parameters for the modelled cells, we decided to remain focus on the impact of passive/morphological parameters. We also note that the experimental results (page 4-5 in manuscript) show minor contribution of h-current emphasizing that the passive properties have the main role in differentiating human and rats. differences between human and rat. 

      Some of the electrophysiological experiments require further control experiments to make robust conclusions.

      Reviewer #3 (Public Review):

      Summary:

      This study indicates that connections across human cortical pyramidal cells have identical latencies despite a larger mean dendritic and axonal length between somas in the human cortex. A precise demonstration combining detailed electrophysiology and modeling indicates that this property is due to faster propagation of signals in proximal human dendrites. This faster propagation is itself due to a slightly thicker dendrite, a larger capacitive load, and stronger hyperpolarizing currents. Hence, the biophysical properties of human pyramidal cells are adapted such that they do not compromise information transfer speed.

      Strengths:

      The manuscript is clear and very detailed. The authors have experimentally verified a large number of aspects that could affect propagation speed and have pinpointed the most important one. This paper provides an excellent comparison of biophysical properties between rat and human pyramidal cells. Thanks to this approach a comprehensive description of the mechanisms underlying the acceleration of propagation in human dendrite is provided.

      Weaknesses:

      Several aspects having an impact on propagation speed are highlighted (dendritic diameter, ionic channels, capacitive load) and there is no clear ranking of their impact on signal propagation speed. It seems that the capacitive load plays a major role, much more than dendritic diameter for which only a 10% increase is observed across species. Both aspects actually indicate that there is an increase in passive signal propagation speed with bigger cells at least close to the soma. This suggests that bigger cells are mechanically more rapid. An intuitive reason why capacitive load increases speed would also help the reader follow the demonstration.

      We thank the referee for both these excellent points. In response to them:

      (i) We now performed a new comprehensive statistical analysis and show the ranking of the effect of the different morphological/cable factors on EPSP propagation. This analysis appears in both Supp. Table 5& 6, Fig. S16 and also in the main text as follows:

      To rank the impact of the various factors affecting EPSP propagation latency in human and rat neurons, we conducted a comprehensive statistical analysis using two complementary approaches: the generalized linear model (GLM) (Kiebel & Holmes, 2007) as well as SHAP (SHapley Additive exPlanations) (Lundberg & Lee, 2017) based on fitting Gradient Tree Boosting  (Friedman, 2002)model. We began by fitting a GLM without interaction terms among the factors affecting EPSP latency (Suppl. Table 5). This enables us to quantify the primary individual factors affecting EPSP propagation. Our analysis revealed the following ranking order: 1) physical distance of synapses from soma had the strongest effect; 2) species differences; 3) conductance load, as demonstrated by our “hybrid cells” manipulation; 4) radii of the apical dendrite, affecting the cables’ space constant, λ; and 5) the specific cable parameters, as revealed when using per-cell fitted parameters versus uniform cable parameters, was minimal. We next performed GLM analysis with interaction terms showing that, as expected, there are significant interactions between the factors affecting EPSP latency (Suppl. Table 6). To further validate the above ranking while incorporating the interactions between the various factors affecting EPSP latency, we performed a SHAP analysis. Notably, even with interactions included, the ranking of the factors affecting signal propagation are aligned with the results from the analysis based on the GLM without interaction terms (see Fig S.16).

      (ii) As for the intuitive explanation required by the referee. We added the following paragraph In the Discussion:

      The intuitive reason for this enhancement is that the large conductance load (the “leaky end” boundary conditions) more effectively “steals” the synaptic (axial) current (like water pouring faster into a large pool). The more mathematical intuition is that the large soma (sink) adds fast time constants to the system (see also related explanation in Fig. 4 in Eyal et al., 2014).

      We thank the editors for considering and revising our manuscript for publication in eLife. We appreciate the positive appreciation of the work and the critical points raised by the reviewers. We have responded in detail to all the excellent comments from all reviewers. We believe that these revisions have significantly improved the quality of our study.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      There are two points that could improve the reading experience of this nice manuscript. These should be easily addressed with minor re-phrasing.

      Credit to conduction velocity literature. Less widely known in the dendrite literature, in the axon literature, the relationship between propagation speed and process diameter is well established. I thought the two articles cited (Jack Noble Tsien and Agmon-Snir & Segev) were not as direct in the treatment of this relationship. The work of Stephen Waxman, for instance, made clear how axon diameter tightly controls propagation speed (see for instance the Scholarpedia entry by Swadlow and Waxman). In my opinion, this is a widely known piece of work, that is part of some introductory books to neuroscience. While the article does not claim they found this relationship, parts of the presentation are better understood if we ignore this well-known fact. I am referring to the abstract, intro, and the beginning of results where 'larger' is presented as synonymous with 'slower'. For instance 'to compensate for the increase neurons' size' (abstract) or 'the increase in size of dendrites and axons might come with a cost of longer signal propagation times' only makes sense if 'size' refers to spatial extent, not diameter.

      We thank for this valid point; leaving out axon diameter references was not intentional. We have now added the suggested reference to our manuscript. In the size comparisons, we have only pointed out the obvious size differences between the body and the dendritic processes. We have reworded sentences with size comparisons.

      In Abstract (lines 1-6):

      Human-specific cognitive abilities depend on information processing in the cerebral cortex, where neurons are significantly larger, their processes are longer and sparser compared to rodents. We found that, in synaptically-connected layer 2/3 pyramidal cells (L2/3 PCs), soma-tosoma signal propagation delay is similar in humans and rodents. Thus, to compensate for the increase in neurons’s longer processes, membrane potential changes must propagate faster in human axons and/or dendrites.

      In section “Effect of dendritic thickness” in Results we have modified it as follows:

      The relationship between conduction velocity and axon diameter is well known for small myelinated and unmyelinated axons (Waxman and Bennett, 1972). Anatomical features of neuronal processes dendrites also have a major influence on signal propagation properties 5,19, thus …

      Waxman, S. G. and Bennett, M. V. L. Relative conduction velocity of small myelinated and nonmyelinated fibres in the central nervous system. Nature New Biol., 238217-219, 1972.

      Two or four dendritic factors? The study identifies two major dendritic factors influencing the propagation speed (diameter and load), however the end of the results highlights four factors. I did not understand how factor 2 was different than factor 1. Neither did I understand how factor 4 was different from the other factors. There seemed to be a little redundancy here that could be streamlined.

      We thank the reviewer for pointing this out. We now have changes the respective text, added the ranking statistics (see above) to assess the effect of the different parameters on signal propagation in dendrites.

      Microcircuits? The study found that the changes in speed arise from the dendrites rather than the axons, as such it seems it would be more precise to replace 'microcircuits' with 'dendrites'.

      We are thankful for this suggestion. We change the title to Accelerated signal propagation speed in human neocortical dendrites.

      Typos

      P3 line 24 'find significant difference the propagation'.

      P6 line 35 'how morphological differences' it would be useful to specify which morphological difference here.

      Corrected.

      Reviewer #2 (Recommendations For The Authors):

      (1) The statistical analyses should be changed. T-testing populations and comparing visual differences of differences ("human minus rats") is a common but egregious error in the field of neurosciences (see doi:10.1038/nn.2886). The conclusion that HCN channels "... do not by themselves explained the differences between the two species" (lines 174-176) is not compelling. The design of the experiments presented in Figure 3 is paired recordings and the addition of a blocker (ZD7288 or TTX cocktail). These are classic 2 x 2 factorial designs (species x drug). The authors will need to perform a repeated-measured analysis of variance (RM-ANOVA) and provide information on the interaction significance. Please revise the figures and improve statistical reporting. Post-hoc comparisons of the velocity populations are required to support the idea of whether h-channels are explaining the observed differences.

      Thank you for drawing our attention to this error. The statistical analysis of the pharmacological experiments was re-performed as suggested. After the 2-way ANOVA with repeated measures and Bonferroni post-hoc correction, we can indeed find significant differences only in the control group, namely that the propagation speed of bAPs in human dendrites was significantly higher. The implementation of the proposed statistical analysis demonstrates that the administration of ZD has no statistically significant effect on the propagation speed of human or rat dendrites. The treatment with TTX cocktail resulted in a significant difference in signal propagation in humans but not in rodents. However the trend is discernible and the P = 0.0588 value is close to the widely accepted 0.05 threshold. After the TTX cocktail treatment, the speed of signal propagation did not differ significantly between the two species. However, on average, the human dendrites remained faster. These alterations in P-values do not affect our primary conclusions. The MS text has been modified accordingly.

      (2) Although ZD7288, in my opinion, influences the bAP (see point #1) the authors subsequently leave the h-current unblocked in the experiments in Figures 3D, E. Here, they use sodium, potassium, and calcium currents as well as synaptic conductances. I am puzzled why (in line 188) they claim the dendrites are "passive" although the data show h-currents are contributing to the shape of the bAP in human neurons. In line 196 they conclude voltage-gated conductances have a "minor" contribution and passive properties a main role. Please revise conclusions or provide better experimental support.

      Thank you for this point. We meant to refer to the state in which no action potential can be generated, although the word 'passive' might be misleading in this context; we rephrase these sentences in the MS accordingly.

      (3) A major concern is the injection of an AP in voltage-clamp mode. Although this is the right choice and I'm in support of the experiment, it is technically challenging to space clamp the soma and fully recapitulate the speed and amplitude of a 100 mV depolarization. The voltage drop in peak amplitude as well as the increased delay between the baseline AP (current clamp) and AP in blocker conditions (voltage clamp) could be fully explained by switching between current- and voltage-clamp modes. In additional control experiments, the authors should add a second voltage follower electrode (CC) at the soma showing whether the authors can preserve the original AP (from CC) in VC/blocker condition. It may well be they need to adjust the injection protocol.

      Our experiments were designed to replicate the work of Stuart et al. (1994), in which they compared the attenuation of active and passive backpropagating signals. When they blocked Na+ channels with TTX they injected simulated action potentials in voltage-clamp mode. They concluded that TTX-sensitive Na+ channels cause somatic action potential entry into the dendritic compartment. They found a comparable attenuation of the backward propagating action potential in the dendrites in control conditions (~70 %). 

      We performed control recordings based on the reviewer’s suggestion (Author response image 1).

      Author response image 1.

      Injection of the previously recorded AP (blue) in VC mode produced a completely similar somatic AP in CC mode (orange). The slight temporal delay between the two signal caused by the different position of the pipettes on the cell body.  The right panel shows the plot of the two peak-aligned APs as a function of each other, close to the blue ‘equality’ line. We concluded that the original AP is well preserved in VC/blocker condition.

      (5) From the paragraph entitled "Modeling EPSP propagation in dendrites" and onwards the authors make countless conclusions based on theory and modelling results but without any statistical support. Multiple neurons are used thus it is rather straightforward to provide numerical support for the assertions. For example, but this is not an exhaustive list, how should we interpret that latency ranges are different (line 240, line 253) etc.? Or were the estimated Cm values of human and rat neurons (0.6 versus 1.1) significantly different? And if so, how does this align with the Cm estimates in the nucleated patch experiments?

      We thank the referee for this comment and now added a set of statistical analyses. The results appear now throughout the whole theoretical paper in revised article. In particular with respect to Figs. 6&7 where we now show that, indeed, our various manipulations (e.g., hybrid vs. original cells) as well as the cable parameters (Cm, Rm) are indeed significantly different between human and rats whereas the membrane time constant is not significantly different between human and rat. As for Cm in human. Our limited sample size shows significant difference between human and rat. Yet, the range of values for Cm that we found in our modeling study does fall within the experimental range reported in the present study.

      Minor

      Line 44. The "simulated EPSP" example in Figure 2C is not a command waveform for an EPSC. Line 526 in the methods states that also ramp currents were used. Please revise to clarify the main text.

      Thank you for bringing this discrepancy to our attention. In the experiments, we used ramp injections. We have made this clear in the main text as follows: ”... we tested orthodromic or forward propagating signal propagation velocity by injecting short-duration current ramps to simulate EPSP (sEPSP) signals in the dendrites and recorded the resultant subthreshold voltage response in the soma”

      Line 522. The authors state the recordings were all carried out "in current clamp mode" but detailed VC method information is lacking. Did they use series resistance compensation?

      We did not use series resistance compensation.

      Line 479 From which region(s) where human "neocortical slices" sampled? Please add this information.

      We have added regions of origin to the Methods section: frontal (n = 21), temporal (n = 20), parietal (n = 20), and occipital (n = 1).  

      Please show higher temporal resolution example traces, for example in Figure 3. Differences are at the micrometer scale, but APs are shown at the millisecond scale. Hard to judge the quality of the data. Showing the command potentials (inset Figure 3D, E) is misleading (see major point #3).

      In response to the reviewer's request, we have redrawn the example traces in Figure 3.

      Please check the labeling of figures. There is information missing. For example, in Figure 5 A to C I am missing information and the units of the axes.

      In the black plots on the right side of panels B and C, the y-axis shows the thickness measurements for the given dendrite stacked on top of each other and the x-axis shows the measurement values, the units for the x-axis are µm as mentioned in the figure legend.

      Line 981 "scalebars" should read scale bars."

      Line 986 "bootstraped" should read "bootstrapped".

      Done.

      Are the dendritic diameters increased for all basal and apical higher-order branches? It is unclear how the model simulations were built on diameters of primary and higher-order branches.

      In our modelling study we took the actual diameter of the reconstructed PCs in both proximal and higher order branches. We did compare per-distance differences in diameter – but it is automatically incorporated into the computation of the basal load (“equivalent cables” in Figs 6&8).

      The velocity calculation for axonal propagation (yielding a ~0.9 m/s conduction velocity, Figure 2B) is incorrect. Using the peak of the action potentials between soma and axon misses the fact that action potentials start earlier and spatially distally from the soma in the axon. Please revise the calculation to include the temporal delay and actual distance travelled by the forward propagating action potential.

      Thank you for this question. We are aware that the AP is generated at the AIS and that it is located between the two recording electrodes and we have to take into account that the signal propagates from the AIS to the soma and this may shorten the delay in the system. To the best of our knowledge, there is no experimental evidence of the location of the AP generation site on the AIS in layer 2-3 pyramidal cells in the human neocortex, so we assumed that it is located 35 microns from the soma, and that the propagation speed from the AIS to the two directions is the same. Consequently, we have corrected our propagation velocity values as follows:

      “For the axon bleb recordings we assumed that the axon initial segment (AIS) of the cells are 35 µm from the axon hillock, and the APs propagate to forward (to the bleb) and backward (to the soma) at the same speed. For the correction of the AIS we used the following formula: (2)

      where vcorr is the corrected propagation speed for AIS position, l is the axonal distance between the soma and the axon bleb, t is the latency between the two measuring point, ais is the assumed position of the AIS alongside the axon (35 µm).”

      What explains the strongly attenuated axonal action potential at the bleb? Is this representative?

      The strongly attenuated axonal action potential at the bleb can be explained by a few key factors:

      (1) Membrane Integrity: Bleb formation often indicates some level of membrane damage or alteration. This can disrupt the normal ionic gradients across the membrane, leading to a failure in generating or propagating action potentials effectively.

      (2) Current Leakage: Bleb formation may create additional pathways for ion leakage, which can dissipate the electrical current that would normally propagate the action potential. This leakage reduces the overall amplitude of the action potential.

      Line 275 "To our delight", please rephrase.

      Corrected.

      Reviewer #3 (Recommendations For The Authors):

      - In Figure 1, the number of cells used to assess intersomatic distance is quite low. A larger number of neuron pairs should be analyzed to be more representative. Or at least an explanation of why such a low sampling can be conclusive.

      We appreciate the reviewer’s concerns on sample sizes of the first set of experiments, where the anatomical pathways were measured through the synapses of coupled cells with electrophysiological recordings. We acknowledge that this is a limitation of our study. However, in this series of experiments, we simply wanted to experimentally confirm already known results which consisted of two parts: first, that in humans the dendrites and axons of neurons are longer, and second, that they have the same time delay in terms of synaptic latency. 

      The reported similarity in synaptic latencies is consistent with the results of a recent study by Campagnola et al. (2022) showing that EPSP latencies of local connections between layer 2/3 pyramidal cells are in the same range in humans and mice (human median latency = 1.73 ms vs. mouse median latency = 1.49 ms). We came to the same conclusion in our previous work where we compared pyramidal basket cell synaptically coupled pairs in human and rat pairs (Molnár et al. 2016). 

      On the other hand, we report interspecific differences in cable pathways from soma to soma, again consistent with the literature suggesting that the length of pyramidal neural processes is longer in humans than in rodents (see Supplementary Figure 1 and e.g. Berg et al. 2021).

      From a practical point of view the collection of experimental data in this hard won experiment is particularly difficult. The electrophysiological recording of a connected pair with an appropriate pre- and postsynaptic series resistance, where human tissue samples are limited, is the first step here. To obtain information about the path of the signals between pre- and postsynaptic cells, an anatomical reconstruction is required. This requires a) a high-quality recovery of postsynaptic dendrites and presynaptic axons, b) successful tracing of all potential contact points between presynaptic axons and postsynaptic dendrites back to the pre- and postsynaptic soma. The difficulty of the latter point in particular arises from the fact that parts of the presynaptic axonal arbor are myelinated and the success of biocytin-based tracing depends on the length of the myelinated axon branches. The success/failure of complete axonal tracing only becomes apparent at the end of these efforts.

      - The author should provide an intuitive explanation of why capacitive load accelerates propagation in the dendrite.

      See answer above  

      - The author should more clearly rank the contribution of each difference between rat and human neurons. The 10% increase in dendritic diameter which affects velocity only via a square root seems a very weak contribution. This should be clarified.

      We now added a set of statistical methods to perform such a ranking in the theoretical part of this study, as described above (and in a new paragraph, attached above) in the revised article. 

      References

      Eyal, G., Mansvelder, H. D., de Kock, C. P. J., & Segev, I. (2014). Dendrites impact the encoding capabilities of the axon. Journal of Neuroscience, 34(24), 8063–8071. https://doi.org/10.1523/JNEUROSCI.5431-13.2014

      Friedman, J. H. (2002). Stochastic gradient boosting. In Computational Statistics & Data Analysis (Vol. 38). www.elsevier.com/locate/csda

      Kiebel, S. J., & Holmes, A. P. (2007). The General Linear Model. In K. Friston, J. Ashburner, S. Kiebel, T. Nichols, & P. William (Eds.), Statistical Parametric Mapping (pp. 101–125). Academic Press.

      Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems, 4768–4777.

    1. Author response:

      The following is the authors’ response to the current reviews.

      Responses to Reviewer #1:

      We thank the reviewer for these additional comments, and more generally for their extensive engagement with our work, which is greatly appreciated. Here, we respond to the three points in their latest review in turn.

      The results of these experiments support a modest but important conclusion: If sub-optimal methods are used to collect retrospective reports, such as simple yes/no questions, inattentional blindness (IB) rates may be overestimated by up to ~8%.

      It is true, of course, that we think the field has overstated the extent of IB, and we appreciate the reviewer characterizing our results as important along these lines. Nevertheless, we respectfully disagree with the framing and interpretation the reviewer attaches to them. As explained in our previous response, we think this interpretation — and the associated calculations of IB overestimation ‘rates’ — perpetuates a binary approach to perception and awareness which we regard as mistaken.

      A graded approach to IB and visual awareness 

      Our sense is that many theorists interested in IB have conceived of perception and awareness as ‘all or nothing’: You either see a perfectly clear gorilla right in front of you, or you see nothing at all. This is implicit in the reviewer’s characterization of our results as simply indicating that fewer subjects fail to see the critical stimulus than previously assumed. To think that way is precisely to assume the orthodox binary position about perception, i.e., that any given subject can neatly be categorized into one of two boxes, saw or didn’t see.

      Our perspective is different. We think there can be degraded forms of perception and awareness that fall neatly into neither of the categories “saw the stimulus perfectly clearly” or “saw nothing at all”. On this graded conception, the question is not: “What proportion of subjects saw the stimulus?” but: “What is the sensitivity of subjects to the stimulus?” This is why we prefer signal detection measures like d′ over % noticing and % correct. This powerful framework has been successful in essentially every domain to which it has been applied, and we think perception and visual awareness are no exception. We understand that the reviewer may not think the same way about this foundational issue, but since part of our goal is to promote a graded approach to perception, we are keen to highlight our disagreement here and so resist the reviewer’s interpretation of our results (even to the extent that it is a positive one!).

      Finally, we note that given this perspective, we are correspondingly inclined to reject many of the summary figures following below in Point (1) by the reviewer. These calculations (given in terms of % noticing and not noticing) make sense on the binary conception of awareness, but not on the SDT-based approach we favor. We say more about this below. 

      (1) In experiment 1, data from 374 subjects were included in the analysis. As shown in figure 2b, 267 subjects reported noticing the critical stimulus and 107 subjects reported not noticing it. This translates to a 29% IB rate if we were to only consider the "did you notice anything unusual Y/N" question. As reported in the results text (and figure 2c), when asked to report the location of the critical stimulus (left/right), 63.6% of the "non-noticer" group answered correctly. In other words, 68 subjects were correct about the location while 39 subjects were incorrect. Importantly, because the location judgment was a 2-alternative-forced-choice, the assumption was that if 50% (or at least not statistically different than 50%) of the subjects answered the location question correctly, everyone was purely guessing. Therefore, we can estimate that ~39 of the subjects who answered correctly were simply guessing (because 39 guessed incorrectly), leaving 29 subjects from the nonnoticer group who were correct on the 2AFC above and beyond the pure guess rate. If these 29 subjects are moved from the non-noticer to the noticer group, the corrected rate of IB for Experiment 1 is 20.86% instead of the original 28.61% rate that would have been obtained if only the Y/N question was used. In other words, relying only on the "Y/N did you notice anything" question led to an overestimate of IB rates by 7.75% in Experiment 1.

      In the revised version of their manuscript, the authors provided the data that was missing from the original submission, which allows this same exercise to be carried out on the other 4 experiments.  

      (To briefly interject: All of these data were provided in our public archive since our original submission and remain available at https://osf.io/fcrhu. The difference now is only that they are included in the manuscript itself.)

      Using the same logic as above, i.e., calculating the pure-guess rate on the 2AFC, moving the number of subjects above this pure-guess rate to the non-noticer group, and then re-calculating a "corrected IB rate", the other experiments demonstrate the following:

      Experiment 2: IB rates were overestimated by 4.74% (original IB rate based only on Y/N question = 27.73%; corrected IB rate that includes the 2AFC = 22.99%)

      Experiment 3: IB rates were overestimated by 3.58% (original IB rate = 30.85%; corrected IB rate = 27.27%)

      Experiment 4: IB rates were overestimated by ~8.19% (original IB rate = 57.32%; corrected IB rate for color* = 39.71%, corrected IB rate for shape = 52.61%, corrected IB rate for location = 55.07%)

      Experiment 5: IB rates were overestimated by ~1.44% (original IB rate = 28.99%; corrected IB rate for color = 27.56%, corrected IB rate for shape = 26.43%, corrected IB rate for location = 28.65%)

      *note: the highest overestimate of IB rates was from Experiment 4, color condition, but the authors admitted that there was a problem with 2AFC color guessing bias in this version of the experiment which was a main motivation for running experiment 5 which corrected for this bias.

      Taken as a whole, this data clearly demonstrates that even with a conservative approach to analyzing the combination of Y/N and 2AFC data, inattentional blindness was evident in a sizeable portion of the subject populations. An important (albeit modest) overestimate of IB rates was demonstrated by incorporating these improved methods.

      We appreciate the work the reviewer has put into making these calculations. However, as noted above, such calculations implicitly reflect the binary approach to perception and awareness that we reject. 

      Consider how we’d think about the single subject case where the task is 2afc detection of a low contrast stimulus in noise. Suppose that this subject achieves 70% correct. One way of thinking about this is that the subject fully and clearly sees the stimulus on 40% of trials (achieving 100% correct on those) and guesses completely blindly on the other 60% (achieving 50% correct on those) for a total of 40% + 30% = 70% overall. However, this is essentially a ‘high threshold’ approach to the problem, in contrast to an SDT approach. On an SDT approach — an approach with tremendous evidential support — on every trial the subject receives samples from probabilistic distributions corresponding to each interval (one noise and one signal + noise) and determines which is higher according to the 2afc decision rule. Thus, across trials, they have access to differentially graded information about the stimulus. Moreover, on some trials they may have significant information from the stimulus (perhaps, well above their single interval detection criterion) but still decide incorrectly because of high noise from the other spatial interval. From this perspective, there is no nonarbitrary way of saying whether the subject saw/did not see on a given trial. Instead, we must characterize the subject’s overall sensitivity to the stimulus/its visibility to them in terms of a parameter such as d′ (here, ~ 0.7).

      We take the same attitude to the subjects in our experiments (and specifically to our ‘super subject’). Instead of calculating the proportion of subjects who saw or failed to see the stimulus (with some characterized as aware and some as unaware), we think the best way to characterize our results is that, across subjects (and so trials also), there was differential graded access to information from the stimulus, and this is best represented in terms of the group-level sensitivity parameter d′. This is why we frame our results as demonstrating that subjects traditionally considered inattentionally blind exhibit significant residual visual sensitivity to the critical stimulus.

      (2) One of the strongest pieces of evidence presented in this paper was the single data point in Figure 3e showing that in Experiment 3, even the super subject group that rated their non-noticing as "highly confident" had a d' score significantly above zero. Asking for confidence ratings is certainly an improvement over simple Y/N questions about noticing, and if this result were to hold, it could provide a key challenge to IB. However, this result can most likely be explained by measurement error.

      In their revised paper, the authors reported data that was missing from their original submission: the confidence ratings on the 2AFC judgments that followed the initial Y/N question. The most striking indication that this data is likely due to measurement error comes from the number of subjects who indicated that they were highly confident that they didn't notice anything on the critical trial, but then when asked to guess the location of the stimulus, indicated that they were highly confident that the stimulus was on the left (or right). There were 18 subjects (8.82% of the high-confidence non-noticer group) who responded this way. To most readers, this combination of responses (high confidence in correctly judging a stimulus feature that one is highly confident in having not seen at all) indicates that a portion of subjects misunderstood the confidence scales (or just didn't read the questions carefully or made mistakes in their responses, which is common for experiments conducted online).

      In the authors' rebuttal to the first round of peer review, they wrote, "it is perfectly rationally coherent to be very confident that one didn't see anything but also very confident that if there was anything to be seen, it was on the left." I respectfully disagree that such a combination of responses is rationally coherent. The more parsimonious interpretation is that a measurement error occurred, and it's questionable whether we should trust any responses from these 18 subjects.

      In their rebuttal, the authors go on to note that 14 of the 18 subjects who rated their 2AFC with high confidence were correct in their location judgment. If these 14 subjects were removed from analysis (which seems like a reasonable analysis choice, given their contradictory responses), d' for the high-confidence non-noticer group would most likely fall to chance levels. In other words, we would see a data pattern similar to that plotted in Figure 3e, but with the first data point on the left moving down to zero d'. This corrected Figure 3e would then provide a very nice evidence-based justification for including confidence ratings along with Y/N questions in future inattentional blindness studies.

      We appreciate the reviewer’s highlighting of this particular piece of evidence as amongst our strongest. (At the same time, we must resist its characterization as a “single data point”: it derives from a large pre-registered experiment involving some 7,000 subjects total, with over 200 subjects in the relevant bin — both figures being far larger than a typical IB experiment.) We also appreciate their raising the issue of measurement error.

      Specifically, the reviewer contends that our finding that even highly confident non-noticers exhibit significant sensitivity is “most likely … explained by measurement error” due to subjects mistakenly inverting our confidence scale in giving their response. In our original reply, we gave two reasons for thinking this quite unlikely; the reviewer has not addressed these in this revised review. First, we explicitly labeled our confidence scale (with 0 labeled as ‘Not at all confident’ and 3 as ‘Highly confident’) so that subjects would be very unlikely simply to invert the scale. This is especially so as it is very counterintuitive to treat “0” as reflecting high confidence. More importantly, however, we reasoned that any measurement error due to inverting or misconstruing the confidence scale should be symmetric. That is: If subjects are liable to invert the confidence scale, they should do so just as often when they answer “yes” as when they answer “no” – after all the very same scale is being used in both cases. This allows us to explore evidence of measurement error in relation to the large number of high-confidence “yes” subjects (N = 2677), thus providing a robust indicator as to whether subjects are generally liable to misconstrue the confidence scale. Looking at the number of such high confidence noticers who subsequently respond to the 2afc question with low confidence (a pattern which might, though need not, suggest measurement error), we found that the number was tiny. Only 28/2677 (1.05%) of high-confidence noticers subsequently gave the lowest level of confidence on the 2afc question, and only 63/2677 (2.35%) subjects gave either of the two lower levels of confidence. For these reasons, we consider any measurement error due to misunderstanding the confidence scale to be extremely minimal.

      The reviewer is correct to note that 18/204 (9%) subjects reported both being highly confident that they didn't notice anything and highly confident in their 2afc judgment, although only 14/18 were correct in this judgment. Should we exclude these 14? Perhaps if we agree with the reviewer that such a pattern of responses is not “rationally coherent” and so must reflect a misconstrual of the scale. But such a pattern is in fact perfectly and straightforwardly intelligible. Specifically, in a 2afc task, two stimuli can individually fall well below a subject’s single interval detection criterion — leading to a high confidence judgment that nothing was presented in either interval. Quite consistent with this, the lefthand stimulus may produce a signal that is much higher than the right-hand stimulus — leading to a high confidence forced-choice judgment that, if something was presented, it was on the left. (By analogy, consider how a radiologist could look at a scan and say the following: “We’re 95% confident there’s no tumor. But even on the 5% chance that there is, our tests completely rule out that it’s a malignant one, so don’t worry.”) 

      (3) In most (if not all) IB experiments in the literature, a partial attention and/or full attention trial is administered after the critical trial. These control trials are very important for validating IB on the critical trial, as they must show that, when attended, the critical stimuli are very easy to see. If a subject cannot detect the critical stimulus on the control trial, one cannot conclude that they were inattentionally blind on the critical trial, e.g., perhaps the stimulus was just too difficult to see (e.g., too weak, too brief, too far in the periphery, too crowded by distractor stimuli, etc.), or perhaps they weren't paying enough attention overall or failed to follow instructions. In the aggregate data, rates of noticing the stimuli should increase substantially from the critical trial to the control trials. If noticing rates are equivalent on the critical and control trials, one cannot conclude that attention was manipulated in the first place.

      In their rebuttal to the first round of peer review, the authors provided weak justification for not including such a control condition. They cite one paper that argues such control conditions are often used to exclude subjects from analysis (those who fail to notice the stimulus on the control trial are either removed from analysis or replaced with new subjects) and such exclusions/replacements can lead to underestimations of inattentional blindness rates. However, the inclusion of a partial or full attention condition as a control does not necessitate the extra step of excluding or replacing subjects. In the broadest sense, such a control condition simply validates the attention manipulation, i.e., one can easily compare the percent of subjects who answered "yes" or who got the 2AFC judgment correct during the critical trial versus the control trial. The subsequent choice about exclusion/replacement is separate, and researchers can always report the data with and without such exclusions/replacements to remain more neutral on this practice.

      If anyone were to follow-up on this study, I highly recommend including a partial or full attention control condition, especially given the online nature of data collection. It's important to know the percent of online subjects who answer yes and who get the 2AFC question correct when the critical stimulus is attended, because that is the baseline (in this case, the "ceiling level" of performance) to which the IB rates on the critical trial can be compared.

      We agree with the reviewer that future studies could benefit from including a partial or full attention condition. They are surely right that we might learn something additional from such conditions. 

      Where we differ from the reviewer is in thinking of these conditions as “controls” appropriate to our research question. This is why we offered the justification we did in our earlier response. When these conditions are used as controls, they are used to exclude subjects in ways that serve to inflate the biases we are concerned with in our work. For our question, the absence of these conditions does not impact the significance of the findings, since such conditions are designed to answer a question which is not the one at the heart of our paper. Our key claim is that subjects who deny noticing an unexpected stimulus in a standard inattentional blindness paradigm nonetheless exhibit significant residual sensitivity (as well as a conservative bias in their response to the noticing question); the presence or absence of partial- or full-attention conditions is orthogonal to that question.

      Moreover, we note that our tasks were precisely chosen to be classic tasks widely used in the literature to manipulate attention. Thus, by common consensus in the field, they are effective means to soak up attention, and have in effect been tested in partial- and full-attention control settings in a huge number of studies. Second, we think it very doubtful that subjects in a full-attention trial would not overwhelmingly have detected our critical stimuli. The reviewer worries that they might have been “too weak, too brief, too far in the periphery, too crowded by distractor stimuli, etc.” But consider E5 where the stimulus was a highly salient orange or green shape, present on the screen for 5 seconds. The reviewer also suggests that subjects in the full-attention control might not have detected the stimulus because they “weren't paying enough attention overall”. But evidently if they weren’t paying attention even in the full-attention trial this would be reason for thinking that there was inattentional blindness even in this condition (a point made by White et al. 2018) and certainly not a reason for thinking there was not an attentional effect in the critical trial. Lastly, the reviewer suggests that a full-attention condition would have helped ensure that subjects were following instructions. But we ensured this already by (as per our pre-registration) excluding subjects who performed poorly in the relevant primary tasks.

      Thus, both in principle and in practice, we do not see the absence of such conditions as impacting the interpretation of our findings, even as we agree that future work posing a different research question could certainly learn something from including such conditions.

      Responses to Reviewer #2:

      We note that this report is unchanged from an earlier round of review, and not a response to our significantly revised manuscript. We believe our latest version fully addresses all the issues which the reviewer originally raised. The interested reader can see our original response below. We again thank the reviewer for their previous report which was extremely helpful.

      —-

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This study presents valuable findings to the field interested in inattentional blindness (IB), reporting that participants indicating no awareness of unexpected stimuli through yes/no questions, still show above-chance sensitivity to specific properties of these stimuli through follow-up forced-choice questions (e.g., its color). The results suggest that this is because participants are conservative and biased to report not noticing in IB. The authors conclude that these results provide evidence for residual perceptual awareness of inattentionally blind stimuli and that therefore these findings cast doubt on the claim that awareness requires attention. Although the samples are large and the analysis protocol novel, the evidence supporting this interpretation is still incomplete, because effect sizes are rather small, the experimental design could be improved and alternative explanations have not been ruled out.

      We are encouraged to hear that eLife found our work “valuable”. We also understand, having closely looked at the reviews, why the assessment also includes an evaluation of “incomplete”. We gave considerable attention to this latter aspect of the assessment in our revision. In addition to providing additional data and analyses that we believe strengthen our case, we also include a much more substantial review and critique of existing methods in the IB literature to make clear exactly the gap our work fills and the advance it makes. (Indeed, if it is appropriate to say this here, we believe one key aspect of our work that is missing from the assessment is our inclusion of ‘absent’ trials, which is what allows us to make the crucial claims about conservative reporting of awareness in IB for the first time.) Moreover, we refocus our discussion on only our most central claims, and weaken several of our secondary claims so that the data we’ve collected are better aligned with the conclusions we draw, to ensure that the case we now make is in fact complete. Specifically, our two core claims are (1) that there is residual sensitivity to visual features for subjects who would ordinarily be classified as inattentionally blind (whether this sensitivity is conscious or not), and (2) that there is a tendency to respond conservatively on yes/no questions in the context of IB. We believe we have very compelling support for these two core claims, as we explain in detail below and also through revisions to our manuscript.

      Given the combination of strengthened and clarified case, as well as the weakening of any conclusions that may not have been fully supported, we believe and hope that these efforts make our contribution “solid”, “convincing”, or even “compelling” (especially because the “compelling” assessment characterizes contributions that are “more rigorous than the current state-of-the-art”, which we believe to be the case given the issues that have plagued this literature and that we make progress on).

      Reviewer #1 (Public review):

      Summary:

      In the abstract and throughout the paper, the authors boldly claim that their evidence, from the largest set of data ever collected on inattentional blindness, supports the views that "inattentionally blind participants can successfully report the location, color, and shape of stimuli they deny noticing", "subjects retain awareness of stimuli they fail to report", and "these data...cast doubt on claims that awareness requires attention." If their results were to support these claims, this study would overturn 25+ years of research on inattentional blindness, resolve the rich vs. sparse debate in consciousness research, and critically challenge the current majority view in cognitive science that attention is necessary for awareness.

      Unfortunately, these extraordinary claims are not supported by extraordinary (or even moderately convincing) evidence. At best, the results support the more modest conclusion: If sub-optimal methods are used to collect retrospective reports, inattentional blindness rates will be overestimated by up to ~8% (details provided below in comment #1). This evidence-based conclusion means that the phenomenon of inattentional blindness is alive and well as it is even robust to experiments that were specifically aimed at falsifying it. Thankfully, improved methods already exist for correcting the ~8% overestimation of IB rates that this study successfully identified.

      We appreciate here the reviewer’s recognition of the importance of work on inattentional blindness, and the centrality of inattentional blindness to a range of major questions. We also recognize their concerns with what they see as a gap between our data and the claims made on their basis. We address this in detail below (as well as, of course, in our revised manuscript). However, from the outset we are keen to clarify that our central claim is only the first one the reviewer mentions — and the one which appears in our title — namely that, as a group, participants can successfully report the location, color, and shape of stimuli they deny noticing, and thus that there is “Sensitivity to visual features in inattentional blindness”. This is the claim that we believe is strongly supported by our data, and all the more so after revising the manuscript in light of the helpful comments we’ve received.

      By contrast, the other claims the reviewer mentions, concerning awareness (as opposed to residual sensitivity–which might be conscious or unconscious) were intended as both secondary and tentative. We agree with the referee that these are not as strongly supported by our data (and indeed we say so in our manuscript), whereas we do think our data strongly support the more modest — and, to us central — claim that, as a group, inattentionally blind participants can successfully report the location, color, and shape of stimuli they deny noticing. 

      We also feel compelled to resist somewhat the reviewer’s summary of our claims. For example, the reviewer attributes to us the claim that “subjects retain awareness of stimuli they fail to report”; but while that phrase does appear in our abstract, what we in fact say is that our data are “consistent with an alternative hypothesis about IB, namely that subjects retain awareness of stimuli they fail to report”. We do in fact believe that our data are consistent with that hypothesis, whereas earlier investigations seemed not to be. We mention this only because we had used that careful phrasing precisely for this sort of reason, so that we wouldn’t be read as saying that our results unequivocally support that alternative.

      Still, looking back, we see how we may have given more emphasis than we intended to some of these more secondary claims. So, we’ve now gone through and revised our manuscript throughout to emphasize that our main claim is about residual sensitivity, and to make clear that our claims about awareness are secondary and tentative. Indeed, we now say precisely this, that although we favor an interpretation of “our results in terms of residual conscious vision in IB … this claim is tentative and secondary to our primary finding”. We also weaken the statements in the abstract that the reviewer mentions, to better reflect our key claims.

      Finally, we note one further point: Dialectically, inattentional blindness has been used to argue (e.g.) that attention is required for awareness. We think that our data concerning residual sensitivity at least push back on the use of IB to make this claim, even if (as we agree) they do not provide decisive evidence that awareness survives inattention. In other words, we think our data call that claim into question, such that it’s now genuinely unclear whether awareness does or does not survive inattention. We have adjusted our claims on this point accordingly as well.

      Comments:

      (1) In experiment 1, data from 374 subjects were included in the analysis. As shown in figure 2b, 267 subjects reported noticing the critical stimulus and 107 subjects reported not noticing it. This translates to a 29% IB rate, if we were to only consider the "did you notice anything unusual Y/N" question. As reported in the results text (and figure 2c), when asked to report the location of the critical stimulus (left/right), 63.6% of the "non-noticer" group answered correctly. In other words, 68 subjects were correct about the location while 39 subjects were incorrect. Importantly, because the location judgment was a 2-alternative-forced-choice, the assumption was that if 50% (or at least not statistically different than 50%) of the subjects answered the location question correctly, everyone was purely guessing. Therefore, we can estimate that ~39 of the subjects who answered correctly were simply guessing (because 39 guessed incorrectly), leaving 29 subjects from the nonnoticer group who may have indeed actually seen the location of the stimulus. If these 29 subjects are moved to the noticer group, the corrected rate of IB for experiment 1 is 21% instead of 29%. In other words, relying only on the "Y/N did you notice anything" question leads to an overestimate of IB rates by 8%. This modest level of inaccuracy in estimating IB rates is insufficient for concluding that "subjects retain awareness of stimuli they fail to report", i.e. that inattentional blindness does not exist.

      In addition, this 8% inaccuracy in IB rates only considers one side of the story. Given the data reported for experiment 1, one can also calculate the number of subjects who answered "yes, I did notice something unusual" but then reported the incorrect location of the critical stimulus. This turned out to be 8 subjects (or 3% of the "noticer" group). Some would argue that it's reasonable to consider these subjects as inattentionally blind, since they couldn't even report where the critical stimulus they apparently noticed was located. If we move these 8 subjects to the non-noticer group, the 8% overestimation of IB rates is reduced to 6%.

      The same exercise can and should be carried out on the other 4 experiments, however, the authors do not report the subject numbers for any of the other experiments, i.e., how many subjects answered Y/N to the noticing question and how many in each group correctly answered the stimulus feature question. From the limited data reported (only total subject numbers and d' values), the effect sizes in experiments 2-5 were all smaller than in experiment 1 (d' for the non-noticer group was lower in all of these follow-up experiments), so it can be safely assumed that the ~6-8% overestimation of IB rates was smaller in these other four experiments. In a revision, the authors should consider reporting these subject numbers for all 5 experiments.

      We now report, as requested, all these subject numbers in our supplementary data (see Supplementary Tables 1 and 2 in our Supplementary Materials).

      However, we wish to address the larger question the reviewer has raised: Do our data only support a relatively modest reduction in IB rates? Even if they did, we still believe that this would be a consequential result, suggesting a significant overestimation of IB rates in classic paradigms. However, part of our purpose in writing this paper is to push back against a certain binary way of thinking about seeing/awareness. Our sense is that the field has conceived of awareness as “all or nothing”: You either see a perfectly clear gorilla right in front of you, or you see nothing at all. Our perspective is different: We think there can be degraded forms of awareness that fall into neither of those categories. For that reason, we are disinclined to see our results in the way that the reviewer suggests, namely as simply indicating that fewer subjects fail to see the stimulus than previously assumed. To think that way is, in our view, to assume the orthodox binary position about awareness. If, instead, one conceives of awareness as we do (and as we believe the framework of signal detection theory should compel us to), then it isn’t quite right to think of the proportion of subjects who were aware, but rather (e.g.) the sensitivity of subjects to the relevant stimulus. This is why we prefer measures like d′ over % noticing and % correct. We understand that the reviewer may not think the same way about this issue as we do, but part of our goal is to promote that way of thinking in general, and so some of our comments below reflect that perspective and approach.

      For example, consider how we’d think about the single subject case where the task is 2afc detection of a low contrast stimulus in noise. Suppose that this subject achieves 70% correct. One way of thinking about that is that the subject sees the stimulus on 40% of trials (achieving 100% correct on those) and guesses blindly on the other 60% (achieving 50% correct on those) for a total of 40% + 30% = 70% overall. However, this is essentially a “high threshold” approach to the problem, in contrast to an SDT approach. On an SDT approach (an approach with tremendous evidential support), on every trial the subject receives samples from probabilistic distributions corresponding to each interval (one noise and one signal + noise) and determines which is higher according to the 2afc decision rule. Thus, across trials they have access to differentially graded information about the stimulus. Moreover, on some trials they may have significant information from the stimulus (perhaps, well above their single interval detection criterion) but still decide incorrectly because of high noise from the other spatial interval. From this perspective, there is no non-arbitrary way of saying whether the subject saw/did not see on a given trial. Instead, we must characterize the subject’s overall sensitivity to the stimulus/its visibility to them in terms of a parameter such as d′ (here, ~ 0.7).

      We take the same attitude to our super subject. Instead of saying that some subjects saw/failed to see the stimuli, instead we suggest that the best way to characterize our results is that across subjects (and so trials also) there was differential graded access to information from the stimulus best represented in terms of the group-level sensitivity parameter d′.

      We acknowledge that (despite ourselves) we occasionally fell into an all-too-natural binary/high threshold way of thinking, as when we suggested that our data show that “inattentionally blind subjects consciously perceive these stimuli after all” and “the inattentionally blind can see after all." (p.17) We have removed such problematic phrasing as well as other problematic phrasing as noted below.

      (2) Because classic IB paradigms involve only one critical trial per subject, the authors used a "super subject" approach to estimate sensitivity (d') and response criterion (c) according to signal detection theory (SDT). Some readers may have issues with this super subject approach, but my main concern is with the lack of precision used by the authors when interpreting the results from this super subject analysis.

      Only the super subject had above-chance sensitivity (and it was quite modest, with d' values between 0.07 and 0.51), but the authors over-interpret these results as applying to every subject. The methods and analyses cannot determine if any individual subject could report the features above-chance. Therefore, the following list of quotes should be revised for accuracy or removed from the paper as they are misleading and are not supported by the super subject analysis: "Altogether this approach reveals that subjects can report above-chance the features of stimuli (color, shape, and location) that they had claimed not to notice under traditional yes/no questioning" (p.6)

      "In other words, nearly two-thirds of subjects who had just claimed not to have noticed any additional stimulus were then able to correctly report its location." (p.6)

      "Even subjects who answer "no" under traditional questioning can still correctly report various features of the stimulus they just reported not having noticed, suggesting that they were at least partially aware of it after all." (p.8)

      "Why, if subjects could succeed at our forced-response questions, did they claim not to have noticed anything?" (p.8)

      "we found that observers could successfully report a variety of features of unattended stimuli, even when they claimed not to have noticed these stimuli." (p.14)

      "our results point to an alternative (and perhaps more straightforward) explanation: that inattentionally blind subjects consciously perceive these stimuli after all... they show sensitivity to IB stimuli because they can see them." (p.16)

      "In other words, the inattentionally blind can see after all." (p.17)

      We thank the reviewer for pointing out how these quotations may be misleading as regards our central claim. We intended them all to be read generically as concerning the group, and not universally as claiming that all subjects could report above-chance/see the stimuli etc. We agree entirely that the latter universal claim would not be supported by our data. In contrast, we do contend that our super-subject analysis shows that, as a group, subjects traditionally considered intentionally blind exhibit residual sensitivity to features of stimuli (color, shape, and location) that they had all claimed not to notice, and likewise that as a group they could succeed at our forced-choice questions. 

      To ensure this claim is clear throughout the paper, and that we are not interpreted as making an unsupported universal claim we have revised the language in all of the quotations above, as follows, as well as in numerous other places in the paper.

      “Altogether this approach reveals that subjects can report above-chance the features of stimuli (color, shape, and location) that they had claimed not to notice under traditional yes/no questioning” (p.6) => “Altogether this approach reveals that as a group subjects can report above-chance the features of stimuli (color, shape, and location) that they had all claimed not to notice under traditional yes/no questioning” (p.6)

      “Even subjects who answer “no” under traditional questioning can still correctly report various features of the stimulus they just reported not having noticed, suggesting that they were at least partially aware of it after all.” (p.8) => “... even subjects who answer “no” under traditional questioning can, as a group, still correctly report various features of the stimuli they just reported not having noticed, indicating significant group-level sensitivity to visual features. Moreover, these results are even consistent with an alternative hypothesis about IB, that as a group, subjects who would traditionally be classified as inattentionally blind are in fact at least partially aware of the stimuli they deny noticing.” (p.8)

      “Why, if subjects could succeed at our forced-response questions, did they claim not to have noticed anything?” (p.8) => “Why, if subjects could succeed at our forcedresponse questions as a group, did they all individually claim not to have noticed anything?” (p.8)

      “we found that observers could successfully report a variety of features of unattended stimuli, even when they claimed not to have noticed these stimuli.” (p.14) => “we found that groups of observers could successfully report a variety of features of unattended stimuli, even when they all individually claimed not to have noticed those stimuli.” (p.14)

      “our results point to an alternative (and perhaps more straightforward) explanation: that inattentionally blind subjects consciously perceive these stimuli after all... they show sensitivity to IB stimuli because they can see them.” (p.16) => “our results just as easily raise an alternative (and perhaps more straightforward) explanation: that inattentionally blind subjects may retain a degree of awareness of these stimuli after all.” (p.16) Here deleting: “they show sensitivity to IB stimuli because they can see them.”

      “In other words, the inattentionally blind can see after all.” (p.17) => “In other words, as a group, the inattentionally blind enjoy at least some degraded or partial sensitivity to the location, color and shape of stimuli which they report not noticing.” (p.17)

      In one case, we felt the sentence was correct as it stood, since it simply reported a fact about our data:

      “In other words, nearly two-thirds of subjects who had just claimed not to have noticed any additional stimulus were then able to correctly report its location.” (p.6)

      After all, if subjects were entirely blind and simply guessed, it would be true to say that 50% of subjects would be able to correctly report the stimulus location (by guessing).

      In addition to these and numerous other changes, we also added the following explicit statement early in the paper to head-off any confusion on this point: “Note that all analyses reported here relate to this super subject as opposed to individual subjects”. 

      (3) In addition to the d' values for the super subject being slightly above zero, the authors attempted an analysis of response bias to further question the existence of IB. By including in some of their experiments critical trials in which no critical stimulus was presented, but asking subjects the standard Y/N IB question anyway, the authors obtained false alarm and correct rejection rates. When these FA/CR rates are taken into account along with hit/miss rates when critical stimuli were presented, the authors could calculate c (response criterion) for the super subject. Here, the authors report that response criteria are biased towards saying "no, I didn't notice anything". However, the validity of applying SDT to classic Y/N IB questioning is questionable.

      For example, with the subject numbers provided in Box 1 (the 2x2 table of hits/misses/FA/CR), one can ask, 'how many subjects would have needed to answer "yes, I noticed something unusual" when nothing was presented on the screen in order to obtain a non-biased criterion estimate, i.e., c = 0?' The answer turns out to be 800 subjects (out of the 2761 total subjects in the stimulus-absent condition), or 29% of subjects in this condition.

      In the context of these IB paradigms, it is difficult to imagine 29% of subjects claiming to have seen something unusual when nothing was presented. Here, it seems that we may have reached the limits of extending SDT to IB paradigms, which are very different than what SDT was designed for. For example, in classic psychophysical paradigms, the subject is asked to report Y/N as to whether they think a threshold-level stimulus was presented on the screen, i.e., to detect a faint signal in the noise. Subjects complete many trials and know in advance that there will often be stimuli presented and the stimuli will be very difficult to see. In those cases, it seems more reasonable to incorrectly answer "yes" 29% of the time, as you are trying to detect something very subtle that is out there in the world of noise. In IB paradigms, the stimuli are intentionally designed to be highly salient (and unusual), such that with a tiny bit of attention they can be easily seen. When no stimulus is presented and subjects are asked about their own noticing (especially of something unusual), it seems highly unlikely that 29% of them would answer "yes", which is the rate of FAs that would be needed to support the null hypothesis here, i.e., of a non-biased criterion. For these reasons, the analysis of response bias in the current context is questionable and the results claiming to demonstrate a biased criterion do not provide convincing evidence against IB.

      We are grateful to the reviewer for highlighting this aspect of our data. We agree with several of these points. For example, it is indeed striking that — given the corresponding hit rate — a false alarm rate of 29% would be needed to obtain an unbiased criterion. At the same time, we would respectfully push back on other points above. In our first experiment that uses the super-subject analysis, for example, d′ is 0.51 and highly significant; to describe that figure, as the reviewer does, as “slightly above zero” seemed not quite right to us (and all the more so given that these experiments involve very large samples and preregistered analysis plans). 

      We also respectfully disagree that our data call into question the validity of applying SDT to classic yes/no IB questioning. The mathematical foundations of SDT are rock solid, and have been applied far more broadly than we have applied them here. In fact, in a way we would suggest that exactly the opposite attitude is appropriate: rather than thinking that IB challenges an immensely well-supported, rigorously tested and broadly applicable mathematical model of perception, we think that the conflict between our SDT-based model of IB and the standard interpretation constitutes strong reason to disfavor the standard interpretation. Several points are worth making here.

      First, it is already surprising that 11.03% of our subjects in E2 (46/417) and 7.24% of our subjects in E5 (200/2761) E5 reported noticing a stimulus when no stimulus was present. But while this may have seemed unlikely in advance of inquiry, this is in fact what the data show and forms the basis of our criterion calculations. Thus, our criterion calculations already factor in a surprising but empirically verified high false alarm rate of subjects answering “yes” when no stimulus was presented and were asked about their noticing. (We also note that the only paper we know of to report a false alarm rate in an IB paradigm, though not one used to calculate a response criterion, found a very consistent false alarm rate of 10.4%. See Devue et al. 2009.)

      Second, while the reviewer is of course correct that a common psychophysical paradigm involves detection of a “threshold-level”/faint stimulus in noise, it is widely recognized that SDT has an extremely broad application, being applicable to any situation in which two kinds of event are to be discriminated (Pastore & Scheirer 1975) and being “almost universally accepted as a theoretical account of decision making in research on perceptual detection and recognition and in numerous extensions to applied domains” quite generally (Estes 2002, see also: Wixted 2020). Indeed, cases abound in which SDT has been successfully applied to situations which do not involve near threshold stimuli in noise. To pick two examples at random, SDT has been used in studying acceptability judgments in linguistics (Huang and Ferreira 2020) and the assessment of physical aggression in childstudent interactions (Lerman et al. 2010; for more general discussion of practical applications, see Swets et al. 2000). Given that the framework of SDT is so widely applied and well supported, and that we see no special reason to make an exception, we believe it can be relied on in the present context.

      Finally, we note that inattentional blindness can in many ways be considered analogous to “near threshold” detection since inattention is precisely thought to degrade or even abolish awareness of stimuli, meaning that our stimuli can be construed as near threshold in the relevant sense. Indeed, our relatively modest d′ values suggest that under inattention stimuli are indeed hard to detect. Thus, even were SDT more limited in its application, we think it still would be appropriate to apply to the case of IB.

      (4) One of the strongest pieces of evidence presented in the entire paper is the single data point in Figure 3e showing that in Experiment 3, even the super subject group that rated their non-noticing as "highly confident" had a d' score significantly above zero. Asking for confidence ratings is certainly an improvement over simple Y/N questions about noticing, and if this result were to hold, it could provide a key challenge to IB. However, this result hinges on a single data point, it was not replicated in any of the other 4 experiments, and it can be explained by methodological limitations. I strongly encourage the authors (and other readers) to follow up on this result, in an in-person experiment, with improved questioning procedures.

      We agree that our finding that even the super-subject group that rated their non-noticing as “highly confident” had a d' score significantly above zero is an especially strong piece of evidence, and we thank the reviewer for highlighting that here. At the same time, we note that while the finding is represented by a single marker in Figure 3e, it seemed not quite right to call this a “single data point”, as the reviewer does, given that it derives from a large pre-registered experiment involving some 7,000 subjects total, with over 200 subjects in the relevant bin — both figures being far larger than a typical IB experiment. It would of course be tremendous to follow up on this result – and we certainly hope our work inspires various follow-up studies. That said, we note that recruiting the necessary numbers of in person subjects would be an absolutely enormous, career-level undertaking – it would involve bringing more than the entire undergraduate population at our own institution, Johns Hopkins, into our laboratory! While those results would obviously be extremely valuable, we wouldn’t want to read the reviewer’s comments as implying that only an experiment of that magnitude — requiring thousands upon thousands of in-person subjects — could make progress on these issues. Indeed, because every subject can only contribute one critical trial in IB, it has long been recognized as an extremely challenging paradigm to study in a sufficiently well-powered and psychophysically rigorous way. We believe that our large preregistered online approach represents a major leap forward here, even if it involves certain trade-offs.

      In the current Experiment 3, the authors asked the standard Y/N IB question, and then asked how confident subjects were in their answer. Asking back-to-back questions, the second one with a scale that pertains to the first one (including a tricky inversion, e.g., "yes, I am confident in my answer of no"), may be asking too much of some subjects, especially subjects paying half-attention in online experiments. This procedure is likely to introduce a sizeable degree of measurement error.

      An easy fix in a follow-up study would be to ask subjects to rate their confidence in having noticed something with a single question using an unambiguous scale:

      On the last trial, did you notice anything besides the cross?

      (1): I am highly confident I didn't notice anything else

      (2): I am confident I didn't notice anything else

      (3): I am somewhat confident I didn't notice anything else

      (4): I am unsure whether I noticed anything else

      (5): I am somewhat confident I noticed something else

      (6): I am confident I noticed something else

      (7): I am highly confident I noticed something else

      If we were to re-run this same experiment, in the lab where we can better control the stimuli and the questioning procedure, we would most likely find a d' of zero for subjects who were confident or highly confident (1-2 on the improved scale above) that they didn't notice anything. From there on, the d' values would gradually increase, tracking along with the confidence scale (from 3-7 on the scale). In other words, we would likely find a data pattern similar to that plotted in Figure 3e, but with the first data point on the left moving down to zero d'. In the current online study with the successive (and potentially confusing) retrospective questioning, a handful of subjects could have easily misinterpreted the confidence scale (e.g., inverting the scale) which would lead to a mixture of genuine high-confidence ratings and mistaken ratings, which would result in a super subject d' that falls between zero and the other extreme of the scale (which is exactly what the data in Fig 3e shows).

      One way to check on this potential measurement error using the existing dataset would be to conduct additional analyses that incorporate the confidence ratings from the 2AFC location judgment task. For example, were there any subjects who reported being confident or highly confident that they didn't see anything, but then reported being confident or highly confident in judging the location of the thing they didn't see? If so, how many? In other words, how internally (in)consistent were subjects' confidence ratings across the IB and location questions? Such an analysis could help screen-out subjects who made a mistake on the first question and corrected themselves on the second, as well as subjects who weren't reading the questions carefully enough.

      As far as I could tell, the confidence rating data from the 2AFC location task were not reported anywhere in the main paper or supplement.

      We are grateful to the reviewer for raising this issue and for requesting that we report the confidence rating data from our 2afc location task in Experiment 3. We now report all this data in our Supplementary Materials (see Supplementary Table 3).

      We of course agree with the reviewer’s concern about measurement error, which is a concern in all experiments. What, then, of the particular concern that some subjects might have misunderstood our confidence question? It is surely impossible in principle to rule out this possibility; however, several factors bear on the plausibility of this interpretation. First, we explicitly labeled our confidence scale (with 0 labeled as ‘Not at all confident’ and 3 as ‘Highly confident’) so that subjects would be very unlikely simply to invert the scale. This is especially so as it is very counterintuitive to treat “0” as reflecting high confidence. However, we accept that it is a possibility that certain subjects might nonetheless have been confused in some other way.

      So, we also took a second approach. We examined the confidence ratings on the 2afc question of subjects who reported being highly confident that they didn't notice anything.

      Reassuringly, the large majority of these high confidence “no” subjects (~80%) reported low confidence of 0 or 1 on the 2afc question, and the majority (51%) reported the lowest confidence of 0. Only 18/204 (9%) subjects reported high confidence on both questions. 

      Still, the numbers of subjects here are small and so may not be reliable. This led us to take a third approach. We reasoned that any measurement error due to inverting or misconstruing the confidence scale should be symmetric. That is: If subjects are liable to invert the confidence scale, they should do so just as often when they answer “yes” as when they answer “no” – after all the very same scale is being used in both cases. This allows us to explore evidence of measurement error in relation to the much larger number of highconfidence “yes” subjects (N = 2677), thus providing a much more robust indicator as to whether subjects are generally liable to misconstrue the confidence scale. Looking at the number of such high confidence noticers who subsequently respond to the 2afc question with low-confidence, we found that the number was tiny. Only 28/2677 (1.05%) of highconfidence noticers subsequently gave the lowest level of confidence on the 2afc question, and only 63/2677 (2.35%) subjects gave either of the two lower levels of confidence. In this light, we consider any measurement error due to misunderstanding the confidence scale to be extremely minimal.

      What should we make of the 18 subjects who were highly confident non-noticers but then only low-confidence on the 2afc question? Importantly, we do not think that these 18 subjects necessarily made a mistake on the first question and so should be excluded. There is no a priori reason why one’s confidence criterion in a yes/no question should carry over to a 2afc question. After all, it is perfectly rationally coherent to be very confident that one didn’t see anything but also very confident that if there was anything to be seen, it was on the left. Moreover, these 18 subjects were not all correct on the 2afc question despite their high confidence (4/18 or 22% getting the wrong answer). 

      Nonetheless, and again reassuringly, we found that the above-chance patterns in our data remained the same even excluding these 18 subjects. We did observe a slight reduction in percent correct and d′ but this is absolutely what one should expect since excluding the most confident performers in any task will almost inevitably reduce performance.

      In this light, we consider it unlikely that measurement error fully explains the residual sensitivity found even amongst highly confident non-noticers. That said, we appreciate this concern. We now raise the issue and the analysis of high confidence noticers which addresses it in our revised manuscript. We also thank the reviewer for pressing us to think harder about this issue, which led directly to these new analyses that we believed have strengthened the paper.

      (5) In most (if not all) IB experiments in the literature, a partial attention and/or full attention trial (or set of trials) is administered after the critical trial. These control trials are very important for validating IB on the critical trial, as they must show that, when attended, the critical stimuli are very easy to see. If a subject cannot detect the critical stimulus on the control trial, one cannot conclude that they were inattentionally blind on the critical trial, e.g., perhaps the stimulus was just too difficult to see (e.g., too weak, too brief, too far in the periphery, too crowded by distractor stimuli, etc.), or perhaps they weren't paying enough attention overall or failed to follow instructions. In the aggregate data, rates of noticing the stimuli should increase substantially from the critical trial to the control trials. If noticing rates are equivalent on the critical and control trials one cannot conclude that attention was manipulated.

      It is puzzling why the authors decided not to include any control trials with partial or full attention in their five experiments, especially given their online data collection procedures where stimulus size, intensity, eccentricity, etc. were uncontrolled and variable across subjects. Including such trials could have actually helped them achieve their goal of challenging the IB hypothesis, e.g., excluding subjects who failed to see the stimulus on the control trials might have reduced the inattentional blindness rates further. This design decision should at least be acknowledged and justified (or noted as a limitation) in a revision of this paper.

      We acknowledge that other studies in the literature include divided and full attention trials, and that they could have been included in our work as well. However, we deliberately decided not to include such control trials for an important reason. As the referee comments, the main role of such trials in previous work has been to exclude from analysis subjects who failed to report the unexpected stimulus on the divided and/or full attention control trials.

      (For example, as Most et al. 2001 write: “Because observers should have seen the object in the full-attention trial (Mack & Rock, 1998), we used this trial as a control … Accordingly, 3 observers who failed to see the cross on this trial were replaced, and their data were excluded from the analyses.") As the reviewer points out, excluding such subjects would very likely have ‘helped' us. However, the practice is controversial. Indeed, in a review of 128 experiments, White et al. 2018 argue that the practice has “problematic consequences” and “may lead researchers to understate the pervasiveness of inattentional blindness". Since we wanted to offer as simple and demanding a test of residual sensitivity in IB as possible, we thus decided not to use any such exclusions, and for that reason decided not to include divided/full attention trials. 

      As recommended, we discuss this decision not to include divided/full attention trials and our logic for not doing so in the manuscript. As we explain, not having those conditions makes it more impressive, not less impressive, that we observed the results we in fact did — it makes our results more interpretable, not less interpretable, and so absence of such conditions from our manuscript should not (in our view) be considered any kind of weakness.

      (6) In the discussion section, the authors devote a short paragraph to considering an alternative explanation of their non-zero d' results in their super subject analyses: perhaps the critical stimuli were processed unconsciously and left a trace such that when later forced to guess a feature of the stimuli, subjects were able to draw upon this unconscious trace to guide their 2AFC decision. In the subsequent paragraph, the authors relate these results to above-chance forced-choice guessing in blindsight subjects, but reject the analogy based on claims of parsimony.

      First, the authors dismiss the comparison of IB and blindsight too quickly. In particular, the results from experiment 3, in which some subjects adamantly (confidently) deny seeing the critical stimulus but guess a feature at above-chance levels (at least at the super subject level and assuming the online subjects interpreted and used the confidence scale correctly), seem highly analogous to blindsight. Importantly, the analogy is strengthened if the subjects who were confident in not seeing anything also reported not being confident in their forced-choice judgments, but as mentioned above this data was not reported.

      Second, the authors fail to mention an even more straightforward explanation of these results, which is that ~8% of subjects misinterpreted the "unusual" part of the standard IB question used in experiments 1-3. After all, colored lines and shapes are pretty "usual" for psychology experiments and were present in the distractor stimuli everyone attended to. It seems quite reasonable that some subjects answered this first question, "no, I didn't see anything unusual", but then when told that there was a critical stimulus and asked to judge one of its features, adjusted their response by reconsidering, "oh, ok, if that's the unusual thing you were asking about, of course I saw that extra line flash on the left of the screen". This seems like a more parsimonious alternative compared to either of the two interpretations considered by the authors: (1) IB does not exist, (2) super-subject d' is driven by unconscious processing. Why not also consider: (3) a small percentage of subjects misinterpreted the Y/N question about noticing something unusual. In experiments 4-5, they dropped the term "unusual" but do not analyze whether this made a difference nor do they report enough of the data (subject numbers for the Y/N question and 2AFC) for readers to determine if this helped reduce the ~8% overestimate of IB rates.

      Our primary ambition in the paper was to establish, as our title suggests, residual sensitivity in IB. The ambition is quite neutral as to whether the sensitivity reflects conscious or unconscious processing (i.e. is akin to blindsight as traditionally conceived). We were evidently not clear about this, however, leading to two referees coming away with an impression of our claims that is different than we intended. We have revised our manuscript throughout to address this. But we also want to emphasize here that we take our data primarily to support the more modest claim that there is residual sensitivity (conscious or unconscious) in the group of subjects who are traditionally classified as inattentionally blind. We believe that this claim has solid support in our data.

      We do in the discussion section offer one reason for believing that there is residual awareness in the group of subjects who are traditionally classified as inattentionally blind. However, we acknowledge that this is controversial and now emphasize in the manuscript that this claim “is tentative and secondary to our primary finding”. We also emphasize that part of our point is dialectical: Inattentional blindness has been used to argue (e.g.) that attention is required for awareness. We think that our data concerning residual sensitivity at least push back on the use of IB to make this claim, even if they do not provide decisive evidence (as we agree) that awareness survives inattention. (Cf. here, Hirshhorn et al. 2024 who take up a common suggestion in the field that awareness is best assessed by using both subjective and objective measures, with claims about lack of awareness ideally being supported by both; our data suggest at a minimum that in IB objective measures do not neatly line up with subjective measures.)

      We hope this addresses the referee’s concern that we dismiss the “the comparison of IB and blindsight too quickly”. We do not intend to dismiss that comparison at all, indeed we raise it because we consider it a serious hypothesis. Our aim is simply to raise one possible consideration against it. But, again, our main claim is quite consistent with sensitivity in IB being akin to “blindsight”.

      We also agree with the referee that a possible explanation of why some subjects say they do not notice something unusual in IB paradigms, is not because they didn’t notice anything but because they didn’t consider the unexpected stimulus sufficiently unusual. However, the reviewer is incorrect that we did not mention this interpretation; to the contrary, it was precisely the kind of concern which led us to be dissatisfied with standard IB methods and so motivated our approach. As we wrote in our main text: “However, yes/no questions of this sort are inherently and notoriously subject to bias…   For example, observers might be under-confident whether they saw anything (or whether what they saw counted as unusual); this might lead them to respond “no” out of an excess of caution.” On our view, this is exactly the kind of reason (among other reasons) that one cannot rely on yes/no reports of noticing unusual stimuli, even though the field has relied on just these sorts of questions in just this way.

      We do not, however, think that this explanation accounts for why all subjects fail to report noticing, nor do we think that it accounts for our finding of above-chance sensitivity amongst non-noticers. This is for two critical reasons. First, whereas the word “unusual” did appear in the yes/no question in our Experiments 1-3, it did not appear in our Experiments 4 and 5 on dynamic IB. (In both cases, we used the exact wording of such questions in the experiments we were basing our work on.) And, of course, we still found significant residual sensitivity amongst non-noticers in Experiments 4 and 5. Second, in relation to our confidence experiment, we think it unlikely that subjects who were highly confident that they did not notice anything unusual only said that because they thought what they had seen was insufficiently unusual. Yet even in this group of subjects who were maximally confident that they did not notice anything unusual, we still found residual sensitivity.

      (7) The authors use sub-optimal questioning procedures to challenge the existence of the phenomenon this questioning is intended to demonstrate. A more neutral interpretation of this study is that it is a critique on methods in IB research, not a critique on IB as a manipulation or phenomenon. The authors neglect to mention the dozens of modern IB experiments that have improved upon the simple Y/N IB questioning methods. For example, in Michael Cohen's IB experiments (e.g., Cohen et al., 2011; Cohen et al., 2020; Cohen et al., 2021), he uses a carefully crafted set of probing questions to conservatively ensure that subjects who happened to notice the critical stimuli have every possible opportunity to report seeing them. In other experiments (e.g., Hirschhorn et al., 2024; Pitts et al., 2012), researchers not only ask the Y/N question but then follow this up by presenting examples of the critical stimuli so subjects can see exactly what they are being asked about (recognition-style instead of free recall, which is more sensitive). These follow-up questions include foil stimuli that were never presented (similar to the stimulus-absent trials here), and ask for confidence ratings of all stimuli. Conservative, pre-defined exclusion criteria are employed to improve the accuracy of their IB-rate estimates. In these and other studies, researchers are very cautious about trusting what subjects report seeing, and in all cases, still find substantial IB rates, even to highly salient stimuli. The authors should consider at least mentioning these improved methods, and perhaps consider using some of them in their future experiments.

      The concern that we do not sufficiently discuss the range of “improved” methods in IB studies is well-taken. A similar concern is raised by Reviewer #2 (Dr. Cohen). To address the concern, we have added to our manuscript a substantial new discussion of such improved methods. However, although we do agree that these methods can be helpful and may well address some of the methodological concerns which our paper raises, we do not think that they are a panacea. Thus, our discussion of these methods also includes a substantial discussion of the problems and pitfalls with such methods which led us to favor our own simple forced-response and 2afc questions, combined with SDT analysis. We think this approach is superior both to the classic approach in IB studies and to the approach raised by the reviewers.

      In particular, we have four main concerns about the follow up questions now commonly used in the field:

      First, many follow up questions are used not to exclude people from the IB group but to include people in the IB group. Thus, Most et al. 2001 asked follow up questions but used these to increase their IB group, only excluding subjects from the IB group if they both reported seeing and answered their follow ups incorrectly: “Observers were regarded as having seen the unexpected object if they answered 'yes' when asked if they had seen anything on the critical trial that had not been present before and if they were able to describe its color, motion, or shape." This means that subjects who saw the object but failed to see its color, say, would be treated as inattentionally blind. This has the purpose of inflating IB rates, in exactly the way our paper is intended to critique. So, in our view this isn’t an improvement but rather part of the approach we take issue with.

      Second, many follow up questions remain yes/no questions or nearby variants, all of which are subject to response bias. For example, in Cohen’s studies which the reviewer mentions, it is certainly true that “he uses a carefully crafted set of probing questions to conservatively ensure that subjects who happened to notice the critical stimuli have every possible opportunity to report seeing them.” We agree that this improves over a simple yes/no question in some ways. However, such follow up probes nonetheless remain yes/no questions, subject to response bias, e.g.:

      (1) “Did you notice anything strange or different about that last trial?”

      (2) “If I were to tell you that we did something odd on the last trial, would you have a guess as to what we did?”

      (3) “If I were to tell you we did something different in the second half of the last trial, would you have a guess as to what we did?”

      (4) “Did you notice anything different about the colors in the last scene?”

      Indeed, follow up questions of this kind can be especially susceptible to bias, since subjects may be reluctant to “take back” their earlier answers and so be conservative in responding positively to avoid inconsistency or acknowledgement of earlier error. This may explain why such follow up questions produce remarkable consistency despite their rather different wording. Thus, Simons and Chabris (1999) report: “Although we asked a series of questions escalating in specificity to determine whether observers had noticed the unexpected event, only one observer who failed to report the event in response to the first question (“did you notice anything unusual?'') reported the event in response to any of the next three questions (which culminated in “did you see a ... walk across the screen?''). Thus, since the responses were nearly always consistent across all four questions, we will present the results in terms of overall rates of noticing.” Thus, while there are undoubtedly merits to these follow ups, they do not resolve problems of bias.

      This same basic issue affects the follow up question used in Pitts et al. 2012 which the reviewer mentions. Pitts et al. write: “If a participant reported not seeing any patterns and rated their confidence in seeing the square pattern (once shown the sample) as a 3 or less (1 = least confident, 5 = most confident), she or he was placed in Group 1 and was considered to be inattentionally blind to the square patterns.” The confidence rating follow-up question here remains subject to bias. Moreover, and strikingly, the inclusion criterion used means that subjects who were moderately confident that they saw the square pattern when shown (i.e. answered 3) were counted as inattentionally blind (!). We do not think this is an appropriate inclusion criterion.

      The third problem is that follow up questions are often free/open-response. For instance, Most et al. (2005) ask the follow up question: "If you did see something on the last trial that had not been present during the first two trials, what color was it? If you did not see something, please guess." This is a much more difficult and to that extent less sensitive question than our binary forced-response/2afc questions. For this reason, we believe our follow up questions are more suitable for ascertaining low levels of sensitivity.

      The fourth and final issue is that whereas 2afc questions are criterion free (in that they naturally have an unbiased decision rule), this is in fact not true of n_afc questions in general, nor is it true in general of _delayed n-alternative match to sample designs. Thus, even when limited response options are given, they are not immune to response biases and so require SDT analysis. Moreover, some such tasks can involve decision spaces which are often poorly understood or difficult to analyze without making substantial assumptions about observer strategy. 

      This last point (as well as the first) is relevant to Hirshhorn et al. 2024. Hirshhorn et al. write that they “used two awareness measures. Firstly, participants were asked to rate stimulus visibility on the Perceptual Awareness Scale (PAS, a subjective measure of awareness: Ramsøy & Overgaard, 2004), and then they were asked to select the stimulus image from an array of four images (an objective measure: Jakel & Wichmann, 2006).”

      While certainly an improvement on simple yes/no questioning, the PAS remains subject to response bias. On the other hand, we applaud Hirshhorn et al.’s use of objective measures in the context of IB which of course our design implements. However, while Hirshhorn et al. 2024 suggest that their task is a spatial 4afc following the recommendation of this design by Jakel & Wichmann (2006), it is strictly a 4-alternative delayed match to sample task, so it is doubtful if it can be considered a preferred psychophysical task for the reasons Jakel & Wichmann offer. Regardless, the more crucial point is that observers in such a task might be biased towards one alternative as opposed to another. Thus, use of d′ (as opposed to percent correct as in Hirshhorn et al. 2024) is crucial in assessing performance in such tasks.

      For all these reasons, then, while we agree that the field has taken significant steps to move beyond the simple yes/no question traditionally used in IB studies (and we have revised our manuscript to make this clear); we do not think it has resolved the methodological issues which our paper seeks to highlight and address, and we believe that our approach contributes something additional that is not yet present in the literature. We have now revised our manuscript to make these points much more clearly, and we thank the reviewer for prompting these improvements.

      Reviewer #2 (Public review):

      In this study, Nartker et al. examine how much observers are conscious of using variations of classic inattentional blindness studies. The key idea is that rather than simply asking observers if they noticed a critical object with one yes/no question, the authors also ask follow-up questions to determine if observers are aware of more than the yes/no questions suggest. Specifically, by having observers make forced choice guesses about the critical object, the authors find that many observers who initially said "no" they did not see the object can still "guess" above chance about the critical object's location, color, etc. Thus, the authors claim, that prior claims of inattentional blindness are mistaken and that using such simple methods has led numerous researchers to overestimate how little observers see in the world. To quote the authors themselves, these results imply that "inattentionally blind subjects consciously perceive these stimuli after all... they show sensitivity to IB stimuli because they can see them."

      Before getting to a few issues I have with the paper, I do want to make sure to explicitly compliment the researchers for many aspects of their work. Getting massive amounts of data, using signal detection measures, and the novel use of a "super subject" are all important contributions to the literature that I hope are employed more in the future.

      We really appreciate this comment and that the reviewer found our work to make these important contributions to the literature. We wrote this paper expecting not everyone to accept our conclusions, but hoping that readers would see the work as making a valuable contribution to the literature promoting an underexplored alternative in a compelling way. Given that this reviewer goes on to express some skepticism about our claims, it is especially encouraging to see this positive feedback up top!

      Main point 1: My primary issue with this work is that I believe the authors are misrepresenting the way people often perform inattentional blindness studies. In effect, the authors are saying, "People do the studies 'incorrectly' and report that people see very little. We perform the studies 'correctly' and report that people see much more than previously thought." But the way previous studies are conducted is not accurately described in this paper. The authors describe previous studies as follows on page 3:

      "Crucially, however, this interpretation of IB and the many implications that follow from it rest on a measure that psychophysics has long recognized to be problematic: simply asking participants whether they noticed anything unusual. In IB studies, awareness of the unexpected stimulus (the novel shape, the parading gorilla, etc.) is retroactively probed with a yes/no question, standardly, "Did you notice anything unusual on the last trial which wasn't there on previous trials?". Any subject who answers "no" is assumed not to have any awareness of the unexpected stimulus.

      If this quote were true, the authors would have a point. Unfortunately, I do not believe it is true. This is simply not how many inattentional blindness studies are run. Some of the most famous studies in the inattentional blindness literature do not simply as observes a yes/no question (e.g., the invisible gorilla (Simons et al. 1999), the classic door study where the person changes (Simons and Levin, 1998), the study where observers do not notice a fight happening a few feet from them (Chabris et al., 2011). Instead, these papers consistently ask a series of follow-up questions and even tell the observers what just occurred to confirm that observers did not notice that critical event (e.g., "If I were to tell you we just did XYZ, did you notice that?"). In fact, after a brief search on Google Scholar, I was able to relatively quickly find over a dozen papers that do not just use a yes/no procedure, and instead as a series of multiple questions to determine if someone is inattentionally blind. In no particular order some papers (full disclosure: including my own):

      (1) Most et al. (2005) Psych Review

      (2) Drew et al. (2013) Psych Science

      (3) Drew et al. (2016) Journal of Vision

      (4) Simons et al. (1999) Perception

      (5) Simons and Levin (1998) Perception

      (6) Chabris et al. (2011) iPerception

      (7) Ward & Scholl (2015) Psych Bulletin and Review

      (8) Most et al. (2001) Psych Science

      (9) Todd & Marois (2005) Psych Science

      (10) Fougnie & Marois (2007) Psych Bulletin and Review

      (11) New and German (2015) Evolution and Human Behaviour

      (12) Jackson-Nielsen (2017) Consciousness and cognition

      (13) Mack et al. (2016) Consciousness and cognition

      (14) Devue et al. (2009) Perception

      (15) Memmert (2014) Cognitive Development

      (16) Moore & Egeth (1997) JEP:HPP

      (17) Cohen et al. (2020) Proc Natl Acad Sci

      (18) Cohen et al. (2011) Psych Science

      This is a critical point. The authors' key idea is that when you ask more than just a simple yes/no question, you find that other studies have overestimated the effects of inattentional blindness. But none of the studies listed above only asked simple yes/no questions. Thus, I believe the authors are mis-representing the field. Moreover, many of the studies that do much more than ask a simple yes/no question are cited by the authors themselves! Furthermore, as far as I can tell, the authors believe that if researchers do these extra steps and ask more follow-ups, then the results are valid. But since so many of these prior studies do those extra steps, I am not exactly sure what is being criticized.

      To make sure this point is clear, I'd like to use a paper of mine as an example. In this study (Cohen et al., 2020, Proc Natl Acad Sci USA) we used gaze-contingent virtual reality to examine how much color people see in the world. On the critical trial, the part of the scene they fixated on was in color, but the periphery was entirely in black and white. As soon as the trial ended, we asked participants a series of questions to determine what they noticed. The list of questions included:

      (1) "Did you notice anything strange or different about that last trial?"

      (2) "If I were to tell you that we did something odd on the last trial, would you have a guess as to what we did?"

      (3) "If I were to tell you we did something different in the second half of the last trial, would you have a guess as to what we did?"

      (4) "Did you notice anything different about the colors in the last scene?"

      (5) We then showed observers the previous trial again and drew their attention to the effect and confirmed that they did not notice that previously.

      In a situation like this, when the observers are asked so many questions, do the authors believe that "the inattentionally blind can see after all?" I believe they would not say that and the reason they would not say that is because of the follow-up questions after the initial yes/no question. But since so many previous studies use similar follow-up questions, I do not think you can state that the field is broadly overestimating inattentional blindness. This is why it seems to me to be a bit of a strawman: most people do not just use the yes/no method.

      We appreciate this reviewer raising this issue. As he (Dr. Cohen) states, his “primary issue” concerns our discussion of the broader literature (which he worries understates recent improvements made to the IB methodology), rather than, e.g., the experiments we’ve run. We take this concern very seriously and address it comprehensively here.

      A very similar issue is identified by Reviewer #1, comment (7). To review some of what we say in reply to them: To address the concern we have added to our manuscript a substantial new discussion of such improved methods. However, although we do agree that these methods can be helpful and may well address some of the methodological concerns which our paper raises, we do not think that they are a panacea. Thus, our discussion of these methods also includes a substantial discussion of the problems and pitfalls with such methods which led us to favor our own simple forced-response and 2afc questions, combined with SDT analysis. We think this approach is superior both to the classic approach in IB studies and to the approach raised by the reviewers.

      In particular, we have three main concerns about the follow up questions now commonly used in the field:

      First, many follow up questions are used not to exclude subjects from the IB group but to include subjects in the IB group. Thus, Most et al. (2001) asked follow up questions but used these to increase their IB group, only excluding subjects from the IB group if they both reported seeing and failed to answer their follow ups correctly: “Observers were regarded as having seen the unexpected object if they answered 'yes' when asked if they had seen anything on the critical trial that had not been present before and if they were able to describe its color, motion, or shape." This means that subjects who saw the object but failed to describe it in these respects would be treated as inattentionally blind. This is problematic since failure to describe a feature (e.g., color, shape) does not imply a complete lack of information concerning that feature; and even if a subject did lack all information concerning these features of an object, this would not imply a complete failure to see the object. Similarly, Pitts et al. (2012) asked subjects to rate their confidence in their initial yes/no response from 1 = least confident to 5 = most confident, and used these ratings to include in the IB group those who rated their confidence in seeing at 3 or less. This is evidently problematic, since there is a large gap between being under confident that one saw something and being completely blind to it. More generally, using follows up to inflate IB rates in such ways raises precisely the kinds of issues our paper is intended to critique. So in our view this isn’t an improvement but rather part of the approach we take issue with.

      Second, many follow up questions remain yes/no questions or nearby variants, all of which are subject to response bias. For example, in the reviewer’s own studies (Cohen et al. 2020, 2011; see also: Simons et al., 1999; Most et al., 2001, 2005; Drew et al., 2013; Memmert, 2014) a series of follow up questions are used to try and ensure that subjects who noticed the critical stimuli are given the maximum opportunity to report doing so, e.g.:

      (1) “Did you notice anything strange or different about that last trial?”

      (2) “If I were to tell you that we did something odd on the last trial, would you have a guess as to what we did?”

      (3) “If I were to tell you we did something different in the second half of the last trial, would you have a guess as to what we did?”

      (4) “Did you notice anything different about the colors in the last scene?”

      We certainly agree that such follow up questions improve over a simple yes/no question in some ways. However, such follow up probes nonetheless remain yes/no questions, intrinsically subject to response bias. Indeed, follow up questions of this kind can be especially susceptible to bias, since subjects may be reluctant to “take back” their earlier answers and so be conservative in responding positively to avoid inconsistency or acknowledgement of earlier error. This may explain why such follow up questions produce remarkable consistency despite their rather different wording. Thus, Simons and Chabris (1999) report: “Although we asked a series of questions escalating in specificity to determine whether observers had noticed the unexpected event, only one observer who failed to report the event in response to the first question (“did you notice anything unusual?'') reported the event in response to any of the next three questions (which culminated in “did you see a ... walk across the screen?''). Thus, since the responses were nearly always consistent across all four questions, we will present the results in terms of overall rates of noticing.” Thus, while there are undoubtedly merits to these follow ups, they do not resolve problems of bias.

      It is also important to recognize that whereas 2afc questions are criterion free (in that they naturally have an unbiased decision rule), this is not true of n_afc nor delayed _n-alternative match to sample designs in general. Performance in such tasks thus requires SDT analysis – which itself may be problematic if the decision space is not properly understood or requires making substantial assumptions about observer strategy.

      Third, and finally, many follow up questions are insufficiently sensitive (especially with small sample sizes). For instance, Todd, Fougnie & Marois (2005) used a 12-alternative match-tosample task (see similarly: Fougnie & Marois, 2007; Devue et al., 2009). And Most et al. (2005) asked an open-response follow-up: “If you did see something on the last trial that had not been present during the first two trials, what color was it? If you did not see something, please guess.” These questions are more difficult and to that extent less sensitive than binary forced-response/2afc questions of the sort we use in our own studies – a difference which may be critical in uncovering degraded perceptual sensitivity.

      For all these reasons, then, while we agree that the field has taken significant steps to move beyond the simple yes/no question traditionally used in IB studies (and we have revised our manuscript to make this clear); we do not think it has resolved the methodological issues which our paper seeks to highlight and address, and we believe that our approach of using 2afc or forced-response questions combined with signal detection analysis is an important improvement on prior methods and contributes something additional that is not yet present in the literature. We have now revised our manuscript to make these points much clearer.

      Other studies that improve on the standard methodology

      This reviewer adds something else, however: A very helpful list of 18 papers which include follow ups and that he believes overcome many of the issues we raise in our paper. To just state our reaction bluntly: We are familiar with every one of these papers (indeed, one of them is a paper by one of us!), and while we think these are all very valuable contributions to the literature, it is our view that none of these 18 papers resolves the worries that led us to conduct our work.  

      Here we briefly comment on the relevant pitfalls in each case. We hope this serves to underscore the importance of our methodological approach.

      (1) Most et al. (2005) Psych Review

      Either a 2-item or 5-item questionnaire was used. The 2-item questionnaire ran as follows:

      (1) On the last trial, did you see anything other than the 4 circles and the 4 squares (anything that had not been present on the original two trials)? Yes No 

      (2) If you did see something on the last trial that had not been present during the original two trials, please describe it in as much detail as possible.

      This clearly does not substantially improve on the traditional simple yes/no question. Moreover, the second question (as well as being open-ended) was used to include additional subjects in the IB group, in that participants were counted as having seen the object only if they responded “yes” to Q1 and in addition “were able to report at least one accurate detail” in response to Q2. In other words, either a subject says “no” (and is treated as unaware), or says “yes” and then is asked to prove their awareness, as it were. If anything, this intensifies the concerns we raise, by inflating IB rates. 

      The 5-item questionnaire looked like this: 

      (1) On the last trial, did you see anything other than the black and white L’s and T’s (anything that had not been present on the first two trials)?

      (2) If you did see something on the last trial that had not been present during the first two trials, please describe it.

      (3) If you did see something on the last trial that had not been present during the first two trials, what color was it? If you did not see something, please guess. (Please indicate whether you did see something or are guessing)

      (4) If you did see something during the last trial that had not been present in the first two trials, please draw an arrow on the “screen” below showing the direction in which it was moving. If you did not see something, please guess. (Please indicate whether you did see something or are guessing)

      (5) If you did see something during the last trial that had not been present during the first two trials, please circle the shape of the object below [4 shapes are presented to choose from]. If you did not see anything, please guess. (Please indicate whether you did see something or are guessing)

      Q5 was not used for analysis purposes. (It suffers from the second issue raised above.) Q1 is the traditional y/n question. Qs 2&3 are open ended. It is unclear how responses to Q4 were analyzed (at the limit it could be considered a helpful, forced-choice question – though it again would suffer from the second issue raised above). However, as noted with respect to the 2-item questionnaire, these responses were not used to exclude people from the IB group but to include people in it. So again, this approach does not in any way address the issues we are concerned about, and if anything, only makes them worse. 

      (2)  Drew et al. (2013) Psych Science

      All follow ups were yes/no: “we asked a series of questions to determine whether they noticed the gorilla: ‘Did the final trial seem any different than any of the other trials?’, ‘Did you notice anything unusual on the final trial?’, and, finally, ‘Did you see a gorilla on the final trial?’”. So, this paper essentially implements the standard methodology we mention (and criticize). 

      (3)  Drew et al. (2016) Journal of Vision

      Follow up questions were used, but the reported procedure does not provide sufficient details to evaluate them (we are only told: “After the final trial, they were asked: ‘On that last trial of the task, did you notice anything that was not there on previous trials?’ They then answered questions about the features of the unexpected stimulus on a separate screen (color, shape, movement, and direction of movement).”). It is not clear that these follow ups were used to exclude any subjects from the analysis. Finally, given that the unexpected object could be the same color as the targets/distractors, it is clear that biases would have been introduced which would need to be considered (but which were not).

      (4)  Simons & Chabris (1999) Perception

      All follow ups were yes/no: “observers were … asked to provide answers to a surprise series of additional questions. (i) While you were doing the counting, did you notice anything unusual on the video? (ii) Did you notice any- thing other than the six players? (iii) Did you see anyone else (besides the six players) appear on the video? (iv) Did you see a gorilla [woman carrying an umbrella] walk across the screen? After any “yes'' response, observers were asked to provide details of what they noticed. If at any point an observer mentioned the unexpected event, the remaining questions were skipped.” As noted previously, the analyses in fact did not use these questions to exclude subjects since answers were so consistent.

      (5)  Simons and Levin (1998) Perception

      This is a change detection paradigm, not a study of inattentional blindness. And in any case, one yes/no follow up was used: “Did you notice that I'm not the same person who approached you to ask for directions?”

      (6)  Chabris et al. (2011) iPerception

      Two yes/no questions were asked: “we asked whether the subjects had seen anything unusual along the route, and then whether they had seen anyone fighting.” It seems that follow up questions (a request to describe the fight) were asked only of those who said yes.

      This is in fact a common procedure – follow up questions only being asked of the “yes” group. As discussed, it is sometimes used to increase rates of IB, compounding the problem we identify in our paper. So this is another example of a follow-up question that makes the problem we identify worse, not better.

      (7) Ward & Scholl (2015) Psych Bulletin and Review

      Two yes/no questions were used: “...observers were asked whether they noticed ‘anything … that was different from the first three trials’ — and if so, to describe what was different. They were then shown the gray cross and asked if they had noticed it—and if so, to describe where it was and how it moved. Only observers who explicitly reported not noticing the cross were counted as ‘nonnoticers’ to be included in the final sample (N = 100).” In each case, combining the traditional noticing question with a request to describe and identify may have induced conservative response biases in the noticing question, since a subject might consider being able to describe or identify the unexpected stimulus a precondition of giving a positive answer to the noticing question.

      (8) Most et al. (2001) Psych Science

      The same 5-item questionnaire discussed above in relation to Most et al. (2005) was used: 

      (1) On the last trial, did you see anything other than the black and white L’s and T’s (anything that had not been present on the first two trials)?

      (2)   If you did see something on the last trial that had not been present during the first two trials, please describe it.

      (3) If you did see something on the last trial that had not been present during the first two trials, what color was it? If you did not see something, please guess. (Please indicate whether you did see something or are guessing)

      (4) If you did see something during the last trial that had not been present in the first two trials, please draw an arrow on the “screen” below showing the direction in which it was moving. If you did not see something, please guess. (Please indicate whether you did see something or are guessing)

      (5) If you did see something during the last trial that had not been present during the first two trials, please circle the shape of the object below [4 shapes are presented to choose from]. If you did not see anything, please guess. (Please indicate whether you did see something or are guessing)

      Q5 was not used for analysis purposes. (It suffers from the second issue raised above.) Q1 is the traditional yes/no question. Qs 2&3 are open ended. It is unclear how responses to Q4 were analyzed (at the limit it could be considered a helpful, forced-choice question – though it again would suffer from the second issue raised above). However, as noted with respect to the two item questionnaire in Most et al. 2005, these responses were not used to exclude people from the IB group but to include people in it. So again this approach does not in any way address the issues we are concerned about, and if anything only makes them worse.

      (9) Todd, Fougnie & Marois (2005) Psych Science

      “participants were probed with three questions to determine whether they had detected the critical stimulus ... .The first question assessed whether subjects had seen anything unusual during the trial; they responded ‘‘yes’’ or ‘‘no’’ by pressing the appropriate key on the keyboard. The second question asked participants to select which stimulus they might have seen among 12 possible objects and symbols selected from MacIntosh font databases. The third question asked participants to select the quadrant in which the critical stimulus may have appeared by pressing one of four keys, each of which corresponded to one of the quadrants.”

      These follow ups were used to include people in the IB group: “In keeping with previous studies (Most et al., 2001), participants were considered to have detected the critical stimulus successfully if they (a) reported seeing an unexpected stimulus and (b) correctly selected its quadrant location.” In line with our third point about sensitivity, the object identity test transpired to be “too difficult even under full-attention conditions … Thus, performance with this question was not analyzed further.”

      (10) Fougnie & Marois (2007) Psych Bulletin and Review

      Same exact methods and problems as with Todd & Marois (2005) Psych Science, just discussed.

      (11) New and German (2015) Evolution and Human Behaviour

      “After the fourth trial containing the additional experimental stimulus, the participant was asked, “Did you see anything in addition to the cross on that trial?” and which quadrant the additional stimulus appeared in. They were then asked to identify the stimulus in an array which in Experiment 1 included two variants chosen randomly from the spider stimuli and the two needle stimuli. Participants in Experiment 2 picked from all eight stimuli used in that experiment.”

      Our second concern about response biases and the need for appropriate SDT analysis of the 4/8 alternative tasks applies to all these questions. We also note that analyses were only performed on groups separately (those who detected/failed to detect, those who located/failed to locate, and those who identified/failed to identify) and on the group which did all three/failed to do any one of the three. Especially in light of the fact that some subjects could clearly detect the stimulus without being able to identity it (e.g.), the most stringent test given our concerns (which were not obviously New and German’s comparative concerns), would be to consider the group which could not detect, identify or localize.

      (12) Jackson-Nielsen (2017) Consciousness and cognition

      This is a very interesting example of a follow-up which used a 3-AFC recognition test:

      “participants were immediately asked, ‘‘which display looks most like what you just saw?’ from 3 alternatives”. However, though such an objective test is definitely to be preferred in our view to an open-ended series of probes, the 3-AFC test administered clearly had issues with response biases, as discussed, and actually yielded significantly below chance performance in one of the experiments.

      (13) Mack et al. (2016) Consciousness and cognition

      The follow ups here were essentially yes/no combined with an assessment of surprise. Participants were asked to enter letters into a box, and if they did so “were immediately asked by the experimenter whether they had noticed anything different about the array on this last trial and if they did not, they were told that there had been no letters and their responses to that news were recorded. Clearly, if they expressed surprise, this would be compelling evidence that they were unaware of the absence of the letters. Those observers who did not enter letters and realized there were no letters present were considered aware of the absence.” So, this again has all of the same problems we identify, considering subjects unaware because they expressed surprise.

      (14) Devue et al. (2009) Perception

      An 8-alternative task was used. The authors were primarily interested in a comparative analysis and so did not use this task to exclude subjects. We note that an 8 alternative task is very demanding – compare the 12-alternative task used in Todd, Fougnie & Marois (2005). There was an attempt to investigate biases in a separate bias trial, however SDT measures were not used.

      (15) Memmert (2014) Cognitive Development

      “After watching the video and stating the number of passes, participants answered four questions (following Simons & Chabris, 1999): (1) While you were counting, did you perceive anything unusual on the video? (2) Did you perceive anything other than the six players? (3) Did you see anyone else (besides the six players) appear on the video? (4) Did you notice a gorilla walk across the screen? After any “yes” reply, children were asked to provide details of what they noticed. If at any point a child mentioned the unexpected event, the remaining questions were omitted.” All of these follow-up questions are yes/no judgments, used to determine awareness in exactly the way we critique as problematic.

      (16) Moore & Egeth (1997) JEP:HPP

      This study (which includes one of us, Egeth, as author) did use forced choice questions. In one case, the question was 2-alternative, in the other it was 4-alternative. In the latter case, SDT would have been appropriate but was not used. In the former case, it may have been that a larger sample would have revealed evidence of sensitivity to the background pattern (as it stood 55% answered the 2-alternative question correctly). Although these results have been replicated, unfortunately the replication in Wood and Simons 2019 used a 6-alternative recognition task and this was not analyzed using SDT. We also note that the task is rather difficult in this study. Wood and Simons report: “Exclusion rates were much higher than anticipated, primarily due to exclusions when subjects failed to correctly report the pattern on the full-attention trial; we excluded 361 subjects, or 58% of our sample.”

      (17) Cohen et al. (2020) Proc Natl Acad Sci

      While this paper improves over a simple yes/no question in some ways, especially in that it used the follow up questions to exclude subjects from the unaware (IB) group, the follow up probes nonetheless remain yes/no questions, subject to response bias, e.g.:

      (1) “Did you notice anything strange or different about that last trial?”

      (2) “If I were to tell you that we did something odd on the last trial, would you have a guess as to what we did?”

      (3) “If I were to tell you we did something different in the second half of the last trial, would you have a guess as to what we did?”

      (4) “Did you notice anything different about the colors in the last scene?”

      Follow up questions of this kind can be especially susceptible to bias, since subjects may be reluctant to “take back” their earlier answers and so be conservative in responding positively to avoid inconsistency or acknowledgement of earlier error. This may explain why such follow up questions can produce remarkable consistency despite their rather different wording. 

      (18) Cohen et al. (2011) Psych Science

      Here are the probes used in this study:

      (1) Did you notice anything different on that trial?

      (2) Did you notice something different about the background stream of images?

      (3) Did you notice that a different type of image was presented in the background that was unique in some particular way?

      (4) Did you see an actual photograph of a natural scene in that stream?

      (5) If I were to tell you that there was a photograph in that stream, can you tell me what it was a photograph of?

      Qs 1-4 are yes/no. Q5 is yes/no with an open-ended response. After this, a 5 or 6-alternative recognition test was administered. So again, this faces the same issues, since y/n questions are subject to bias in the way we have described, and many-alternative tests are more problematic than 2afc tests.

      In summary

      We really appreciate the care that went into compiling this list, and we agree that these papers and the improved methods they contain are relevant. But as hopefully made clear above, the approaches in each of these papers simply don’t solve the foundational issues our critique is aimed at (though they may address other issues). This is why we felt our new approach was necessary. And we continue to feel this way even after reading and incorporating these comments from Dr. Cohen.

      Nevertheless, there is clearly lots for us to do in light of these comments. And so as noted earlier we have now added a very substantial new section to our discussion section to more fairly and completely portray the state of the art in this literature. This is really to our benefit in the end, since we now not only better acknowledge the diverse approaches present, but also set up ourselves to make our novel contribution exceedingly clear.

      Main point 2: Let's imagine for a second that every study did just ask a yes/no question and then would stop. So, the criticism the authors are bringing up is valid (even though I believe it is not). I am not entirely sure that above chance performance on a forced choice task proves that the inattentionally blind can see after all. Could it just be a form of subliminal priming? Could there be a significant number of participants who basically would say something like, "No I did not see anything, and I feel like I am just guessing, but if you want me to say whether the thing was to the left or right, I will just 100% guess"? I know the literature on priming from things like change and inattentional blindness is a bit unclear, but this seems like maybe what is going on. In fact, maybe the authors are getting some of the best priming from inattentional blindness because of their large sample size, which previous studies do not use.

      I'm curious how the authors would relate their studies to masked priming. In masked priming studies, observers say the did not see the target (like in this study) but still are above chance when forced to guess (like in this study). Do the researchers here think that that is evidence of "masked stimuli are truly seen" even if a participant openly says they are guessing?

      We’re grateful to the reviewer for raising this question. As we say in response to Reviewer #1, our primary ambition in the paper is to establish, as our title suggests, residual sensitivity in IB. The ambition is quite neutral as to whether the sensitivity reflects conscious or unconscious processing (i.e. is akin to blindsight as traditionally conceived, or what the reviewer here suggests may be happening in masked priming). Since we were evidently insufficiently clear about this we have revised our manuscript in several places to clarify that we take our data primarily to support the more modest claim that there is residual sensitivity (conscious or unconscious) in the group of subjects who are traditionally classified as inattentionally blind. We believe that this claim has much more solid support in our data than our secondary and tentative suggestion about awareness.

      This said, we do consider masked priming studies to be susceptible to the critique that performance may reflect degraded conscious awareness which is unreported because of conservative response criteria. There is good evidence that response criteria tend to be conservative near threshold (Björkman et al. 1993; see also: Railo et al. 2020), including specifically in masked priming studies (Sand 2016, cited in Phillips 2021). So, we consider it a perfectly reasonable hypothesis that subjects who say they feel they are guessing in fact have conscious access to a degraded signal which is insufficient to reach a conservative response criterion but nonetheless sufficient to perform above chance in 2afc detection. Of course, we appreciate that this hypothesis is controversial, so it is not one we argue for in our paper (though we are happy to share our feelings about it here).

      Main point 3: My last question is about how the authors interpret a variety of inattentional blindness findings. Previous work has found that observers fail to notice a gorilla in a CT scan (Drew et al., 2013), a fight occurring right in front of them (Chabris et al., 2011), a plane on a runway that pilots crash into (Haines, 1991), and so forth. In a situation like this, do the authors believe that many participants are truly aware of these items but simply failed to answer a yes/no question correctly? For example, imagine the researchers made participants choose if the gorilla was in the left or right lung and some participants who initially said they did not notice the gorilla were still able to correctly say if it was in the left or right lung. Would the authors claim "that participant actually did see the gorilla in the lung"? I ask because it is difficult to understand what it means to be aware of something as salient as a gorilla in a CT scan, but say "no" you didn't notice it when asked a yes/no question. What does it mean to be aware of such important, ecologically relevant stimuli, but not act in response to them and openly say "no" you did not notice them?

      Our view is that in such cases, observers may well have a “degraded” percept of the relevant feature (gorilla, plane, fight etc.). But crucially we do not suggest that this percept is sufficient for observers to recognize the object/event as a gorilla, plane, fight etc. Our claim is only that, in our studies at least, observers (as a group) do have enough information about the unexpected stimuli to locate them, and discriminate certain low level features better than chance. Crudely, it may be that subjects see the gorilla simply as a smudge or the plane as a shadowy patch etc. (One of us who is familiar with the gorilla CT scan stimuli notes that the gorilla is in fact rather hard to see even when you know which slide it is on, suggesting that they are not as “salient” as the reviewer suggests!) 

      More precisely, in the paper we write that in our view perhaps “...unattended stimuli are encoded in a partial or degraded way. Here we see a variety of promising options for future work to investigate. One is that unattended stimuli are only encoded as part of ensemble representations or summary scene statistics (Rosenholtz, 2011; Cohen et al., 2016). Another is that only certain basic “low-level” or “preattentive” features (see Wolfe & Utochkin, 2019 for discussion) can enter awareness without attention. A final possibility consistent with the present data is that observers can in principle be aware of individual objects and higher-level features under inattention but that the precision of the corresponding representations is severely reduced. Our central aim here is to provide evidence that awareness in inattentional blindness is not abolished. Further work is needed to characterize the exact nature of that awareness.” We hope this sheds light on our perspective while still being appropriately cautious not to go too far beyond our data.

      Overall: I believe there are many aspects of this set of studies that are innovative and I hope the methods will be used more broadly in the literature. However, I believe the authors misrepresent the field and overstate what can be interpreted from their results. While I am sure there are cases where more nuanced questions might reveal inattentional blindness is somewhat overestimated, claims like "the inattentionally blind can see after all" or "Inattentionally blind subjects consciously perceive thest stimuli after all" seem to be incorrect (or at least not at all proven by this data).

      Once again, we would like to thank this reviewer for his feedback, which obviously comes from a place of tremendous expertise on these issues. We appreciate his assessment that our studies are innovative and that our methodological advances will be of use more broadly. We also hear the reviewer loud and clear about the passages in question, which on reflection we agree are not as central to our case as the other claims we make (regarding residual sensitivity and conservative responding), and so we have now edited them accordingly to refocus our discussion on only those claims that are central and supported. Thank you for making our paper stronger!

      Reviewer #3 (Public review):

      Summary:

      Authors try to challenge the mainstream scientific as well as popularly held view that Inattentional

      Blindness (IB) signifies subjects having no conscious awareness of what they report not seeing (after being exposed to unexpected stimuli). They show that even when subjects indicate NOT having seen the unexpected stimulus, they are at above chance level for reporting features such as location, color or movement of these stimuli. Also, they show that 'not seen' responses are in part due to a conservative bias of subjects, i.e. they tend to say no more than yes, regardless of actual visibility. Their conclusion is that IB may not (always) be blindness, but possibly amnesia, uncertainty etc.

      We just thought to say that we felt this was a very accurate summary of our claims, and in ways underscore the modesty we had hoped to convey. This is especially true of the reviewer’s final sentence: “Their conclusion is that IB may not (always) be blindness, but possibly amnesia, uncertainty etc.”; as we noted in response to other reviewers, our claim is not that IB doesn’t exist, that subjects are always conscious of the stimulus, etc.; it is only that the cohort of IB subjects show sensitivity to the unattended stimulus in ways that suggest they are not as blind as traditionally conceived. Thank you for reading us as intended!

      Strengths:

      A huge pool of (25.000) subjects is used. They perform several versions of the IB experiments, both with briefly presented stimuli (as the classic Mack and Rock paradigm), as well as with prolonged stimuli moving over the screen for 5 seconds (a bit like the famous gorilla version), and all these versions show similar results, pointing in the same direction: above chance detection of unseen features, as well as conservative bias towards saying not seen.

      We’re delighted that the reviewer appreciated these strengths in our manuscript!

      Weaknesses:

      Results are all significant but effects are not very strong, typically a bit above chance. Also, it is unclear what to compare these effects to, as there are no control experiments showing what performance would have been in a dual task version where subjects have to also report features etc for stimuli that they know will appear in some trials

      The backdrop to the experiments reported here is the “consensus view” (Noah & Mangun, 2020) according to which inattention completely abolishes perception, such that subjects undergoing IB “have no awareness at all of the stimulus object” (Rock et al., 1992) and that “one can have one’s eyes focused on an object or event … without seeing it at all” (Carruthers, 2015). In this context, we think our findings of significant above-chance sensitivity (e.g., d′ = 0.51 for location in Experiment 1; chance, of course, would be d′ = 0 here) are striking and constitute strong evidence against the consensus view. We of course agree that the residual sensitivity is far lower than amongst subjects who noticed the stimulus. For this reason, we certainly believe that inattention has a dramatic impact on perception. To that extent, our data speak in favor of a “middle ground” view on which inattention substantially degrades but crucially does not abolish perception/explicit encoding. We see this as an importantly neglected option in a literature which has overly focused on seen/not seen binaries (see our section ‘Visual awareness as graded’).

      Regarding the absence of a control condition, we think those conditions wouldn’t have played the same role in our experiments as they typically play in other experiments. As Reviewer #1 comments, the main role of such trials in previous work has been to exclude from analysis subjects who failed to report the unexpected stimulus on the divided and/or full attention control trials. As Reviewer #1 points out, excluding such subjects would very likely have ‘helped’ us. However, the practice is controversial. Indeed, in a review of 128 experiments, White et al. 2018 argue that the practice has “problematic consequences” and “may lead researchers to understate the pervasiveness of inattentional blindness". Since we wanted to offer as simple and demanding a test of residual sensitivity in IB as possible, we thus decided not to use any exclusions, and for that reason decided not to include divided/full attention trials.

      As recommended, we discuss this decision not to include divided/full attention trials and our logic for not doing so in the manuscript. As we explain, not having those conditions makes it more impressive, not less impressive, that we observed the results we in fact did — it makes our results more interpretable, not less interpretable, and so absence of such conditions from our manuscript should not (in our view) be considered any kind of weakness.

      There are quite some studies showing that during IB, neural processing of visual stimuli continues up to high visual levels, for example, Vandenbroucke et al 2014 doi:10.1162/jocn_a_00530 showed preserved processing of perceptual inference (i.e. seeing a kanizsa illusion) during IB. Scholte et al 2006 doi: 10.1016/j.brainres.2005.10.051 showed preserved scene segmentation signals during IB. Compared to the strength of these neural signatures, the reported effects may be considered not all that surprising, or even weak.

      We agree that such evidence of neural processing in IB is relevant to — and perhaps indeed consistent with — our picture, and we’re grateful to the reviewer for pointing out further studies along those lines. Previously, we mentioned a study from Pitts et al., 2012 in which, as we wrote, “unexpected line patterns have been found to elicit the same Nd1 ERP component in both noticers and inattentionally blind subjects (Pitts et al., 2012).” We have added references to both the studies which the reviewer mentions – as well as an additional relevant study – to our manuscript in this context. Thank you for the helpful addition.

      We do however think that our studies are importantly different to this previous work. Our question is whether processing under IB yields representations which are available for explicit report and so would constitute clear evidence of seeing, and perhaps even conscious experience. As we discuss, evidence for this kind of processing remains wanting: “A handful of prior studies have explored the possibility that inattentionally blind subjects may retain some visual sensitivity to features of IB stimuli (e.g., Schnuerch et al., 2016; see also Kreitz et al., 2020, Nobre et al., 2020). However, a recent meta-analysis of this literature (Nobre et al., 2022) argues that such work is problematic along a number of dimensions, including underpowered samples and evidence of publication bias that, when corrected for, eliminates effects revealed by earlier approaches, concluding “that more evidence, particularly from well-powered pre-registered experiments, is needed before solid conclusions can be drawn regarding implicit processing during inattentional blindness” (Nobre et al., 2022).” Our paper is aimed at addressing this question which evidence of neural processing can only speak to indirectly.

      Recommendations for the authors:  

      Reviewer #1 (Recommendations for the authors):

      (1) Please report all of the data, especially the number of subjects in each experiment that answered Y/N and the numbers of subjects in each of the Y and N groups that guessed a feature correctly/incorrectly on the 2AFC tasks. And also the confidence ratings for the 2AFC task (for comparison with the confidence ratings on the Y/N questions).

      We now report all this data in our (revised) Supplementary Materials. We agree that this information will be helpful to readers.

      (2) Consider adding a control condition with partial attention (dual task) or full attention (single task) to estimate the rates of seeing the critical stimulus when it's expected.

      This is the only recommendation we have chosen not to implement. The reason, as we explain in detail above (especially in response to Reviewer #1 comment 5), is that this would not in fact be a “control condition” in our studies, and indeed would only inflate the biases we are concerned with in our work. As the referee comments, the main role of such trials in previous work has been to exclude from analysis subjects who failed to report the unexpected stimulus on the divided and/or full attention control trials. And the practice is controversial: Indeed, in a review of 128 experiments, White et al. 2018 argue that the practice has “problematic consequences” and “may lead researchers to understate the pervasiveness of inattentional blindness" (emphasis added). So, our choice not to have such conditions ensures an especially stringent test of our central claim. Not having those conditions (and their accompanying exclusions) makes our results more interpretable, not less interpretable, and so the absence of such conditions from our manuscript should not (in our view) be considered any kind of weakness.

      We have added a paragraph to our “Design and analytical approach” section explaining the logic behind our deliberate decision not to include divided or full attention trials in our experiments. (For even fuller discussion, see our response to Reviewer #1’s comment 5 above.)

      (3) Consider revising the interpretations to be more precise about the distinction between the super subject being above chance versus each individual subject who cannot be at chance or above chance because there was only a single trial per subject.

      We have now done this throughout the manuscript, as discussed above. We have also added a substantive additional discussion to our “Design and analytical approach” section discussing what should be said about individual subjects in light of our group level data.

      This was a very helpful point, and greatly clarifies the claims we wish to make in the paper. Thank you for this comment, which has certainly made our paper stronger.

      Reviewer #2 (Recommendations for the authors):

      I would be curious to hear the authors' response to two points:

      (1) What do they have to say about prior studies that do more than just ask yes/no questions (and ask several follow-ups)? Are those studies "valid"?

      A very substantial new discussion of this important point has been added. As you will see above, we comment on every one of the 18 papers this reviewer raised (as well as the general argument made); we contend that while many of these papers improve on past methodology in various ways, most in fact do “just ask yes/no questions”, and none of them makes the methodological advance we offer in our manuscript. However, this discussion has helped us clarify that very advance, and so working through this issue has really helped us improve our paper and make its relation to existing literature that much clearer. Thank you for raising this crucial point.

      (2) Do the authors think it is possible that in many cases, people are just guessing about a critical item's location or color and this is at least in part a form of priming?

      We have clarified our discussion in numerous places to further emphasize that our main point concerns above-chance sensitivity, not awareness. Given this, we take very seriously the hypothesis that something like priming of a kind sometimes proposed to occur in cases of blindsight or other putative cases of unconscious perception could be what is driving the responses in non-noticers.

      Reviewer #3 (Recommendations for the authors):

      (1) Control dual task version with expected stimuli would be nice

      We have added a paragraph to our “Design and analytical approach” section explaining the logic behind our deliberate decision not to include divided or full attention trials, which would not in fact be a “control” task in our experiments. For full discussion, see our response to Reviewer 3 above, as well as our summary here in the Recommendations for Authors section in responding to Reviewer 1, recommendation (2).

      (2) Please do a better job in discussing and introducing experiments about neural signatures during IB.

      A discussion of Vandenbroucke et al. 2014 and Scholte et al. 2006 has been added to our discussion of neural signatures in IB, as well as an additional reference to an important early study of semantic processing in IB (Rees et al., 1999). Thank you for these very helpful suggestions!

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer 1:

      (1) The notion of a “root” causal gene - which the authors define based on a graph theoretic notion of topologically sorting graphs - requires a graph that is directed and acyclic. It is the latter that constitutes an important weakness here - it simply is a large simplification of human biology to draw out a DAG including hundreds of genes and a phenotype Y and to claim that the true graph contains no cycles.

      We agree that real causal graphs in biology often contain cycles. We now include additional experimental results with cyclic directed graphs in the Supplementary Materials. RCSP outperformed the other algorithms even in this setting, but we caution the reader that the theoretical interpretation of the RCS score may not coincide with a root causal effect when cycles exist:

      “We also evaluated the algorithms on directed graphs with cycles. We generated a linear SEM over ρ + 1 = 1000 variables in . We sampled the coefficient matrix β from a Bernoulli (1/(p − 1)) distribution but did not restrict the non-zero coefficients to the upper triangular portion of the matrix. We then proceeded to permute the variable ordering and weight each entry as in the Methods for the DAG. We repeated this procedure 30 times and report the results in Supplementary Figure 3.

      RCSP again outperformed all other algorithms even in the cyclic case. The results suggest that conditioning on the surrogate ancestors also estimates the RCS well even in the cyclic case. However, we caution that an error term E<sub>i</sub> can affect the ancestors of when cycles exist. As a result, the RCS may not isolate the causal effect of the error term and thus not truly coincide with the notion of a root causal effect in cyclic causal graphs.”

      (2) I also encourage the authors to consider more carefully when graph structure learned from Perturb-seq can be ported over to bulk RNA-seq. Presumably this structure is not exactly correct - to what extent is the RCSP algorithm sensitive to false edges in this graph? This leap - from cell line to primary human cells - is also not modeled in the simulation. Although challenging - it would be ideal for the RCSP to model or reflect the challenges in correctly identifying the regulatory structure.

      We now include additional experimental results, where we gradually increased the incongruence between the DAG modeling the Perturb-seq and the DAG modeling the bulk RNA-seq using a mixture of graphs. The performance of RCSP degraded gradually, rather than abruptly, with increasing incongruence. We therefore conclude that RCSP is robust to differences between the causal graphs representing Perturb-seq and bulk RNA-seq:

      “We next assessed the performance of RCSP when the DAG underlying the Perturb-seq data differs from the DAG underlying the bulk RNA-seq data. We considered a mixture of two random DAGs in bulk RNA-seq, where one of the DAGs coincided with the Perturb-seq DAG and second alternate DAG did not. We instantiated and simulated samples from each DAG as per the previous subsection. We generated 0%, 25%, 50%, 75%, and 100% of the bulk RNA-seq samples from the alternate DAG, and the rest from the Perturb-seq DAG. We ideally would like to see the performance of RCSP degrade gracefully, as opposed to abruptly, as the percent of samples derived from the alternate DAG increases.

      We summarize results in Supplementary Figure 4. As expected, RCSP performed the best when we drew all samples from the same underlying DAG for Perturb-seq and bulk RNA-seq. However, the performance of RCSP also degraded slowly as the percent of samples increased from the alternate DAG. We conclude that RCSP can accommodate some differences between the underlying DAGs in Perturb-seq and bulk RNA-seq with only a mild degradation in performance.”

      (3) It should also be noted that in most Perturb-seq experiments, the entire genome is not perturbed, and frequently important TFs (that presumably are very far “upstream” and thus candidate “root” causal genes) are not expressed highly enough to be detected with scRNA-seq. In that context - perhaps slightly modifying the language regarding RCSP’s capabilities might be helpful for the manuscript - perhaps it would be better to describe it as an algorithm for causal discovery among a set of genes that were perturbed and measured, rather than a truly complete search for causal factors. Perhaps more broadly it would also benefit the manuscript to devote slightly more text to describing the kinds of scenarios where RCSP (and similar ideas) would be most appropriately applied - perhaps a well-powered, phenotype annotated Perturb-seq dataset performed in a disease relevant primary cell.

      We now clarify that Perturb-seq can only identify root causal genes among the perturbed set of genes in the Discussion:

      “Modern genome-wide Perturb-seq datasets also adequately perturb and measure only a few thousand, rather than all, gene expression levels. RCSP can only identify root causal genes within this perturbed and measured subset.”

      We now also describe the scenario where RCSP can identify root causal genes well in the Introduction:

      “Experiments demonstrate marked improvements in performance, when investigators have access to a large bulk RNA-seq dataset and a genome-wide Perturb-seq dataset from a cell line of a disease-relevant tissue.”

      Reviewer 2:

      (1) The process from health-to-disease is not linear most of the time with many checks along the way that aim to prevent the disease phenotype. This leads to a non-deterministic nature of the path from health-to-disease. In other words, with the same root gene perturbations, and depending on other factors outside of gene expression, someone may develop a phenotype in a year, another in 10 years and someone else never. Claiming that this information is included in the error terms might not be sufficient to address this issue. The authors should discuss this limitation.

      The proposed approach accommodates the above non-deterministic nature. The error terms of model factors that are outside of gene expression. We model the relation from gene expression to Y as probabilistic rather than deterministic because , where E<sub>Y</sub> introduces stochasticity. Thus, two individuals with the same instantiations of the root causes may develop disease differently. We now clarify this in Methods:

      “The error terms model root causes that are outside of gene expression, such as genetic variation or environmental factors. Moreover, the relation from gene expression to Y is stochastic because , where E<sub>Y</sub> introduces the stochasticity. Two individuals may therefore have the exact same error term values over but different instantiations of Y.”

      (2) The paper assumes that the network connectivity will remain the same after perturbation. This is not always true due to backup mechanisms in the cells. For example, suppose that a cell wants to create product P and it can do it through two alternative paths: Path #1: ABP, Path #2: ACP. Now suppose that path #1 is more efficient, so when B can be produced, path #2 is inactive. Once the perturbation blocks element B from being produced, the graph connectivity changes by activation of path #2. I did not see the authors taking this into consideration, which seems to be a major limitation in using Perturb-seq results to infer conductivities.

      We agree that backup mechanisms can exist and therefore now include additional experimental results, where we gradually increased the incongruence between the DAG modeling the Perturb-seq and the DAG modeling the bulk RNA-seq using a mixture of graphs. The performance of RCSP degraded gradually, rather than abruptly, with increasing incongruence. We therefore conclude that RCSP is robust to differences between the causal graphs representing Perturb-seq and bulk RNA-seq:

      “We next assessed the performance of RCSP when the DAG underlying the Perturb-seq data differs from the DAG underlying the bulk RNA-seq data. We considered a mixture of two random DAGs in bulk RNA-seq, where one of the DAGs coincided with the Perturb-seq DAG and second alternate DAG did not. We generated 0%, 25%, 50%, 75%, and 100% of the bulk RNA-seq samples from the alternate DAG, and the rest from the Perturb-seq DAG. We ideally would like to see the performance of RCSP degrade gracefully, as opposed to abruptly, as the percent of samples derived from the alternate DAG increases.

      We summarize results in Supplementary Figure 4. As expected, RCSP performed the best when we drew all samples from the same underlying DAG for Perturb-seq and bulk RNA-seq. However, the performance of RCSP also degraded slowly as the percent of samples increased from the alternate DAG. We conclude that RCSP can accommodate some differences between the underlying DAGs in Perturb-seq and bulk RNA-seq with only a mild degradation in performance.”

      (3) There is substantial system heterogeneity that may cause the same phenotype. This goes beyond the authors claim that although the initial gene causes of a disease may differ from person to person, at some point they will all converge to changes in the same set of “root genes.” This is not true for many diseases, which are defined based on symptoms and lab tests at the patient level. You may have two completely different molecular pathologies that lead to the development of the same symptoms and test results. Breast cancer with its subtypes is a prime example of that. In theory, this issue could be addressed if there is infinite sample size. However, this assumption is largely violated in all existing biological datasets.

      The proposed method accommodates the above heterogeneity. We do not assume that the root causes affect the same set of root causal genes. Instead the root causes and root causal genes may vary from person to person. We write in the Introduction:

      “The problem is further complicated by the existence of complex disease, where a patient may have multiple root causal genes that differ from other patients even within the same diagnostic category... We thus also seek to identify patient-specific root causal genes in order to classify patients into meaningful biological subgroups each hopefully dictated by only a small group of genes.”

      The root causal genes may further affect different downstream genes at the patient-specific level. However root causal genes tend to have many downstream effects so that virtually every gene expression level becomes correlated with Y. We now clarify this by describing the omnigenic root causal model in the Introduction as follows:

      “Finally, application of the algorithm to two complex diseases with disparate pathogeneses recovers an omnigenic root causal model, where a small set of root causal genes drive pathogenesis but impact many downstream genes within each patient. As a result, nearly all gene expression levels are correlated with the diagnosis at the population level.”

      (4) Were the values of the synthetic variables Z-scored?

      Yes, all variables were z-scored. We now clarify this in Methods:

      “We also standardized all variables before running the regressions to prevent gaming of the marginal variances in causal discovery (Reisach et al., 2021; Ng et al., 2024).”

      (5) The algorithm seems to require both RNA-seq and Perturb-seq data (Algorithm 1, page 14). Can it function with RNA-seq data only? What will be different in this case?

      The algorithm cannot function with observational bulk RNA-seq data only. We included Perturb-seq because causal discovery with observational RNA-seq data alone tends to be inaccurate and unstable, as highlighted by the results of CausalCell. We further emphasize that we do not rely on d-separation faithfulness in Methods, which is typically required for causal discovery from observational data alone:

      “We can also claim the backward direction under d-separation faithfulness. We however avoid making this additional assumption because real biological data may not arise from distributions obeying d-separation faithfulness in practice.”

      (6) Synthetic data generation: how many different graphs (SEMs) did they start from? (30?) How many samples per graph? Did they test different sample sizes?

      We now clarify that we generate 30 random SEMs, each associated with a DAG. We used 200 samples for the bulk RNA-seq to mimic a relatively large but common sample size. We also drew 200 samples for each perturbation or control in the Perturb-seq data. We did not consider multiple sample sizes due to the time required to complete each run. Instead, we focused on a typical scenario where investigators would apply RCSP. We now write the following in the Methods:

      “We drew 200 samples for the bulk RNA-seq data to mimic a large but common dataset size. We introduced knockdown perturbations in Perturb-seq by subtracting an offset of two in the softplus function: . We finally drew 200 samples for the control and each perturbation condition to generate the Perturb-seq data. We repeated the above procedure 30 times.” We also include the following in Results:

      “We obtained 200 cell samples from each perturbation, and another 200 controls without perturbations. We therefore generated a total of 2501 × 200 = 500,200 single cell samples for each Perturb-seq dataset. We simulated 200 bulk RNA-seq samples.”

      (7) The presentation of comparative results (Supplementary Figures 4 and 7) is not clear. No details are given on how these results were generated. (what does it mean “The first column denotes the standard deviation of the outputs for each algorithm?”) Why all other methods have higher SD differences than RCSP? Is it a matter of scaling? Shouldn’t they have at least some values near zero since the authors “added the minimum value so that all histograms begin at zero?”

      Each of these supplementary figures contains a 6 by 3 table of figures. By the first column, we mean column one (with rows 1 through 6) of each figure. The D-RCS and D-SD scores represent standard deviations of the RCS and SD scores from zero of each gene, respectively. We can similarly compute the standard deviation of the outputs of the algorithms. We now clarify this in the Supplementary Materials:

      “The figure contains 6 rows and 3 columns. Similar to the D-RCS, we can compute the standard deviation of the output of each algorithm from zero for each gene. The first column in Supplementary Figure 7 denotes the histograms of these standard deviations across the genes.”

      Many histograms do not appear to start at zero because the bars are too small to be visible. We now clarify this in the Supplementary Materials as well:

      “Note that the bars at zero are not visible for many algorithms, since only a few genes attained standard deviations near the minimum.”

      (8) Why RCSP results are more like a negative binomial distribution and every other is kind of normal?

      All other methods have higher standard deviations than RCSP because they fail to compute an accurate measure of the root causal effect. Recall that, just like a machine has a few root causal problems, only a few root casual genes have large root causal effects under the omnigenic root causal model. The results of RCSP look more like a negative binomial distribution because most RCS scores are concentrated around zero and only a few RCS scores are large – consistent with the omnigenic root causal model. The other algorithms fail to properly control for the upstream genes and thus attain large standard deviations for nearly all genes. We now clarify these points in the Supplementary Materials as follows:

      “If an algorithm accurately identifies root causal genes, then it should only identify a few genes with large conditional root causal effects under the omnigenic root causal model. The RCSP algorithm had a histogram with large probability mass centered around zero with a long tail to the right. The standard deviations of the outputs of the other algorithms attained large values for nearly all genes. Incorporating feature selection and causal discovery with CausalCell introduced more outliers in the histogram of ANM. We conclude that only RCSP detected an omnigenic root causal model.”

      (9) What is the significance of genes changing expression “from left to right” in a UMAP plot? (e.g., Fig. 3h and 3g)

      The first UMAP dimension captured the variability of the RCS scores for most root causal genes. As a result, we could focus our analysis on the black cluster in Figure 3 (g) with large RCS scores in the subsequent pathway enrichment analysis summarized in Figure 3 (j). If two dimensions were involved, then we would need to analyze at least two clusters (e.g., black and pink), but this was not the case. We now clarify this in Results:

      “The RCS scores of most of the top genes exhibited a clear gradation increasing only from the left to the right hand side of the UMAP embedding; we plot an example in Figure 3 (h). We found three exceptions to this rule among the top 30 genes (example in Figure 3 (i) and see Supplementary Materials). RCSP thus detected genes with large RCS scores primarily in the black cluster of Figure 3 (g). Pathway enrichment analysis within this cluster alone yielded supra-significant results on the same pathway detected in the global analysis...”

      (10) The authors somewhat overstate the novelty of their algorithm. Representation of GRNs as causal graphs dates back in 2000 with the work of Nir Friedman in yeast. Other methods were developed more recently that look on regulatory network changes at the single sample level which the authors do not seem to be aware (e.g., Ellington et al, NeurIPS 2023 workshop GenBio and Bushur et al, 2019, Bioinformatics are two such examples). The methods they mention are for single cell data and they are not designed to connect single sample-level changes to a person’s phenotype. The RCS method needs to be put in the right background context in order to bring up what is really novel about it.

      We agree that many methods already exist for uncovering associational, predictive (Markov, neighborhood) and causal gene regulatory networks. We now cite the above papers. However, the novelty in our manuscript is not causal graph discovery, but rather estimation of root causal effects, detection of root causal genes, and the proposal of the omnigenic root causal model. We now clarify this in the

      Introduction:

      “Many algorithms focus on discovering associational or predictive relations, sometimes visually represented as gene regulatory networks (Costa et al., 2017; Ellington et al., 2023). Other methods even identify causal relations (Friedman et al., 2000; Wang et al., 2023; Wen et al., 2000; Buschur et al., 2000), but none pinpoint the first gene expression levels that ultimately generate the vast majority of pathogenesis. Simply learning a causal graph does not resolve the issue because causal graphs do not summarize the effects of unobserved root causes, such as unmeasured environmental changes or variants, that are needed to identify all root causal genes. We therefore define the Root Causal Strength (RCS) score...”

      Reviewer 3:

      (1) Several assumptions of the method are problematic. The most concerning is that the observational expression changes are all causally upstream of disease. There is work using Mendelian randomization (MR) showing that the opposite is more likely to be true: most differential expression in disease cohorts is a consequence rather than a cause of disease (Porcu et al., 2021). Indeed, the oxidative stress of AMD has known cellular responses including the upregulation of p53. The authors need to think carefully about how this impacts their framework. Can the theory say anything in this light? Simulations could also be designed to address robustness.

      Strictly speaking, we believe that differential expression in disease most likely has a cyclic causal structure: gene expression causes a diagnosis or symptom severity, and a diagnosis or symptom severity lead to treatments and other behavioral changes that perturb gene expression. For example, revTMWR in Porcu et al. (2021) uses trans-variants that are less likely to directly cause gene expression and instead directly cause a phenotype. However, TWMR as proposed in Porcu et al. (2019) instead uses cis-eQTLs and finds many putative causal relations from gene expression to phenotype. Thus, both causal directions likely hold.

      RCSP uses disease-relevant tissue believed to harbor gene expression levels that cause disease. However, RCSP theoretically cannot handle the scenario where Y is a non-sink vertex and is a parent of a gene expression level because modern Perturb-seq datasets usually do not perturb or measure Y. We therefore empirically investigated the degree of error by running experiments, where we set Y to a non-sink vertex, so that it can cause gene expression. We find that the performance of RCSP degrades considerably for gene expression levels that contain Y as a parent. Thus RCSP is sensitive to violations of the sink target assumption:

      “We finally considered the scenario where Y is a non-sink (or non-terminal) vertex. If Y is a parent of a gene expression level, then we cannot properly condition on the parents because modern Perturbseq datasets usually do not intervene on Y or measure Y . We therefore empirically investigated the degradation in performance resulting from a non-sink target Y, in particular for gene expression levels where Y is a parent. We again simulated 200 samples from bulk RNA-seq and each condition of Perturbseq with a DAG over 1000 vertices, an expected neighborhood size of 2 and a non-sink target Y . We then removed the outgoing edges from Y and resampled the DAG with a sink target. We compare the results of RCSP for both DAGs in gene expression levels where Y is a parent. We plot the results in Supplementary Figure 5. As expected, we observe a degradation in performance when Y is not terminal, where the mean RMSE increased from 0.045 to 0.342. We conclude that RCSP is sensitive to violations of the sink target assumption.”

      (2) A closely related issue is the DAG assumption of no cycles. This assumption is brought to bear because it is required for much classical causal machinery, but is unrealistic in biology where feedback is pervasive. How robust is RCSP to (mild) violations of this assumption? Simulations would be a straightforward way to address this.

      We agree that real causal graphs in biology often contain cycles. We now include additional experimental results with cyclic directed graphs in the Supplementary Materials. RCSP outperformed the other algorithms even in this setting, but we caution the reader that the theoretical interpretation of the RCS score may not coincide with a root causal effect when cycles exist:

      “We also evaluated the algorithms on directed graphs with cycles. We generated a linear SEM over p + 1 = 1000 variables in . We sampled the coefficient matrix β from a Bernoulli (1/(p − 1)) distribution but did not restrict the non-zero coefficients to the upper triangular portion of the matrix. We then proceeded to permute the variable ordering and weight each entry as in the Methods for the DAG. We repeated this procedure 30 times and report the results in Supplementary Figure 3.

      RCSP again outperformed all other algorithms even in the cyclic case. The results suggest that conditioning on the surrogate ancestors also estimates the RCS well even in the cyclic case. However, we caution that an error term E<sub>i</sub> can affect the ancestors of , when cycles exist. As a result, the RCS may not isolate the causal effect of the error term and thus not truly coincide with the notion of a root causal effect in cyclic causal graphs.”

      (3) The authors spend considerable effort arguing that technical sampling noise in X can effectively be ignored (at least in bulk). While the mathematical arguments here are reasonable, they miss the bigger picture point that the measured gene expression X can only ever be a noisy/biased proxy for the expression changes that caused disease: 1) Those events happened before the disease manifested, possibly early in development for some conditions like neurodevelopmental disorders. 2) bulk RNA-seq gives only an average across cell-types, whereas specific cell-types are likely “causal.” 3) only a small sample, at a single time point, is typically available. Expression in other parts of the tissue and at different times will be variable.

      We agree that many other sources of error exist. The causal model of RNA-expression in Methods corresponds to a single snapshot in time for each sample. We now clarify this in the Methods as follows:

      “We represent a snapshot of a biological causal process using an SEM over obeying Equation (3).”

      We thus only detect the root causal genes in a single snapshot in time for each sample in bulk RNA-seq. If we cannot detect the root causal effect in a gene due to the signal washing out over time as in (1), or if the root causal effect in different cell types cancel each other out to exactly zero in bulk as in (2), then we cannot detect those root causal genes even with an infinite sample size.

      (4) While there are connections to the omnigenic model, the latter is somewhat misrepresented. The authors refer to the “core genes” of the omnigenic model as being at the end (longitudinal) of pathogenesis. The omnigenic model makes no statements about temporal ordering: in causal inference terminology the core genes are simply the direct causes of disease.

      We now clarify that we use the word pathogenesis to mean the causal cascade from root causes to the diagnosis. In this case, the direct causes of the diagnosis correspond to the end of pathogenesis, while the root causes correspond to the beginning. For example, if , with Y a diagnosis, then X<sub>1</sub> is a root causal gene while X<sub>2</sub> is a core (direct causal) gene. We now clarify this in the Introduction:

      Root causes of disease correspond to the most upstream causes of a diagnosis with strong causal effects on the diagnosis. Pathogenesis refers to the causal cascade from root causes to the diagnosis. Genetic and non-genetic factors may act as root causes and affect gene expression as an intermediate step during pathogenesis. We introduce root causal gene expression levels – or root causal genes for short – that correspond to the initial changes to gene expression induced by genetic and non-genetic root causes that have large causal effects on a downstream diagnosis (Figure 1 (a)). Root causal genes differ from core genes that directly cause the diagnosis and thus lie at the end, rather than at the beginning, of pathogenesis (Boyle et al., 2017).”

      (5) A key observation underlying the omnigenic model is that genetic heritability is spread throughout the genome (and somewhat concentrated near genes expressed in disease relevant cell types). This implies that (almost) all expressed genes, or their associated (e)SNPs, are “root causes”.

      We now clarify that genetic heritability can be spread throughout the genome in the omnigenic root causal model as well in the Discussion:

      “Further, each causal genetic variant tends to have only a small effect on disease risk in complex disease because the variant can directly cause Y or directly cause any causal gene including those with small root causal effects on Y ; thus, all error terms that cause Y can model genetic effects on Y. However, the root causal model further elaborates that genetic and non-genetic factors often combine to produce a few root causal genes with large root causal effects, where non-genetic factors typically account for the majority of the large effects in complex disease. Many variants may therefore cause many genes in diseases with only a few root causal genes.”

      We finally add Figure 5 into the Discussion as a concrete example illustrating the omnigenic root causal model:

      (6) The claim that root causal genes would be good therapeutic targets feels unfounded. If these are highly variable across individuals then the choice of treatment becomes challenging. By contrast the causal effects may converge on core genes before impacting disease, so that intervening on the core genes might be preferable. The jury is still out on these questions, so the claim should at least be made hypothetical.

      We clarify that we do not claim that root causal genes are better treatment targets than core genes in terms of magnitudes of causal effects on the phenotype. For example, in the common cold with a virus as the root cause, giving a patient an antiviral will eliminate fever and congestion, but so will giving a decongestant and an antipyretic. We only claim that treating root causal genes can eliminate disease near its pathogenic onset, just like giving an antiviral can eliminate the viral load and stop pathogenesis. We write the following the Introduction:

      “Treating root causal genes can modify disease pathogenesis in its entirety, whereas targeting other causes may only provide symptomatic relief... Identifying root causal genes is therefore critical for developing treatments that eliminate disease near its pathogenic onset.”

      We also further clarify in the Discussion that root causal genes account for deleterious causal effects not captured by the diagnosis Y:

      “We finally emphasize that the root causal model accounts for all deleterious effects of the root causal genes, whereas the core gene model only captures the deleterious effects captured by the diagnosis Y. For example, the disease of diabetes causes retinopathy, but retinopathy is not a part of the diagnostic criteria of diabetes. As a result, the gene expression levels that cause retinopathy but not the diagnosis of diabetes are not core genes, even though they are affected by the root causal genes.”

      We do agree that root causal genes may differ substantially between patients, although it is unclear if the heterogeneity is too great to develop treatments.

      (7) The closest thing to a gold standard I believe we have for “root causal genes” is integration of molecular QTLs and GWAS, specifically coloc/MR. Here the “E” of RCSP are explicitly represented as SNPs. I don’t know if there is good data for AMD but there certainly is for MS. The authors should assess the overlap with their results. Another orthogonal avenue would be to check whether the root causal genes change early in disease progression.

      Colocalization and Mendelian randomization unfortunately cannot identify root causal effects because they all attempt, either heuristically (colocalization) or rigorously (MR), to identify variants that cause each gene expression level rather than variants that directly cause each gene expression level and thus make up the error terms. We therefore need new methods that can identify direct causal variants in order to assess overlap.

      We checked whether root causal genes change early in disease progression using knowledge of pathogenesis. In particular, oxidative stress induces pathogenesis in AMD, and RCSP identified root causal genes involved in oxidative stress in AMD:

      “The pathogenesis of AMD involves the loss of RPE cells. The RPE absorbs light in the back of the retina, but the combination of light and oxygen induces oxidative stress, and then a cascade of events such as immune cell activation, cellular senescence, drusen accumulation, neovascularization and ultimately fibrosis (Barouch et al., 2007). We therefore expect the root causal genes of AMD to include genes involved in oxidative stress during early pathogenesis. The gene MIPEP with the highest D-RCS score in Figure 3 (d) indeed promotes the maturation of oxidative phosphorylation-related proteins (Shi et al., 2011). The second gene SLC7A5 is a solute carrier that activates mTORC1 whose hyperactivation increases oxidative stress via lipid peroxidation (Nachef et al., 2021; Go et al., 2020). The gene HEATR1 is involved in ribosome biogenesis that is downregulated by oxidative stress (Turi et al., 2018). The top genes discovered by RCSP thus identify pathways known to be involved in oxidative stress.”

      Similarly, T cell infiltration across the blood brain barrier initiates pathogenesis in MS, and RCSP identified root causal genes involved in this infiltration:

      “Genes with the highest D-RCS scores included MNT, CERCAM and HERPUD2 (Figure 4 (d)). MNT is a MYC antagonist that modulates the proliferative and pro-survival signals of T cells after engagement of the T cell receptor (Gnanaprakasam et al., 2017). Similarly, CERCAM is an adhesion molecule expressed at high levels in microvessels of the brain that increases leukocyte transmigration across the blood brain barrier (Starzyk et al., 2000). HERPUD2 is involved in the endoplasmic-reticulum associated degradation of unfolded proteins (Kokame et al., 2000). Genes with the highest D-RCS scores thus serve key roles in known pathogenic pathways of MS.”

      (8) The available Perturb-seq datasets have limitations beyond on the control of the authors. 1) The set of genes that are perturbed. The authors address this by simply sub-setting their analysis to the intersection of genes represented in the perturbation and observational data. However, this may mean that a true ancestor of X is not modeled/perturbed, limiting the formal claims that can be made. Additionally, some proportion of genes that are nominally perturbed show little to no actual perturbation effect (for example, due to poor guide RNA choice) which will also lead to missing ancestors.

      We now clarify that Perturb-seq can only identify root causal genes among the adequately perturbed set of genes in the Discussion:

      “Modern genome-wide Perturb-seq datasets also only adequately perturb and measure a few thousand, rather than all, gene expression levels. RCSP can only identify root causal genes within this perturbed and measured subset.”

      (9) The authors provide no mechanism for statistical inference/significance for their results at either the individual or aggregated level. While I am a proponent of using effect sizes more than p-values, there is still value in understanding how much signal is present relative to a reasonable null.

      We now explain that RCSP does not perform statistical inference in Methods because it is not clear how to define the appropriate cut-off for the RCS score under the null distribution:

      “We focus on statistical estimation rather than statistical inference because Φ<sub>i</sub> > 0 when E<sub>i</sub> causes Y under mild conditions, so we reject the null hypothesis that Φ<sub>i</sub> \= 0 for many genes if many gene expression levels cause Y. However, just like a machine typically breaks down due to only one or a few root causal problems, we hypothesize that only a few genes have large RCS scores Φ<sub>i</sub> ≫ 0 even in complex disease.”

      (10) I agree with the authors that age coming out of a “root cause” is potentially encouraging. However, it is also quite different in nature to expression, including being “measured” exactly. Will RCSP be biased towards variables that have lower measurement error?

      We tested the above hypothesis by plotting sequencing depth against the D-RCS scores of each gene. We observed a small negative correlation between sequencing depth and D-RCS scores, indicating the D-RCS scores are slightly biased upwards with low sequencing depth. However, genes with the largest D-RCS scores exhibited a wide variety of sequencing depths in both MS and AMD, suggesting that sequencing depth has minimal effect on the largest D-RCS scores. We now explain these results for AMD in the Supplementary Materials:

      “Theorem 1 states that RCS scores may exhibit bias with insufficient sequencing depth. The genes with large D-RCS scores may therefore simply have low sequencing depths. To test this hypothesis, we plotted sequencing depth against D-RCS scores. Consistent with Theorem 1, we observed a small negative correlation between D-RCS and sequencing depth (ρ \= −0.16, p=2.04E-13), and D-RCS scores exhibited greater variability at the lowest sequencing depths (Supplementary Figure 8). However, genes with the largest D-RCS scores had mean sequencing depths interspersed between 20 and 3000. We conclude that genes with the largest D-RCS scores had a variety of sequencing depths ranging from low to high.”

      We also report the results for MS:

      “We plot sequencing depth against the D-RCS scores of each gene similar to the AMD dataset. We again observed a small negative correlation (ρ \= −0.136, p_<_2.2E-16), indicating that genes with low sequencing depths had slightly higher D-RCS scores on average (Supplementary Figure 12). However, genes with the largest D-RCS scores again had a variety of sequencing depths. We conclude that sequencing depth has minimal correlation with the largest D-RCS scores.”

      (11) Finally, it’s a stretch to call K562 cells “lymphoblasts.” They are more myeloid than lymphoid.

      We now clarify that K562 cells are undifferentiated blast cells that can be induced to differentiate into lymphoblasts in Results:

      “We next ran RCSP on 137 samples collected from CD4+ T cells of multiple sclerosis (MS; GSE137143) as well as Perturb-seq data of 1,989,578 undifferentiated blast cells that can be induced to differentiate into lymphoblasts, or the precursors of T cells and other lymphocytes.”

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this manuscript, Dong et al. study the directed cell migration of tracheal stem cells in Drosophila pupae. The migration of these cells which are found in two nearby groups of cells normally happens unidirectionally along the dorsal trunk towards the posterior. Here, the authors study how this directionality is regulated. They show that inter-organ communication between the tracheal stem cells and the nearby fat body plays a role. They provide compelling evidence that Upd2 production in the fat body and JAK/STAT activation in the tracheal stem cells play a role. Moreover, they show that JAK/STAT signalling might induce the expression of apicobasal and planar cell polarity genes in the tracheal stem cells which appear to be needed to ensure unidirectional migration. Finally, the authors suggest that trafficking and vesicular transport of Upd2 from the fat body towards the tracheal cells might be important.

      Strengths:

      The manuscript is well written. This novel work demonstrates a likely link between Upd2JAK/STAT signalling in the fat body and tracheal stem cells and the control of unidirectional cell migration of tracheal stem cells. The authors show that hid+rpr or Upd2RNAi expression in a fat body or Dome RNAi, Hop RNAi, or STAT92E RNAi expression in tracheal stem cells results in aberrant migration of some of the tracheal stem cells towards the anterior. Using ChIP-seq as well as analysis of GFP-protein trap lines of planar cell polarity genes in combination with RNAi experiments, the authors show that STAT92E likely regulates the transcription of planar cell polarity genes and some apicobasal cell polarity genes in tracheal stem cells which appear to be needed for unidirectional migration. Moreover, the authors hypothesise that extracellular vesicle transport of Upd2 might be involved in this Upd2-JAK/STAT signalling in the fat body and tracheal stem cells, which, if true, would be quite interesting and novel.

      Overall, the work presented here provides some novel insights into the mechanism that ensures unidirectional migration of tracheal stem cells that prevents bidirectional migration. This might have important implications for other types of directed cell migration in invertebrates or vertebrates including cancer cell migration.

      Weaknesses:

      It remains unclear to what extent Upd2-JAK/STAT signalling regulates unidirectional migration. While there seems to be a consistent phenotype upon genetic manipulation of Upd2-JAK/STAT signalling and planar cell polarity genes, as in the aberrant anterior migration of a fraction of the cells, the phenotype seems to be rather mild, with the majority of cells migrating towards the posterior.

      We agree that the phenotype is mild, as perturbing JAK/STAT signaling in the progenitors specifically affects the coordinated migration of the cells rather than alters their direction or completely blocks migration. Our data indicate that inter-organ communication ensures coordinated behavior of the progenitor cells, although the differential responses exhibited by individual cells represent an interesting unresolved issue that awaits future in-depth investigation.

      While I am not an expert on extracellular vesicle transport, the data presented here regarding Upd2 being transported in extracellular vesicles do not appear to be very convincing.

      We performed additional PLA experiments which support the interaction between Upd2 and the core components of extracellular vesicles (revised Figure 8). Furthermore, we performed electron microscopy to visualize the Lbm-containing vesicles in fat body (Figure 8-figure supplement 1D).

      These data are now provided in the revised manuscript.

      Major comments:

      (1) The graphs showing the quantification of anterior (and in some cases also posterior migration) are quite confusing. E.g. Figure 1F (and 5E and all others): These graphs are difficult to read because the quantification for the different conditions is not shown separately. E.g. what is the migration distance for Fj RNAi anterior at 3h in Fig5E? Around -205micron (green plus all the other colors) or around -70micron (just green, even though the green bar goes to -205micron). If it's -205micron, then the images in C' or D' do not seem to show this strong phenotype. If it's around -70, then the way the graph shows it is misleading, because some readers will interpret the result as -205. Moreover, it's also not clear what exactly was quantified and how it was quantified. The details are also not described in the methods. It would be useful, to mark with two arrowheads in the image (e.g. 5 A' -D') where the migration distance is measured (anterior margin and point zero).

      Overall, it would be better, if the graph showed the different conditions separately. Also, n numbers should be shown in the figure legend for all graphs.

      We apologize for those inappropriate presentation and insufficient description and thank you for kindly pointing them out. We used different colors to represent different genotypes, and the columns were superimposed. we chose to show the quantification in different conditions separately in the revised Figures. The anterior migration distance for Fj RNAi is around 70 µm.

      We now provided detailed description in the revised methods. For migration distance measurement, we took snapshots at 0hr\ 1hr\ 2hr and 3hr, and measured the distance from the starting point (the junction of TC and DT) to the leading edge of progenitor clusters. The velocity formula: v=d (micrometer)/t (min). As you kindly suggested, we indicated the anterior margin and point zero in the corresponding panels. We have added n number in the legends.

      (2) Figure 2-figure supplement 1: C-L and M: From these images and graph it appears that Upd2 RNAi results in no aberrant anterior migration. Why is this result different from Figures 2D-F where it does?

      The fat body-expressing lsp2-Gal4 was used in Figure 2-figure supplement 1C-L and Figure 2D-F, while trachea specific btl-Gal4 was used in Figure 2-figure supplement 1K-L. The lsp2-Gal4-driven but not btl-Gal4-driven upd2RNAi causes aberrant anterior migration, suggesting that fat bodyderived Upd2 plays a role. We have further clarified this in the text.

      (3) Figure 5F: The data on the localisation of planar cell polarity proteins in the tracheal stem cell group is rather weak. Figure 5G and J should at least be quantified for several animals of the same age for each genotype. Is there overall more Ft-GFP in the cells on the posterior end of the cell group than on the opposite side? Or is there a more classic planar cell polarity in each cell with FtGFP facing to the posterior side of the cell in each cell? Maybe it would be more convincing if the authors assessed what the subcellular localisation of Ft is through the expression of Ft-GFP in clones to figure out whether it localises posteriorly or anteriorly in individual cells.

      We staged the animals, measured several animals for each genotype and provided the quantifications in the revised manuscript. The level of Ft-GFP is higher in the cells at the frontal edge. We tried to examine the expression of Ft-GFP at single-cell level. However, this turned out to be technically difficult because the tracheal stem cells are not regularly arranged as epithelial cells and the proximal-distant axis of the tracheal stem cells remains unclear. We thus decided to measure the fluorescence signal of groups of stem cells along the DT regardless of their individual polarity within cells.

      (4) Regarding the trafficking of Upd2 in the fat body, is it known, whether Grasp65, Lbm, Rab5, and 7 are specifically needed for extracellular vesicle trafficking rather than general intracellular trafficking? What is the evidence for this?

      In our experiments, knocking down rab5, rab7, grasp65 or lbm in trachea using btl-Gal4 did not cause abnormality in the disciplined migration, which excludes their intracellular contribution in the trachea (Figure 7-figure supplement 1). Perturbation of Grasp65 or Lbm in fat body increased intracellular upd2-containing vesicles, indicating that intracellular production is functional (Figure 6J). The Grasp65 is specifically required for Upd2 production. Lbm, Rab5 and Rab7 are important of vesicle trafficking. Our conclusion does not pertain to extracellular or intracellular compartment.

      (5) Figure 8A-B: The data on the proximity of Rab5 and 7 to the Upd2 blobs are not very convincing.

      The confocal images indicate the proximity of Rab5 and Rab7 to the Upd2 vesicles. We interpret the proximity together with the results from Co-IP and PLA data (Figure 8E-K).

      (6) The authors should clarify whether or not their work has shown that "vesicle-mediated transport of ligands is essential for JAK/STAT signaling". In its current form, this manuscript does not appear to provide enough evidence for extracellular vesicle transport of Upd2.

      Lbm belongs to the tetraspanin protein family that contains four transmembrane domains, which are the principal components of extracellular vesicles. We show that Lbm interacts with Upd2. The JAK/STAT signaling depends on the Upd2 in the fat body as well as vesicle trafficking machinery. Furthermore, we performed electron microscopy and show the presence of Lbm-containing vesicles in fat body (Figure 8-figure supplement 1D).

      (7) What is the long-term effect of the various genetic manipulations on migration? The authors don't show what the phenotype at later time points would be, regarding the longer-term migration behaviour (e.g. at 10h APF when the cells should normally reach the posterior end of the pupa). And what is the overall effect of the aberrant bidirectional migration phenotype on tracheal remodelling?

      We observed that the integrity of tracheal network especially the dorsal trunk was impaired, which may be due to incomplete regeneration (Figure 3-figure supplement1E-I).

      (8) The RNAi experiments in this manuscript are generally done using a single RNAi line. To rule out off-target effects, it would be important to use two non-overlapping RNAi lines for each gene.

      We validated the phenotype using several independent RNAi alleles.

      Reviewer #2 (Public review):

      Summary:

      This work by Dong and colleagues investigates the directed migration of tracheal stem cells in Drosophila pupae, essential for tissue homeostasis. These cells, found in two nearby groups, migrate unidirectionally along the dorsal trunk towards the posterior to replenish degenerating branches that disperse the FGF mitogen. The authors show that inter-organ communication between tracheal stem cells and the neighboring fat body controls this directionality. They propose that the fat body-derived cytokine Upd2 induces JAK/STAT signaling in tracheal progenitors, maintaining their directional migration. Disruption of Upd2 production or JAK/STAT signaling results in erratic, bidirectional migration. Additionally, JAK/STAT signaling promotes the expression of planar cell polarity genes, leading to asymmetric localization of Fat in progenitor cells. The study also indicates that Upd2 transport depends on Rab5- and Rab7-mediated endocytic sorting and Lbm-dependent vesicle trafficking. This research addresses inter-organ communication and vesicular transport in the disciplined migration of tracheal progenitors.

      Strengths:

      This manuscript presents extensive and varied experimental data to show a link between Upd2JAK/STAT signaling and tracheal progenitor cell migration. The authors provide convincing evidence that the fat body, located near the trachea, secretes vesicles containing the Upd2 cytokine. These vesicles reach tracheal progenitors and activate the JAK-STAT pathway, which is necessary for their polarized migration. Using ChIP-seq, GFP-protein trap lines of planar cell polarity genes, and RNAi experiments, the authors demonstrate that STAT92E likely regulates the transcription of planar cell polarity genes and some apicobasal cell polarity genes in tracheal stem cells, which seem to be necessary for unidirectional migration.

      Weaknesses:

      Directional migration of tracheal progenitors is only partially compromised, with some cells migrating anteriorly and others maintaining their posterior migration.

      Our results suggest that Upd2-JAK/STAT signaling is required for the consistency of disciplined migration. Although only a few tracheal progenitors display anterior migration, these cells lose the commitment of directional movement. We acknowledge that the phenotype is moderate.

      Additionally, the authors do not examine the potential phenotypic consequences of this defective migration.

      We examined the long-term effects of the aberrant migration and observed an impairment of tracheal integrity and melanized tracheal branches (Figure 3-figure supplement1E-I).

      It is not clear whether the number of tracheal progenitors remains unchanged in the different genetic conditions. If there are more cells, this could affect their localization rather than migration and may change the proposed interpretation of the data.

      We examined the progenitor cell number in bidirectional movement samples and control group. The results show that cell number does not exhibit a significant difference between control and bidirectional movement groups (Figure 3-figure supplement 1).

      Upd2 transport by vesicles is not convincingly shown.

      We performed additional PLA experiments to further support the interaction between Upd2 and the core components of extracellular vesicles. Furthermore, we performed electron microscopy and show the presence of Lbm-containing vesicles in fat body (Figure 8-supplement 1D). Additional experiments such as colocalization and Co-IP assay and better quantification are provided in the revised manuscript (see revised Figure 8).

      Data presentation is confusing and incomplete.

      We used different colors to represent different genotypes, and the columns were superimposed. we changed the graphs to show the quantification in different conditions separately. We revised data presentation to avoid confusing.

      Reviewer #3 (Public review):

      Summary:

      Dong et al tackle the mechanism leading to polarized migration of tracheal progenitors during Drosophila metamorphosis. This work fits in the stem cell research field and its crucial role in growth and regeneration. While it has been previously reported by others that tracheal progenitors migrate in response to FGF and Insulin signals emanating from the fat body in order to regenerate tracheal branches, the authors identified an additional mechanism involved in the communication of the fat body and tracheal progenitors.

      Strengths:

      The data presented were obtained using a wide range of complementary techniques combining genetics, molecular biology, quantitative, and live imaging techniques. The authors provide convincing evidence that the fat body, found in close proximity to the trachea, secrete vesicles containing the Upd2 cytokine that reach tracheal progenitors leading to JAK-STAT pathway activation, which is required for their polarized migration. In addition, the authors show that genes regulating planar cell polarity are also involved in this inter-organ communication.

      Weaknesses:

      (1) Affecting this inter-organ communication leads to a quite discrete phenotype where polarized migration of tracheal progenitors is partially compromised. The study lacks data showing the consequences of this phenotype on the final trachea morphology, function, and/or regeneration capacities at later pupal and adult stages. This could potentially increase the significance of the findings.

      Regarding your kind suggestion, we examined the long-term effects of the aberrant migration and observed the impairment of tracheal integrity and melanized tracheal branches (Figure 3-figure supplement1E-I).

      (2) The conclusions of this paper are mostly well supported by data, but some aspects of data acquisition and analysis need to be clarified and corrected, such as recurrent errors in plotting of tracheal progenitor migration distance that mislead the reader regarding the severity of the phenotype.

      We used different colors to represent different genotypes, and the columns were superimposed. we changed the graphs to show the quantification in different conditions separately. We thank you for kindly pointing it out.

      (3) The number of tracheal progenitors should be assessed since they seem to be found in excess in some genetic conditions that affect their behavior. A change in progenitor number could lead to crowding, thus affecting their localization rather than migration capacities, thereby changing the proposed interpretation. In addition, the authors show data suggesting a reduced progenitor migration speed when the fat body is affected, which would also be consistent with a crowding of progenitors.

      We examined the cell number in bidirectional movement samples and control group. We examined cell number and cell proliferation and observed that there was no significance between control and bidirectional movement groups (Figure 3-figure supplement 2).

      (4) The authors claim that tracheal progenitors display a polarized distribution of PCP proteins that is controlled by JAK-STAT signaling. However, this conclusion is made from a single experiment that is not quantified and for which there is no explanation of how the plot profile measurements were performed. It also seems that this experiment was done only once. Altogether, this is insufficient to support the claim. Finally, a quantification of the number of posterior edges presenting filopodia rather than the number of filopodia at the anterior and posterior leading edges would be more appropriate.

      We staged the animals, measured several animals for each genotype and provided the quantifications in the revised manuscript. The level of Ft-GFP is higher in the cells at the frontal edge. We tried to examine the expression of Ft-GFP at single-cell level. However, this turned out to be difficult due to the fact that the tracheal stem cells are not regularly patterned as epithelial cells and the proximaldistant axis of tracheal stem cells is not well defined. We thus decided to measure the fluorescence signal of groups of stem cells along the DT regardless of their individual polarity.

      (5) The authors demonstrate that Upd2 is transported through vesicles from the fat body to the tracheal progenitors where they propose they are internalized. Since the Upd2 receptor Dome ligand binding sites are exposed to the extracellular environment, it is difficult to envision in the proposed model how Upd2 would be released from vesicles to bind Dome extracellularly and activate the JAK-STAT pathway. Moreover, data regarding the mechanism of the vesicular transport of Upd2 are not fully convincing since the PLA experiments between Upd2 and Rab5, Rab7, and Lbm are not supported by proper positive and negative controls and co-immunoprecipitation data in the main figure do not always correlate to the raw data.

      We use molecular modeling to show that Upd2 and Lbm intermingle, and Upd2 is not entirely encapsulated in vesicles (Figure 8-supplement 1E). We performed PLA experiments using the animals not expressing upd2-Cherry as negative control (Figure 8 E-J). We corrected the Co-IP panel and apologize for this error.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Minor comments:

      (1) Figure 1-figure supplement 1: E: How was the migration velocity assessed? By live imaging individual cells or following the cell front of the group? Over what time period? Do the data points in the graph correspond to individual cells or the cell group? It would be important to show confocal images that go along with this quantification.

      We took snapshots of pupae at 0hr\ 1hr\ 2hr and 3hr, and measured the distance covered by the migrating progenitor cells from the start place (the junction of TC and DT) to the leading edge of progenitor groups. We then calculated the migration rate by v=d (micrometer)/t (min). As the progenitor cells revolve around and migrate along the DT, tracking single tracheoblast through intact cuticle is technically challenging. We have therefore measured the leading edge as a proxy to the whole cell group. We agree with you that time-lapse imaging is favorable for analysis of migration.

      (2) Figure 1-figure supplement 1: F: Why is there Gal80ts in the genotype? (and in Figure 1H). Also, what pupal age was used for this quantification?

      Expression of hid and rpr in L3 stage impaired fat body integrity and adipocyte abundance, and caused lethality. Gal80ts was used for controlling the expression of rpr.hid. The pupal at 0hr APF were used in EdU experiment.

      (3) Figure 2C: what is shown in the 6 columns (why 3 each for control and rpr/hid)?

      We conducted 3 replicates of each group for control and rpr.hid.

      (4) In the methods, several Drosophila stocks are listed as 'source:" from a particular person (e.g. Dr Ma). Please list the real source of this stock, e.g. Bloomington stock number, or the lab and publication in which the stock was originally made.

      We provide the information on these stocks in the revised methods.

      (5) The SKOV3 carcinoma cell and S2 cell work is not described in the methods.

      We added detailed description of this experiment in the revised method-Cell culture and transfection. 

      (6) Figure 6 (F) 'Bar graph plots the abundance of Upd2-mCherry-containing vesicles in progenitors.' What does abundance mean? What was quantified, the number of vesicles, or the mean intensity? This is also not mentioned in the methods.

      We counted the number of Upd2-mCherry-containing vesicles in fat body cells and trachea progenitors and added the description of measurement in the method.

      (7) There are a few language mistakes throughout the manuscript. E.g.

      (a) Line 117 and other places: Language: 'fat body' should be 'the fat body'.

      We thank you for pointing out these errors and corrected it accordingly.

      (b) Line 1276 Language mistakes: 'Video 1 3D-view of confocal image stacks of tracheal progenitors and fat body. Scale bar: 100 μm. Genotypes: UAS-mCD8-GFP/+;lsp2-Gal4,P[B123]-RFP-moe/+.' :stacks and genotypes should be singular.

      We fixed these errors and thank you for kindly pointing them out. We also proofread the entire manuscript to assure accuracy.

      (8) In general, it is hard to figure out the exact genotypes used in experiments. This is mostly not written very clearly in the figure legends. E.g. Figure 2: genotype for A-C missing in figure legend (is B from control animals?)

      We added genotypes in the figure legends. For Figure 2, A and C lsp2-Gal4,P[B123]-RFP-moe/+ for control, UAS-rpr-hid/+;Gal80ts/+;lsp2-Gal4,P[B123]-RFP-moe/+ for rpr.hid; B from control animals.

      Reviewer #2 (Recommendations for the authors):

      Major comments:

      (1) The phenotype resulting from Upd2 downregulation by RNAi is subtle and shown by unconvincing images. In addition, these phenotypes are analyzed using only one RNAi line.

      We used two independent alleles of upd2RNAi from THFC (THU1288 and THU1331), and observed similar phenotype. For RNAi experiments, we always use multiple independent alleles.

      (2) The authors should analyze the phenotypic consequences of directional migration changes. Is there an effect on tracheal remodeling?

      We observed that the integrity of tracheal network especially the dorsal trunk was impaired and that melanized tracheal branches were present, which may be due to incomplete regeneration (Figure 3figure supplement1E-I).

      (3) The number of tracheal progenitors should be quantified, as some genetic conditions may affect cell numbers, as is apparent in some panels.

      We examined cell number and cell proliferation and observed that there was no significance between control and bidirectional movement groups (Figure 3-figure supplement 1).

      (4) The data on PCP protein distribution are unconvincing, unquantified, and insufficient to support one of the main conclusions of the study, which is stated in the abstract: "JAK/STAT signaling promotes the expression of genes involved in planar cell polarity, leading to asymmetric localization of Fat in progenitor cells."

      We staged the animals, measured several animals for each genotype and provided the quantifications in the revised manuscript. The level of Ft-GFP is higher in the cells at the frontal edge. We tried to examine the expression of Ft-GFP at single-cell level. However, this turned out to be difficult due to the fact that the tracheal stem cells are not regularly patterned as epithelial cells and the proximaldistant axis of tracheal stem cells is not well defined. We thus decided to measure the fluorescence signal of groups of stem cells along the DT regardless of their individual polarity.

      Minor comments:

      (1) Language should be revised. In many places in the manuscript, starting in line 113, "fat body" should be "the fat body".

      Thank you for pointing out this error. We corrected it accordingly.

      (2) Genotypes used in experiments should be described.

      We added all the genotypes. We proofread the entire manuscript to complete the figure legends for genotypes.

      (3) Line 67, the reference to "The progenitor cells reside in Tr4 and Tr5 metameres and start to move along the tracheal branch" should include (Chen and Krasnow, Science 2014).

      We added the reference in the manuscript.

      (4) Line 1081, Figure 7 Legend. "Bar graph plots the abundance of Upd2-mCherry-containing vesicles" Abundance is the number of vesicles? The graph displays the average number of vesicles? Please explain and describe the quantification.

      The bar graph represents the number of Upd2-mCherry-containing vesicles in different conditions. We quantified the number of vesicles per area.

      (5) Figure 1 (I-J) What is shown on the panels? Progenitors marked with? This information is not present in the figure or figure legend. Same for Figure 2 (D-E).

      Figure 1I-J show the vector of migrating progenitors. We added the information in the legends. The tracheal cells were labeled by nls-mCherry in Figure 1I-J. In Figure 2D-E, the progenitors were marked with P[B123]-RFP-moe.

      (6) Figure 3 Q, Stat92E-GFP values in the graph are not well-explained. What do the numbers in the y-axis refer to?

      y-axis represents the intensity of Stat92E-GFP normalized to control. We have changed the y-axis label to ‘normalized Stat92E-GFP intensity’ in the legends.

      (7) In general, figures and figure legends must be revised. Sometimes stainings are not well-defined, some scale bars are missing and plots do not say what the values are.

      We apologized for inadequate information and have revised the figures and legends accordingly.

      Reviewer #3 (Recommendations for the authors):

      Several points should be addressed by the authors in order to improve their manuscript.

      Major points:

      (1) The phenotype obtained from decreasing the inter-organ signaling is quite discrete. It is further weakened by the fact that the images chosen to illustrate the measures are not really convincing. No image at 1h APF shows any clear anterior migration. Based on the scale, most of the images at 3h APF do not show a striking difference compared to the control, and in any case, stronger phenotypes would be missed anteriorly since they would thus be out of frame. In addition, at 3h APF, progenitors migrating anteriorly from Tr5 position get mixed with those migrating posteriorly from Tr4 so it is not clear how measurements were made. Given that most phenotypes are observed upon the use of RNAis, it is possible that phenotypes are weak due to persistent gene expression. Using null clones for dome, hop, or stat in progenitors could therefore aggravate the phenotypes and support further the significance of the study. Finally, assessing the consequences of compromised fat body-tracheal communication on trachea morphology, function, and regeneration later in pupal development and on adult flies would also help strengthen the importance of the findings.

      We agree with you that anteriorly migrated Tr5 progenitors adjoining Tr4 progenitor hinders measurements and that mutants may give stronger phenotype than RNAi lines. We only measured Tr4 progenitors (instead of Tr5) when assessing anterior migration. Thus, we performed experiments using mutant alleles, which gave aberrant migration of tracheal progenitors (Figure 3-figure supplement1A-D). We can now show that the integrity of tracheal network especially dorsal trunk was impaired, which may be due to incomplete regeneration (Figure 3-figure supplement1E-I).

      (2) Although the authors did not observe defects in tracheal progenitor proliferation, progenitors seem to be present in excess in some key genetic background (e.g, upon expression of rpr.hid, statRNAi, Rab-RNAi or in the presence of BFA). This excess could be the result of another mechanism than proliferation (recruitment of extra progenitors since it is not clear how they originate, defect in apoptosis...) and could impact the localization of progenitors, those being pushed anteriorly as a consequence of crowding. A proper characterization of tracheal progenitor number would thus help to discriminate between defects in migration or crowding. This point could also be addressed by performing individual tracking of tracheal progenitors, to find out whether each progenitor is indeed migrating in the wrong direction or if the movement assessed by the global tracking method that is used is just a consequence of progenitor excess.

      We examined the cell number in bidirectional movement samples and control group. The results show that there was no significance between control and bidirectional movement groups (Figure 3figure supplement 1). We also tried to follow every progenitor, but were unable to obtain convincing results with P[B123]-RFP-moe, as tracking single tracheoblast through intact cuticle is technically challenging.

      (3) Regarding the ChIP-seq experiment, an explanation of why choosing the "establishment of planar polarity" family should be provided since data indicate a quite low GeneRatio. Indeed, the "cell adhesion" family seems a more obvious candidate, which would be further supported by the fact that the JAK-STAT pathway has been shown to affect cell adhesion components such as ECadherin and FAK (Silver and Montell 2001, Mallart et al 2024). Also, have these known targets of JAK-STAT signaling been found in the ChIP-seq data? Since filopodia polarization is affected in tracheal progenitors when JAK-STAT signaling is decreased, the same question also applies to enabled, which is involved in filopodia formation and has been recently identified as a target of JAK-STAT signaling.

      As you kindly suggested, we tested a number of cell adhesion-related genes such as E-Cadherin (shg), fak, robo2 and enabled (ena). We did not observe an apparent aberrancy in the migration of tracheal progenitors (Figure 5-supplement 1J).

      (4) Data investigating PCP protein distribution is not convincing, not quantified, and not sufficient to draw one of the main conclusions of the study, which is even written in the abstract "JAK/STAT signaling promotes the expression of genes involved in planar cell polarity leading to asymmetric localization of Fat in progenitor cells."

      We better quantified the abundance of Ft in in the progenitors in the frontal edge and those lagging behind. The traces plot multiple replicates in the figures. The level of Ft-GFP is higher in the cells at the frontal edge.

      (5) Overall, the figures together with their caption and/or the material and methods section lack some important information for the reader to fully understand the data. In addition, some errors are found in multiple plots throughout the article and must be corrected. Here are some examples:

      According to your suggestion, we revised legends and methods section to include sufficient information.

      (a) Migration distance plots from Figure 3E do not match the data presented in the source data file. It seems that, when creating the plot, instead of superimposing the bars, bars were stacked. This should be corrected for all migration distance plots from Figure 3E onward, including in supplementary figures.

      We apologized for misleading representation. We revised it accordingly and show the quantification in different conditions separately.

      (b) The number of analyzed flies and/or clusters of tracheal progenitors from different flies should be stated for all quantification or observations made on images. This information is lacking for all migration distance plots, for progenitor migration tracking (Figure 1 I, J), for DIPF reporter in Figure 2J, for plot profiles (Figure 5G, J), for Upd2-Rab5/Rab7/Lbm co-detections, PLA, CoIP, and lbm-pHluorin experiments. This also applies to RNA seq, ChIP seq, and surface proteomics, for which the number of pupae and number of replicates is not indicated.

      We changed the graphs to show the quantification and n number in different conditions separately.

      We also added the n number of replicates in methods.

      (c) How quantifications were performed is not sufficiently explained. For example, the reference point for migration distance measurement is not defined, and neither is whether the measures were made on fixed or live imaging samples. In fluorescence intensity measurements and Upd2 vesicle counting, information on whether measures were made on a single z slice or on a projection of several z slices should be stated together with what ROI and which FIJI tool for quantification were used. For plot profiles, the same information regarding z slices misses together with how the orientation, the thickness, and the length of the line were chosen, and again the number of times the experiment was conducted should be mentioned and error bars should appear on graphs.

      We thank this reviewer for the suggestions which help clarify the methodology of our experiments and improve presentation of our data. We have made the changes according to the suggestions and modified our methods section and the related figures to incorporate these changes.

      For measuring the migration distance of tracheal progenitors, we took snapshots of living pupae at 0hr\ 1hr\ 2hr and 3hr APF, and measured the migration distance of tracheal progenitors from the start place (the junction of TC and DT) to the leading edge of progenitor groups.

      For the measurements of fluorescent intensity of stat92E-GFP and DIPF, we took z-stack confocal images of samples and quantified the fluorescent intensity using FIJI. Specifically, intensity was quantified for regions of interest, using the Analysis and Measurement tools. To quantify Upd2mCherry vesicles, z-stack confocal images of fat body were taken and the cell counting function of FIJI was used to measure the vesicle number.

      To quantify the fluorescent intensity of in vivo tagged Ds, Ft and Fj proteins, a single z slice was used. The expression level of the protein was assessed as the integrated fluorescent intensity normalized to area.

      For the measurement of Ft-GFP distribution, a single z slice of the progenitors immediately proximal to the DT was imaged. An arbitrary line was drawn along the migration direction from the starting TC-DT junction to the leading front (the length of the line corresponds to the distribution range of tracheal stem cell clusters). Then, fluorescent intensity along the line was automatically calculated with the imbedded measurement function of Zeiss confocal software.

      Minor points:

      (1) In several instances, the authors generalize that stem cells migrate to leave their niche, but this is not the case for all stem cells.

      The phenomenon that stem cells leave their niche when they are activated is commonly observed. We interpreted the general mechanism from our system of tracheal stem cells. We fully agree with you that it may not be the case for all stem cells. We modified the text accordingly.

      (2) Line 122 -a reference paper or an image showing the expression pattern of the lsp2-Gal4 driver is missing.

      We added the reference in the manuscript.

      (3) Line 136 - The term "traces of individual progenitors" is overstated and should be reformulated as the method used does not seem to be individual cell tracking.

      We rephrased accordingly in the revised manuscript.

      (4) Line 146 - Fat body and tracheal progenitors are qualified as interdependent organs, in which aspect do tracheal progenitors affect the fat body?

      Current knowledge suggests a close inter-organ crosstalk between trachea and fat body: The fly trachea provides oxygen to the body and influences the oxidation and metabolism of the whole body. When the trachea is perturbed, the body is in hypoxia, which causes inflammatory response in adipose tissue as an important immune organ (Shin et al., 2024).

      (5) Line 163 - Not all the genes tested are cytokines, so the sentence should be reformulated. In addition, in supplementary Fig2-1 C-J, the KD of hh seems to abolish completely tracheal progenitor migration, which is not commented on.

      According to your suggestion, we revised the description on information of the genes tested. We added comments in the revised manuscript regarding phenotypes of hh knockdown. 

      (6) Line 180 - Conclusion is made on Dome expression while using a dome-Gal4 construct, which does not necessarily recapitulate the endogenous pattern of dome expression, so it should be reformulated. Ideally, dome expression should be assessed in another way. Also, it is not clear whether GFP is present only in progenitors since images are zoomed.

      We revised statement and provided larger view of dome>GFP that shows an enriched expression in the tracheal progenitors (Figure 2-figure supplement 2E), an expression pattern that is consistent with FlyBase.

      (7) Line 199 - Is it upd-Gal4 or upd2-Gal4 that is used? Since the conclusion of the experiment is made on upd2, the use of upd-gal4 would not be relevant. If upd2-gal4 is used, it should be corrected. In general, the provenance of the Gal4 lines should be provided. In addition, a strong GFP signal in the trachea is visible on the image in Supplementary Figure 2-2F but not commented on and seems contradictory with the conclusion mentioning that fat body and gut are the main source of Upd2 production.

      We removed data obtained from the use of this irrelevant upd-Gal4 line.

      (8) Figures:

      -  Figure 1 G, H - Scale bar is missing.

      We added it accordingly.

      -  Figure 1 I, J - The information on the staining is missing.

      We added it in the revised manuscript.

      -  Figure 2A - Providing explanations of the terms "Count" and "Gene ratio" in the caption would be helpful for readers who are not used to this kind of data. In addition, the color code is confusing since the same color is used for the selected gene family and for high p-values (the same applies to other similar graphs).

      Gene ratio refers to the proportion of genes in a dataset that are associated with a particular biological process, function, or pathway. Count indicates the number of genes from input gene list that are associated with a specific GO term. We used redness to indicate a smaller p-value and a higher significance.

      -  Figure 2 B, C - What does the color scale represent? What do the columns in C correspond to, different time points, different replicates?

      The color scale represents the normalized expression. The columns in C correspond to different replicates of control and rpr.hid.

      -  Figure 2 F - The error bars on the 3h APF posterior bars are missing.

      We added error bars accordingly.

      -  Figure 2 G - The legend "Down-Stable-Up" is in comparison to what?

      The control group was generated from the reaction without H2O2. The comparison was relative to the control group.

      -  Figure 2 J - The specificity of the DIPF tool that has been created should be validated in other tissues displaying known JAK-STAT activity and/or in conditions of decreased JAK-STAT signaling. In addition, the added value of the tool as compared to the JAK-STAT activity reporter used later, which has been well characterized, is not obvious.

      We added the signal of DIPF in fat body and salivary gland, both of which harbor active JAK/STAT signaling (Figure 2-figure supplement 2F-H). As opposed to the well characterized Stat92E-GFP reporter that assays the downstream transcription activity, the DIPF reporter measures the upstream event of receptor dimerization.

      -  Figure 3 I-P - Reporter tool validation in Images I-L could be moved to supplementary data. In images M-P, staining of nuclei and/or membranes would be useful to assess cell integrity.

      We revised the figures accordingly.

      -  Figure 3Q and similar plots in the following figures do not explain the normalization performed and how it can be higher than 1 in control conditions.

      In these figures, we normalized the signal relative to control groups, e.g., The value of Stat92E-GFP in btl-GFP control group was set to 1 in the previous Figure 3Q (revised Figure 3-supplementary

      Figure B-J).

      -  Figure 4C - These representations lack explanations to be fully understood by a broad audience.

      The figure showing that Stat92E binding was detected in the promoters and intronic regions (the orange peaks) of genes functioning in distal-to-proximal signaling, such as ds, fj, fz, stan, Vang and fat2. We added the information in figure legend according to your suggestion.

      -  Figure 5 K,L - What is the x-axis missing, together with the method of tracking used?

      The x-axis refers to time of recording from a t stack series with a time interval of 5 min. We revised method section and provide detailed procedure of this experiment.

      -  Figures 6 and 8- The overall figures lack a wider view of the cells/tissues/organs and/or additional staining to understand what is presented.

      We showed preparation of fat body. In order to obtain the high resolution of vesicles, we used high magnification. We now added wider views of the tissues under investigation (e.g. Figure 6-figure supplement 1).

      -  Figure 6 D,E - The scale bar is missing.

      We added it accordingly.

      -  Figure 8 O-S - What is the blue staining?

      The blue staining shows DAPI-stained nuclei. We have added the information in the legend.

      -  PLA experiments can give a lot of non-specific background. What kind of controls have been used in Figure 8 F-J? Negative controls should be done on cells that do not express upd2-mCherry using both antibodies to detect non-specific background, which does not usually appear completely black.

      If possible, a positive control using a known protein interacting with Rab5-GFP should be included.

      We used the control samples without one of the primary antibodies in previous Figure 8. In the revised Figure 8, we conducted experiment as you suggested with controls that do not express upd2mCherry (Figure 8 E-J).

      -  Co-IP experiments - The raw data file for blots is quite hard to read through. Some legends are not facing the right lane and some blots presented in the main figure are difficult to track since several blots are presented in the raw data file. e.g.

      (a)  Raw blot for Figure 8 K: the band for mCherry in the IP anti-GFP blot (lane one in K) is not convincing, it is not distinguishable from other aspecific bands. On the reverse IP presented only in raw data, on the input from blot IB anti-mCherry, both lanes present exactly the same bands at 72kb when one of the lanes corresponds to extract from flies not expressing upd2-mCherry.

      We thank you for pointing out the incorrect labels. We apologized for the errors and corrected it accordingly.

      (b)  Raw blot for Figure 8 L: on the input blot IB anti-GFP, there is a band corresponding to Rab7-GFP in the lane of the extract from flies not expressing Rab7-GFP.

      We corrected it.

      (c)  Raw data for Figure 8 M: on the last blot, legends are missing above the input Ib anti-GFP blot.

      We added the missing legends in the figure.

      Shin, M., Chang, E., Lee, D., Kim, N., Cho, B., Cha, N., Koranteng, F., Song, J.J., and Shim, J. (2024). Drosophila immune cells transport oxygen through PPO2 protein phase transition. Nature 631, 350-359.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      (1) Summary:

      In this manuscript, the model's capacity to capture epistatic interactions through multi-point mutations and its success in finding the global optimum within the protein fitness landscape highlights the strength of deep learning methods over traditional approaches.

      We thank the reviewer for his/her recognition of our model’s potential and advantages.

      (2) Strengths:

      It is impressive that the authors used AI combined with limited experimental validation to achieve such significant enhancements in protein performance. Besides, the successful application of the designed antibody in industrial settings demonstrates the practical and economic relevance of the study. Overall, this work has broad implications for future AI-guided protein engineering efforts.

      We are thankful for the editor’s appreciation on our work, especially acknowledged the practical application of our model.

      (3) Weaknesses:

      However, the authors should conduct a more thorough computational analysis to complement their manuscript. While the identification of improved multi-point mutants is commendable, the manuscript lacks a detailed investigation into the mechanisms by which these mutations enhance protein properties. The authors briefly mention that some physicochemical characteristics of the mutants are unusual, but they do not delve into why these mutations result in improved performance. Could computational techniques, such as molecular dynamics simulations, be employed to explore the effects of these mutations?

      We thank the reviewer for this good question, which allows us to provide a deeper investigation into the mechanisms by which the mutations significantly enhance the alkali-resistance of proteins. By following the reviewer’s suggestion, we have expanded our analysis by incorporating molecular dynamics (MD) simulations to understand the impact of the mutations. As an example, we focused on the representative alkali-resistant mutant, A57D;P29T, and examined its MD simulation results. As shown in Figure S4A, the two-point mutant of A57D;P29T has a Tm increase of around 8 ℃ and a much stronger binding affinity than the WT. Our analysis of the MD trajectories indicates that the A57D;P29T mutant has a more rigid structure than that of WT due to its lower root mean squared deviation (RMSD) of protein (Figure S4B). Furthermore, we calculated the root mean squared fluctuation (RMSF) for each residue, and realized that the mutant displayed less fluctuation at residue 29 but similar flexibility at residue 57. Interestingly, residues at positions 10, 108 and 118 which spatially distant from residues 29 and 57 in the mutant exhibited remarkable weakened fluctuations than those in the WT (Figure S4C), implying a more rigid structure of the mutant contributing to its improved resistance on high temperature and strong alkalinity. However, Figure S4D shows the AlphaFold3 predicted structures of the WT and the mutant are quite similar.

      To unveil the origin of change on structural flexibility, we computed the intramolecular interactions, such as salt bridges and hydrogen bonds for both WT and the mutant. We observed that the mutations increased the number of hydrogen bonds between the mutation sites and the rest of the protein (Figure S4E). However, the overall structure of the mutant did not show significant changes, which is also evident from the solvent-accessible surface area (SASA) analysis (Figure S4F). We also analyzed changes in salt bridges and found that although residue 57 mutated to Histidine, no new salt bridges were formed. Additionally, RMSF results showed that residues 10, 108, and 118 became more rigid, but further analysis revealed that there was no significant change in hydrogen bonds or other interactions in these regions. Overall, the MD results suggest that more hydrogen bonds introduced by the mutations of A57D;P29T stabilize the protein, leading to the enhanced alkali resistance observed in the mutant. These results are now presented in Figure S4 and discussed in detail in the revised manuscript.

      Specifically, we have added the following discussion in the main text:

      “In order to gain deeper insights into the mechanisms by which the identified mutations enhance protein properties, we performed molecular dynamics (MD) simulations on the best alkali-resistant mutant. The simulation results revealed several key observations that help explain the observed improvements in protein stability and alkali resistance. As shown in Figure S4A, the two-point mutant of A57D;P29T has a Tm increase of around 8℃ and a much stronger binding affinity than the WT. Our analysis of the MD trajectories indicates that the A57D;P29T mutant has a more rigid structure than that of WT due to its lower root mean squared deviation (RMSD) of protein (Figure S4B). Furthermore, we calculated the root mean squared fluctuation (RMSF) for each residue, and realized that the mutant displayed less fluctuation at residue 29 but similar flexibility at residue 57. Interestingly, residues at positions 10, 108 and 118 which spatially distant from residues 29 and 57 in the mutant exhibited remarkable weakened fluctuations than those in the WT (Figure S1C), implying a more rigid structure of the mutant contributing to its improved resistance on high temperature and strong alkalinity. However, Figure S4D shows the AlphaFold3 predicted structures of the WT and the mutant are quite similar. To unveil the origin of change on structural flexibility, we computed the intramolecular interactions, such as salt bridges and hydrogen bonds for both WT and the mutant. We observed that the mutations increased the number of hydrogen bonds between the mutation sites and the rest of the protein (Figure S4E). However, the overall structure of the mutant did not show significant changes, which is also evident from the solvent-accessible surface area (SASA) analysis (Figure S4F). We also analyzed changes in salt bridges and found that although residue 57 mutated to Histidine, no new salt bridges were formed. Additionally, RMSF results showed that residues 10, 108, and 118 became more rigid, but further analysis revealed that there were no significant changes in hydrogen bonds or other interactions in these regions. Taken together, these findings suggest that the enhanced alkali resistance of the mutant is likely due to an overall increase in protein stability, rather than a dramatic change in its structural conformation. The MD simulation results, which are detailed in Figure S4, provide a deeper understanding of how specific mutations can improve protein properties and offer valuable insights for future protein engineering applications.”

      And we also included the following content in the SI:

      “Molecular Dynamics (MD) simulations

      The initial structures for molecular dynamics (MD) simulations of both the wild type and the mutant were predicted using AlphaFold3. To simulate experimental conditions, each protein was placed in a cubic water box containing 0.1 M NaCl. The CHARMM27 force field and the TIP4P water model were applied throughout the simulations. After an initial energy minimization of 50,000 steps, the systems were heated and equilibrated for 1 ns in the NVT ensemble at 300 K followed by an additional 1 ns in the NPT ensemble at 1 atm. The production phase then involved 200-ns simulations with periodic boundary conditions, using a 2 fs integration time step. The LINCS algorithm was used to constrain covalent bonds involving hydrogen atoms, while Lennard-Jones interactions were cut off at 10 Å. Electrostatic interactions were computed with the particle mesh Ewald method, using a 10 Å cutoff and a grid spacing of approximately 1.6 Å with a fourth-order spline. Temperature and pressure were regulated by the velocity rescaling thermostat and Parrinello-Rahman algorithm, respectively. All simulations were performed using GROMACS 2020.4 software packages. Both systems have reached equilibrium according to the analyses of root mean squared deviation (RMSD).”

      (4) Additionally, the authors claim that their method is efficient. However, the selected VHH is relatively short (<150 AA), resulting in lower computational costs. It remains unclear whether the computational cost of this approach would still be acceptable when designing larger proteins (>1000 AA). Besides, the design process involves a large number of prediction tasks, including the properties of both single-site saturation and multi-point mutants. The computational load is closely tied to the protein length and the number of mutation sites. Could the authors analyze the model's capability boundaries in this regard and discuss how scalable their approach is when dealing with larger proteins or more complex mutation tasks?

      In our prior work, we have demonstrated that our method is applicable to larger proteins as well [Jiang et al., Sci. Adv. 10, eadr2641 (2024)]. For instance, when engineering a protein with 1000 amino acids, inferring the fitness of one million mutants using the model on a single 4090 GPU takes approximately 20 hours. However, it remains infeasible to explore all possible mutations when designing multi-point mutants due to the vast space. To address this challenge, we propose the design of a reliable mutant library. In the first round of experiments, we used the model to score all single-point mutations, and then constructed the multi-point mutant library by combining experimentally tested single-point mutations. In this way, even when designing five-point mutants, we only need to score on the order of millions of mutants, making the inference process time-efficient and fully acceptable. As a result, the number of single-point mutations selected for combination into the multi-point mutant library becomes a crucial parameter that affects both inference time and scope. We limited the number of single-point mutations to between 30 and 50 to strike a balance between efficiency and accuracy.

      These results are discussed in the revised manuscript. Specifically, we have added the following discussion at the section 2.2 in the main text:

      “Although the model inference is fast, it is not feasible to explore all possible mutations when designing multi-point mutants due to the exponential increase in the number of potential combinations. To manage this challenge, we constructed a mutant library based on a two-stage design process. In the first stage, we scored all single-point mutations using the model, and in the second stage, we combined experimentally validated single-point mutations to create the multi-point mutant library. This approach ensures that even when designing multi-point mutants (e.g., five-point mutants), the number of mutants to score remains in the millions, which is computationally efficient and practical. The number of single-point mutations selected for the multi-point mutant library is a key factor influencing both the computational load and the scope of the design space. To maintain a balance between efficiency and accuracy, we limited the number of single-point mutations to between 30 and 50. This strategic approach allows us to achieve both scalability and precision in our protein engineering tasks.”

      Reviewer #2 (Public review):

      In this paper, the authors aim to explore whether an AI model trained on natural protein data can aid in designing proteins that are resistant to extreme environments. While this is an interesting attempt, the study's computational contributions are weak, and the design of the computational experiments appears arbitrary.

      The reviewer’s comments give us an opportunity to further state the novelty of this study. Despite the AI model has been reported in our previous work [Sci. Adv. 10, eadr2641 (2024)], the unnatural physicochemical properties of proteins, to the best of our knowledge, have never been predicted using AI models. Our preceding work [Sci. Adv. 10, eadr2641 (2024)] has demonstrated that the large language model can predict the performances of the mutants on thermostability, catalytic activity, and binding affinity, etc. However, whether the AI models are able to evaluate the unnatural properties of the mutants remains unexplored. Our work has shown that AI models trained on the natural proteins can be used to design the mutants that resistant extreme conditions, such as strong alkalinity, substantially expanding the application of AI for bioengineering. Moreover, our design of the computational experiments was driven by the nature of the task and the availability of experimental data. We employed different strategies for designing single-point and multi-point mutants, specifically using a zero-shot approach for single-point mutations to overcome the challenge of rare data and fine-tuning the model for multi-point mutations to leverage the experimental data of single-point mutations.

      (1) The writing throughout the paper is poor. This leaves the reader confused.

      The manuscript has been revised accordingly, and we would like to address the reader’s questions if anything is confused.

      (2) The main technical issue the authors address is whether AI can identify protein mutations that adapt to extreme environments based solely on natural protein data. However, the introduction could be more concise and focused on the key points to better clarify the significance of this question.

      We thank the reviewer for this comment. We have revised the manuscript, particularly the introduction, where we focused on the research questions, methods, and main findings, while removing excessive background information to improve the manuscript’s conciseness and clarity.

      “Protein engineering, situated at the nexus of molecular biology, bioinformatics, and biotechnology, focuses on the design of proteins to introduce novel functionalities or enhance existing attributes[1-3]. With the exponential growth of biological data and computational power, protein engineering has experienced a significant shift towards advanced computational methodologies, particularly deep learning, to expedite the design process and unravel complex protein-function relationships[4-9]. However, a significant challenge in industrial protein engineering is designing proteins with inherent resistance to extreme conditions, such as high temperature and extreme pH environments (acidic or alkaline)[17, 18]. Unlike proteins in natural ecosystems, those used in industrial processes often encounter harsh physical and chemical conditions, necessitating exceptional resilience to maintain functionality[19, 20]. Previous efforts to enhance protein resistance have often relied on rational design and mutant library screening. These methods are typically labor-intensive, inefficient, and yield limited improvements[23-26]. Consequently, the industrial demand for proteins resilient to harsh environments poses a notable absence within the training datasets of Artificial Intelligence (AI) models. Exploring whether AI can achieve the evolution of protein resistance to extreme environments is crucial for broadening protein applications and improving modification efficiency.

      Recent advances in large-scale protein language models (LLMs) have enabled zero-shot predictions of protein mutants based on self-supervised learning from natural protein sequences. Although AI-guided protein design has been applied to predict the mutants with greater thermostability and higher activity[34-36], it is unexplored whether these models based on the natural protein information can find the mutants that adapt the unnatural extreme environments, such as the alkaline solution with the pH value higher than 13.

      Here, we employed a LLM (large language model) developed by our group, the Pro-PRIME model[27], to predict dozens of mutants of a nano-antibody against growth hormone (a VHH antibody), and examined their fitness, including alkali resistance and thermostability, to evaluate their performance under extreme environments.

      We utilized the Pro-PRIME model to score saturated single-point mutations of the VHH in a zero-shot setting, and selected the top 45 mutants for experimental testing. Some mutants exhibited improved alkali resistance, while others demonstrated higher thermal stability or affinity. Subsequently, we fine-tuned the Pro-PRIME model to predict dozens of multi-point mutations. As a result, we obtained three multi-point mutants with enhanced alkali resistance, higher thermostability, as well as strong affinity to the targeted protein. Also, the dynamic binding capacity of the selected mutant did not show significant decline after more than 100 cycles, making it suitable for practical application in industrial production. The selected mutant has been used in practical production and lower the cost for over one million dollars in a year. To the best of our knowledge, this is the first protein product developed by a LLM that has been successfully applied in mass production. Due to the Pro-PRIME model's ability to achieve precise predictions of multi-point mutations with reliance on a small amount of experimental data, our two-round design process involved experimental validation of only 65 mutants in two months, demonstrating remarkable high efficiency. Furthermore, we performed a systematic analysis of these findings and determined that the model can yield more valuable predictive outcomes while remaining consistent with rational design principles. Specifically, within the framework of multi-point combinations, the model's incorporation of negative single-point mutations into the combinatorial space led to exceptional results, showcasing its capacity to capture epistatic interactions. Notably, in striving for global optimum, deep learning methods offer distinct advantages over traditional rational design approaches.”

      (3) The authors did not develop a new model but instead used their previously developed Pro-PRIME model. This significantly weakens the novelty and contribution of this work.

      While it is true that the Pro-PRIME model was previously developed, the novelty and contribution of this work lie in its novel application to design proteins with properties that are not naturally found or are rare in nature. In our original work, the Pro-PRIME model was used to optimize proteins for existing, well-established properties, such as thermal stability, enzymatic activity, and affinity. However, in this study, we extended the model’s capabilities to design proteins that exhibit resilience to extreme environments, such as high pH—properties that are not inherently present in most natural proteins. To our knowledge, no existing model has addressed the challenge of engineering alkali-resistant proteins, nor is there relevant dataset available for training such models.

      This shift from optimizing existing characteristics to engineering entirely new properties represents a significant step forward in the field of protein design. By focusing on the design of proteins that can survive and function in harsh, unnatural environments, we have demonstrated the broader applicability of the Pro-PRIME model beyond its initial scope. This expansion of the model's application is a novel contribution that has the potential to accelerate the development of proteins for industrial, agricultural, and biotechnological applications.

      Thus, while the Pro-PRIME model itself is not new, its application to the new challenge of engineering proteins with alkali resistance and other novel properties significantly enhances the impact and novelty of this work. Moreover, this work is groundbreaking not only in terms of the model’s novel application but also because no previous studies have specifically targeted alkali resistance or provided data for training models on such extreme properties. Therefore, our approach is unique, marking a new direction in protein engineering.

      We have made the following revisions to the conclusions section of the manuscript:

      “Through two rounds of evolution, we successfully designed a VHH antibody with strong resistance to extreme environments and enhanced affinity using the Pro-PRIME model. Although rare case can tolerate the extreme pH and saline conditions in our pre-training dataset, the Pro-PRIME model showed impressive performance after supervised learning with limited data, especially on capturing the epistatic effects. The analysis of these 65 mutants revealed that the Pro-PRIME model is adept at exploring the large space of protein fitness, being less susceptible to local optima, and having greater potential to find the global optimum. Our efficient method of designing mutants that consider multiple properties improvement holds promise for industrial application of proteins. Specifically, the VHH antibody has been deployed in practical production and significantly enhancing the efficiency of the entire production line after our design. While the Pro-PRIME model itself has been reported, this work demonstrates its first-time application to the challenge of designing proteins with alkali resistance and other extreme properties that are not found in natural proteins, nor have previous studies addressed or provided data for such applications. This shift from optimizing existing protein properties to engineering entirely new, unnatural traits is a significant advance in the field. This study shows that the AI models, such as Pro-PRIME, can not only guide the evolution of protein thermal stability, enzymatic activity, ligand affinity, etc., but also enable to develop the mutants adapting the harsh unnatural environments, such as extreme pH and concentrated salt, largely expanding its application. The novelty of this work lies in the ability to design and engineer proteins with novel properties, specifically alkali resistance, which is an unprecedented achievement in AI-assisted protein engineering. The great potential of AI model is expected to significantly accelerate the development of proteins for diverse applications in medicine, agriculture, bioengineering, etc.”

      (4) The computational experiments are not well-justified. For instance, the authors used a zero-shot setting for single-point mutation experiments but opted for fine-tuning in multiple-point mutation experiments. There is no clear explanation for this discrepancy. How does the model perform in zero-shot settings for multiple-point mutations? How would fine-tuning affect single-point mutation results? The choice of these strategies seems arbitrary and lacks sufficient discussion.

      We appreciate the reviewer’s comment regarding the use of zero-shot and fine-tuning settings for single-point and multi-point mutation experiments, and we are grateful for the opportunity to further clarify this aspect of our work.

      In the first round of design, we used the zero-shot approach for single-point mutations because the number of possible single-point mutations is limited, and no prior experimental data was available. In the absence of relevant data, the zero-shot approach allows the model to make predictions based on the learned sequence patterns from the pre-trained protein language model. Given that single-point mutations are relatively fewer in number and computationally feasible to evaluate, the zero-shot approach was deemed appropriate for this task.

      However, when it comes to designing multi-point mutants, the number of potential combinations increases exponentially, making it computationally impractical to explore all possible mutations in a reasonable timeframe. Furthermore, since we had already obtained some experimental data for single-point mutations in the first round, we fine-tuned the model with this data in the second round to improve the accuracy of predictions for multi-point mutants. Fine-tuning helps the model better capture the specific features that contribute to protein functionality, which are critical when dealing with multi-point mutations where multiple residues interact. This allows the model to produce more reliable and targeted predictions for multi-point mutants, ultimately leading to better design outcomes.

      Regarding the model's performance in zero-shot settings for multi-point mutations, we tested this approach, and the results did not align well with the experimental data for multi-point mutants. Specifically, the Spearman correlation coefficient between the zero-shot predictions and experimental results was -0.71, indicating that zero-shot predictions for multi-point mutations were not as accurate as those from the fine-tuned model.

      In summary, the choice of using zero-shot for single-point mutations and fine-tuning for multi-point mutations was driven by the nature of the task and the availability of experimental data. Fine-tuning the model improves its predictive performance, especially for more complex multi-point mutation tasks. We have now clarified these choices in the manuscript and have added further discussion on the trade-offs between zero-shot and fine-tuning approaches.

      Specifically, we have added the following discussion at the section 2.2 in the main text:

      “Note that we employed different strategies for designing single-point and multi-point mutants, specifically using a zero-shot approach for single-point mutations and fine-tuning the model for multi-point mutations. These choices were made based on the distinct characteristics of the two tasks and the availability of experimental data. For single-point mutations, the number of possible mutations is relatively limited, and at the outset, there were no experimental data available. In such cases, the zero-shot setting was chosen because it allows the model to predict the fitness of mutants based solely on the information learned during pre-training on a large protein sequence dataset. Since single-point mutations are computationally manageable, this approach was deemed appropriate to generate initial predictions for protein engineering. However, when designing multi-point mutants, the situation changes significantly. The potential combinations of mutations increase exponentially, and without prior data, it becomes computationally infeasible to evaluate every possible combination within a reasonable timeframe. Moreover, by the time we reached the multi-point mutation design stage, experimental data for several single-point mutations had already been obtained. This data enabled us to fine-tune the model to better capture the specific structural and functional features that contribute to protein stability and resistance, especially in the context of multiple interacting mutations. Fine-tuning improves the model’s accuracy by adjusting its parameters to align more closely with the experimental data, ensuring that the predicted multi-point mutants are more likely to meet the desired engineering goals. After the second round of design, the fitness of the mutants was further improved. In improving alkali resistance, experimental results showed that 15 of the 45 designed mutants exhibited positive responses, yielding a success rate of 30%, close to the 35% success rate achieved in the second round. Compared to the wild type, the best single-point mutant improved alkali resistance by approximately 44.7%, while the best multi-point mutant achieved a 67.7% increase. For thermal stability enhancement, the success rate in the first round was 77.8%, rising to 100% in the second round. The top single-point mutant exhibited a Tm increase of 6.37°C over the wild type, while the best multi-point mutant had a Tm increase of 10.02°C. We also tested the performance of the zero-shot approach for multi-point mutants, and the results showed that this method did not yield satisfactory predictions. The Spearman correlation coefficient between the zero-shot predictions and experimental results for multi-point mutants was -0.71, indicating a significant discrepancy. This further highlights the importance of fine-tuning the model for multi-point mutations, as the fine-tuned model provided more accurate and reliable results. In summary, the choice of zero-shot for single-point mutations and fine-tuning for multi-point mutations was driven by practical considerations regarding computational feasibility and the availability of experimental data. Fine-tuning the model significantly enhances its predictive performance, particularly for complex multi-point mutations where multiple residues interact. We believe this strategy strikes an optimal balance between computational efficiency and predictive accuracy, making it well-suited for practical protein engineering applications.”

    1. Author response:

      We would like to thank the reviewers and the editors for carefully reading and commenting our manuscript and plan to prepare a revised manuscript. Particularly, we want to thank reviewer 2 for spotting a major oversight regarding the use of the TKO (TRiP-CRISPR knockout) and TOE (TRiP-CRISPR Over Expression) systems and the MiMIC alleles. As the reviewer pointed out, these lines were not used as intended, therefore our results and conclusions regarding the genetic interactions between Pink1 and several of genes in the paper (PIG-A, Rab7, Ccz1, CG10646, Mon1, FASN2, CG17712) that we attempted to target, are incorrect and based on a technical mistake. These results need to be removed from the manuscript.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews: 

      Reviewer #1 (Public Review): 

      The authors assess the effectiveness of electroporating mRNA into male germ cells to rescue the expression of proteins required for spermatogenesis progression in individuals where these proteins are mutated or depleted. To set up the methodology, they first evaluated the expression of reporter proteins in wild-type mice, which showed expression in germ cells for over two weeks. Then, they attempted to recover fertility in a model of late spermatogenesis arrest that produces immotile sperm. By electroporating the mutated protein, the authors recovered the motility of ~5% of the sperm, although the sperm regenerated was not able to produce offspring using IVF.

      We actually did not write that “sperm regenerated was not able to produce offspring using IVF” but rather that IVF was not attempted because the number of rescued sperm was too low. To address this important point, the ability of sperm to produce embryos was therefore challenged by two different assisted reproduction technologies, that are IVF and ICSI. To increase the number of motile sperm for IVF experiments, we have injected both testes from one male. We also conducted intracytoplasmic sperm injection (ICSI) experiments, using only rescued sperm, identified as motile sperm with a normal flagellum. The results of these new experiments have demonstrated that the rescued ARMC2 sperm successfully fertilized eggs and produced embryos at the two-cell stage by IVF and blastocysts by ICSI. These outcomes are presented in Figure 12.

      This is a comprehensive evaluation of the mRNA methodology with multiple strengths. First, the authors show that naked synthetic RNA, purchased from a commercial source or generated in the laboratory with simple methods, is enough to express exogenous proteins in testicular germ cells. The authors compared RNA to DNA electroporation and found that germ cells are efficiently electroporated with RNA, but not DNA. The differences between these constructs were evaluated using in vivo imaging to track the reporter signal in individual animals through time. To understand how the reporter proteins affect the results of the experiments, the authors used different reporters: two fluorescent (eGFP and mCherry) and one bioluminescent (Luciferase). Although they observed differences among reporters, in every case expression lasted for at least two weeks. 

      The authors used a relevant system to study the therapeutic potential of RNA electroporation. The ARMC2-deficient animals have impaired sperm motility phenotype that affects only the later stages of spermatogenesis. The authors showed that sperm motility was recovered to ~5%, which is remarkable due to the small fraction of germ cells electroporated with RNA with the current protocol. The 3D reconstruction of an electroporated testis using state-of-the-art methods to show the electroporated regions is compelling. 

      The main weakness of the manuscript is that although the authors manage to recover motility in a small fraction of the sperm population, it is unclear whether the increased sperm quality is substantial to improve assisted reproduction outcomes. The quality of the sperm was not systematically evaluated in the manuscript, with the endpoints being sperm morphology and sperm mobility. 

      We would like to thank the reviewers for their comments. As previously stated above, we produced additional rescue experiments and performed CASA, morphology observation, IVF and ICSI with the rescued sperm. The rescued ARMC2 sperm exhibited normal morphology (new figure 11 and Supp Fig 8), motility (figure 11), and fecundity (figure 12).  Whereas sperm from untreated KO males were unable to fertilize egg by IVF, the rescued sperm fertilized eggs in vitro at a significant level (mean 62%, n=5), demonstrating that our strategy improves the sperm quality and assisted reproduction outcome (from 0 to 62%). 

      Some key results, such as the 3D reconstruction of the testis and the recovery of sperm motility, are qualitative given the low replicate numbers or the small magnitude of the effects. The presentation of the sperm motility data could have been clearer as well. For example, on day 21 after Armc2-mRNA electroporation, only one animal out of the three tested showed increased sperm motility. However, it is unclear from Figure 11A what the percentage of sperm motility for this animal is since the graph shows a value of >5% and the reported aggregate motility is 4.5%. It would have been helpful to show all individual data points in Figure 11A. 

      We provide now in figure 11A, a graph showing the percentage of rescued sperm for all animals. (scatter dot plot). Moreover, we performed additional CASA experiments to analyze in detail sperm motility (Figure 11A2-A3). Individual CASA parameters for motile sperm cells were extracted as requested by reviewer 3 and represented in a new graph (Fig 11 A2). 

      The expression of the reporter genes is unambiguous; however, better figures could have been presented to show cell type specificity. The DAPI staining is diffused, and it is challenging to understand where the basement membranes of the tubules are. For example, in Figures 7B3 and 7E3, the spermatogonia seems to be in the middle of the seminiferous tubule. The imaging was better for Figure 8. Suboptimal staining appears to lead to mislabeling of some germ cell populations. For example, in Supplementary Figure 4A3, the round spermatid label appears to be labeling spermatocytes. Also, in some instances, the authors seem to be confusing, elongating spermatids with spermatozoa, such as in the case of Supplementary Figures 4D3 and D4.

      Thanks for the comments, some spermatogenic cells were indeed mislabeled as you mentioned. We have therefore readjusted the labeling accordingly. We also changed spermatozoa to mature spermatids. The new sentence is now: “At the cellular level, fluorescence was detectable in germ cells (B1-B3) including Spermatogonia (Sg), Spermatocytes (Scytes),round Spermatids (RStids), mature spermatids (m-Sptids) and Sertoli cells (SC)”. Moreover, to indicate the localization of the basal membrane, we have also labelled myoid cells.

      The characterization of Armc2 expression could have been improved as well. The authors show a convincing expression of ARMC2 in a few spermatids/sperm using a combination of an anti-ARMC2 antibody and tubules derived from ARMC2 KO animals. At the minimum, one would have liked to see at least one whole tubule of a relevant stage.  

      Thanks for the remark. 

      We present now new images showing transversal section of seminiferous tubules as requested (see supp fig 6). In this new figure, it is clear that Armc2 is only expressed in spermatids. We have also added in this figure an analysis of the RNA-seq database produced by Gan's team (Gan, Wen et al. 2013), confirming that ArmC2 expression is predominantly expressed at the elongated spermatid stage. This point is now clearly indicated in the text.

      Overall, the authors show that electroporating mRNA can improve spermatogenesis as demonstrated by the generation of motile sperm in the ARMC2 KO mouse model. 

      Thank you

      Reviewer #2 (Public Review): 

      Summary: 

      Here, the authors inject naked mRNAs and plasmids into the rete testes of mice to express exogenous proteins - GFP and later ARMC2. This approach has been taken before, as noted in the Discussion to rescue Dmc1 KO infertility. While the concept is exciting, multiple concerns reduce reviewer enthusiasm. 

      Strengths: 

      The approach, while not necessarily novel, is timely and interesting.  Weaknesses: 

      Overall, the writing and text can be improved and standardized - as an example, in some places in vivo is italicized, in others it's not; gene names are italicized in some places, others not; some places have spaces between a number and the units, others not. This lack of attention to detail in the preparation of the manuscript is a significant concern to this reviewer - the presentation of the experimental details does cast some reasonable concern with how the experiments might have been done. While this may be unfair, it is all the reviewers have to judge. Multiple typographical and grammatical errors are present, and vague or misleading statements. 

      Thanks for the comment, we have revised the whole manuscript to remove all the mistakes. We have also added new experiments/figures to strengthen the message. Finally, we have substantially modified the discussion.

      Reviewer #3 (Public Review):

      Summary: 

      The authors used a novel technique to treat male infertility. In a proof-of-concept study, the authors were able to rescue the phenotype of a knockout mouse model with immotile sperm using this technique. This could also be a promising treatment option for infertile men. 

      Strengths: 

      In their proof-of-concept study, the authors were able to show that the novel technique rescues the infertility phenotype in vivo. 

      Weaknesses: 

      Some minor weaknesses, especially in the discussion section, could be addressed to further improve the quality of the manuscript. 

      We have substantially modified the discussion, following the remarks of the reviewers.

      It is very convincing that the phenotype of Armc2 KO mice could (at least in part) be rescued by injection of Armc2 RNA. However, a central question remains about which testicular cell types have been targeted by the constructs. From the pictures presented in Figures 7 and 8, this issue is hard to assess. Given the more punctate staining of the DNA construct a targeting of Sertoli cells is more likely, whereas the more broader staining of seminiferous tubules using RNA constructs is talking toward germ cells. Further, the staining for up to 119 days (Figure 5) would point toward an integration of the DNA construct into the genome of early germ cells such as spermatogonia and/or possibly to Sertoli cells. 

      Thanks for the comment. We would like to recall the peculiar properties of the non-insertional Enhanced Episomes Vector (EEV) plasmid, which is a non-viral episome based on the Epstein-Barr virus (EBV: Epstein-Barr Virus). It allows the persistence of the plasmid for long period of time without integration. Its maintenance within the cell is made possible by its ability to replicate in a synchronous manner with the host genome and to segregate into daughter cells. This is due to the fact that EEV is composed of two distinct elements derived from EBV: an origin of replication (oriP) and an EpsteinBarr Nuclear Antigen 1 (EBNA1) expression cassette (Gil, Gallaher, and Berk, 2010).   The oriP is a locus comprising two EBNA1-binding domains, designated as the Family of Repeats (FR) and Dyad Symmetry (DS). The FR is an array of approximately 20 EBNA1-binding sites (20 repeats of 30 bp) with high affinity, while the DS comprises four lower-affinity sites operating in tandem (Ehrhardt et al., 2008). 

      The 641-amino-acid EBNA1 protein contains numerous domains. The N-terminal domains are rich in glycines and alanines, which enable interaction with host chromosomes. The C-terminal region is responsible for binding to oriP (Hodin, Najrana, and Yates, 2013). The binding of EBNA1 to the DS element results in the recruitment of the origin of replication. This results in the synchronous initiation of extra-chromosomal EEV replication with host DNA at each S phase of the cell cycle (Düzgüneş, Cheung, and Konopka 2018). Furthermore, EBNA1 binding to the FR domain induces the formation of a bridge between metaphase chromosomes and the vector during mitosis. This binding is responsible for the segregation of the EEV episome in daughter cells (Düzgüneş, Cheung, and Konopka 2018). It is notable that EEV is maintained at a rate of 90-95% per cell division.

      Because of the intrinsic properties of EEV described above, the presence of the reporter protein at 119 day after injection was likely due to the maintenance of the plasmid, mostly in Sertoli cells, and not to the DNA integration of the plasmid.

      Of note, the specificity of EEV was already indicated in the introduction (lines 124-128 clean copy). Nevertheless, we have added more information about EEV to help the readers.  

      Given the expression after RNA transfection for up to 21 days (Figure 4) and the detection of motile sperm after 21 days (Figure 11), this would point to either round spermatids or spermatocytes.  These aspects need to be discussed more carefully (discussion section: lines 549-574).

      We added a sentence to highlight that spermatids are transfected and protein synthetized at this stage and this question is discussed in details (see lines 677-684 clean copy).

      It would also be very interesting to know in which testicular cell type Armc2 is endogenously expressed (lines 575-591)

      Thanks for the remarks. We present now new images showing the full seminiferous tubules as requested by reviewer 1 (see supp fig 6). In this new figure, it is clear that Armc2 is only expressed in spermatids. We have also added in this figure an analysis of the RNA-seq database produced by Gan's team (Gan, Wen et al. 2013), confirming that Armc2 is predominantly expressed at the elongated spermatid stage. This point is now clearly indicated in the text. (lines 570-579 clean copy).

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors): 

      The article is well-structured and easy to read. Nonetheless, there are typos and mistakes in some places that are distracting to the reader, such as the capitalization of the word "Oligo-" in the title of the manuscript, the use of the word "Materiel" in the title of the Materials and methods and the presence of space holders "Schorr staining was obtained from Merck (XXX)".  Thank you, we corrected the misspelling of "Materials and Methods" and corrected our error: "obtained from Merck (Darmstadt, Germany)". We also carefully corrected the manuscript to remove typos and mistakes.

      The discussion is too lengthy, with much repetition regarding the methods used and the results obtained. For example, these are two sentences from the discussion. "The vector was injected via the rete testis into the adult Armc2 KO mice. The testes were then electroporated." I would recommend shortening these passages.

      Thanks for your comments, we removed the sentences and we have substantially modified the discussion, following the remarks of the reviewers.

      The work is extensive, and many experiments have been done to prove the points made. However, a more in-depth analysis of critical experiments would have benefited the manuscript significantly. A more thorough analysis of sperm mobility and morphology using the CASA system would have been an initial step.

      In response to the observations made, additional CASA experiments and sperm motility analysis were conducted, as illustrated in Figure 11 (A2-A3). Individual CASA parameters for motile sperm cells were extracted as suggested and represented in a new graph (Fig 11 A2). We have observed significant differences between WT and rescued sperm. In particular, the VSL and LIN parameters were lower for rescued sperm. Nevertheless, these differences were not sufficient to prevent IVF, maybe because the curvilinear velocity (VCL) was not modified.

      In the case of ARMC2 localization, an analysis of the different stages of spermatogenesis to show when ARMC2 starts to be expressed. 

      Thanks for the remarks. This is an important remark pointed out by all reviewers. As explained above, we have performed more experiments. We present now new images showing transversal section of seminiferous tubules as requested (see supp fig 6). In this new figure, it is clear that Armc2 is only expressed in spermatid layers. We have also added in this figure an analysis of the RNA-seq database produced by Gan's team (Gan, Wen et al. 2013), confirming that ArmC2 expression is predominantly expressed at the elongated spermatid stage. This point is now clearly indicated in the text. (lines 575579 clean copy).

      Finally, exploring additional endpoints to understand the quality of the sperm generated, such as the efficiency of ICSI or sperm damage, could have helped understand the degree of the recovery.

      This point was underlined in public review. We paste here our answer: “To address this important point, the ability of sperm to produce embryos was therefore challenged by two different assisted reproduction technologies, that are IVF and ICSI. To increase the number of motile sperm for IVF experiments, we have injected both testes from one male. We also conducted intracytoplasmic sperm injection (ICSI) experiments, using only rescued sperm, identified as motile sperm with a normal flagellum. The results of these new experiments have demonstrated that the rescued ARMC2 sperm successfully fertilized eggs and produced embryos at the two-cell stage by IVF and blastocysts by ICSI. These outcomes are presented in Figure 12.”

      Reviewer #2 (Recommendations For The Authors):

      38,74 intracellular

      Thanks, we changed it accordingly: "Intracytoplasmic sperm injection (ICSI) is required to treat such a condition, but it has limited efficacy and has been associated with a small increase in birth defects" and "such as intracytoplasmic sperm injection (ICSI)".

      39 "limited efficacy" Versus what? And for what reason? "small increase in birth defects" - compared to what? 

      We changed to “… but it is associated with a small increase in birth defect with comparison to pregnancies not involving assisted conception.”

      40 Just thinking through the logic of the argument thus far - the authors lay out that there are people with OAT (true), ICSI must be used (true), ICSI is bad (not convincing), and therefore a new strategy is needed... so is this an alternative to ICSI? And this is to restore fertility, not "restore spermatogenesis"

      - because ICSI doesn't restore spermatogenesis. This logic flow needs to be cleaned up some

      Thanks we changed it accordingly: “restore fertility.”

      45 "mostly"?

      Thank you, we removed the word: “We show that mRNA-coded reporter proteins are detected for up to 3 weeks in germ cells, making the use of mRNA possible to treat infertility.”

      65 Reference missing. 

      We added the following reference Kumar, N. and A. K. Singh (2015). "Trends of male factor infertility, an important cause of infertility: A review of literature." J Hum Reprod Sci 8(4): 191-196.

      68 Would argue meiosis is not a reduction of the number of chromosomes - that happens at the ends of meiosis I and II - but the bulk of meiosis is doubling DNA and recombination; would re-word; replace "differentiation" with morphogenesis, which is much more commonly used:

      Thank you, we have changed the sentence accordingly: "proliferation (mitosis of spermatogonia), reduction of the number of chromosomes (meiosis of spermatocytes), and morphogenesis of sperm (spermiogenesis)".

      70 "almost exclusively" is an odd term, and a bit of an oxymoron - if not exclusively, then where else are they expressed? Can you provide some sense of scale rather than using vague words like "large", "almost", "several", "strongly" and "most...likely" - need some support for these claims by being more specific: 

      Thanks for the comment, we changed the sentence: "The whole process involves around two thousand genes, 60% of which are expressed exclusively in the testes."

      73 "severe infertility" is redundant - if they are infertile, is there really any more or less about it? I think what is meant is patients with immotile sperm can be helped by ICSI - so just be more specific... 

      We changed the transition : “Among infertility disorders, oligo-astheno-teratozoospermia  (OAT) is the most frequent (50 % (Thonneau, Marchand et al. 1991); it is likely to be of genetic origin. Spermatocytograms of OAT patients show a decrease in sperm concentration, multiple morphological defects and defective motility. Because of these combined defects, patients are infertile and can only conceive by IntraCytoplasmic Sperm Injection (ICSI). IntraCytoplasmic Sperm Injection (ICSI) can efficiently overcome the problems faced. However, there are …”

      75 "some" is vague - how many concerns, and who has them? Be specific!

      Thanks for the comment, we removed the word.

      76-7 Again, be specific - "real" has little meaning - what is the increased risk, in % or fold? This is likely a controversial point, so make sure you absolutely support your contention with data .

      77 "these"? There was only one concern listed - increased birth defects; and "a number" is vague - what number, 1 or 1,000,000? A few (2-3), dozens, hundreds? 

      Thanks for the comment, we have reworded the sentence: “Nevertheless, concerns persist regarding the potential risks associated with this technique, including blastogenesis defect, cardiovascular defect, gastrointestinal defect, musculoskeletal defect, orofacial defect, leukemia, central nervous system tumors, and solid tumors. Statistical analyses of birth records have demonstrated an elevated risk of birth defects, with a 30–40% increased likelihood in cases involving ICSI, and a prevalence of birth defects between 1% and 4%.” We have added a list of references to support these claims.

      79-81 So, basically transgenesis? Again, vague terms "widely" - I don't think it's all that widely used yet... and references are missing to support the statement that integration of DNA into patient genomes is widely used. Give specific numbers, and provide a reference to support the contention. 

      Thanks for the comment, we removed the word widely and add references.

      81-5 Just finished talking about humans, but now it appears the authors have switched to talking about mice - got to let the readers know that! Unless you're talking about the Chinese group that deleted CCR5 in making transgenic humans? 

      Your feedback is greatly appreciated. In response to your comments, the sentence in question has been amended to provide a more comprehensive understanding. Indeed, the text refers to experiences carried in mice. The revised wording is as follows: “Given the genetic basis of male infertility, the first strategy, tested in mice, was to overcome spermatogenic failure associated with monogenic diseases by delivery of an intact gene to deficient germ cells (Usmani, Ganguli et al. 2013). 

      84-5 "efficiently" and "high" - provide context so the reader can understand what is meant - do the authors mean the experiments work efficiently, or that a high percentage of cells are transfected? And give some numbers or range of numbers - you're asking the readers to take your word for things when you choose adjectives - instead, provide values and let the readers decide for themselves.

      Thanks for the comment, we have reworded the sentence: Gene therapy is effective in germ cells, as numerous publications have shown that conventional plasmids can be transferred into spermatogonia in several species with success, allowing their transcription in all cells of the germinal lineage (Usmani, Ganguli et al. 2013, Michaelis, Sobczak et al. 2014, Raina, Kumar et al. 2015, Wang, Liu et al. 2022).

      93 Reference at the end of the sentence "most countries"

      Thanks, we changed the sentence and added the reference: the new sentence is "… to avoid any eugenic deviations, transmissible changes in humans are illegal in 39 countries (Liu 2020)” (Liu, S. (2020). "Legal reflections on the case of genomeedited babies." Glob Health Res Policy 5: 24

      93-4 Odd to say "multiple" and then list only one. 

      Thanks for the comment, we have reworded the sentence: “Furthermore, the genetic modification of germ cell lines poses biological risks, including the induction of cancer, off-target effects, and cell mosaicism. Errors in editing may have adverse effects on future generations. It is exceedingly challenging to anticipate the consequences of genetic mosaicism, for instance, in a single individual. (Sadelain, Papapetrou et al. 2011, Ishii 2017).”

      97 Is this really a "small" change? Again, would use adjectives carefully - to this reviewer, this is not a small change, but a significant one! And "should be" is not altogether convincing

      Thanks for the comment, we have reworded the sentence: “Thanks to this change, the risk of genomic insertion is avoided, and thus there is no question of heritable alterations.”

      What chance is there of retrotransposition? Is there any data in the literature for that, after injecting millions of copies of RNA one or more might be reverse transcribed and inserted into the genome?

      This is certainly possible and is the putative origin for multiple intronless spermatid-expressed genes: 

      The expert poses an interesting question, but one that unfortunately remains unanswered at present. Most papers on mRNA therapy state that there is no risk concerning genomic integration, but no reference is given (for instance see mRNA-based therapeutics: looking beyond COVID-19 vaccines. Lancet. 2024 doi: 10.1016/S0140-6736(23)02444-3). This is an important question, which deserves to be evaluated, but is beyond the scope of this manuscript. Nevertheless is remaining very debating (Igyarto and Qin 2024).

      98 Odd to say "should be no risk" and then conclude with "there is no question" - so start the sentence with 'hedging', and then end with certainty - got to pick one or the other.

      Thanks for the comment, we have reworded the sentence

      99 "Complete" - probably not, would delete:

      We removed the word: “The first part of this study presents a characterization of the protein expression patterns obtained following transfection of naked mRNA coding for reporter genes into the testes of mice”

      101-2 Reference missing, as are numbers - what % of cases? 

      Thank you, we changed the sentence and added the reference: “Among infertility disorders, oligoastheno-teratozoospermia  (OAT) is the most frequent (50 % (Thonneau, Marchand et al. 1991)” Thonneau, P., S. Marchand, A. Tallec, M. L. Ferial, B. Ducot, J. Lansac, P. Lopes, J. M. Tabaste and A. Spira (1991). "Incidence and main causes of infertility in a resident population (1,850,000) of three French regions (1988-1989)." Hum Reprod 6(6): 811-816.

      103 Once again, the reference is missing:

      We have added these references: (Colpi, Francavilla et al. 2018) (Cavallini 2006)

      104-5 Awkward transition.

      Thanks, we changed the transition: “The first part of this study presents a characterization of the protein expression patterns obtained following transfection of naked mRNA coding for reporter genes into the testes of mice. The second part is to apply the protocol to a preclinical mouse model of OAT.”

      105 Backslash is odd - never seen it used in that way before

      Removed

      108 "completely infertile" is redundant;

      Thank you, we changed it accordingly: “Patients and mice carrying mutations in the ARMC2 gene present a canonical OAT phenotype and are infertile”.

      and is a KO mouse really "preclinical"? 

      The definition of preclinical research, is research involving the use of animals to ascertain the potential efficacy of a drug, procedure, or treatment. Preclinical studies are conducted prior to any testing in humans. Our KO mouse model has been shown to mimic human infertility. Indeed Armc2-/-mice exhibit a phenotype that is identical to that observed in humans. Our study is in line with this definition. For this reason, we have decided to maintain our current position and to use the term "preclinical" in the article. 

      110  Delete "sperm".

      Thank you, we changed it accordingly: “The preclinical Armc2 deficient (Armc2 KO) mouse model is therefore a valuable model to assess whether in vivo injection of naked mRNA combined with electroporation can restore spermatogenesis”

      111  "Easy"? Really? 

      We changed it accordingly: “We chose this model for several reasons: first, Armc2 KO mice are sterile and all sperm exhibit short, thick or coiled flagella [13].”

      112-3 "completely immobile" is redundant - either they are immobile or not.

      Thank you, we changed it accordingly: “As a result, 100 % of sperm are immobile, thus it should be easy to determine the efficacy of the technique by measuring sperm motility with a CASA system.”

      108-33 Condense this lengthy text into a coherent few sentences to give readers a sense of what you sought to accomplish, broadly how it was done, and what you found. This reads more like a Results section

      Thanks for the comment, we shortened the text.

      Materials and Methods 

      The sections appear to have been written by different scientists - the authors should standardize so that similar detail and formatting are used - e.g., in some parts the source is in parentheses with catalog number, in others not, some have city, state, country, others do not... the authors should check eLife mandates for this type of information and provide. 

      We are grateful for your feedback. We standardized the text, and if we had missed some, as outlined on the E-Life website, we can finish to format the article once it has been accepted for publication in the journal before sending the VOR.

      134 Misspelling

      We corrected the misspelling  

      142 Just reference, don't need to spell it out.

      Thanks, we changed it accordingly: “and the Armc2 KO mouse strain obtained by CRISPR-Cas9 (Coutton, Martinez et al. 2019). Experiments”

      150 What is XXX?

      We would like to express our gratitude for bringing this error to our attention. We have duly rectified the issue: “obtained from Merck (Darmstadt, Germany).”

      157-60 Are enough details provided for readers to repeat this if necessary? Doesn't seem so to this reviewer; if kits were followed, then can say "using manufacturer's protocol", or refer to another manuscript - but this is too vague. 

      Thanks, we change it accordingly: After expansion, plasmids were purified with a NucleoBond Xtra Midi kit (740410-50; Macherey-Nagel, Düren, Germany) using manufacturer's protocol.”

      165 Again, too few details - how was it purified? What liquid was it in?

      Thanks for the comment, the EEV plasmids were purified like all other plasmids. We change the text: “All plasmids,EEV CAGs-GFP-T2A-Luciferase,((EEV604A-2), System Bioscience, Palo Alto, CA, USA), mCherry plasmid ( given by Dr. Conti MD at UCSF, San Francisco, CA, USA) and EEV-Armc2-GFP plasmid (CUSTOM-S017188-R2-3,Trilink,San Diego, USA) were amplified by bacterial transformation” 

      170 Seems some words are missing - and will everyone know Dr. Conti by last name alone? Would spell out, and the details of the plasmid must either be provided or a reference given; how was amplification done? Purification? What was it resuspended in? 

      Thank for the remark, the mcherry plasmids were purified like all other plasmids. We change the text: “All plasmids,EEV CAGs-GFP-T2A-Luciferase,((EEV604A-2), System Bioscience, Palo Alto, CA, USA), mCherry plasmid ( given by Dr. Conti MD, UCSF, San Francisco, CA, USA) and EEV-Armc2-GFP plasmid (CUSTOM-S017188-R2-3,Trilink,San Diego, USA) were amplified by bacterial transformation”

      175 Again, for this plasmid provide more information - catalog number, reference, etc; how amplified and purified, what resuspension buffer?

      Thank you for the remark, as We mentioned, we add this sentence for the preparation: “All plasmids, EEV CAGs-GFP-T2A-Luciferase,((EEV604A-2), System Bioscience, Palo Alto, CA, USA), mCherry plasmid (given by Dr. Conti MD at UCSF, San Francisco, CA, USA) and EEV-Armc2-GFP plasmid (CUSTOMS017188-R2-3,Trilink,San Diego, USA) were amplified by bacterial transformation” and we add these sentence “The EEV-Armc2-GFP plasmid used for in vivo testes microinjection and electroporation was synthesized and customized by Trilink (CUSTOM-S017188-R2-3,San Diego, USA).”

      183 What sequence, or isoform was used? Mouse or human? 

      Thanks, we changed accordingly: “This non-integrative episome contains the mice cDNA sequences of Armc2 (ENSMUST00000095729.11)”

      186-7 Provide sequence or catalog number; what was it resolubilized in?

      Thanks we changed accordingly “the final plasmid concentration was adjusted to 9 μg μL-1 in water.” We provided the sequence of EEV-Armc2-GFP in supp data 6.

      207-219 Much better, this is how the entire section needs to be written! 

      237-240 Font

      Thanks for the comment, we changed it accordingly

      246 Cauda, and sperm, not sperm cells

      Thanks for the comment, we changed it accordingly

      255-6 Which was done first? Would indicate clearly.

      Thanks for the comment, we changed the sentence: “Adult mice were euthanized by cervical dislocation and then transcardiac perfused  with 1X PBS”

      281-2 Provide source for software - company, location, etc: 

      We changed it accordingly: FIJI software (Opened source software) was used to process and analyze images and Imaris software (Oxford Instruments Tubney Woods, Abingdon, Oxon OX13 5QX, UK) for the 3D reconstructions.  

      323 um, not uM. 

      Thanks for the comment, we changed our mistake: “After filtration (100 µm filter)”

      Results 

      369 Weighed.  

      Thanks for the comment, we changed our mistake: “the testes were measured and weighed”

      371 No difference in what, specifically?

      Thanks for the comment, we changed the sentence to: “No statistical differences in length and weight were observed between control and treated testes”

      375 "was respected"? What does this mean?

      Thanks for the comment, we changed the sentence to “The layered structure of germ cells were identical in all conditions”

      378  This is highly unlikely to be true, as even epididymal sperm from WT animals are often defective - the authors are saying there were ZERO morphological defects? Or that there was no difference between control and treated? Only showing 2-3 sperm for control vs treatment is not sufficient.

      Your observation that the epididymal spermatozoa from wild-type animals exhibited defective morphology is indeed true. The prevalence of these defects varies by strain, with an average incidence of 20% to 40% (Kawai, Hata et al., 2006; Fan, Liu et al., 2015). To provide a more comprehensive representation, we conducted a Harris-Shorr staining procedure and included a histogram of the percentage of normal sperm in each condition (new figure 2F4). Furthermore, Harris-Shorr staining of the epididymal sperm cells revealed that there were no discernible increases in morphological defects when mRNA and EEV were utilized, in comparison with the control. We add the sentence “At last, Harris-Shorr staining of the epididymal sperm cells demonstrated that there were no increases in morphological defects when mRNA and EEV were used in comparison with the control”.

      379  "safe" is not the right word - better to say "did not perturb spermatogenesis". 

      Thanks, we changed it accordingly: “these results suggest that in vivo microinjection and electroporation of EEV or mRNA did not perturb spermatogenesis”

      382-3 This sentence needs attention, doesn't make sense as written: 

      Thanks for the remark, we changed the sentence to: “No testicular lesions were observed on the testes at any post injection time”

      389  How long after injection? 

      Thanks for the comment, we changed the sentence to: “It is worth noting that both vectors induced GFP expression at one day post-injection”

      390  Given the duration of mouse spermatogenesis (~35 days), for GFP to persist past that time suggests that it was maintained in SSCs? How can the authors explain how such a strong signal was maintained after such a long period of time? How stable are the episomally-maintained plasmids, are they maintained 100% for months? And if they are inherited by progeny of SSCs, shouldn't they be successively diluted over time? And if they are inherited by daughter cells such that they would still be expressed 49 days after injection, shouldn't all the cells originating from that SSC also be positive, instead of what appear to be small subsets as shown in Fig. 3H2? Overall, this reviewer is struggling to understand how a plasmid would be inherited and passed through spermatogenesis in the manner seen in these results. 

      Thanks for the comment. 

      This point was already underlined in public review. We paste here our answer: “The non-insertional Enhanced Episomes Vector (EEV) plasmid is a non-viral episome based on the Epstein-Barr virus (EBV: Epstein-Barr Virus). Its maintenance within the cell is made possible by its ability to replicate in a synchronous manner with the host genome and to segregate into daughter cells. This is due to the fact that EEV is composed of two distinct elements derived from EBV: an origin of replication (oriP) and an Epstein-Barr Nuclear Antigen 1 (EBNA1) expression cassette (Gil, Gallaher, and Berk, 2010).   The oriP is a locus comprising two EBNA1-binding domains, designated as the Family of Repeats (FR) and Dyad Symmetry (DS). The FR is an array of approximately 20 EBNA1-binding sites (20 repeats of 30 bp) with high affinity, while the DS comprises four lower-affinity sites operating in tandem (Ehrhardt et al., 2008). 

      The 641-amino-acid EBNA1 protein contains numerous domains.The N-terminal domains are rich in glycines and alanines, which enable interaction with host chromosomes. The C-terminal region is responsible for binding to oriP (Hodin, Najrana, and Yates, 2013a). The binding of EBNA1 to the DS element results in the recruitment of the origin of replication. This results in the synchronous initiation of extra-chromosomal EEV replication with host DNA at each S phase of the cell cycle (Düzgüneş, Cheung, and Konopka 2018a). Furthermore, EBNA1 binding to the FR domain induces the formation of a bridge between metaphase chromosomes and the vector during mitosis. This binding is responsible for the segregation of the EEV episome in daughter cells (Düzgüneş, Cheung, and Konopka 2018b). It is notable that EEV is maintained at a rate of 90-95% per cell division.”

      Because of the intrinsic properties of EEV described above, the presence of the reporter protein at 119 day after injection was likely due to the maintenance of the plasmid, mostly in Sertoli cells, and not to the DNA integration of the plasmid.

      Of note, the specificity of EEV was already indicated in the introduction. Nevertheless, we have added more information about it to help the readers (lines 124-128 clean copy)  

      398 Which "cell types"? 

      Your feedback is greatly appreciated, and the sentence in question has been amended to provide a more comprehensive understanding. The revised wording is as follows: These results suggest that GFPmRNA and EEV-GFP targeted different seminiferous cell types, such as Sertoli cells and all germline cells, or that there were differences in terms of transfection efficiency.

      409 Why is it important to inject similar copies of EEV and mRNA? Wouldn't the EEV be expected to generate many, many more copies of RNA per molecule than the mRNAs when injected directly?? 

      We removed the word importantly. 

      415 How is an injected naked mRNA stably maintained for 3 weeks? What is the stability of this mRNA?? Wouldn't its residence in germ cells for 21 days make it more stable than even the most stable endogenous mRNAs? Even mRNAs for housekeeping genes such as actin, which are incredibly stable, have half-lives of 9-10 hours.

      We appreciate your inquiry and concur with your assessment that mRNA stability is limited.  It is our hypothesis that the source of the confusion lies in the fact that we injected mRNA coding for the GFP protein, rather than mRNA tagged with GFP. After a three-week observation period, we did not observe the mRNA, but we observed the expression of the GFP protein induced by the mRNA. To draw the reader's attention to this point, we have added the following sentence to the text “It is important to underline that the signal measured is the fluorescence emitted by the GFP. This signal is dependent of both the half-lives of the plasmid/mRNA and the GFP. Therefore, the kinetic of the signal persistence (which is called here expression) is a combination of the persistence of the vector and the synthetized protein. See lines 469-472 clean copy. 

      This being said, it is difficult to compare the lifespan of a cellular mRNA with that of a mRNA that has been modified at different levels, including 5’Cap, mRNA body, poly(A)tail modifications, which both increase mRNA stability and translation (see The Pivotal Role of Chemical Modifications in mRNA Therapeutics  (2022) https://doi.org/10.3389/fcell.2022.901510). This question is discussed lines 687698 clean copy

      467 "safely" should be deleted

      Thanks, we removed the word: “To validate and confirm the capacity of naked mRNA to express proteins in the testes after injection and electroporation”

      470  Except that apoptotic cells were clearly seen in Figure 2:

      We would like to thank the reviewer for their comment. We agree that the staining of the provided sections were of heterogenous quality. To address the remark, we carried out additional HE staining for all conditions, and we now present testis sections correctly stained obtained in the different condition in Fig. 2 and Supp. 7. Our observations revealed that the number of apoptotic cells remained consistent across all conditions.

      471  "remanence"?

      We appreciate your feedback and have amended the sentence to provide clear meaning. The revised wording is as follows: “The assessment of the temporal persistence of testicular mCherry fluorescent protein expression revealed a robust red fluorescence from day 1 post-injection, which remained detectable for at least 15 days (Fig. Supp. 3 B2, C2, and D2).”

      489 IF measures steady-state protein levels, not translation; should say you determined when ARMC2 was detectable. 

      Thanks for the remark, we changed the sentence to: “ By IF, we determined when ARMC2 protein was detectable during spermatogenesis.”

      491 Flagella

      Thanks for the comment, we changed our mistake: “in the flagella of the elongated spermatids (Fig 9A)”

      Discussion 

      The Discussion is largely a re-hashing of the Methods and Results, with additional background.

      Message stability must be addressed - how is a naked mRNA maintained for 21 days?

      As previously stated, it is our hypothesis that the source of the confusion lies in the fact that we injected mRNA coding for the GFP protein, rather than mRNA tagged with GFP. After a three-week observation period, we did not observe the mRNA, but we observed the synthetized GFP protein. This point and the stability of protein in the testis is now discussed lines 677-684 (clean copy).

      556 How do the authors define "safe"?

      Thanks for the comment, we changed the sentence to be clearer: “Our results also showed that the combination of injection and electroporation did not perturb spermatogenesis when electric pulses are carefully controlled”

      563 Synthesized

      Thanks, we changed it accordingly

      602 Again, this was not apparent, as there were more apoptotic cells in Fig. 2 - data must be provided to show "no effect".

      As previously stated, we carried out additional HE staining for all conditions, as can be observed in Fig. 2 . Our observations revealed that the number of apoptotic cells remained consistent across all conditions.

      629-30 This directly contradicts the authors' contention in the Introduction that ICSI was unsafe - how is this procedure going to be an advancement over ICSI as proposed, if ICSI needs to be used?? Why not just skip all this and do ICSI then?? Perhaps if this technique was used to 'repair' defects in spermatogonia or spermatocytes, then that makes more sense. But if ICSI is required, then this is not an advancement when trying to rescue a sperm morphology/motility defect.

      In light of the latest findings (Fig 12), we have revised this part of the discussion and this paragraph no longer exist.

      Nevertheless, to address specifically the reviewer’s remark, we would like to underline that ICSI with sperm from fertile donor is always more efficient than ICSI with sperm from patient suffering of OAT condition. Our strategy, by improving sperm quality, will improve the efficiency of ICSI and at the end will increase the live birth rate resulting from the first fresh IVF cycle.

      640-2 What is meant by "sperm organelles" And what examples are provided for sperm proteins being required at or after fertilization? 

      This paragraph was also strongly modified and the notion of protein persistence during spermatogenesis was discussed in the paragraph on fluorescent signal duration. See lines 698-705.

      651 "Dong team"??

      Thanks for the comment, we added the references. 

      Figure 2D2 - tubule treated with EEV-GFP appears to have considerably more apoptotic cells - this reviewer counted ~10 vs 0 in control; also, many of the spermatocytes appear abnormal in terms of their chromatin morphology - the authors must address this by staining for markers of apoptosis - not fair to conclude there was no difference when there's a very obvious difference! 

      We would like to thank the reviewer for their comment. This point was already addressed. As previously stated, we provide now new testis sections for all condition (see Fig. 2). Our observations revealed that the number of apoptotic cells remained consistent across all conditions.

      Figure 2D3 staining is quite different than D1-2, likely a technical issue - looks like no hematoxylin was added? Need to re-stain so results can be compared to the other 2 figures 

      As previously stated, we carried out additional HE staining for all conditions, and new images are provided, with similar staining. 

      Figure 3 - the fluorescent images lack any context of tubule structure so it is nearly impossible to get a sense of what cells express GFP, or whether they're in the basal vs adluminal compartment - can the authors outline them? Indicate where the BM and lumen are. 

      We would like to thank the reviewer for their comment. This figure provides actually a global view of the green fluorescent protein (GFP) expression at the surface of the testis. The entire testis was placed under an inverted epifluorescence microscope, and a picture of the GFP signal was recorded. For this reason, it is impossible to delineate the BM and the lumen. It should be noted that the fluorescence likely originates from different seminiferous tubules.

      Author response image 1.

      So, for Figure 3 if the plasmid is being uptaken by cells and maintained as an episome, is it able to replicate? Likely not. 

      Yes! it is the intrinsic property of the episome, see the detailed explanation provided above about the EEV plasmid

      So, initially, it could be in spermatogonia, spermatocytes, and spermatids. As time progressed those initially positive spermatids and then spermatocytes would be lost - and finally, the only cells that should be positive would be the progeny of spermatogonia that were positive - but, as they proliferate shouldn't the GFP signal decline? 

      Because EEV is able  to replicate in a synchronous manner with the host genome and to segregate into daughter cells at a level of 90% of the mother cell, the expected decline is very slow.

      And, since clones of germ cells are connected throughout their development, shouldn't the GFP diffuse through the intercellular bridges so entire clones are positive? Was this observed? 

      We did not perform IF experiments further than 7 days after injection, a time too short to observe what the reviewer suggested. Moreover, if at 1 day after injection, GFP synthesized from injected EEV was found in both germ cells and Sertoli cells (Fig 7), after one week, the reporter proteins were only observable in Sertoli cells. This result suggests that EEV is maintained only in Sertoli cells, thus preventing the observation of stained clones.

      Can these sections be stained for the ICB TEX14 so that clonality can be distinguished? Based on the apparent distance between cells, it appears some are clones, but many are not... 

      We thank the reviewer for this suggestion but we are not able to perform testis sectioning and costaining experiments because the PFA treatment bleaches the GFP signal. We also tested several GFP antibodies, but all failed.  

      Nevertheless, we were able to localize and identify transfected cells thank to the whole testis optical clearing, combined with a measure of GFP fluorescence and three-dimensional image reconstructions. 

      For Figure 4, with the mRNA-GFP, why does the 1-day image (which looks similar to the plasmidtransfected) look so different from days 7-21? 

      And why do days 7-21 look so different from those days in Fig 3? 

      Thank you for your feedback. It is an excellent question. Because of the low resolution of the whole testis epifluorescences imaging and light penetration issue, we decided to carry-out whole testis optical clearing and three-dimensional image reconstructions experiments, in order to get insights on the transfection process. At day 1, GFP synthesized from EEV injection was found in spermatogonia, spermatocytes and Sertoli cells (Fig 7).  After one week, the reporter protein synthesized from injected EEV was only observable in Sertoli cells.

      In contrast, for mRNA, on day 1 and day 7 post-injection, GFP fluorescent signal was associated with both Sertoli cells and germ cells. This explains why patterns between mRNA-GFP and EEV-GFP are similar at day 1 and different at day 7 between both conditions. 

      Why do the authors think the signal went from so strong at 21 to undetectable at 28? What changed so drastically over those 7 days?

      What is the half-life of this mRNA supposed to be? It seems that 21 days is an unreasonably long time, but then to go to zero at 28 seems also odd... Please provide some explanation, and context for whether the residence of an exogenous mRNA for 21 days is expected. 

      As previously stated, it is our hypothesis that the source of the confusion lies in the fact that we injected mRNA coding for the GFP protein, rather than mRNA tagged with GFP. After a three-week observation period, we did not observe the mRNA, but we observed the GFP protein produced by the mRNA. The time of observation of the reporter proteins expressed by the respective mRNA molecules (mCherry, luciferase, or GFP) ranged from 15 to 21 days. Proteins have very different turnover rates, with half-lives ranging from minutes to days. Half-lives depend on proteins but also on tissues. As explained in the discussion, it has been demonstrated that proteins involved in spermatogenesis exhibit a markedly low turnover rate and this explains the duration of the fluorescent signal. 

      The authors should immunostain testis sections from controls and those with mRNA and plasmid and immunostain with established germ cell protein fate markers to show what specific germ cell types are GFP+

      Thank you for your feedback. As previously mentioned, we were unable to perform testis sectioning and co-staining because the PFA treatment bleaches the GFP signal and because we were unable to reveal GFP with an GFP antibody, for unknown reasons.

      For the GFP signal to be maintained past 35 days, the plasmid must have integrated into SSCs - and for that to happen, the plasmid would have to cross the blood-testis-barrier... is this expected? 

      We are grateful for your observation. 

      First, as explained above, we do not think that the plasmid has been integrated. 

      Concerning the blood-testing barrier.  It bears noting that electroporation is a technique that is widely utilized in biotechnology and medicine for the delivery of drugs and the transfer of genes into living cells (Boussetta, Lebovka et al. 2009). This process entails the application of an electric current, which induces the formation of hydrophilic pores in the lipid bilayer of the plasma membrane (Kanduser, Miklavcic et al. 2009). The pores remain stable throughout the electroporation process and then close again once it is complete. Consequently, as electroporation destabilizes the cell membrane, it can also destabilize the gap junctions responsible of the blood-testis barrier. This was actually confirmed by several studies, which have observed plasmid transfection beyond the blood-testis barrier with injection into rete testis following electroporation (Muramatsu, Shibata et al. 1997, Kubota, Hayashi et al. 2005, Danner, Kirchhoff et al. 2009, Kanduser, Miklavcic et al. 2009, Michaelis, Sobczak et al. 2014).

      Figure 9 - authors should show >1 cell - this is insufficient; also, it's stated it's only in the flagella, but it also appears to be in the head as well. And is this just the principal piece?? And are the authors sure those are elongating vs condensing spermatids? Need to show multiple tubules, at different stages, to make these claims

      We have partly answered to this question in the public review; We pastehere  our answer

      “We present now new images showing the full seminiferous tubules as requested (see supp fig 6). In this new figure, it is clear that Armc2 is only expressed in spermatids. We have also added in this figure an analysis of the RNA-seq database produced by Gan's team (Gan, Wen et al. 2013), confirming that ArmC2 expression is predominantly expressed at the elongated spermatid stage. This point is now clearly indicated in the text.”

      Concerning the localization of the protein in the head, we confirm that the base of the manchette is stained but we have no explanation so far. This point is now indicated in the manuscript.

      Figure 10B2 image - a better resolution is necessary

      We are grateful for your feedback. We concede that the quality of the image was not optimal. Consequently, We have replaced it with an alternative.

      Figure 11 - in control, need to show >1 sperm; and lower-mag images should be provided for all samples to show population-wide effects; showing 1 "normal" sperm per group (white arrows) is insufficient: 

      We are grateful for your feedback. We conducted further experiments and provide now additional images in Supp. figure 8.

      Reviewer #3 (Recommendations For The Authors)

      In this study, Vilpreux et al. developed a microinjection/electroporation method in order to transfect RNA into testicular cells. The authors studied several parameters of treated testis and compared the injection of DNA versus RNA. Using the injection of Armc2 RNA into mice with an Armc2 knockout the authors were able to (partly) rescue the fertility phenotype. 

      Minor points. 

      Figure 6 + lines 553+554: might it be that the staining pattern primarily on one side of the testis is due to the orientation of the scissor electrode during the electroporation procedure and the migration direction of negatively charged RNA molecules (Figure 6)? 

      Your input is greatly appreciated. We concur that the observed peripheral expression is due to both the electroporation and injection. Accordingly, we have amended the sentence as follows: "The peripheral expression observed was due to the close vicinity of cells to the electrodes, and to a peripheral dispersal of the injected solution, as shown by the distribution of the fluorescent i-particles NIRFiP-180."

      Discussion of the safety aspect (lines 601-608): The authors state several times that there are no visible tissue changes after the electroporation procedure. However, in order to claim that this procedure is "safe", it is necessary to examine the offspring born after microinjection/electroporation. 

      Your input is greatly appreciated. Consequently, the term "safe" has been replaced with "did not perturb spermatogenesis" in accordance with the provided feedback. Your assertion is correct; an examination of the offspring born would be necessary to ascertain the safety of the procedure. Due to the quantity of motile sperm obtained, it was not possible to produce offspring through natural mating. However, novel Armc2-/--rescued sperm samples have been produced and in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) experiments have been conducted. The results demonstrate that the Armc2-/--rescued sperm can successfully fertilize eggs and produce two-cell embryos by IVF and blastocysts by ICSI. These outcomes are visually represented in Figure 12. The development of embryos up to the blastocyst stage is a step in the right direction.

      The discussion section could be shortened. Lines 632-646 are largely a repetition of the introductory section. In addition, the Dong paper (ref. 25) may be interesting; however, this part could also be shortened (lines 647-676). This reviewer would prefer the authors to focus on the technique (different application sites and applied nucleotides) and proof of concept for (partial) phenotype rescue in the knockout mice. 

      Your contribution is highly valued. In light of your observations and the latest findings, we have substantially revised the discussion accordingly.

      Line 63: oocytes rather than eggs.

      We are grateful for your input, but we have decided to retain our current position and to use the term "eggs" rather than "oocytes" in our writing because the definition of an oocyte is a female gametocyte or germ cell involved in reproduction. In other words, oocyte corresponds to a germ cell inside the ovary and after ovulation become an egg.  

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    1. Author response:

      Reviewer 1:

      Summary: This work presents an Interpretable protein-DNA Energy Associative (IDEA) model for predicting binding sites and affinities of DNA-binding proteins. Experimental results demonstrate that such an energy model can predict DNA recognition sites and their binding strengths across various protein families and can capture the absolute protein-DNA binding free energies.

      We appreciate the reviewer’s careful assessment of the paper, and we thank the reviewer for the insightful suggestions and comments.

      Strengths:

      (1) The IDEA model integrates both structural and sequence information, although such an integration is not completely original. (2) The IDEA predictions seem to have agreement with experimental data such as ChIP-seq measurements.

      We appreciate the reviewer’s comments on the strength of the paper.

      Weaknesses:

      (1) The authors claim that the binding free energy calculated by IDEA, trained using one MAX-DNA complex, correlates well with experimentally measured MAX-DNA binding free energy (Figure 2) based on the reported Pearson Correlation of 0.67. However, the scatter plot in Figure 2A exhibits distinct clustering of the points and thus the linear fit to the data (red line) may not be ideal. As such. the use of the Pearson correlation coefficient that measures linear correlation between two sets of data may not be appropriate and may provide misleading results for non-linear relationships.

      We thank the reviewer for the insightful comments and agree that the linear fit between our predictions and the experimental data may not be ideal. The primary utility of the IDEA model is for assessing the relative binding affinities of different DNA sequences. To further support this, we plan to conduct additional statistical analyses that are independent of the linear correlation assumption but instead focus on the ranked order of DNA sequence binding affinities.

      (2) In the same vein, the linear Pearson Correlation analysis performed in Figure 5A and the conclusion drawn may be misleading.

      We thank the reviewer for the insightful comments. We will perform the same analysis for Figure 5A as detailed in our response to the previous comments.

      (3) The authors included the sequences of the protein and DNA residues that form close contacts in the structure in the training dataset, whereas a series of synthetic decoy sequences were generated by randomizing the contacting residues in both the protein and DNA sequences. In particular, synthetic decoy binders were generated by randomizing either the DNA (1000 sequences) or protein sequences (10,000 sequences) from the strong binders. However, the justification for such randomization and how it might impact the model’s generalizability and transferability remain unclear.

      We thank the reviewer for the insightful comments. We will perform additional analyses to assess the robustness of our model predictions with respect to the number of randomized decoys. Additionally, we will examine how randomization would potentially affect the model’s generalizability and transferability.

      (4) The authors performed Receiver Operating Characteristic (ROC) analysis and reported the Area Under the Curve (AUC) scores in order to quantitate the successful identification of the strong binders by IDEA. It would be beneficial to analyze the precision-recall (PR) curve and report the PRAUC metric which could be more robust.

      We agree with Reviewer 1 that more statistical metrics should be used to evaluate our model’s performance. We will include a more robust approach, such as PRAUC, to evaluate our model.

      Reviewer 2:

      Summary:

      Zhang et al. present a methodology to model protein-DNA interactions via learning an optimizable energy model, taking into account a representative bound structure for the system and binding data. The methodology is sound and interesting. They apply this model for predicting binding affinity data and binding sites in vivo. However, the manuscript lacks discussion of/comparison with state-of-the-art and evidence of broad applicability. The interpretability aspect is weak, yet over-emphasized.

      We appreciate the reviewer’s excellent summary of the paper, and we thank the reviewer for the insightful suggestions and comments.

      Strengths:

      The manuscript is well organized with good visualizations and is easy to follow. The methodology is discussed in detail. The IDEA energy model seems like an interesting way to study a protein-DNA system in the context of a given structure and binding data. The authors show that an IDEA model trained on one system can be transferred to other structurally similar systems. The authors show good performance in discriminating between binding-vs-decoy sequences for various systems, and binding affinity prediction. The authors also show evidence of the ability to predict genome-wide binding sites.

      We appreciate the reviewer’s strong assessment of the strengths of this paper.

      Weaknesses:

      An energy-based model that needs to be optimized for specific systems is inherently an uncomfortable idea. Is this kind of energy model superior to something like Rosetta-based energy models, which are generally applicable? Or is it superior to family-specific knowledge-based models? It is not clear.

      We thank the reviewer for the insightful comments. We will include predictions by generic protein-DNA energy models, such as the Rosetta-based energy model or family-specific knowledge-based model, to compare with our model performance.

      Prediction of binding affinity is a well-studied domain and many competitors exist, some of which are well-used. However, no quantitative comparison to such methods is presented. To understand the scope of the presented method, IDEA, the authors should discuss/compare with such methods (e.g. PMID 35606422).

      We thank the reviewer for the insightful comments. In our initial submission, Figure S5 presents a comparison between our model’s prediction and those of an existing method using 10-fold cross-validation. We agree a more comprehensive comparison with other methods is needed and will include a discussion and comparison of the IDEA model’s performance with additional state-of-the-art models.

      The term “interpretable” has been used lavishly in the manuscript while providing little evidence on the matter. The only evidence shown is the family-specific residue-nucleotide interaction/energy matrix and speculations on how these values are biologically sensible. Recent works already present more biophysical, fine-grained, and sometimes family-independent interpretability (e.g. PMID 39103447, 36656856, 38352411, etc.). The authors should put into context the scope of the interpretability of IDEA among such works.

      We agree that “interpretability” should be discussed in a relevant context. We will discuss the scope of IDEA interoperability within the context of recent works, including those suggested by the reviewers.

      The manuscript disregards subtle yet important differences in commonly used terminology in the field. For example, the authors use the term ”specificity” and ”affinity” almost interchangeably (for example, the caption for Figure 3A uses ”specificity” although the Methods text describes the prediction as about ”affinity”). If the authors are looking to predict specificity, IDEA needs to be put in the context of the corresponding state-of-the-art (PMID 36123148, 39103447, 38867914, 36124796, etc).

      We really appreciate the reviewer for pointing out our conflation of “specificity” and “affinity” in the manuscript. To clarify, IDEA’s primary function is to predict the binding affinities of protein-DNA pairs in a sequence-specific manner. The acquired binding affinities of target DNA sequences can then be used to assess the specific binding motifs. We will revise our text to clarify this point.

      It is not clear how much the learned energy model is dependent on the structural model used for a specific system/family. It would be interesting to see the differences in learned model based on different representative PDB structures used. Similarly, the supplementary figures show a lack of discriminative power for proteins like PDX1 (homeodomain family), POU, etc. Can the authors shed some light on why such different performances?

      We thank the reviewer for the insightful comments and agree that the familyspecific energy model could provide insight into the model predictions. We will examine different energy models based on the protein family, and especially investigate whether they can explain the lack of discriminative power for certain proteins.

      It is also not clear if IDEA’s prediction for reverse complement sequences is the same for a given sequence. If so, how is this property being modelled? Either this description is lacking or I missed it.

      We thank the reviewer for the insightful comments. The IDEA model treats reverse complementary sequences separately. We will provide additional details on how these sequences are modeled.

      Reviewer 3:

      Summary:

      Protein-DNA interactions and sequence readout represent a challenging and rapidly evolving field of study. Recognizing the complexity of this task, the authors have developed a compact and elegant model. They have applied well-established approaches to address a difficult problem, effectively enhancing the information extracted from sparse contact maps by integrating artificial sequences decoy set and available experimental data. This has resulted in the creation of a practical tool that can be adapted for use with other proteins.

      We appreciate the reviewer’s excellent summary of the paper, and we thank the reviewer for the insightful suggestions and comments.

      Strengths:

      (1) The authors integrate sparse information with available experimental data to construct a model whose utility extends beyond the limited set of structures used for training. (2) A comprehensive methods section is included, ensuring that the work can be reproduced. Additionally, the authors have shared their model as a GitHub project, reflecting their commitment to transparency of research.

      We appreciate the reviewer’s strong assessment of the strengths of this paper.

      Weaknesses:

      (1) The coarse-graining procedure appears artificial, if not confusing, given that full-atom crystal structures provide more detailed information about residue-residue contacts. While the selection procedure for distance threshold values is explained, the overall motivation for adopting this approach remains unclear. Furthermore, since this model is later employed as an empirical potential for molecular modeling, the use of P and C5 atoms raises concerns, as the interactions in 3SPN are modeled between C<sub>α</sub> and the nucleic base, represented by its center of mass rather than P or C5 atoms.

      We appreciate the reviewer’s insightful comments. The selection of P and C5 atoms will augment our model prediction, but the prediction is robust without this selection scheme. We will provide more details on the motivation behind this selection.

      Regarding the simulation model, we acknowledge a potential disconnection between the coarse-grained level of the 3SPN model (3 coarse-grained sites per nucleotide) and the data-driven model (1 coarse-grained site per nucleotide). The selection of nucleic bases for molecular interactions in the 3SPN model follows the PI’s previous work [PMID: 34057467] and its code implementation. We will test the simulation model by incorporating interactions between Cff and P atoms. In the future, we will work on implementing IDEA model output for 1-bead-per-nucleotide DNA simulation models.

      (2) Although the authors use a standard set of metrics to assess model quality and predictive power, some ∆∆G predictions compared to MITOMI-derived ∆∆G values appear nonlinear, which casts doubt on the interpretation of the correlation coefficient.

      We thank the reviewer for the insightful comments and agree that the linear fit between our model’s prediction and the experimental data may not be ideal. The primary utility of the IDEA model is for assessing the relative binding affinities of different DNA sequences. To this end, we plan to perform additional statistical analyses that are independent of the linear correlation assumption but instead focus on the ranked order of DNA sequence binding affinities.

      (3) The discussion section lacks information about the model’s limitations and a comprehensive comparison with other models. Additionally, differences in model performance across various proteins and their respective predictive powers are not addressed.

      We thank the reviewer for the insightful comments and will compare the performance of the IDEA model with state-of-the-art methods. We will also perform detailed analyses of the learned energy models across different proteins and examine their correlation with the model’s predictive powers.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Hahn et al use bystander BRET, NanoBiT assays, and APEX2 proteomics to investigate endosomal signaling of CCR7 by two agonists, CCL19 and CCL21. The authors suggest that CCR7 signals from early endosomes following internalisation. They use spatial proteomics to try to identify novel interacting partners that may facilitate this signaling and use this data to specifically enhance a Rac1 signaling pathway. Many of the results in the first few figures showing simultaneous recruitment of Barr and G proteins by CCR7 have been shown previously (Laufer et al, 2019, Cell Reports), as has signaling from endomembranes, and Rac1 activation at intracellular sites. The new findings are the APEX2 proteomics studies, which could be useful to the scientific community. Unfortunately, the authors only follow up on a single finding, and the expansion of this section would improve the manuscript.

      First of all, we would like to thank the reviewer for helping with the manuscript. The summary is mostly accurate except for the statement that simultaneous recruitment of barr and G protein to CCR7 has been shown before. It should also be noted that it has not been demonstrated that CCR7 activates G proteins from endosomes previously nor has the functional role of this signaling mechanism. However, that CCR7 activity at endomembranes is associated with Rac1 signaling was demonstrated in the Laufer et al. study as the reviewer correctly points out.

      Strengths:

      (1) The APEX2 resource will be valuable to the GPCR and immunology community. It offers many opportunities to follow up on findings and discover new biology. The resource could also be used to validate earlier findings in the current manuscript and in previous manuscripts. Was there enrichment of early endosomal markers, Barr and Gi as this would provide further evidence for their earlier claims regarding endosomal signaling? Previous studies have suggested signaling from the TGN, so it is possible that the different ligands also direct to different sites. This could easily be investigated using the APEX2 data.

      Thank you for your comment. We do in fact observe enrichment of TGN/Golgi markers in response to chemokine stimulation, which we now have highlighted in the manuscript (fourth paragraph on page 7).

      (2) The results section is well written and can be followed very easily by the reader.

      We are glad that the reviewer found the results section very readable.

      (3) Some findings verify previous studies (e.g. endomembrane signalling). This should be acknowledged as this shows the validity of the findings of both studies.

      This is correct. We have now included more discussion of previous work related to CCR7 signaling at endomembranes (thirdparagraph on page 10).

      Weaknesses:

      (1) The findings are interesting although the studies are almost all performed in HEK293 cells. I understand that these are commonly used in GPCR biology and are easy to transfect and don't express many GPCRs at high concentrations, but their use is still odd when there are many cell-lines available that express CCR7 and are more reflective of the endogenous state (e.g. they are polarised, they can perform chemotaxis/ migration). Some of the findings within the study should also be verified in more physiologically relevant cells. At the moment only the final figure looks at this, but findings need to be verified elsewhere.

      We thank the reviewer for raising this point and giving us an opportunity to elaborate in further detail. The major goal of our study was to investigate whether CCR7 activates G protein from endosomes, the underlying mechanism, and functions of this potential signaling mechanism. The reason we chose CCR7 as our model receptor was that it belongs to a group of GPCRs, the chemokine receptors, that most often have features associated with the ability to promote endosomal G protein activation (phosphorylation site clusters in the C-terminal region).

      Specific detection of G protein activation at distinct subcellular compartments is currently very challenging in truly endogenous systems despite new innovative biosensors that are available (not just related to CCR7, but GPCRs in general). To our knowledge, most if not all studies that detect direct activation of G protein at a specific compartment whether at the plasma membrane, endosome, Golgi, or other compartments, have overexpressed either the receptor, G protein, or both. This is why we choose the HEK293 cell system for most of our experiments, which are easy to manipulate. That being said, we did confirm major findings in an indirect manner using Jurkat T-cells, which express CCR7 endogenously and are physiological relevant. Our hope is that in the future we will be able to use highly sensitive biosensors to directly confirm our findings in such a cell system as the reviewer wisely suggests.

      (2) The authors acknowledge that the kinetic patterns of the signals at the early endosome are not consistent with the rates of internalisation. They mention that this could be due to trafficking elsewhere. This could be easily looked at in their APEX2 data. Is there evidence of proximity to markers of other membranes? Perhaps this could be added to the discussion. Similarly, previous studies have shown that CCR7 signaling may involve the TGN. Was there enrichment of these markers? If not, this could also be an interesting finding and should be discussed. It is also possible that the Rab5 reporter is just not as efficient as the trafficking one, especially as in later figures the very convincing differences in the two ligands are not as robust as the differences in trafficking.

      Excellent point. We have now highlighted the possibility of CCR7 being further trafficked to the trans-Golgi network (TGN) as possible explanation for the transient translocation of activated CCR7 to the early endosome in Fig. 1G-H (second paragraph on page 3).

      Furthermore, in the APEX2 experiment we observe enrichment of proteins involved in lysosomal trafficking (LAMP1, VPS16, VAMP7, WDR91, and PP4P1) by CCL19 stimulation at 25 min, and recycling endosomes/TGN markers (SNX6, RAB7L, and GGA) by CCL21 stimulation at 25 min. In addition to this, several markers of TGN/Golgi (SNX3, COG5, YIF1A, SC22B, and AP3S1) were enriched as well in response to both CCL19 and CCL21 stimulation. We have now included a statement in the manuscript, which describes the likely trafficking of CCR7 to the TGN/Golgi in response to CCL19 and CCL21 stimulation (fourth paragraph on page 7).

      (3) In the final sentence of paragraph 2 of the results the authors state that the internalisation is specific to CCR7 as there isn't recruitment to V2R. I'm not sure this is the best control. The authors can only really say it doesn't recruit to unrelated receptors. The authors could have used a different chemokine receptor which does not respond to these ligands to show this.

      The point with this control experiment was to demonstrate that the loss of NanoBiT signal in response to CCL19 in CCR7-SmBiT/LgBiT-CAAX expressing cells, but not in V2R-SmBiT/LgBiT-CAAX expressing cells, was a result of bona fide CCR7 internalization rather than potential artifactual effects of CCL19 on the NanoBiT system. Our intent was not to demonstrate specificity of CCL19 among chemokine receptors, which already has been thoroughly tested in previous studies. We have now modified the sentence (second paragraph on page 3) “Moreover, CCL19/CCL21-stimulation of receptor internalization to endosomes is specific to CCR7 as none of the chemokines promote internalization or trafficking to endosomes of the vasopressin type 2 receptor (V<sub>2</sub>R)-SmBiT construct (Fig. S1E-F)” to “Moreover, CCL19/CCL21-stimulation did not promote internalization or trafficking to endosomes of the vasopressin type 2 receptor (V<sub>2</sub>R)-SmBiT construct, which validates that these chemokines act specifically via the CCR7-SmBiT system (Fig. S1E-F).”

      (4) The miniGi-Barr1 and imaging showing co-localisation could be more convincing if it was also repeated in a more physiological cell line as in the final figure. Imaging of CCR7, miniGi, and Barr1 would also provide further evidence that the receptor is also present within the complex.

      We agree with the reviewer’s assessment. However, as mentioned above it is currently extremely challenging to detect endogenous G protein coupling/activation to endogenous receptors. In addition, we are not sure if overexpressing fluorophore-tagged receptor, miniG, and barr1 in a physiological-relevant cell line would provide truly physiological conditions as the expression of these proteins still would be artificially high. This is why we chose to conduct these mechanistic experiments in HEK293 cells and then indirectly verify key findings in an endogenous and physiological-relevant cell line.

      (5) The findings regarding Rac1 are interesting, although an earlier paper found similar results (Laufer et al, 2019, Cell Reports), so perhaps following up on another APEX2-identified protein pathway would have been more interesting. The authors' statement that Rac1 is specifically activated, and RhoA and Cdc42 are not, is unconvincing from the current data. Only a single NanoBiT assay was used, and as raw values are not reported it is difficult for the reader to glean some essential information. The authors should show evidence that these reporters work well for other receptors (or cite previous studies) and also need evidence from an independent (i.e. non-NanoBiT or BRET) assay.

      The major focus of the study was to investigate whether CCR7 can activate G protein after having been internalized into endosomes via formation of CCR7-Gi/o-barr megaplexes, and to dissect out potential functions of said endosomal G protein signaling. To do this, we used CCL19 and CCL21 which stimulate G protein to the same extent but differ in their ability of promote barr recruitment and receptor internalization with CCL19 being superior to CCL21. To this end, we found that CCL19 also promote endosomal G protein activation to a greater extent than CCL21, and therefore, we specifically looked for proteins enriched by CCL19 in our APEX experiment. This led us to some Rho GTPase regulators that were differentially enriched by CCL19 and CCL21. We agree that there were other interesting effectors related to CCR7 biology identified in the APEX experiment such as EYA2, GRIP2, and EI24. However, those proteins were enriched similar by CCL19 and CCL21 challenge, and thus, do not seem to be activated specifically at endosomes. Following the same argument, we also did not observe any difference in the activity of RhoA or Cdc42 when stimulated with CCL19 or CCL21, so we cannot conclude that these signaling proteins are activated specifically in endosomes. On the other hand, Rac1 was stimulated to a larger degree by CCL19 than CCL21, its activity was inhibited by the Gi/o inhibitor PTX and endocytosis inhibitors Dyngo-4a and PitStop2. CCR7-mediated Rac1 signaling was also inhibited by expression of a dominant negative dynamin mutant that inhibits receptor internalization, and Rac1 was not activated by an internalization-deficient CCR7-DS/T mutant. Finally, the involvement of Rac1 in CCR7 mediated chemotaxis of Jurkat T cells was also demonstrated. We believe that these findings together provide strong basis for the claim that endosomal Gi/o protein signaling by CCR7 activates Rac1.

      Following the reviewer’s suggestion, we have now included experiments to show that the activation of RhoA, Rac1, and Cdc42 by CXCR4 also can be detected by the NanoBiT biosensors (Fig. S7D-F). We have also added the appropriate references to the original studies where these biosensors were developed in the results section (first paragraph on page 8).

      (6) At present, the studies in Figure 7 do not go beyond those in the previous Laufer et al study in which they showed blocking endocytosis affected Rac1 signalling. The authors could show that Rac1 signalling is from early endosomes to improve this, otherwise, it could be from the TGN as previously reported.

      The major purpose of Figure 7 was to indirectly confirm findings from HEK293 cells experiments and to tie them to physiological functions. Our experiments using Jurkat T-cells show that CCL19 promote stronger chemotactic response than CCL21 despite similar Gi/o response. In addition, we showed that CCR7-mediated Gi/o activation, receptor endocytosis, as well as Rac1 activity, are required to drive chemotaxis. The Laufer et al. study did not investigate whether CCR7 activates G protein after having been internalized into endosomes via formation of CCR7-Gi/o-barr megaplexes, and thus, did not focus on functional outcomes of this signaling mechanism. Based on this, we believe our work provides new and valuable knowledge to the field.

      Reviewer #2 (Public Review):

      Summary:

      This manuscript describes a comprehensive analysis of signalling downstream of the chemokine receptor CCR7. A comprehensive dataset supports the authors' hypothesis that G protein and beta-arrestin signalling can occur simultaneously at CCR7 with implications for continued signalling following receptor endocytosis.

      We would like to thank the reviewer for helping with the manuscript. We agree on all points made and have now updated the manuscript accordingly.

      Strengths:

      The experiments are well controlled and executed, employing a wide range of assays using - in the main - CCR7 transfectants. Data are well presented, with the authors' claims supported by the data. The paper also has an excellent narrative which makes it relatively easy to follow. I think this would certainly be of interest to the readership of the journal.

      We appreciate the positive assessment of strengths.

      Weaknesses:

      Since the authors show a differential enrichment of RhoGTPases by CCR7 stimulation with CCL19 versus CCL21, I think that they also need to show that the Gi/o coupling of HEK-292-CCR7-APEX2 cells to both CCL19 and CCL21 is not perturbed by the modification. Currently, the authors only show data for CCL19 signalling, which leaves the potential for a false negative finding in terms of CCL21 signalling being selectively impaired. This should be relatively easy to do and should strengthen the authors' conclusions.

      We agree with the reviewer and have now included experiments to show that both CCL19- and CCL21-mediated CCR7-APEX2 stimulation leads to Gi/o activation (Fig. S4C). In addition, our proteomics experiments show strong effects of both CCL19 and CCL21 stimulation, which suggest that the receptor is activated by both ligands.

      The authors conclude the discussion by suggesting that their findings highlight endosomal signalling as a general mechanism for chemokine receptors in cell migration. I think this is an overreach. The authors chose several studies of CXC chemokine receptors to support their argument that C-terminal truncation or mutation of the C-terminal phosphorylation sites impairs endocytosis and chemotaxis (refs 40-42). However, in some instances e.g. at the related chemokine receptor CCR4, C-terminal removal of these sites impairs endocytosis but promotes chemotaxis (Nakagawa et al, 2014); Anderson et al, 2020). I therefore think that either the final statement needs to be tempered down or the counterargument discussed a little.

      We appreciate the reviewer highlighting this point. We have now modified the concluding sentence from “Thus, the findings from our study highlight endosomal G protein signaling by chemokine receptors as a potential general mechanism that regulates key aspects of cell migration” to “Thus, the findings from our study highlight endosomal G protein signaling by some chemokine receptors as a potential mechanism that regulates key aspects of cell migration.” We hope that the temper level of this sentence is more appropriate.

      References:

      Anderson, C. A. et al. A degradatory fate for CCR4 suggests a primary role in Th2 inflammation. J Leukocyte Biol 107, 455-466 (2020).

      Nakagawa, M. et al. Gain-of-function CCR4 mutations in adult T cell leukaemia/lymphoma. Journal of Experimental Medicine 211, 2497-2505 (2014).

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) The results section is well written, although the introduction needs more information on what is known about CCR7 trafficking and endomembrane signaling. I understand this is because the authors wanted to focus on GPCR signaling, but the study will equally be of interest to researchers in the immunology and chemokine fields, and therefore more CCR7-focussed discussion in the introduction would be useful. Similarly, the discussion would benefit from more discussion of previous studies of CCR7 trafficking and endomembrane signaling (in particular the Laufer et al paper) to acknowledge that many of the findings within this paper verify previous studies.

      We have now included additional immunology/endomembrane background information about CCR7 at the place where the receptor is introduced (first paragraph on page 3). We have also expanded our discussion of our work in relation to the Laufer et al. study (third paragraph on page 10).

      (2) On page 5, the authors state that 'The response to chemokine stimulation was not observed in mock transfected HEK293 cells'. Figure S4D does not have a legend so it is difficult to see what they mean by mock transfected. Do they mean not transfecting with anything or not with the receptor? The better control would be transfecting the reporters but not the receptor. This may have been done, but the wording needs clarifying and S4D needs a legend.

      Thanks for pointing this out. We believe the reviewer refers to Figure S2D and we have now highlighted/clarified the legend better. Mock transfected conditions refer to HEK293 cells transfected with the reporter, but not the receptor. This is written in the legend as “(D) Change in luminescence signal generated between SmBiT-barr1 and LgBiT-miniGi in response to 100 nM CCL19 or 100 nM CCL21 in mock transfected HEK293 cells (no CCR7)”, which we believe should be clear to the audience.

      (3) The validation of the APEX2 receptor construct relies on a single assay with one ligand. The authors should show that the receptor expresses at the cell surface, is internalised normally, and that both ligands activate the receptor.

      We have now included additional data to show that (1) the receptor is expressed at the cell surface, (2) that the CCR7-APEX2 recruits barr1 to the plasma membrane, (3) that this association leads to barr1 translocation to the early endosomes as an indirect measurement of receptor internalization, and (4) that both CCL19- and CCL21-stimulation inhibit forskolin induced cAMP production (Fig.S4A-C, and described in fifth paragraph on page 6).

      (4) The APEX2 section is very short, especially as this is novel data. It lacks some important information, e.g. when the authors state that 'we identified a total of 579 proteins', is this in total for both ligands, separately or were some shared? More information on each ligand separately and combined would make this clearer.

      We have now specified that the identified total proteins enriched from our APEX2 approach is when the cells are stimulated with either CCL19 or CCL21 (third paragraph on page 7). Furthermore, we have included a Venn diagram in Fig. S5C to show how many proteins were enriched by CCL19 or CCL21 stimulation and how many of those were shared at different time points.

      (5) The discussion would benefit from some further work. The current first two paragraphs just reiterate the introduction and don't discuss the current paper so could be removed completely. The Laufer et al study needs much more discussion as they report many of the findings of the current paper (signaling following endocytosis, Rac1 endomembrane signaling) five years ago. The APEX2 findings that are discussed, though interesting, are not followed up by further experimental evidence and there is little discussion of why the two ligands have different responses or what the physiological effects could be.

      We appreciate the reviewer’s effort in helping with the discussion. To this end, we have now expanded our discussion of the mentioned paper further as suggested (third paragraph on page 10). We agree that the findings from our APEX experiment are interesting, but the focus of this study relates to proteins enriched specifically at endosomes. Several of the most enriched proteins did not show this localization bias, which is why these proteins were not further investigated.

      Minor changes:

      (1) The authors should remove the word 'recent' at the start of the first sentence of the third paragraph. Endosomal signaling by GPCRs was described 15 years ago so cannot really be seen as recent anymore.

      We have now adjusted the manuscript accordingly.

      (2) Tukey defaulted to Turkey in some places.

      We thank the reviewer for pointing out these typos, which now have been corrected.

      Reviewer #2 (Recommendations For The Authors):

      Minor Points:

      (1) ACKRs do not couple to G proteins so it is peculiar to see them in this table. I would limit the table to the conventional CCR1-10, CXCR1-6 and XCR1. The ligand for XCR1 is XCL1 which is absent from the table.

      We have now modified the table accordingly.

      (2) CCL19 (formerly known as ELC) has been long known to be a more efficacious and potent ligand in chemotaxis assays (Bardi et al, 2001). This earlier reference should be added to the citations in the preceding statement on page 10.

      This is an important study showing that CCL19 is more efficacious than CCL21 in promoting chemotaxis and that this has been known for decades. We have now included the reference accordingly (reference 59 in second paragraph on page 11).

      (3) Figure 6, Panel Q. I think the legends for CCR7 and CCR7 delta ST might be flipped.

      We thank the reviewer for pointing out this error. We have now corrected the figure panel.

      (4) Figure S5 (or 5) might benefit from simple Venn diagrams showing the numbers of differentially enriched proteins following treatment with the two ligands at different time points.

      We have included a Venn diagram in Fig. S5C to show how many proteins were enriched by CCL19 or CCL21 stimulation and how many of those where shared.

      Reference:

      Bardi, G., Lipp, M., Baggiolini, M. & Loetscher, P. The T cell chemokine receptor CCR7 is internalized on stimulation with ELC, but not with SLC. European Journal of Immunology 31, 3291-3297 (2001).

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary:

      Understanding the mechanisms of how organisms respond to environmental stresses is a key goal of biological research. Assessment of transcriptional responses to stress can provide some insights into those underlying mechanisms. The researchers quantified traits, fitness, and gene expression (transcriptional) response to salinity stress (control vs stress treatments) for 130 accessions of rice (three replicates for each accession), which were grown in the field in the Philippines. This experimental design allowed for many different types of downstream analyses to better understand the biology of the system. These analyses included estimating the strength of selection imposed on transcription in each environment, evaluating possible trade-offs in gene expression, testing whether salinity induces transcriptional decoherence, and conducting various eQTL-type analyses.

      Strengths:

      The study provides an extensive analysis of gene expression responses to stress in rice and offers some insights into underlying mechanisms of salinity responses in this important crop system. The fact that the study was conducted under field conditions is a major plus, as the gene expression responses to soil salinity are more realistic than if the study was conducted in a greenhouse or growth chamber. The preprint is generally well-written and the methods and results are mostly well-described.

      Weaknesses:

      While the study makes good use of analyzing the dataset, it is not clear how the current work advances our understanding of gene regulatory evolution or plant responses to soil salinity generally. Overall, the results are consistent with other prior studies of gene expression and studies of selection across environmental conditions. Some of the framing of the paper suggests that there is more novelty to this study than there is in reality. That said, the results will certainly be useful for those working in rice and should be interesting to scientists interested in how gene expression responses to stress occur under field conditions. I detail other concerns I had about the preprint below:

      The abstract on lines 33-35 illustrates some of my concerns about the overstatement of the novelty of the current study. For example, is it really true that the role of gene expression in mediating stress response and adaptation is largely unexplored? There have been numerous studies that have evaluated gene expression responses to stresses in a wide range of organisms. Perhaps, I am missing something critically different about this study. If so, I would recommend that the authors reword this sentence to clarify what gap is being filled by this study. Further, is it really the case that none of them have evaluated how the correlational structure of gene expression changes in response to stresses in plants, as implied in lines 263-265? Don't the various modules and PC analyses of gene expression get at this question?

      We have re-worded these sentences, and highlighted the novelty of our work.

      There were some places in the methods of the preprint that required more information to properly evaluate. For example, more information should be provided on lines 664-668 about how G, E, and GxE effects were established, especially since this is so central to this study. What programs/software (R? SAS? Other?) were used for these analyses? If R, how were the ANOVAs/models fit? What type of ANOVA was used? How exactly was significance determined for each term? Which effects were considered fixed and which were random? If the goal was to fit mixed models, why not use an approach like voom-limma (Law et al. 2014 Genome Biology)? More details should also be added to lines 688-709 about these analyses, including what software/programs were used for these analyses.

      We have added more details in the methods. Also, although we could in priciple use voom-limma to fit our mixed model, to be able to partition variance into G, E and G×E, we need to use the function fitExtractVarPartModel (from package VariancePartition) which requires all categorical variables to be modeled as random effects. Therefore, we couldn’t model environment as a fixed effect.

      One thing that I found a bit confusing throughout was the intermixing of different terms and types of selection. In particular, there seemed to be some inconsistencies with the usage of quantitative genetics terms for selection (e.g. directional, stabilizing) vs molecular evolution terms for selection (e.g. positive, purifying). I would encourage the authors to think carefully about what they mean by each of these terms and make sure that those definitions are consistently applied here.

      We have defined the selection terms used in the study and used these terms consistently throughout the manuscript.

      It would be useful to clarify the reasons for the inherent bias in the detection of conditional neutrality (CN) and antagonistic pleiotropy (AP; Lines 187-196). It is also not clear to me what the authors did to deal with the bias in terms of adjusting P-value thresholds for CN and AP the way it is currently written. Further, I found the discussion of antagonistic pleiotropy and conditional neutrality to be a bit confusing for a couple of reasons, especially around lines 489-491. First of all, does it really make sense to contrast gene expression versus local adaptation, when lots of local adaptation likely involves changes in gene expression? Second, the implication that antagonistic pleiotropy is more common for local adaptation than the results found in this study seems questionable. Conditional neutrality appears to be more common for local adaptation as well: see Table 2 of Wadgymar et al. 2017 Methods in Ecology and Evolution. That all said, it is always difficult to conclude that there are no trade-offs (antagonistic pleiotropy) for a particular locus, as the detecting trade-offs may only manifest in some years and not others and can require large sample sizes if they are subtle in effect.

      We have now explained the cause of the inherent bias in the detection of CN, and also elaborated on how we deal with this bias. Also, we have edited our discussion and added relevant citations to indicate both conditional neutrality and antagonistic pleiotropy can lead to local adaptations and added the caveat regarding detecting antagonistic pleiotropy.

      Reviewer #2 (Public Review):

      The authors investigate the gene expression variation in a rice diversity panel under normal and saline growth conditions to gain insight into the underlying molecular adaptive response to salinity. They present a convincing case to demonstrate that environmental stress can induce selective pressure on gene expression, which is in agreement to their earlier study (Groen et al, 2020). The data seems to be a good fit for their study and overall the analytic approach is robust.

      (1) The work started by investigating the effect of genotype and their interaction at each transcript level using 3'-end-biased mRNA sequencing, and detecting a wide-spread GXE effect. Later, using the total filled grain number as a proxy of fitness, they estimated the strength of selection on each transcript and reported stronger selective pressure in a saline environment. However, this current framework relies on precise estimation of fitness and, therefore can be sensitive to the choice of fitness proxy.

      We now acknowledge this caveat in the discussion.

      (2) Furthermore, the authors decomposed the genetic architecture of expression variation into cis- and trans-eQTL in each environment separately and reported more unique environment-specific trans-eQTLs than cis-. The relative contribution of cis- and trans-eQTL depends on both the abundance and effect size. I wonder why the latter was not reported while comparing these two different genetic architectures. If the authors were to compare the variation explained by these two categories of eQTL instead of their frequency, would the inference that trans-eQTLs are primarily associated with expression variation still hold?

      We have now also reported the effect sizes for both cis- and trans-eQTLs in the two environments and showed that the trans-eQTLs have higher effect sizes as compared to cis-eQTLs, indicating that they are able to explain higher proportion of variation in transcript abundances in the two environments.

      (3) Next, the authors investigated the relationship between cis- and trans-eQTLs at the transcript level and revealed an excess of reinforcement over the compensation pattern. Here, I struggle to understand the motivation for testing the relationship by comparing the effect of cis-QTL with the mean effect of all trans-eQTLs of a given transcript. My concern is that taking the mean can diminish the effect of small trans-eQTLs potentially biasing the relationship towards the large-effect eQTLs.

      We wanted to estimate compensating vs reinforcing effects, which essentially entails identifying genes that have opposing directionality of cis and trans-effects. To get the total trans-effect we decided to take the mean effect of trans-eQTLs. This mean was only used to identify the compensating/reinforcing genes and although the mean effects diminishes the effect of small trans-eQTLs, this mean was not used in downstream analyses.

      Reviewer #3 (Public Review):

      In this work, the authors conducted a large-scale field trial of 130 indica accessions in normal vs. moderate salt stress conditions. The experiment consists of 3 replicates for each accession in each treatment, making it 780 plants in total. Leaf transcriptome, plant traits, and final yield were collected. Starting from a quantitative genetics framework, the authors first dissected the heritability and selection forces acting on gene expression. After summarizing the selection force acting on gene expression (or plant traits) in each environment, the authors described the difference in gene expression correlation between environments. The final part consists of eQTL investigation and categorizing cis- and trans-effects acting on gene expression.

      Building on the group's previous study and using a similar methodology (Groen et al. 2020, 2021), the unique aspect of this study is in incorporating large-scale empirical field works and combining gene expression data with plant traits. Unlike many systems biology studies, this study strongly emphasizes the quantitative genetics perspective and investigates the empirical fitness effects of gene expression data. The large amounts of RNAseq data (one sample for each plant individual) also allow heritability calculation. This study also utilizes the population genetics perspective to test for traces of selection around eQTL. As there are too many genes to fit in multiple regression (for selection analysis) and to construct the G-matrix (for breeder's equation), grouping genes into PCs is a very good idea.

      Building on large amounts of data, this study conducted many analyses and described some patterns, but a central message or hypothesis would still be necessary. Currently, the selection analysis, transcript correlation structure change, and eQTL parts seem to be independent. The manuscript currently looks like a combination of several parallel works, and this is reflected in the Results, where each part has its own short introduction (e.g., 185-187, 261-266, 349-353). It would be great to discuss how these patterns observed could be translated to larger biological insights. On a related note, since this and the previous studies (focusing on dry-wet environments) use a similar methodology, one would also wonder what the conclusions from these studies would be. How do they agree or disagree with each other?

      We acknowledge that the manuscript currently presents some analyses in a somewhat independent manner. Although it would be ideal to have a central hypothesis/message, our study is meant to broadly outline the various responses and fitness effects of salinity stress in rice. Throughout the manuscript, we have also included comparisons between our findings and that of our previous studies on drought stress to highlight any consistent themes or novel insights.

      Many analyses were done separately for each environment, and results from these two environments are listed together for comparison. Especially for the eQTL part, no specific comparison was discussed between the two environments. It would be interesting to consider whether one could fit the data in more coherent models specifically modeling the X-by-environment effects, where X might be transcripts, PCs, traits, transcript-transcript correlation, or eQTLs.

      We do plan to consider fitting models that explicitly incorporate X-by-environment interactions to provide a more detailed understanding of the genetics of plasticity between the two environments, but it is beyond the scope of this paper. This will be explored in a separate report.

      As stated, grouping genes into PCs is a good idea, but although in theory, the PCs are orthogonal, each gene still has some loadings on each PC (ie. each PC is not controlled by a completely different set of genes). Another possibility is to use any gene grouping method, such as WGCNA, to group genes into modules and use the PC1 of each module. There, each module would consist of completely different sets of genes, and one would be more likely to separate the biological functions of each module. I wonder whether the authors could discuss the pros and cons of these methods.

      We recognize that individual genes can contribute to multiple PCs, and this is precisely why we choose PCA clustering over WGCNA where one gene can belong to only one module. Our aim was to recognize all biological processes that could be under selection in either environment, and since one gene can be involved in various different processes, we wanted to identify the contribution of these genes to different processes which can be done effectively by a PCA analyses.

      Reviewer #4 (Public Review):

      The manuscript examines how patterns of selection on gene expression differ between a normal field environment and a field environment with elevated salinity based on transcript abundances obtained from leaves of a diverse panel of rice germplasm. In addition, the manuscript also maps expression QTL (eQTL) that explains variation in each environment. One highlight from the mapping is that a small group of trans-mapping regulators explains some gene expression variation for large sets of transcripts in each environment. The overall scope of the datasets is impressive, combining large field studies that capture information about fecundity, gene expression, and trait variation at multiple sites. The finding related to patterns indicating increased LD among eQTLs that have cis-trans compensatory or reinforcing effects is interesting in the context of other recent work finding patterns of epistatic selection. However, other analyses in the manuscript are less compelling or do not make the most of the value of collected data. Revisions are also warranted to improve the precision with which field-specific terminology is applied and the language chosen when interpreting analytical findings.

      Selection of gene expression:

      One strength of the dataset is that gene expression and fecundity were measured for the same genotypes in multiple environments. However, the selection analyses are largely conducted within environments. The addition of phenotypic selection analyses that jointly analyze gene expression across environments and or selection on reaction norms would be worthwhile.

      We do plan to consider fitting models that explicitly incorporate G×E interactions to provide a more detailed understanding of the genetics of plasticity between the two environments, but it is beyond the scope of this paper. This will be explored in a separate report.

      Gene expression trade-offs:

      The terminology and possibly methods involved in the section on gene expression trade-offs need amendment. I specifically recommend discontinuing reference to the analysis presented as an analysis of antagonistic pleiotropy (rather than more general trade-offs) because pleiotropy is defined as a property of a genotype, not a phenotype. Gene expression levels are a molecular phenotype, influenced by both genotype and the environment. By conducting analyses of selection within environments as reported, the analysis does not account for the fact that the distribution of phenotypic values, the fitness surface, or both may differ across environments. Thus, this presents a very different situation than asking whether the genotypic effect of a QTL on fitness differs across environments, which is the context in which the contrasting terms antagonistic pleiotropy and conditional neutrality have been traditionally applied. A more interesting analysis would be to examine whether the covariance of phenotype with fitness has truly changed between environments or whether the phenotypic distribution has just shifted to a different area of a static fitness surface.

      We recognize that pleiotropy is a property of a genotype, and not phenotype, but since our phenotype (gene expression) is strongly coupled with the genotype, we choose to call trade-offs as antagonistic pleiotropy. That being said, we did test whether the covariance of gene expression with phenotype significantly varies between environments, and found that to indeed be the case.

      Biological processes under selection / Decoherence: PCs are likely not the most ideal way to cluster genes to generate consolidated metrics for a selection gradient analysis. Because individual genes will contribute to multiple PCs, the current fractional majority-rule method applied to determine whether a PC is under direct or indirect selection for increased or decreased expression comes across as arbitrary and with the potential for double-counting genes. A gene co-expression network analysis could be more appropriate, as genes only belong to one module and one can examine how selection is acting on the eigengene of a co-expression module. Building gene co-expression modules would also provide a complementary and more concrete framework for evaluating whether salinity stress induces "decoherence" and which functional groups of genes are most impacted.

      We recognize that individual genes can contribute to multiple PCs, and this is precisely why we choose PCA clustering over WGCNA where one gene can belong to only one module. Our aim was to recognize all biological processes that could be under selection in either environment, and since one gene can be involved in various different processes, we wanted to identify the contribution of these genes to different processes which can be done effectively by a PCA analyses. But again as pointed out by the reviewer, our PCs did contain contribution (even negligible) of each gene, so to identify the ‘primary’ biological processes represented by the PCs, we chose the majority rule. As for testing decoherence, we agree that a co-expression module analyses would have provided additional support to the specific test performed in our manuscript, but since it would just be additional support, we choose to not add it in the manuscript.

      But based on the recommendation of the reviewer(s), we did perform a WGCNA analyses and found a total of 14 and 13 modules in normal and saline conditions, of which 0 and 2 modules (with no significant GO enrichment) were under directional selection. This supports our reasoning of potentially missing on identification of processes under selection.

      Selection of traits:

      Having paired organismal and molecular trait data is a strength of the manuscript, but the organismal trait data are underutilized. The manuscript as written only makes weak indirect inferences based on GO categories or assumed gene functions to connect selection at the organismal and molecular levels. Stronger connections could be made for instance by showing a selection of co-expression module eigengene values that are also correlated with traits that show similar patterns of selection, or by demonstrating that GWAS hits for trait variation co-localize to cis-mapping eQTL.

      We did perform a GWAS for all the traits collected in both normal and saline environment, and only found significant hits for fecundity (in both normal and saline environment) and chlorophyll_a content (in the saline environment). But these regions did not overlap with any candidate genes or cis-mapping eQTL. Hence we choose to mention it in the manuscript. Additionally, using the WGCNA modules, we found that the only two module under selection in the saline environment were not significantly correlated with any of the traits measured.

      Genetic architecture of gene expression variation:

      The descriptive statistics of the eQTL analysis summarize counts of eQTLs observed in each environment, but these numbers are not broken down to the molecular trait level (e.g., what are the median and range of cis- and trans-eQTLs per gene). In addition, genetic architecture is a combination of the numbers and relative effect sizes of the QTLs. It would be useful to provide information about the relative distributions of phenotypic variance explained by the cis- vs. trans- eQTLs and whether those distributions vary by environment. The motivation for examining patterns of cis-trans compensation specifically for the results obtained under high salinity conditions is unclear to me. If the lines sampled have predominantly evolved under low salinity conditions and the hypothesis being evaluated relates to historical experience of stabilizing selection, then my intuition is that evaluating the eQTL patterns under normal conditions provides the more relevant test of the hypothesis.

      We have added the median number of eQTLs per gene in each environment. Additionally, we recognize that genetic architecture is a combination id numbers and effect size, and we have added information regarding the effect sizes of eQTLs by type and by environment as recommend by another reviewer. We did explore the distributions of phenotypic variance explained by the cis- vs. trans- eQTLs as recommended here, and found that trans-eQTLs explain more phenotypic variance than cis-eQTLs in both environments and that the distribution of either type of eQTL does not vary by environment. We are choosing to not add this in the main text due to space limitations. Lastly, we examined the patterns of cis-trans compensation/reinforcement under both normal and salinity conditions and have compared and contrasted the results from both in the main text.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Lines 126: I would recommend citing those who originally developed the 3' end targeted RNA sequencing methods (e.g. Meyer et al 2011 Molecular Ecology).

      We have cited the recommended paper.

      Lines 128-130: It would be useful to include a description here of what models were fit to the data to partition out G, E, and GxE effects.

      Due to space limitations, we have in brief added a sentence to this effect.

      Line 139: I would suggest changing "found little" to "no" since the test was not significant.

      The sentence has been modified to say no evidence.

      Line 313: I think you mean directional selection instead of positive selection.

      We have corrected the text

      Lines 362-363: Would the authors also expect an enrichment of reinforcing genes for most scenarios where that has been divergent selection, such as local adaptation among populations?

      Based on our hypothesis, we would indeed expect an enrichment of reinforcing genes for scenarios of local adaptation where different alleles are maintained in different populations due to local adaptation.

      Reviewer #3 (Recommendations For The Authors):

      Figures 1d-e are not mentioned in the Results.

      The figures have been referenced in appropriate places.

      Lines 41-45: Terms such as reinforcement and compensation need to be explained in this specific context. Also "different selection regimes" is a bit broad and vague.

      Due to word-count limitation, we are choosing to not elaborate the terms reinforcement and compensation in the abstract (since these are commonly used in the literature, and we have also defined these in the main text). Additionally, we now explicitly state the selection pressures associated with cis and trans eQTLs.

      Table 1: Please explain S and C in the footnote.

      We have added the recommended footnote

      Figures: Some panel labels (a, b, c...) are mingled with the graphs.

      We are re-made our figure such that the panel labels do not mingle with the plots.

      Lines 588-591: font.

      Modified

      Lines 620-633: Please describe how these RNAseq libraries were allocated/pooled into different sequencing lanes to avoid potential batch effects among sequencing lanes.

      The sequencing was performed on the same Illumina NextSeq 500 machine and we have added the sequencing libraries/pool plan in the methods (lines 688-689). 

      Lines 690-692: At the beginning of this paragraph, it was mentioned that the un-standardized coefficients were estimated. But here, it seems like the transcript data were already standardized in the data preparation step. What do lines 687-688 refer to? Further standardizing those estimated coefficients so that the whole distribution has mean=0 and sd=1?

      Thank you pointing out our oversight. We checked our scripts and data preparation did not include transcript standardization, and we have removed the above line from the manuscript.

      Lines 705-711: Please explain why assigning the positive/negative selection status for each gene is important. "Positive selection" here is defined as genes whose increased expression also increases fitness, but traditionally positive selection was defined as "the derived state is favored over the ancestral state". For a gene whose ancestral expression is high but lower expression increases fitness in this experiment, could we also say this gene is under positive selection? Given that we don't know the ancestral state here, maybe the authors could explain whether this definition is necessary. Also, given that many genes positively or negatively regulate each other in a pathway, it is also unclear whether it is necessary to assign the positive/negative status for a PC using the majority rule (lines 710-711).

      We have now defined the different selection terms with respect to our study and use them consistently throughout the manuscript.

      Lines 711-715: If I understand correctly, PCs were used as traits, and by definition PCs should all be orthogonal. Is this section saying only retaining PCs whose correlation < 0.6 with each other? What is the rationale?

      PCA were performed on transcript abundance and the resulting orthogonal PCs explaining over 0.5% variance were all retained for selection analyses.

      We also performed selection analyses on the functional traits measured in the field, but since these functional traits are correlated (and as such would not satisfy the independent variable requirement of regression analyses), we retained only those functional traits which had a Pearson correlation coefficient < 0.6.

      Line 729: Please briefly describe what CLIP is doing.

      We have added the required description.

      Lines 736-741: The accession numbers do not add up to 125.

      Thank you for catching our oversight. We have edited the text, and now the numbers add upto 125.

      Line 796: Please remind readers where these 247k SNPs come from. Supposedly all accessions have been whole-genome sequenced, so the total number of SNPs should be larger than this.

      We have detailed method detailing how the SNPs were obtained and processed in the lines preceding this. Indeed the number of SNPs would have been much bigger, but the stringent cutoffs and linkage disequilibrium pruning reduced our dataset to about 247k SNPs.

      Lines 154-160: This is a bit confusing. The authors first mentioned, for the raw selection differentials, the mean and variance differ between environments, meaning they are misleading (why?). The next sentence then says non-standardized selection differentials will be used.

      The mean and variance for transcript abundances vary between the two environments. Because traits are usually measured in different scales, it is recommended to standardize trait values using variance or mean before estimating selection coefficients. Multiplying this variance (or mean) standardized selection differential with heritability gives the expected response to selection in standard deviation (or mean) units. But if the trait variance (or mean) varies between traits or environments, it leads to a conflation between the standardized selection differential and trait variance (or mean), which can be misleading. So to avoid this, and given that our traits (transcript abundance in this case) were all measured on the same scale, we chose to not standardize our trait values and estimated raw selection differentials.

      Figure 1 c-e: Please explain how the horizontal axis values were obtained. Is it assuming these selection differentials have a normal distribution of mean=0 & sd=1?

      Yes, horizontal axis represents theorical quantile for selection differential assuming they have a normal distribution with mean=0 and sd=1. This has been added to the figure legend.

      Line 162-168: Please clarify this part. What does “general trend towards stronger positive compared to negative selection on gene expression” mean? Does it mean the whole distribution of S is significantly different from 0, the difference in the number of genes in the S>0 vs S<0 category, or the a-bit-higher median |S| in the S>0 vs S<0 category? If it is the last one, are the small differences biological meaningful (0.053 vs. 0.047 for control & 0.051 vs. 0.050 for salt conditions), given that the authors defined |S|<0.1 as neutral?

      By “general trend towards stronger positive compared to negative selection on gene expression”, we mean that more transcripts were under positive directional selection as compared to negative directional selection. We have also clarified this in the text now.

      Line 177-178: This sentence implies disruptive selection is more important than stabilizing selection in the saline environment, but the test was not significant (line 176).

      Although there was no significant difference in the magnitude of stabilizing vs disruptive selection within the saline environment, the number of transcripts experiencing stronger disruptive selection in the saline condition was greater than the number of transcripts experiencing disruptive selection in the normal conditions. And so comparing between conditions, disruptive selection plays an important role in the saline conditions.

      Line 188-190: How CN vs. AP was statistically defined was not mentioned in the Methods section.

      We have added in the main text within the Results section.

      Line 203-214: How do these results fit with the previous observations that almost all transcripts have significant heritability?

      Although we do find that all but three transcripts have a have significant genetic effect (and thus have significant heritability), the median broad-sense heritability for 51 antagonistically pleiotropic genes is 0.23. Give that, we would only be able to detect SNPs regulating gene expression with high effect size since our sample size is n=130. Additionally, we used a very stringent criteria (FDR < 0.001) to define eQTLs. These two factors in combination could lead to us not being able to detect significant eQTLs for AP genes.

      Line 246-250: Please explain why the current conclusion would be opposite from the previous study. Supposedly the PCA, G matrix, and breeder’s equation were done for each environment separately. It makes sense that the G matrix and response to selection could be different between saline and drought treatments, but for the control treatments in the two studies, do they still differ? Why? Also in Table S7, it would be nice to show the % variation explained by each PC.

      Although both our studies had largely overlapping samples, about 20% samples were unique to each study. Additionally, although the site where the study was performed was the same across the two studies, we found significant temporal differences in gene expression due to micro-environmental differences. Both these factors can lead to changes in direct and indirect selection and its response, and we are examining these differences as part of a separate study. We also highlight these caveats in our discussion.

      Information on percent explained by each PCs is given in Table S5.

      Figure 2b: The vertical axis was labeled as “selection gradient”, but I think the responses to selection (D, I, T) have different units.

      We have re-labeled the vertical axis as “selection”.

      Reviewer #4 (Recommendations For The Authors):

      The manuscript mixes terminology for selection from quantitative genetics with that from population genetics. This is problematic, and the adjectives positive and negative should be replaced as descriptors of selection by instead rewording, for example, positive directional selection as directional selection for higher transcript abundance.

      Lines 193-196: The phrasing here reads as if the selection is solely acting on the presence/absence of expression rather than on quantitative variation in expression. During revision, it would be worth considering including an analysis of genes that parses genes that show the presence/absence of variation of expression within or across environments separately from genes that are expressed to non-trivial levels in both environments.

      We have modified the sentence in question now. Also, we pre-processed RNA-seq data to remove all transcripts with low expression signals (sigma signal < 20), and further retained only transcripts that had non-trivial expression in at least 10% of the population, which we believe represents presence/absence of variation of expression within or across environments.

      Lines 216-231: Is this analysis solely for directional selection? Not clear since previous sections examined both directional and stabilizing selection.

      Yes, we performed this analysis for only directional selection, and have clarified this in the text too.

      Lines 224-226: The meaning of this sentence is unclear and should be written more concretely.

      We have rephrased the sentence to be more clear.

      Lines 232-241: The description of the scientific logic here could be read as implying that genes interacting in networks are the sole source of indirect selection. I recommend revising the language to indicate this cause is one of several potential causes.

      We have reworded the sentence such that we indicate selection acting on interacting genes is just one of the causes of indirect selection.

      The strength of the conclusions of the decoherence analysis should be evaluated in light of caveats with such analyses (see Cai and Des Marais New Phytologist 2023).

      We have added the caveat with relevant citation in the manuscript.

      Rename this section as "Selection on Organismal Traits", as the previous sections have also been investigating selection on traits, just molecular traits.

      We have renamed the section as recommended

      Lines 314-318: Rewrite for clarity. Most environments select for an optimal phenotype; it is just the case here that the phenotypic distribution in the high salinity environment overlaps with the optimum.

      We have rephrased and clarified the statement.

      Lines 343-345: Rephrase to "These results indicate that natural variation in gene regulation under..."

      Rephrased.

      Line 354: "most" reads as too strong a descriptor here if the majority is ~60%.

      We have reworded the sentence to read “more than half”

      Lines 359-361: It is unclear to me how this interpretation follows from the above analysis.

      We have reworded the sentence so that the claim follows our analysis.

      Line 372: Is the expectation here more specifically one of epistatic selection? Other processes could stochastically lead to the genetic fixation of compensatory/reinforcing variants, but I think only epistasis for fitness would cause the interesting patterns of LD observed.

      The expectation here is that certain cis and trans variants only exists to compensate/reinforce, potentially through epistasis. We have clarified this in the text.

      Line 405: Change "adaptive organismal responses of organisms" to "organismal responses." As written, the sentence reads as being about plasticity rather than evolutionary responses, which are by populations, not organisms. None of the analyses included the manuscript test specifically test for adaptive plasticity.

      Rephrased.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews: 

      Reviewer #1 (Public review): 

      The conserved AAA-ATPase PCH-2 has been shown in several organisms including C. elegans to remodel classes of HORMAD proteins that act in meiotic pairing and recombination. In some organisms the impact of PCH-2 mutations is subtle but becomes more apparent when other aspects of recombination are perturbed. Patel et al. performed a set of elegant experiments in C. elegans aimed at identifying conserved functions of PCH-2. Their work provides such an opportunity because in C. elegans meiotically expressed HORMADs localize to meiotic chromosomes independently of PCH-2. Work in C. elegans also allows the authors to focus on nuclear PCH-2 functions as opposed to cytoplasmic functions also seen for PCH-2 in other organisms. 

      The authors performed the following experiments: 

      (1) They constructed C. elegans animals with SNPs that enabled them to measure crossing over in intervals that cover most of four of the six chromosomes. They then showed that doublecrossovers, which were common on most of the four chromosomes in wild-type, were absent in pch-2. They also noted shifts in crossover distribution in the four chromosomes. 

      (2) Based on the crossover analysis and previous studies they hypothesized that PCH-2 plays a role at an early stage in meiotic prophase to regulate how SPO-11 induced double-strand breaks are utilized to form crossovers. They tested their hypothesis by performing ionizing irradiation and depleting SPO-11 at different stages in meiotic prophase in wild-type and pch-2 mutant animals. The authors observed that irradiation of meiotic nuclei in zygotene resulted in pch-2 nuclei having a larger number of nuclei with 6 or greater crossovers (as measured by COSA-1 foci) compared to wildtype. Consistent with this observation, SPO11 depletion, starting roughly in zygotene, also resulted in pch-2 nuclei having an increase in 6 or more COSA-1 foci compared to wild type. The increased number at this time point appeared beneficial because a significant decrease in univalents was observed. 

      (3) They then asked if the above phenotypes correlated with the localization of MSH-5, a factor that stabilizes crossover-specific DNA recombination intermediates. They observed that pch-2 mutants displayed an increase in MSH-5 foci at early times in meiotic prophase and an unexpectedly higher number at later times. They conclude based on the differences in early MSH-5 localization and the SPO-11 and irradiation studies that PCH-2 prevents early DSBs from becoming crossovers and early loading of MSH-5. By analyzing different HORMAD proteins that are defective in forming the closed conformation acted upon by PCH-2, they present evidence that MSH-5 loading was regulated by the HIM-3 HORMAD. 

      (4) They performed a crossover homeostasis experiment in which DSB levels were reduced. The goal of this experiment was to test if PCH-2 acts in crossover assurance. Interestingly, in this background PCH-2 negative nuclei displayed higher levels of COSA-1 foci compared to PCH-2 positive nuclei. This observation and a further test of the model suggested that "PCH-2's presence on the SC prevents crossover designation." 

      (5) Based on their observations indicating that early DSBS are prevented from becoming crossovers by PCH-2, the authors hypothesized that the DNA damage kinase CHK-2 and PCH2 act to control how DSBs enter the crossover pathway. This hypothesis was developed based on their finding that PCH-2 prevents early DSBs from becoming crossovers and previous work showing that CHK-2 activity is modulated during meiotic recombination progression. They tested their hypothesis using a mutant synaptonemal complex component that maintains high CHK-2 activity that cannot be turned off to enable crossover designation. Their finding that the pch-2 mutation suppressed the crossover defect (as measured by COSA-1 foci) supports their hypothesis. 

      Based on these studies the authors provide convincing evidence that PCH-2 prevents early DSBs from becoming crossovers and controls the number and distribution of crossovers to promote a regulated mechanism that ensures the formation of obligate crossovers and crossover homeostasis. As the authors note, such a mechanism is consistent with earlier studies suggesting that early DSBs could serve as "scouts" to facilitate homolog pairing or to coordinate the DNA damage response with repair events that lead to crossing over. The detailed mechanistic insights provided in this work will certainly be used to better understand functions for PCH-2 in meiosis in other organisms. My comments below are aimed at improving the clarity of the manuscript. 

      We thank the reviewer for their concise summary of our manuscript and their assessment of our work as “convincing” and providing “detailed mechanistic insight.”

      Comments 

      (1) It appears from reading the Materials and Methods that the SNPs used to measure crossing over were obtained by mating Hawaiian and Bristol strains. It is not clear to this reviewer how the SNPs were introduced into the animals. Was crossing over measured in a single animal line? Were the wild-type and pch-2 mutations made in backgrounds that were isogenic with respect to each other? This is a concern because it is not clear, at least to this reviewer, how much of an impact crossing different ecotypes will have on the frequency and distribution of recombination events (and possibly the recombination intermediates that were studied). 

      We have clarified these issues in the Materials and Methods of our updated preprint. The control and pch-2 mutants were isogenic in either the Bristol or Hawaiian backgrounds. Control lines were the original Bristol and Hawaiian lines and pch-2 mutants were originally made in the Bristol line and backcrossed at least 3 times before analysis. Hawaiian pch-2 mutants were made by backcrossing pch-2 mutants at least 8 times to the Hawaiian background and verifying the presence of Hawaiian SNPs on all chromosomes tested in the recombination assay. To perform the recombination assays, these lines were crossed to generate the relevant F1s.

      (2) The authors state that in pch-2 mutants there was a striking shift of crossovers (line 135) to the PC end for all of the four chromosomes that were tested. I looked at Figure 1 for some time and felt that the results were more ambiguous. Map distances seemed similar at the PC end for wildtype and pch-2 on Chrom. I. While the decrease in crossing over in pch-2 appeared significant for Chrom. I and III, the results for Chrom. IV, and Chrom. X. seemed less clear. Were map distances compared statistically? At least for this reviewer the effects on specific intervals appear less clear and without a bit more detail on how the animals were constructed it's hard for me to follow these conclusions. 

      We hope that the added details above makes the results of these assays more clear. Map distances were compared and did not satisfy statistical significance, except where indicated. While we agree that the comparisons between control animals and pch-2 mutants may seem less clear with individual chromosomes, we argue that more general, consistent patterns become clear when analyzing multiple chromosomes. Indeed, this is why we expanded our recombination analysis beyond Chromosome III and the X Chromosome, as reported in Deshong, 2014. We have edited this sentence to: “Moreover, there was a striking and consistent shift of crossovers to the PC end of all four chromosomes tested.”

      (3) Figure 2. I'm curious why non-irradiated controls were not tested side-by-side for COSA-1 staining. It just seems like a nice control that would strengthen the authors' arguments. 

      We have added these controls in the updated preprint as Figure 2B.

      (4) Figure 3. It took me a while to follow the connection between the COSA-1 staining and DAPI staining panels (12 hrs later). Perhaps an arrow that connects each set of time points between the panels or just a single title on the X-axis that links the two would make things clearer. 

      To make this figure more clear, we have generated two different cartoons for the assay that scores GFP::COSA-1 foci and the assay that scores bivalents. We have also edited this section of the results to make it more clear.

      Reviewer #2 (Public review): 

      Summary: 

      This paper has some intriguing data regarding the different potential roles of Pch-2 in ensuring crossing over. In particular, the alterations in crossover distribution and Msh-5 foci are compelling. My main issue is that some of the models are confusingly presented and would benefit from some reframing. The role of Pch-2 across organisms has been difficult to determine, the ability to separate pairing and synapsis roles in worms provides a great advantage for this paper. 

      Strengths: 

      Beautiful genetic data, clearly made figures. Great system for studying the role of Pch-2 in crossing over. 

      We thank the reviewers for their constructive and useful summary of our manuscript and the analysis of its strengths. 

      Weaknesses: 

      (1) For a general audience, definitions of crossover assurance, crossover eligible intermediates, and crossover designation would be helpful. This applies to both the proposed molecular model and the cytological manifestation that is being scored specifically in C. elegans. 

      We have made these changes in an updated preprint.

      (2) Line 62: Is there evidence that DSBs are introduced gradually throughout the early prophase? Please provide references. 

      We have referenced Woglar and Villeneuve 2018 and Joshi et. al. 2015 to support this statement in the updated preprint.

      (3) Do double crossovers show strong interference in worms? Given that the PC is at the ends of chromosomes don't you expect double crossovers to be near the chromosome ends and thus the PC? 

      Despite their rarity, double crossovers do show interference in worms. However, the PC is limited to one end of the chromosome. Therefore, even if interference ensures the spacing of these double crossovers, the preponderance of one of these crossovers toward one end (and not both ends) suggest something functionally unique about the PC end.

      (4) Line 155 - if the previous data in Deshong et al is helpful it would be useful to briefly describe it and how the experimental caveats led to misinterpretation (or state that further investigation suggests a different model etc.). Many readers are unlikely to look up the paper to find out what this means. 

      We have added this to the updated preprint: “We had previously observed that meiotic nuclei in early prophase were more likely to produce crossovers when DSBs were induced by the Mos transposon in pch-2 mutants than in control animals but experimental caveats limited our ability to properly interpret this experiment.”

      (5) Line 248: I am confused by the meaning of crossover assurance here - you see no difference in the average number of COSA-1 foci in Pch-2 vs. wt at any time point. Is it the increase in cells with >6 COSA-1 foci that shows a loss of crossover assurance? That is the only thing that shows a significant difference (at the one time point) in COSA-1 foci. The number of dapi bodies shows the loss of Pch-2 increases crossover assurance (fewer cells with unattached homologs). So this part is confusing to me. How does reliably detecting foci vs. DAPI bodies explain this? 

      We have removed this section to avoid confusion.

      (6) Line 384: I am confused. I understand that in the dsb-2/pch2 mutant there are fewer COSA-1 foci. So fewer crossovers are designated when DSBs are reduced in the absence of PCH-2.

      How then does this suggest that PCH-2's presence on the SC prevents crossover designation? Its absence is preventing crossover designation at least in the dsb-2 mutant. 

      We have tried to make this more clear in the updated preprint. In this experiment, we had identified three possible explanations for why PCH-2 persists on some nuclei that do not have GFP::COSA-1 foci: 1) PCH-2 removal is coincident with crossover designation; 2) PCH-2 removal depends on crossover designation; and 3) PCH-2 removal facilitates crossover designation. The decrease in the number of GFP::COSA-1 foci in dsb2::AID;pch-2 mutants argues against the first two possibilities, suggesting that the third might be correct. We have edited the sentence to read: “These data argue against the possibility that PCH-2’s removal from the SC is simply in response to or coincident with crossover designation and instead, suggest that PCH-2’s removal from the SC somehow facilitates crossover designation and assurance.”

      (7) Discussion Line 535: How do you know that the crossovers that form near the PCs are Class II and not the other way around? Perhaps early forming Class I crossovers give time for a second Class II crossover to form. In budding yeast, it is thought that synapsis initiation sites are likely sites of crossover designation and class I crossing over. Also, the precursors that form class I and II crossovers may be the same or highly similar to each other, such that Pch-2's actions could equally affect both pathways. 

      We do not know that the crossovers that form near the PC are Class II but hypothesize that they are based on the close, functional relationship that exists between Class I crossovers and synapsis and the apparent antagonistic relationship that exists between Class II crossovers and synapsis. We agree that Class I and Class II crossover precursors are likely to be the same or highly similar, exhibit extensive crosstalk that may complicate straightforward analysis and PCH-2 is likely to affect both, as strongly suggested by our GFP::MSH-5 analysis. We present this hypothesis based on the apparent relationship between PCH-2 and synapsis in several systems but agree that it needs to be formally tested. We have tried to make this argument more clear in the updated preprint.

      Reviewer #3 (Public review): 

      Summary: 

      This manuscript describes an in-depth analysis of the effect of the AAA+ ATPase PCH-2 on meiotic crossover formation in C. elegant. The authors reach several conclusions, and attempt to synthesize a 'universal' framework for the role of this factor in eukaryotic meiosis. 

      Strengths: 

      The manuscript makes use of the advantages of the 'conveyor' belt system within the c.elegans reproductive tract, to enable a series of elegant genetic experiments. 

      We thank this reviewer for the useful assessment of our manuscript and the articulation of its strengths.

      Weaknesses: 

      A weakness of this manuscript is that it heavily relies on certain genetic/cell biological assays that can report on distinct crossover outcomes, without clear and directed control over other aspects and variables that might also impact the final repair outcome. Such assays are currently out of reach in this model system. 

      In general, this manuscript could be more generally accessible to non-C.elegans readers. Currently, the manuscript is hard to digest for non-experts (even if meiosis researchers). In addition, the authors should be careful to consider alternative explanations for certain results. At several steps in the manuscript, results could ostensibly be caused by underlying defects that are currently unknown (for example, can we know for sure that pch-2 mutants do not suffer from altered DSB patterning, and how can we know what the exact functional and genetic interactions between pch-2 and HORMAD mutants tell us?). Alternative explanations are possible and it would serve the reader well to explicitly name and explain these options throughout the manuscript. 

      We have made the manuscript more accessible to non-C. elegans readers and discuss alternate explanations for specific results in the updated preprint. 

      Recommendations for the authors:  

      Reviewing Editor Comments: 

      (1) Please provide 'n' values for each experiment. 

      n values are now included in the Figure legends for each experiment.

      (2) Line 129: Please represent the DCOs as percent or fraction (1%-9.8%, instead of 1-13). 

      We have made this change.

      (3) Figure 3A legend: the grey bar should read 20hr. COSA-1/ 32 hr DAPI. In Figure 3E, it is not clear why 36hr Auxin and 34hr Auxin show a significant difference in DAPI bodies between control and pch-2, but 32hr Auxin treatment does not. Here again 'n' values will help. 

      We have made this change. We also are not sure why the 32 hour auxin treatment did not show a significant difference in DAPI stained bodies. We have included the n values, which are not very different between timepoints and therefore are unlikely to explain the difference. The difference may reflect the time that it takes for SPO-11 function to be completely abrogated.

      (4) Line 360: Please provide the fraction of PCH-2 positive nuclei in dsb-2.

      We have made this change. 

      Please also address all reviewer comments. 

      Reviewer #1 (Recommendations for the authors): 

      (1) Page 3, line 52. While I agree that crossing over is important to generate new haplotypes, work has suggested that the contribution by an independent assortment of homologs to generate new haplotypes is likely to be significantly greater. One reference for this is: Veller et al. PNAS 116:1659. 

      We deeply appreciate this reviewer pointing us to this paper, especially since it argues that controlling crossover distribution contributes to gene shuffling and now cite it in our introduction! While we agree that this paper concludes that independent assortment likely explains the generation of new haplotypes to a greater degree than crossovers, the authors performed this analysis with human chromosomes and explicitly include the caveat that their modeling assumes uniform gene density across chromosomes. For example, we know this is not true in C. elegans. It would be interesting to perform the same analysis with C. elegans chromosomes in control and pch-2 mutants, taking into account this important difference.

      (2) Figure 2. It would really help the reader if an arrow and text were shown below each irradiation sign to indicate the stage in meiosis in which the irradiation was done as well as another arrow in the late pachytene box to show when the COSA-1 foci were analyzed. In general, having text in the figures that help stage the timing in meiosis would help the non C. elegans reader. This is also an issue where staging of C. elegans is shown (Figure 4). 

      We have made these changes to Figure 2. To help readers interpret Figure 4, we have added TZ and LP to the graphs in Figure 4B and 4D and indicated what these acronyms (transition zone and late pachytene, respectively) are in the Figure legend.

      (3) Page 12, line 288. It would be valuable to first outline why the him3-R93Y and htp-3H96Y alleles were chosen. This was eventually done on Page 13, but introducing this earlier would help the reader. 

      We have introduced these mutations earlier in the manuscript.

      (4) Page 13, line 323. A one sentence description of the OLLAS tagging system would be useful. 

      We have added this sentence: “we generated wildtype animals and pch-2 mutants with both GFP::MSH-5 and a version of COSA-1 that has been endogenously tagged at the Nterminus with the epitope tag, OLLAS, a fusion of the E. coli OmpF protein and the mouse Langerin extracellular domain”

      Reviewer #2 (Recommendations for the authors): 

      (1) The title is a little awkward. Consider: PCH-2 controls the number and distribution of crossovers in C. elegans by antagonizing their formation 

      We have made this change.

      (2) Abstract: 

      Consider removing "that is observed" from line 20. 

      We have made this change.

      I'm confused by the meaning of "reinforcement of crossover-eligible intermediates" from line 27. 

      We have removed this phrase from the abstract.

      A definition of crossover assurance would be helpful in the abstract. 

      We have added this to the abstract: “This requirement is known as crossover assurance and is one example of crossover control.”

      (3) Line 36: I know a stickler but many meioses only produce one haploid gamete (mammalian oocytes, for example) 

      Thanks for the reminder! We have removed the “four” from this sentence.

      (4) Line 284 - are you defining MSH-5 foci as crossover-eligible intermediates? If so, please state this earlier. 

      We have added this to the introduction to this section of the results: “In C. elegans, these crossover-eligible intermediates can be visualized by the loading of the pro-crossover factor MSH-5, a component of the meiosis-specific MutSγ complex that stabilizes crossover-specific DNA repair intermediates called joint molecules”

      (5) Can the control be included in Figure S1? 

      We have made this change.

      (6) Can you define that crossover designation is the formation of a COSA-1 focus? 

      We did this in the section introducing GFP::MSH-5: “In the spatiotemporally organized meiotic nuclei of the germline, a functional GFP tagged version of MSH-5, GFP::MSH-5, begins to form a few foci in leptotene/zygotene (the transition zone), becoming more numerous in early pachytene before decreasing in number in mid pachytene to ultimately colocalize with COSA-1 marked sites in late pachytene in a process called designation” 

      (7) Would it be easier to see the effect of DSB to crossover eligible intermediates in Spo-11, Pch-2 vs. Spo-11 mutant with irradiation using your genetic maps? At least for early vs. late breaks? 

      Unfortunately, irradiation does not show the same bias towards genomic location that endogenous double strand breaks do so it is unlikely to recapitulate the effects on the genetic map.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Weaknesses:

      In my estimation, the following would improve this manuscript:

      (1) The physiological relevance of these data could be better highlighted. For instance, future work could revolve around incubating oocytes with oviduct fluid (or OVGP1) to reduce polyspermy in porcine IVF, and naturally improve sperm selection in human IVF.

      Thank you for the suggestions. We have added these physiological relevance points at the end of the discussion.

      (2) Biological and technical replicate values for each experiment are unclear - for semen, oocytes, and oviduct fluid pools. I suggest providing in the Materials and Methods and/or Figure legends.

      Biological and technical replicates are now indicated in M&M. Number of oocytes or ZPs used were already indicated in every Supplementary Table.

      (3) Although differences presented in the bar charts seem obvious, providing statistical analyses would strengthen the manuscript.

      Statistical analyses are now indicated in each bar chart.

      (4) Results are presented as {plus minus} SEM (line 677); however, I believe standard deviation is more appropriate.

      This was a mistake; all the results are indicated as standard deviation.

      (5) Given the many independent experimental variables and combinations, a schematic depiction of the experimental design may benefit readers.

      A schematic depiction of the experimental design is now included as Figure 1. This new Figure modifies the number assigned to the rest of Figures.

      (6) Attention to detail can be improved in parts, as delineated in the "author recommendation" review section.

      Done

      Reviewer #2 (Public review):

      Weaknesses:

      The authors postulate a role for oviductal fluid in species-specific fertilization, but in my opinion, they cannot rule out hormonal effects or differences in the method of oocyte maturation employed.

      As we indicate below, the effect of hormones has been analyzed, and we have demonstrated that it is not the cause of zona pellucida specificity.

      They also cannot unequivocally prove that OVGP1 is the oviductal protein involved in the effect. Additional experiments are necessary to rule out these alternative explanations.

      Our work does not demonstrate that other proteins could be involved, but it does show that OVGP1 is involved in the process.

      When performing the EZPT assay on mouse oocytes obtained either from the ovary or from the oviduct, the oocytes obtained from the ovary came from mice primed with eCG, whereas the ones collected from the oviduct were obtained from superovulated mice (eCG plus hCG). This difference in the hormonal environment may make a difference in the properties of the ZP. Additionally, the ones obtained from the ovary were in vitro matured, which is also different from the freshly ovulated eggs and, again, may change the properties of the ZP. I suggest doing this experiment superovulating both groups of mice but collecting the fully matured MII eggs from the ovary before they get ovulated. In that way the hormonal environment will be the same in both groups and in both groups, oocytes will be matured in vivo. Hence, the only difference will be the exposure to oviductal fluids.

      In Figure 2, we compare ZPs from murine oocytes obtained from the ovary using only PMSG with ZPs from oviductal oocytes treated with both HCG and PMSG. But in Figure 7, however, we compared ZPs from murine oocytes exposed only to PMSG, with the only difference being whether or not they had been in contact with OVGP1. This shows that it is not the effect of the hormone but rather the contact with OVGP1 that determines their specificity.

      Mice with OVGP1 deletion are viable and fertile. It would be quite interesting to investigate the species-specificity of sperm-ZP binding in this model. That would indicate whether OVGP1 is the only glycoprotein involved in determining species-specificity. Alternatively, the authors could immunodeplete OVGP1 from oviductal fluid and then ascertain whether this depleted fluid retains the ability to impede cross-species fertilization.

      We agree with the reviewer that it would be interesting to investigate sperm-ZP binding in this model. Unfortunately, we do not have the OVGP1 knockout mouse strain. We also believe that immunodepletion of OVGP1 would not completely remove the protein, so its effect would likely not be entirely eliminated.

      What is the concentration of OVGP1 in the oviduct? How did the authors decide what concentration of protein to use in the experiments where they exposed ZPs to purified OVGP1? Why did they use this experimental design to check the structure of the ZP by SEM? Why not do it on oocytes exposed to oviductal fluid, which would be more physiological?

      We have included in the manuscript that the concentration of OVGP1 in the oviductal fluid was quantified using ImageJ software by comparing the mean gray value of the band in the oviductal fluid to the band in the recombinant protein lane. By establishing this relationship, along with the known concentration of protein amount in the recombinant one and in the total protein amount of oviductal fluid, the concentration of OVGP1 in the oviductal fluid was determined as the average of three western blots. The concentration of OVGP1 in oviductal fluids was in the range of 100-150 ng/µl in mice and 150-200 ng/µL in cow. We have included also in the manuscript the concentration that we have use for the EZPTs, 30 ng/µL of recombinants OVGP1 (bovine, murine and human) for 30 minutes in 20µL drops. With this concentration, we observed a clear effect on zona specificity with no negative impact on the gametes.

      As you can see in supplementary Fig S8B, we already realized SEM of oocytes exposed to oviductal fluid.

      None of the figures show any statistical analysis. Please perform analysis for all the data presented, include p values, and indicate which statistical tests were performed. The Statistical analysis section in the Methods indicating that repeated measures ANOVA was used must refer to the tables. Was normality tested? I doubt all the data are normally distributed, in which case using ANOVA is not appropriate.

      Statistical results are now included in each Figure and Table. All the statistical analysis are included, all the data pass normality, homogeneity of variance and independence; for this reason the data analysis was conducted by using a one-way ANOVA, followed by Tukey´s post hoc test. Significance level was set at p <0.05.

      Why was OVGP1 selected as the probable culprit of the species specificity? In the Results section entitled "Homology of bovine, human and murine OVGP1 proteins..." the authors delve into the possible role of this protein without any rationale for investigating it. What about other oviductal proteins?

      A sentence indicating this rationale for investigating OVGP1 has been introduced in this paragraph.

      Reviewer #3 (Public review):

      Weaknesses:

      The manuscript began with a well-written introduction, but problems started to surface in the Results section, in the Discussion, as well as in the Materials and Methods. Major concerns include inconsistencies, misinterpretation of results, lacking up-to-date literature search, numerous errors found in the figure legends, misleading and incorrect information given in the Materials and Methods, missing information regarding statistical analysis, and inadequate discussion. These concerns raise questions regarding the authenticity of the study, reliability of the findings, and interpretation of the results. The manuscript does not provide solid and convincing findings to support the conclusion.

      We have modified and clarified all the issues, some of which are misunderstandings, we have also performed the suggested experiment of putting sperm in contact with OVGP1.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Ensure consistency in (past) tense, for example, "decondensed" (line 102), "induced" (line 103), and elsewhere.

      Done

      (2) Replace "table" with "Table" throughout.

      Done

      (3) The authors often refer to "co-incubation". I believe this should read "incubation". My understanding is that oocytes were incubated with oviduct fluid or sperm but never both simultaneously as "co-incubation" implies.

      Done

      (4) Synonymous terms "OVGP1" and "oviductin" are used interchangeably. Consider using one or the other for consistency.

      We believe that by using both terms, reading is more fluid.

      (5) Delete "around" on line 256 and "approximately" on line 263 and provide actual percentages.

      Done

      (6) The point of the sentence on lines 311-313 is unclear to me.

      Rewritten

      (7) Suggest specifying "wildtype" on line 419.

      All the mice used in this work are wildtype

      (8) Do the authors have details regarding cattle oocyte donor breeds?

      Done

      (9) What do the authors mean by "strengthen" on line 500?

      The word strengthen has been changed to carefully isolated

      (10) Ponceau and vinculin (Figure 3) details are not provided in the manuscript.

      Ponceau and vinculin details are now included in the manuscript

      (11) Address formatting issues (e.g. citation 26 among others).

      Done

      (12) Primary and secondary antibody controls for immunofluorescent imaging (to fully exclude autofluorescence) are lacking.

      Controls for immunofluorescent imaging are indicated in Supplementary Figure S7.

      (13) The corresponding author on the manuscript and in the eLife submission system are different

      It was a problem during submission, now it is corrected.

      Reviewer #2 (Recommendations for the authors):

      (1) For the experiment depicted in Figures 3C and D, the authors need to perform a negative control to demonstrate that this fluorescent signal is specific. What happens if they express a different FLAG-tagged protein instead of bOVGP1 and mOVGP1? FLAG antibodies give quite strong non-specific binding. Or if they expressed untagged bovine and mouse OVGP1?

      The negative controls are in the supplementary Figure S7. A rabbit polyclonal antibody to the human OVGP1 was used for murine and bovine IVM ZPs from ovaries and murine superovulated ZPs recovered from mouse oviducts. There is a remarkable difference in the ones that are not incubated with any OVGP1 and the endogenous one, given the specificity of the antibody.

      Also, IVM mouse and bovine oocytes incubated or not with OF were immunoblotted with anti-Flag-tag antibody. Since any of them present OVGP1 tagged to Flag, there is not signal in the immunofluorescence.

      (2) For the Western blots of recombinant proteins, why are the authors not showing the blots using His and FLAG tag antibodies? Is the 50-kDa band observed for the mouse OVGP1 detected with His-Tag antibody?

      We have included a supplementary figure S6 with the western blot with anti-His and anti-Flag. The protein around 50 kDa is not a specific band (there is not signal with anti-Flag). This new figure modifies the number assigned to the rest of supplementary figures (S6-S8).

      (3) How was the estrous cycle stage determined in mice? It is not described in the Methods.

      Estrous cycle stage was determined in mice by visual examination of the vaginal opening and cytological examination of the vagina smear. This is now included in the M&M

      (4) For sperm binding, what does the percentage mean?

      It was a mistake, percentages were related to pronuclear formation and cleavage not to sperm binding, this is now corrected.

      (5) In Figure 3A, the labels for regions C, D, and E are mixed up. It is regions A and C that are conserved (or orange and blue, if the letters are incorrect). The purple region is only present in the mouse (E?), and the red region (D?) is only in the human form. Also, the legend for this panel is repeated verbatim in the Results section. Please remove one of them.

      Errors in Figure 3a have been corrected. Legend repetition is removed.

      (6) In the title of Figure 1B and in different places in the text, it should be mouse (not mice) oocytes.

      Done

      (7) In line 140, I would change the part indicating "We extracted the cytoplasmic contents from the oocytes". It is not only the cytoplasm, but all the oocyte, including the nucleus and membranes, that are being removed.

      Done

      (8) Please rephrase the sentence in lines 245-247, as it is quite confusing.

      Done

      (9) In line 236, the authors indicate that "During in vitro maturation (IVM), oocytes displayed a porous ZP structure...". Do they mean after IVM? When were those oocytes collected for SEM?

      The sentence has been modified by “after IVF”. Bovine oocytes were collected from slaughterhouse ovaries and were similar to those used in the rest of the experiments in the manuscript.

      (10) In the legend of Figure 1, please indicate what the parthenogenic group is.

      Done

      (11) In the legend to Figure 1G, the text indicates "Note sperm only appear outside the zona". However, I cannot see any sperm in that image.

      The phrase has been removed, as when enlarging the image to better see the sperm that are inside the area, the vision of those that are outside has been lost.

      (12) In the legend to Figure 2 describing the different zona pictures, the letters of the panels are not correct.

      Done

      (13) In line 999, please provide the right concentration for NMase (it indicates 10 μ/mL).

      Done

      (14) Where does the model depicted at the end of the manuscript go? Is it a Figure? A graphical abstract? In that model, please correct some typos: it should be "ZP obtained from ovarian oocytes"; and change specie for species in all three panels.

      Done. It is a model (Fig. 10)

      (15) The FITC-PNA staining to visualize acrosomes is not described in the Methods section.

      Done

      Reviewer #3 (Recommendations for the authors):

      The present study reports findings from a series of experiments suggesting that bovine oviductal fluid and species-specific oviductal glycoprotein (OVGP1 or oviductin) from bovine, murine, or human sources modulate the species specificity of bovine and murine oocytes. The manuscript began with a well-written introduction, but problems started to surface in the Results section, Discussion as well as in the Materials and Methods. Major concerns include inconsistencies, misinterpretation of results, lacking up-to-date literature search, numerous errors found in the figure legends, misleading and incorrect information given in the Materials and Methods, missing information regarding statistical analysis, and inadequate discussion.

      We have modified and clarified all the issues, some of which are misunderstandings, we have also performed the suggested experiment of putting sperm in contact with OVGP1.

      Specific comments:

      (1) Lines 142 to 143 on page 5: It is stated that "Because this experiment was done on empty ZPs, we called this test "empty zona penetration test" (EZPT)". In fact, the experiment was not actually done on empty ZPs, but on oocytes with the ooplasm extracted. Therefore, the zona pellucidae used in the experiment were not empty but contained an intact zona matrix of glycoproteins. The term "EZPT" used by the authors in the manuscript is a misnomer. A better term should be used to reflect the ZPs which were intact and not empty.

      We extracted the cytoplasmic containing all the organelles, nucleus and membranes, and the polar body. This has been clarified in the text.

      (2) The authors need to distinguish between sperm penetration and sperm binding in the manuscript. In lines 169 to 177 on page 6, the authors mixed up the terms "penetration" and "binding" in the text. In writing about events leading to fertilization in reproductive biology, the term "sperm binding" refers to the interaction between the sperm plasma membrane and the oocyte zona pellucida (ZP), whereas the term "sperm penetration" refers to the passage of the sperm through the ZP. Therefore, the statements in lines 169 to 177 describing the binding of bovine, murine, and human sperm to bovine oocytes with and without prior treatment with oviductal fluid are misleading and not correct. In fact, Figure 2 and Table 6 show sperm penetration and not sperm binding.

      Figure 2A and B (now 3A and 3B), and Tables S6 show both sperm penetration (% penetration rate and average sperm in penetrated ZPs) and sperm binding (average sperm bound to ZPs). Throughout the manuscript, a clear distinction is made between sperm attached to the ZP and sperm that have penetrated it.

      (3) Lines 182 to 187 on page 6: What is being described in the text here does not match what is being shown in Figure 3A. As a result, the information provided in lines 182 to 187 is not correct and misleading. For example, it is stated in lines 182 to 183 that "As depicted in Fig. 3A, the sequences of these three OVGP1 have five distinct regions (A, B, C, D and E)." However, Figure 3A shows that hOVGP1 and mOVGP1 both have only 4 regions and bOVGP1 has only 3 regions. None of the three has 5 regions. In lines 183 to 184, the authors continued to state that "Regions A and D are conserved in the different mammals." This statement is also not true because Figure 3A shows that only region A is conserved in all three species but not region D which is found only in the human. What is stated in lines 186 to 187 is also not correct based on the information provided in Figure 3A. It is stated here that "Region C is an insertion present only in the mouse (Mus) and region E is typical of human oviductin." However, based on the color codes provided in Figure 3A, region C is present in all three species while region E is present only in the mouse.

      Errors with naming regions in Figure 3A (now 4A) have been corrected.

      (4) In lines 195 to 197 on page 6, the authors stated that "Western blots of the three OVGP1 recombinants indicated expected sizes based on those of the proteins: 75 kDa for human and murine OVGP1 and around 60 kDa for bovine OVGP1 (Fig. 3B)." However, the expected size of the recombinant human OVGP1 is not in agreement with what has been published in literature regarding the molecular weight of recombinant human OVGP1. It has been previously reported that a single protein band of approximately 110-150 kDa was detected for recombinant human OVGP1 using an antibody against human OVGP1. The authors provided Western blots of murine oviductal fluid and bovine oviductal fluid in Figure 3B but not a Western blot of native human oviductal fluid. The latter should have been included for a comparison with the recombinant human OVGP1.

      We do not have human oviductal fluid, but we have included now a supplementary figure 6S of a western blot with antibody again His and Flag (present in the recombinant OVGP1) which shows that the size of the recombinant protein is as indicated in the Figure 3B (now 4B).

      (5) Lines 220 to 229 on page 7: In this experiment, the authors conducted the EZPT using ZPs from bovine oocytes that were either treated with or without bOVGP1 followed by incubation, respectively, with homologous sperm (bovine) and heterologous sperm (human and murine). This is a logical experiment to determine if OVGP1 plays a species-specific role in setting the specificity of the zona pellucida. However, in the in vivo situation, sperm that reach the lumen of the ampulla region of the oviduct where fertilization takes place are also exposed to oviductal fluid of which OVGP1 is a major constituent. Therefore, an additional experiment in which sperm are treated with OVGP1 prior to incubation with ZP should be carried out for a comparison.

      The additional experiment in which sperm are treated with OVGP1 prior to incubation with ZP has been done (Table S9). No effects were observed. This is now included in the manuscript.

      (6) Regarding the results obtained with the use of neuraminidase (lines 278 to 293 on pages 8 to 9), if neuraminidase treatment of bovine ZP prevented bovine sperm penetration regardless of whether ZPs had been or had not been in contact with OVGP1, that means OVGP1 is not responsible for penetration despite the description of earlier findings in the manuscript. Sialic acid is likely associated with the sugar side chains of ZP glycoproteins and not sugar side chains of OVGP1. To attribute the species-specific property of sialic acid to OVGP1 for sperm binding, an experiment in which OVGP1 will be treated with neuraminidase prior to performing the EZPT is needed.

      We conducted the experiment by treating only OVGP1 with neuraminidase and then isolating OVGP1 from the enzyme previously to incubate treated OVGP1 with ZPs. The results agree with our previous findings, indicating the importance of sialic acid on OVGP1 for sperm binding and penetration, and confirming that OVGP1 is responsible for species-specific penetration. Results are shown in Fig. 9 and Table S14.

      (7) The Discussion appears superficial and a more in-depth discussion regarding the results obtained in the present study in relation to other reports about OVGP1 published in literature is needed (e.g. a recent paper published by Kenji Yamatoya et al. (2023) Biology of Reproduction https://doi.org/10.1093/biolre/ioad159). Lines 317 to 342 of the Discussion on pages 10 to 11 should belong to the Introduction.

      Results of Yamatoya are now included in discussion. Part of the discussion from 317 to 342 are now in the introduction

      (8) In is not clear what the authors exactly want to say in lines 343 to 344 of the Discussion on page 11. It is stated here that "The empty zona penetration test (EZPT) enables heterologous sperm to overcome the oocyte's second barrier, the plasma membrane or oolemma." Do the authors mean that the sperm can now enter the empty space encircled by the ZP without having to go through the plasma membrane or oolemma? In Figure S4 which depicts the method used to empty the ooplasm in the bovine oocyte, does the method extract only the ooplasm (or cytoplasmic contents) leaving behind the intact plasma membrane or oolemma? This needs to be clearly shown and clearly explained. High magnifications of the zona pellucida are also needed to show whether the plasma membrane (or oolemma) is still present and intact after extraction of the ooplasm.

      This is clearly explained in the text. To obtain empty ZP, everything except ZP (nucleus, organelles, membranes and cytoplasmic contents of the oocytes) was removed using a micromanipulator, following the procedure outlined in Figure S4.

      (9) The authors stated in the Discussion in lines 383 to 383 on page 12 that "After ovulation, the changes reported in the carbohydrate composition of the ZP (3, 25) are likely induced by the addition of glycoproteins of oviductal origin, as we have seen here with OVGP1." There is no evidence in the present study to suggest that OVGP1 or glycoproteins of oviductal origin have changed or can change the carbohydrate composition of the ZP. At present, it is not known if OVGP1 or glycoproteins of oviductal origin directly interact with ZP glycoproteins (including ZP1, ZP2, ZP3 and/or ZP4) that make up the zona matrix.

      There is scientific evidence suggesting that oviductal glycoproteins, including OVGP1, interact with the zona pellucida (ZP) glycoproteins of the oocyte. Studies have shown that OVGP1 binds to the ZP of the oocyte. Specifically, OVGP1 is thought to interact with ZP glycoproteins, such as ZP2 and ZP3, in a way that may help stabilize the oocyte or modify the ZP structure during its passage through the oviduct. This interaction is believed to influence processes like sperm binding, oocyte maturation, and potentially the prevention of polyspermy during fertilization. For example, in several studies, the absence of OVGP1 in knockout animals (such as in Ovgp1-KO hamsters) has been associated with impaired fertilization and embryonic development, which indicates the importance of this interaction. However, the detailed molecular mechanisms and functional significance of these interactions require further exploration. We have use the work “likely” to soften this statement.

      Velásquez, J. G., Canovas, S., Barajas, P., Marcos, J., Jiménez‐Movilla, M., Gallego, R. G., ... & Coy, P. (2007). Role of sialic acid in bovine sperm–zona pellucida binding. Molecular reproduction and development, 74(5), 617-628.

      Kunz, P., et al. (2013). "The role of oviductal glycoprotein 1 in sperm–egg interaction and early embryonic development." Reproduction, 145(3), 225-233. DOI: 10.1530/REP-12-0300

      Yamatoya, K., Kurosawa, M., Hirose, M., Miura, Y., Taka, H., Nakano, T., ... & Araki, Y. (2024). The fluid factor OVGP1 provides a significant oviductal microenvironment for the reproductive process in golden hamster. Biology of reproduction, 110(3), 465-475.

      (10) Lines 390 to 391 page 12: The statement "This determines that OVGP1 modifications are critical to define the barrier among the different species of mammals." needs to be rephrased because there is no evidence in the present study showing that OVGP1 has been modified. There are many concerns with errors, important information that is missing, and inconsistencies as well as wrong and misleading information in the Materials and Methods which are troublesome. These concerns raise questions regarding the authenticity and reliability of the study. Some of the major concerns are listed below:

      All concerns have been fixed

      (11) It says in line 399 on page 13 that "Human semen samples were obtained from a normozoospermic donor...". Do the authors really mean that the semen samples were obtained from only one donor?

      Samples were obtained from 3 normozoospermic donor, this is now indicated in M&M

      (12) In lines 409 to 411 on page 13, what do the authors mean by "...the samples were frozen into pellets..."? Was centrifugation of the samples carried out prior to freezing the samples? Secondly, what do the authors mean by "....and stored in liquid nitrogen at -196{degree sign}C or lower.", particularly what do the authors mean by "or lower"? The temperature of liquid nitrogen is -196{degree sign}C. What is the "lower" temperature?

      Centrifugation of the samples were no carried out at this time. A more detailed protocol is now included The word lower has been removed.

      (13) Line 424 on page 13: Provide the full name of "M2" when it is first used in the text then followed by the abbreviation.

      Done

      (14) Is there a reason why different counting chambers were used to determine sperm concentrations? In line 432 on page 13, a Thomas cell counting chamber was used to determine the sperm count of epididymal mouse sperm whereas it is mentioned in line 441 on page 14 that a Neubauer cell counting chamber was used to determine epididymal cat sperm. Furthermore, where did the cat's sperm come from?

      The cat sperm was obtained and processed at the Faculty of Veterinary Medicine and the rest of the samples were processed in the INIA-CSIC lab, and different chambers were used in both places.

      (15) The mention of the use of cat spermatozoa in line 439 on page 14 is a worrisome problem of the manuscript. The present study used bovine, mouse, and human sperm and not cat. Therefore, the sudden mentioning of the use of cat spermatozoa in the Materials and Methods is troublesome and worrisome. It appears that the paragraph from lines 439 to 450 was directly copied and pasted from previously published work. Furthermore, lines 441 to 445 do not flow and do not make sense. In fact, what is described in this paragraph (lines 439 to 450) does not appear to correspond to the method(s) used to obtain the results presented in the Results section of the manuscript.

      I don't understand why the reviewer says we don't use cat sperm. This study uses cat sperm. Results of cat sperm are indicated in the Figure 1A (now 2A). We have modified the M&M to clarify frozen description.

      (16) Similarly, several problems are also found in the paragraphs (lines 453-478 on page 14) describing the methods and procedures to obtain homologous and heterologous IVF of bovine oocytes. Firstly, it is mentioned here (in line 460) that COCs were co-incubated with selected sperm without removing the cumulus cells. However, the results of the sperm penetration experiment indicated otherwise. Figures 2 and 3 show that the oocytes were denuded of cumulus cells. Secondly, it is very worrisome and troublesome to read what is written in line 468 on page 14 that "...from other species (cat, human, mouse, and rabbit)." One wonders where the cat and rabbit came from. Again, it appears that this paragraph was directly copied and pasted from previously published work.

      Cat sperm was used in this manuscript and it is correctly indicated in every section and figures. About IVF and EZPT protocols, in the protocol of IVF for bovine oocytes, COCs were used without removing the cumulus cells. For the EZPT cumulus cells were removed, this is described in the following sections of the material and methods. The word rabbit was a mistake and it has been removed.

      (17) In lines 468 to 469 on page 14, it is mentioned that "Sperm-egg interactions were assessed through a sperm-ZP binding assay...". The authors only examined sperm penetration in their study. Therefore, this needs to be specified in the Materials and Methods. Secondly, the authors did not use the conventional sperm-ZP binding assay in their study. Instead, they used the EZPT in their study. There appear to be many inconsistencies throughout the manuscript.

      When the IVF experiments using bovine COCs were done (Fig 2A and C, Fig 1S to 3S, and Tables 1S to 4S) conventional sperm-egg interaction was assessed at 2.5 hours after IVF. EZPT was used in the rest of experiments. IVF with COCs and EZPT with ZPs are different experiments.

      (18) Lines 480 to 489 on page 15 under the sub-heading of "In vitro culture of presumptive zygotes to first cleavage embryos on Day 2" do not provide the correct methodology used for obtaining the results presented in the manuscript. In line 482, it is not clear where the "synthetic oviductal fluid" came from. In fact, in the Results section, none of the results came from the use of synthetic oviductal fluid. In line 487, humans and rabbits are mentioned here. However, human and rabbit oocytes were not used in the present study. It is very strange indeed to read human and rabbit in the sentence.

      SOF reference is now included. Human results are in Fig 1A; the sentence is referred about the cultures of bovine oocytes inseminated with sperm of bull, human, mouse or cat). Rabbit word is a mistake and is now eliminated of the manuscript.

      (19) In line 500 on page 15, what do the authors mean by "Each oviduct was strengthen by removing the adjacent tissue..."?

      The sentence has been modified.

      (20) On page 15 in the Materials and Methods, the authors described the collection of bovine and mouse oviductal fluid. However, there is no mention of human oviductal fluid and how it was collected. This important information is missing.

      We have not use human oviductal fluid in this manuscript.

      (21) Line 510 on page 15: The sub-heading of "Preparation of empty zonae pellucidae from bovine ovarian oocytes" should be rephrased. As pointed out earlier in my review, the ZPs prepared by the authors were intact and not "empty". It was the oocyte which was empty after extraction of the ooplasm.

      Everything except the ZP were removed from the oocyte, this is now clarified in the manuscript.

      (22) Line 518 on page 16 and line 553 on page 17: "Figure S5" should be "Figure 4S".

      Done

      (23) Line 538 and line 547 on page 16: "mice oocytes" should be "mouse oocytes".

      Done

      (24) On page 17, the procedures for in vitro fertilization, sperm penetration, and binding assessment in mice were described here in lines 560 to 574. Several problems are noted in this paragraph as listed below:<br /> a. As mentioned earlier the authors in the present manuscript mixed up sperm penetration and sperm binding which are two separate events. Based on the results presented in the manuscript, they represent sperm penetration and not sperm binding. Therefore, the authors need to precisely explain in the manuscript whether the results presented refer to sperm penetration or sperm binding.

      Both sperm penetration and binding have been analyzed in this work.

      b. In line 570 on page 17, the term "insemination" is wrongly used here. Insemination is the introduction of semen into the female reproductive tract either through sexual intercourse or through an instrument. The procedure used in the present study was carried out in vitro in a co-incubation manner and not by transferring sperm into the female reproductive tract.

      The word insemination has been changed to incubation

      c. Information regarding procedures for treatment with various oviductal fluid and OVGP1s are all missing in the Materials and Methods.

      This information is now in M&M

      d. The concentrations of various oviductal fluids and OVGP1s used and the number of ZPs used in each incubation are also missing.

      Concentrations are now indicated in the manuscript. All the numbers and ZPs used are indicated in supplementary figures.

      (25) Lines 577 to 603 on pages 17 to 18: Were recombinant bovine and murine glycoproteins prepared using the same methodology? In line 595 on page 18, it is stated that "Supernatant was saved in subsequent experiments." It is not clear exactly what experiments the supernatant was subsequently used in.

      Details about how the bovine and murine glycoproteins were prepared are now included. Sentence about subsequent experiment is delete; supernatant was used for the next steps of protein purification.

      (26) What is being described in lines 604 to 609 on page 18 is problematic. The paragraph starts by saying that "Human recombinant oviductin was obtained from Origene Technologies....". Strangely, the paragraph continues by saying that the recombinant proteins were produced by transfection in HEK293T...". If recombinant human OVGP1 had already been obtained from Origene Technologies, why did the authors want to produce it again? It does not make sense.

      We briefly described the method that Origene used for the production of the human recombinant OVGP1

      (27) In lines 626 to 627 on page 18, it is stated that "Zonae pellucidae previously incubated with OVGP1 proteins from several species and murine oviductal fluid...". Were the zonae pellucidae previously incubated with only murine oviductal fluid or also with others?

      It was only incubated with OVGP1 or with oviductal fluid, this is now clarified in the text.

      (28) In lines 638 and 639 on page 19, can the authors please explain the difference between "endogenous OVGP1 and bOVGP1" and "exogenous recombinant hOVGP1 and mOVGP1"?

      This is now clarified

      (29) As stated in lines 676 to 679 on page 20, statistical analysis was performed in the study. Strangely, no "n" numbers and p values were provided in any of the figures that require statistical analysis. This is problematic.

      Statistical analysis and significant differences are now included in the figures, all the numbers used are included in the supplementary tables that are related with the figures.

      There are also many errors noted in the Figure Legends. These concerns raise questions regarding the reliability of the findings and interpretation of the results. Some major ones that require attention are listed below:

      (30) Figure legend 1 on page 27: In line 912, where did the "cat sperm" come from? In line 913, where did the "feline sperm" come from? In line 918, as pointed out earlier, the term "empty zona penetration test (EZPT)" is a misnomer and should be replaced with a better term. In line 924, it is stated that "Note sperm only appear outside the zona." However, no sperm can be seen outside the zona pellucida shown in Figure 1.

      Cat sperm is used in this manuscript. Term EZPT is now clarified The sentence about sperm outside of ZP is removed

      (31) Figure legend 2 on page 27 (lines 928 to 940) needs to be rewritten. Some of the sentences are not clearly written. Authors, please check all the capital labeling letters some of which appear to be wrong.

      Done

      (32) As is written, Figure legend 3 on pages 28 and 29 (lines 943 to 959) presents many problems:

      a. Contrary to what is stated in the figure legend, not all five regions are present in the hOVGP1, mOVGP1, and bOVGP1.

      Done

      b. Contrary to what is stated in line 946, region D is not conserved in the mouse and bull as shown in Figure 3A, and region C is not present only in the mouse.

      Done

      c. Based on what is shown in Figure 3A, region E is present only in the mouse and not in the human.

      Done

      d. What is stated in line 951 that "Proteins were expressed in mammalian cells..." is not correct. Based on the information provided in the manuscript, recombinant human OVGP1 was obtained from Origene Technologies and was not expressed in mammalian cells as claimed.

      All the recombinant proteins were produced in mammalian cells.

      (33) Figure legend 6 on page 28: In lines 985 to 986, what do the authors mean by "...and combinations of the three oviductins with sperm of the three species."? As is written, it appears that the bovine ZPs were pretreated with a combination of all three oviductins and then co-incubated with sperm from the bull, mouse and human together.

      We have clarified this sentence

      (34) What is described in the figure legend for the supplemental figure (Figure S7) does not make sense.

      Legend of Fig S7 (now S8) is related to pictures A to E, the legend is now clarified.

      (35) In addition to the figures and supplemental figures provided in the manuscript, there is also an additional figure labeled with "Model" showing three diagrams. Strangely, there is no mention of this additional figure in the manuscript. There is no figure legend for or description of this figure. It is not clear what is being shown in this figure, and it is not clear about the purpose of the use of this figure.

      We have included a legend to the model that is now Figure 10.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this study, a chromosome-level genome of the rose-grain aphid M. dirhodum was assembled with high quality, and A-to-I RNA-editing sites were systematically identified. The authors then demonstrated that: 1) Wing dimorphism induced by crowding in M. dirhodum is regulated by 20E (ecdysone signaling pathway); 2) an A-to-I RNA editing prevents the binding of miR-3036-5p to CYP18A1 (the enzyme required for 20E degradation), thus elevating CYP18A1 expression, decreasing 20E titer, and finally regulating the wing dimorphism of offspring.

      Strengths:

      he authors present both genome and A-to-I RNA editing data. An interesting finding is that a A-to-I RNA editing site in CYP18A1 ruin the miRNA binding site of miR-3036-5p. And loss of miR-3036-5p regulation lead to less 20E and winged offspring.

      Weaknesses:

      How crowding represses the miR-3036-5p is still unclear.

      Reviewer #2 (Public Review):

      Summary:

      Environmental influences on development are ubiquitous, affecting many phenotypes in organisms. However molecular genetic and cellular mechanisms transducing environmental signals are still only barely understood. This study examines part of one such intracellular mechanism in a polyphenic (or dimorphic) aphid.

      Strengths:

      While other published reports have linked phenotypic plasticity to RNA editing before, this study reports such an interaction in insects. The study uses a wide array of molecular tools to identify connections upstream and downstream of the RNA editing to elucidate the regulatory mechanism, which is illuminating.

      Weaknesses:

      While this system is intriguing, this report does not foster confidence in its conclusions. Many of the analyses seem based on very small sample sizes. It is itself problematic that sample sizes are not obvious in most figures, although based on Methods section covering RNAseq, they seem to be either 3, 6 or 9, depending on whether stages were pooled, but that point is not made clear. With such small sample sizes, statistical tests of any kind are unreliable. Besides the ambiguity on sample sizes, it's unclear what error bars or whiskers show in plots throughout this study. When sample sizes are small estimates of variance are not reliable. Student's t-test is not appropriate for comparisons with such small sample sizes. Presently, it is not possible to replicate the tests shown in Figures 3, 4 and 6. (Besides the HT-seq reads, other data should also be made publicly available, following the journal's recommendations.) Regardless, effect sizes in some comparisons (Fig 3J, 4A-C, 6E, H) are clearly not large, making confidence in conclusions low. The authors should be cautious about over-interpreting these data.

      We appreciate very much for the reviewers’ time spent on our manuscript and the referees for the valuable suggestions and comments.

      To Reviewer #1:

      At present, researches on miRNAs mainly focus on its role in gene regulation by binding to the mRNA of target genes, “how miRNAs are regulated” has received less attention.

      Recent researches indicated that the expression of miRNAs is also regulated at the transcriptional or post transcriptional level. Transcriptional regulation including changes in the promoter of microRNA genes, and post-transcriptional mechanisms such as changes in miRNA processing and stability can both affect the final expression level of miRNAs.

      This article did not address how crowding treatment regulates miRNA expression. But this will be a very interesting issue, and we will pay attention to it in our future research.

      Thank you for this suggestion.

      To Reviewer #2:

      (1) “Transgenerational wing dimorphism was observed in M. dirhodum in which crowding of the parent (100 mother aphids in a 10 cm³ tube) increased the winged offspring (Fig 3E).” In this experiment, over 250 offsprings were used to calculate the proportion of winged and wingless individuals in normal (277), crowding (255) and crowding+20E (272) groups, respectively.

      “The RNAi-mediated knockdown of CYP18A1 and ADAR2 can significantly increase the titer of 20E (Fig. 4E) and reduce the number of winged offspring by 29.6% and 24.4% (Fig. 4F), respectively.” In this experiment, over 245 offsprings were used to calculate the proportion of winged and wingless individuals in dsEGFP (273), dsCYP18A1(248), and dsADAR2 (250) groups, respectively.

      “miR-3036-5p agomir and antagomir treatments could affect the proportion of winged offspring under normal conditions (Fig. 6F), but have no effect on the wing dimorphism of offspring under crowded conditions (Fig. 6L).” In this experiment, over 235 offsprings were used to calculate the proportion of winged and wingless individuals in each group, respectively.

      So I think our conclusion that crowding treatment, A-to-I RNA editing, and miRNAs could affect the wing dimorphism of offspring in M. dirhodum is very reliable. Because the number of aphids we use to count the results is sufficient.

      (2) The quantitative PCR method is used to detect changes in gene expression levels of CYP18A1 and ADAR2 after treatment with crowding, 20E, dsRNA, miRNA agomir and antagomir, and the results are shown in Fig. 3J, 4A-C, 5B, 6B, H, respectively. 5 biological replicates (more than 100 aphids were used for each biological replicate) were used in each sample, which might be sufficient for qPCR experiments. And among these biological replicates, the differences in gene expression levels are relatively small.

      (3) The titer of 20E was detected after treatment with crowding, 20E, dsRNA, miRNA agomir and antagomir, and the results are shown in Fig. 3I, 4E, 6E, K, respectively. 8 biological replicates (more than 100 aphids were used for each biological replicate) were used in each sample.

      The number of biological replicates used in each analysis and the number of aphids included in each biological replicate have been added in the Materials and Methods section. Thank you very much for pointing out this important issue.

      Reviewer #1 (Recommendations For The Authors):

      Several questions:

      (1) This study was conducted on the rose-grain aphid M. dirhodum. However, pea aphid Acyrthosiphon pisum seems to be a better object in wing dimorphism and development studies. Have the authors also identified the A-to-I RNA editing on pea aphids or other aphids?

      Wheat is one of the main grain crops in China as well as in the world. Metopolophium dirhodum is one of the most important wheat aphids around China, and has posed a significant threat to grain production. The current study was conducted to determine the regulatory mechanism of wing dimorphism on M. dirhodum, which might be of great significance to better control this pest in wheat production.

      Surely the pea aphid offers more established experimental tools and genomic resources. However, with the development of high-throughput sequencing technology, the chromosome level genomes of many insect species have been assembled. That means any of various insects might be studied as a model species, and not limited to Drosophila melanogaster, Acyrthosiphon pisum, etc.

      We didn’t identify the A-to-I RNA editing on pea aphids or other aphids. A recent study has shown that editing events are poorly conserved across different Xenopus species. Even sites that are detected in both X. laevis and X. tropicalis show largely divergent editing levels or developmental profiles. In protein-coding regions, only a small subset of sites that are found mostly in the brain are well conserved between frogs and mammals. The conservation of RNA editing in aphids is still unknown, and we will continue to pay attention to this issue in our future research works.

      Reference: Nguyen TA, Heng JWJ, Ng YT, Sun R, Fisher S, Oguz G, Kaewsapsak P, Xue S, Reversade B, Ramasamy A, Eisenberg E, Tan MH. Deep transcriptome profiling reveals limited conservation of A-to-I RNA editing in Xenopus. BMC Biology. 2023, 21(1):251.

      (2) "Two miRNA-target prediction software programs, miRanda and RNAhybrid, were used to identify the miRNAs that potentially act on CYP18A1. The results showed that miR-3036-5p could bind to the sequence containing edited position (editing site 528) of CYP18A1 in M. dirhodum." Is there any other miRNA that can also act on CYP18A1, thereby regulating its expression?

      The predicted results indicate that there are several other miRNAs can act on CYP18A1, but none of them can bind to this editing site (editing site 528). Therefore, we did not pay attention to other miRNAs.

      (3) 11678 A-to-I RNA-editing sites were systematically identified in M. dirhodum. Does that mean RNAi-mediated knockdown of ADAR2 may affect the RNA-editing and expression of a large number of genes? Please clarify.

      It is of course possible that RNAi-mediated knockdown of ADAR2 may affect the RNA-editing and expression of a large number of genes. A-to-I RNA editing was also observed in 5 other genes that involved in 20E biosynthesis and signaling pathway, but no evident difference was identified for the RNA editing and expression levels of these 5 genes after crowding treatment (Fig. S2, Table S5). That means the A-to-I RNA editing of CYP18A1 might be crucial in 20E-mediated wing dimorphism in M. dirhodum.

      (4) It is interesting that "the transcriptional level of ADAR2 was 2.19 fold higher in the crowding+20E treatment parent than that in the normal group, but no significant difference was identified between the crowding and normal groups". ADAR2 can be induced by 20E, rather than crowding. How should the author explain? It seems that 20E induction can also cause many RNA editing events.

      20-hydroxyecdysone (20E) can affect the growth and development, molting, metamorphosis, and reproductive processes of insects. According to this result, 20E induction can also cause RNA editing events by regulating the expression of ADAR2, and which may provide valuable references for the future study on 20E. Meanwhile, we will also continue to pay attention to this issue in our future research works.

      (5) Authors provided a lot of text to describe the genome assembly. I don't think it's necessary, authors can make appropriate deletions.

      Thank you for this suggestion. This is the first high-quality chromosome-level genome of M. dirhodum, which will be very helpful for the cloning, functional verification, and evolutionary analysis of genes in this important species or even other Hemiptera insects. Therefore, I think it is necessary to provide a detailed description. We will also make appropriate deletions in the “Result and Discussion” sections.

      Reviewer #2 (Recommendations For The Authors):

      Additional concerns

      - With an existing genome sequence available for the peas aphid *Acyrthosiphon pisum*, why have these authors chosen to use the rose-grain aphid for this study? It would be helpful to address any limitations in *Acyrthosiphon pisum* or advantages in *Metopolophium dirhodum* that explain that decision.

      Wheat is one of the main grain crops in China as well as in the world. Metopolophium dirhodum is one of the most important wheat aphids around China, and has posed a significant threat to grain production. The current study was conducted to determine the regulatory mechanism of wing dimorphism on M. dirhodum, which might be of great significance to better control this pest in wheat production.

      Surely the pea aphid offers more established experimental tools and genomic resources. However, with the development of high-throughput sequencing technology, the chromosome level genomes of many insect species have been assembled. That means any of various insects might be studied as a model species, and not limited to Drosophila melanogaster, Acyrthosiphon pisum, etc.

      - In Figure 5E, what anatomy is being shown in FISH? Moreover, this represents a single sample. It would be preferable to include a supplemental figure with comparable images from at least 3 additional specimens.

      It is the whole aphid body, and we have already uploaded additional 2 FISH images to the supplementary material Fig. S5. Thank you for this suggestion.

      - L190: Conservation alone seems inadequate to conclude that a chromosome functions as a sex chromosome. It would be fine to note the homology between Chr1 and the X of other Aphidini, but there are other explanations for that. Inference that Chr 1 is a sex chromosome might come from observations in karyotypes (by relative size comparisons or ideally from FISH) or from comparison of reads mapped to the chromosomes, suggesting Chr1 is hemizygous in males.

      Karyotype analysis experiment was not conducted in this research, so here the sex chromosome was determined based on chromosome homology between M. dirhodum and A. pisum genome. We have made appropriate modifications to the description in the article. Thank you for this suggestion.

      - L205: It's unclear to me how to interpret RNA editing results, based on RNAseq data, that map to "intergenic regions", especially when this is such a large fraction (37.3%) of the total result. Does this suggest a fundamental problem with the analysis, that so much RNAseq data maps to parts of the genome that are not annotated as genes?

      Non-coding RNA regions often account for a large proportion in the genome, and this RNAseq data is mapped to non-coding RNA transcription regions (37.3%) between protein-coding genes (intergenic regions).

      - L288-290: What degrees of confidence are attached to the predictions of these miRNA targets?

      There is no clear research indicating the accuracy of miRNA target prediction software. However, by comprehensively utilizing multiple prediction tools and experimental verification, the accuracy and reliability of prediction can be significantly improved.

      Actually, the prediction of miRNA targets is only a preliminary identification step, and we have subsequently demonstrated that miR-3036-5p can act on CYP18A1 through dual-luciferase reporter assay, RNA immunoprecipitation and FISH, etc.

      - L296-298: The mechanism proposed in this study seems to imply that miR-3036-5p should be absent (not expressed) in aphids under crowded conditions. Therefore, relative realtime PCR is not particularly useful here. Finding that the miR relative expression is reduced by 48.8% is meaningless, because in *relative* expression, zero has no special meaning. In this case, absolute quantitative PCR, measuring actual transcript numbers, would be far more informative.

      miR-3036-5p is not absent in aphids under crowded conditions. Only a significant decrease of miR-3036-5p in expression level under crowded conditions was identified compared to normal feeding conditions (Fig. 5B). So it should be reasonable to use relative quantitative methods for expression level analysis.

      - L361: Isn't alternative mRNA splicing a more common post-transcriptional modification?

      I'm very sorry, this sentence has been modified to “A-to-I RNA editing is one of the most prevalent forms of posttranscriptional modification in animals, plants, and other organisms.” Thank you for this suggestion.

      - L372: "Functional wing polymorphism is commonly observed in insects as a form of adaptation and a source of variation for natural selection (14)." The relationship between plastic phenotypic variation and natural selection is complex, and there is a large theoretical literature in evolutionary biology and evo-devo on this topic, but it is not a focus in the cited review by Zhang et al.. It would be helpful if the authors could expand on this idea with reference to some of this literature (e.g. Levins 1968; Harrison 1980; Moran 1992; Roff 1996; West-Eberhard 2003; Zera 2009).

      I have changed the citation and expanded on this idea. “Wing polymorphism is commonly observed in insects, resulting from variation in both genetic factors and environmental factors (Zera 2009).”

      - L404: Use the word "accurate" seems inappropriate in this context. Both morphs are equally "accurate".

      This sentence has been modified to “resulting in the alteration of CYP18A1 expression and wing dimorphism of offspring regulated by miR-3036-5p”, Thank you for this suggestion.

      - L412: Reference 67 seems irrelevant to this point.

      References have been changed and added.

      67. E.J. Duncan, C.B. Cunningham, P.K. Dearden. Phenotypic plasticity: what has DNA methylation got to do with it? Insects. 13(2):110 (2022).

      68. K.J. Rangan, S.L. Reck-Peterson, RNA recoding in cephalopods tailors microtubule motor protein function. Cell 186, 2531-2543 (2023).

      - L443: Is this referring to "mixed stage" aphids?

      Yes. To make it clearer, this sentence has been modified to “Approximately 200 mg of fresh M. dirhodum with mixed stages (including first- to fourth-instar nymphs and winged and wingless adults)”.

      - L483: What mass or number of individual aphids was used? I assume multiple individuals were pooled?

      Each sample contains approximately 200 aphids.

      - L499: Why was k = 17 used? The default is k = 21.

      The selection of k is usually an odd number between 15 and 21, which ensures that the types of k-mers can cover the genome while being small enough to avoid erroneous effects. Therefore, using 17 is very reasonable.

      - L574: what does it mean "multiple editing types"? What different types are possible? Are you referring to things other than A-to-I editing?

      That means besides A-to-I, this locus may also have other editing situations, such as A-to-C. If this situation occurs, it will be discarded.

      - L635: Which luciferase construct or plasmid has been used in this experiment? Citation to that source is necessary.

      PmirGLO vector (Promega, Leiden, Netherlands) was used in this experiment, and a reference has been added.

      B. Zhu, L. Li, R. Wei, P. Liang, X. Gao. Regulation of GSTu1-mediated insecticide resistance in Plutella xylostella by miRNA and lncRNA. PLoS Genetics. 17(10), e1009888 (2021).

      - L644: Did cDNA synthesis employ random primers or a poly-dT primer?

      This kit provides mixed primers, including random and poly-dT primers. (PrimeScript™ RT reagent Kit with gDNA Eraser (Perfect Real Time), Takara Biotechnology, Dalian, China).

      - Fig 4D: Seems like this panel should be divided to cover the two sites, as in Fig 3F. Right now the x-axis labels seem redundant.

      Done. Thank you for this suggestion.

      - Fig 7: Consider adding ADAR2 to this figure.

      Done. Thank you for this suggestion.

      - Table 1: It would be helpful to represent this data in a figure where the phylogenetic relationships among the species can be shown.

      The phylogenetic relationships among the species were shown in Fig. 1D, and the table here may present genome information in more detail.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review)

      This paper focuses on secondary structure and homodimers in the HIV genome. The authors introduce a new method called HiCapR which reveals secondary structure, homodimer, and long-range interactions in the HIV genome. The experimental design and data analysis are well-documented and statistically sound. However, the manuscript could be further improved in the following aspects.

      Major comments:

      (1) Please give the full name of an abbreviation the first time it appears in the paper, for example, in L37, "5' UTR" "RRE".

      Thank you for your suggestion. We have added the full name of these abbreviations.

      (2) The introduction could be strengthened by discussing the limitations of existing methods for studying HIV RNA structures and interactions and highlighting the specific advantages of the HiCapR method.

      Thank you for your insightful suggestion. We have modifed sentences in the introduction section (line 66 -line 71, line 80-line 81 in the revised manuscript).

      (3) Please reorganize Results Part 1.

      Thank you for your advice. We have reorganized results part 1. We hope the revision provides a logical flow and clarity to the results, making it easier for readers to follow the progression of the study and the significance of the findings regarding to the HiCapR method.

      (4) Is there any reason that the authors mention "genome structure of SARS-CoV-2" in L95?

      Thank you for your insightful question. We have deleted this sentence in the revised paper.

      Initially, the mention of our previous work on SARS-CoV-2 serves two purposes: firstly, to demonstrate our capability to perform proximity ligation assays on viral samples; and secondly, to underscore the necessity of the hybridization step, which is particularly relevant for the study of HIV.

      Unlike SARS-CoV-2, which is highly abundant in infected cells and does not require post-library hybridization, HIV-1 presents a unique challenge due to its typically low viral RNA input within cells. The simplified SPLASH protocol, while effective for more abundant viral RNAs, does not provide the necessary coverage for high-resolution analysis when applied directly to HIV samples.

      Now, we have deleted this sentence according to your comments, and discuss the technical difference elsewhere.

      (5) L102: Please clarify the purpose of comparing "NL4-3" and "GX2005002." Additionally, could you explain what NL4-3 and GX2005002 are? The connection between NL4-3, GX2005002, and HIV appears to be missing.

      Thank you for your question, and we apologize for the misleading. "NL4-3" and "GX2005002" are two distinct HIV-1 strains that exhibit different prevalence patterns in various geographical regions. The NL4-3 strain is a well-characterized laboratory strain that is widely used in HIV research and is representative of the HIV-1 subtype B, which is highly prevalent in Europe and the Americas. On the other hand, GX2005002 is a primary isolate of the CRF01_AE subtype, which is one of the most prevalent strains in Southeast Asia, particularly in China.

      The reason for comparing these two strains in our study is twofold. Firstly, it allows us to assess the applicability and versatility of our HiCapR method across different HIV-1 strains that may have distinct genetic and structural features. This is crucial for understanding the potential broad utility of our method in studying various HIV-1 strains globally. Secondly, by comparing these strains, we can begin to elucidate any strain-specific differences in RNA structure, homodimer formation, and long-range interactions, which may have implications for viral pathogenesis, transmission, and response to therapeutic interventions.

      The connection between NL4-3, GX2005002, and HIV lies in their representation of different subtypes of the HIV-1 virus, which exhibit genetic diversity and are associated with different geographical distributions. This diversity is epidemiologically and clinically relevant, as it may be associated with different pathogenesis and resistance mechanisms, and might has implications for vaccine development and treatment strategies.

      (6) Figure 1A is not able to clearly present the innovation point of HiCapR.

      Thank you for your comment. We have revised this figure to more clearly illustrate the steps and principles of the post-library capture process using HIV pooled probes hybridization and streptavidin pull down to enrich HIV RNA-derived chimeras.

      (7) Please compare the contact metrics detected by HiCapR and current techniques like SHAPE on the local interactions to assess the accuracy of HiCapR in capturing local RNA interactions relative to established methods.

      Thank you for your request to compare the contact metrics detected by HiCapR and current techniques like SHAPE on local interactions to assess the accuracy of HiCapR in capturing local RNA interactions relative to established methods.

      In this study, HiCapR has demonstrated its ability to identify key structural elements within the HIV genome, including TAR, polyA, SL1, SL2, and SL3, as well as the polyA-SL1 in the monomeric conformation. These elements are crucial for understanding the local RNA structures involved in HIV replication and pathogenesis. By visualizing the base pairing probability as a heatmap, we have identified the most stable base pairs in the 5’ UTR of HIV, which is consistent across both NL4-3 and GX2005002 strains (Figure 2D). This consistency suggests robustness in the overall structure despite sequence variations and alternative RNA conformations, indicating a high level of agreement between HiCapR and SHAPE methods in detecting local interactions.

      Furthermore, HiCapR not only confirms the presence of known structural elements but also reveals alternative conformations of the 5'UTR that support the alternative conformations found in SHAPE analysis. This additional layer of information provides a more comprehensive view of the RNA structures, highlighting HiCapR's ability to capture local RNA interactions with a high degree of accuracy comparable to established methods like SHAPE.

      (8) The paper needs further language editing.

      We have thoroughly revised the paper. We hope it’s improved significantly.

      Reviewer #2 (Public review):

      Summary:

      In the manuscript "Mapping HIV-1 RNA Structure, Homodimers, Long-Range Interactions and 1 persistent domains by HiCapR" Zhang et al report results from an omics-type approach to mapping RNA crosslinks within the HIV RNA genome under different conditions i.e. in infected cells and in virions. Reportedly, they used a previously published method which, in the present case, was improved for application to RNAs of low abundance.

      Their claims include the detection of numerous long-range interactions, some of which differ between cellular and virion RNA. Further claims concern the detection and analysis of homodimers.

      Strengths:

      (1) The method developed here works with extremely little viral RNA input and allows for the comparison of RNA from infected cells versus virions.

      (2) The findings, if validated properly, are certainly interesting to the community.

      Thank you for your comprehensive review and insightful comments on our manuscript. We appreciate your recognition of the strengths of our HiCapR method and the potential interest of our findings to the scientific community.

      Weaknesses:

      (1) On the communication level, the present version of the manuscript suffers from a number of shortcomings. I may be insufficiently familiar with habits in this community, but for RNA afficionados just a little bit outside of the viral-RNA-X-link community, the original method (reference 22) and the presumed improvement here are far too little explained, namely in something like three lines (98-100). This is not at all conducive to further reading.

      Thank you for your feedback on the clarity of our manuscript, particularly regarding the explanation of the HiCapR method and its improvements over the original method mentioned in reference 22

      In response to your feedback, we expand on the description of the HiCapR method in the revised manuscript to ensure that it is accessible to a broader audience. We will provide a more thorough comparison between HiCapR and the original method, detailing the specific improvements and how they enable the analysis of low-abundance viral RNAs like HIV. This will include:

      Post-library Hybridization: Unlike the original method, HiCapR incorporates a post-library hybridization step. This innovation allows for the capture of target RNA involved in interactions after library construction, offering additional flexibility and enhancing the resolution of the analysis.

      Enhanced Sensitivity: HiCapR has been optimized to work with extremely low viral RNA input, which is a significant advancement over the original method. This is crucial for studying viruses like HIV, where obtaining high quantities of viral RNA can be challenging. As a matter of fact,

      (2) Experimentally, the manuscript seems to be based on a single biological replicate, so there is strong concern about reproducibility.

      Thank you for raising the issue of reproducibility in our study. We understand the importance of experimental replication in ensuring the reliability of our findings. In response to your concern, we would like to provide the following clarification and additional details regarding the reproducibility of our HiCapR experiments:

      Replicates in HiCapR Experiments: All ligation and control samples in our HiCapR experiments were performed in three biological replicates. This was done to ensure the high reproducibility of our results. The high degree of correlation (r > 0.99) between these replicates underscores the reliability of our findings.

      Dimer Validation Experiments: To validate the dimer formation of RRE and 5’-UTR, we employed multiple independent methods, including Native agarose gel electrophoresis, Agilent 4200 TapeStation Capillary electrophoresis, and Biomolecular Binding Kinetics Assays. These methods provide complementary perspectives on the dimer formation, enhancing the robustness of our validation process. The data presented in Figure 3C and Supplementary figure S12 are representative results from these experiments, which consistently support our findings on dimer formation.

      Agreement Between Cellular and Virion RNA: Our study also demonstrates a significant similarity between virions in the supernatant and infected cells from the same viral strain, as shown in Supplementary Figure S3. This consistency further validates the reproducibility and reliability of our HiCapR method in capturing RNA structures and interactions under different conditions.

      Consistency across two strains: Our study includes a comprehensive analysis of two distinct HIV-1 strains, NL4-3 and GX2005002, which are prevalent in Europe and Southeast Asia, respectively. The consistency in our findings across these strains serves as a strong indicator of the reproducibility and general applicability of our HiCapR method. Specifically, presence of key structural elements such as TAR, polyA, SL1, SL2, and SL3 in both NL4-3 and GX2005002 strains, suggests a robust structural framework that is conserved across different strains, despite sequence variations. Additionally, our study reveals approximately 20 candidate dimer peaks conserved between the NL4-3 and GX2005002 strains along the genome. The conservation of these dimer peaks across strains indicates a reproducible pattern of dimerization.

      (3) The authors perform an extensive computational analysis from a limited number of datasets, which are in thorough need of experimental validation

      Thank you for your comment.

      In response to your concern, we would like to clarify that while our manuscript does present an extensive computational analysis, we have also conducted a series of experiments. Specifically, we have validated dimer formation using multiple independent methods (afore discussed).

      Given the time-consuming nature of additional experiments, we have chosen to share the HiCapR data with the community in a timely manner. This approach allows for more immediate communication and evaluation of the data on HIV structure, which we believe is valuable for advancing the field.

      We are committed to further investigating the functional implications of our structural findings. We plan to conduct more experiments to explore the functional linking between the structural insights of HIV, which will help to deepen our understanding of the virus's replication and potential antiviral strategies.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      I suggest a major revision of the manuscript.

      Minor comments:

      (1) The article lacks consistency in its presentation. The expression of the proper noun is wrong in the paper. For example, (a) L89, "RNA:RNA interaction" →RNA-RNA interaction; (b) L431, "SARS-COV-2" → SARS-CoV-2;

      We are sorry for the inconsistency. We have corrected the mistakes.

      (2) "We identified dimers based on the methodology described in23." is not a complete sentence.

      Thank you for your insightful comment. We have revised the sentence to provide a complete and clear description of our methodology. The revised sentence is as follows: "Homodimers were identified in accordance with the methods previously reported in the literature."

      Reviewer #2 (Recommendations for the authors):

      (1) The authors perform an extensive computational analysis from a limited number of datasets, which are in thorough need of experimental validation. There is a single series on in vitro validation of the interaction of an homodimerization site, described in five lines (278-283) plus the Figure panel 3c with a very brief legend, and an extremely minimalist Figure S12. The panel to Figure 3c contains Kd values which have not been assessed for significant digits.

      Thank you for your constructive feedback on our manuscript.

      We acknowledge that our computational analysis is based on a limited number of datasets. Due to the initial exploratory nature of our study and the logistical challenges of generating additional datasets, we have focused on in-depth analysis of the available data. We are currently working on further validating our findings and are committed to publishing these results in a follow-up study.

      Regarding Experimental Validation:

      We agree that the initial description of our in vitro validation of the homodimerization site was concise. To address this, we have expanded the description of our experimental procedures. Specifically, we have detailed the methods used for the in vitro transcription, the preparation of RNA samples, and the use of the Octet R8 platform for biomolecular binding kinetics assays.

      For the Kd values presented in Figure 3c. We have now included standard error of the mean and have defined the significant digits in the figure legend. This revision provides a more accurate representation of the binding affinities.

      (2) As a further example to be experimentally validated, splice sites are discussed after lines 354, for which unsophisticated validation techniques such as targeted RT-PCR are widely accepted.

      In response to your comment, we would like to clarify that the splice sites mentioned in our study are well-established and widely recognized in the literature. They have been previously characterized and are considered canonical within the HIV research community. Given their established nature, we have relied on this foundational knowledge in our analysis.

      However, we concur with the importance of validating the regulatory role of homodimers in splicing, which is a significant aspect of HIV biology. While we have provided evidence for the presence of these homodimers and their potential implications for splicing, we acknowledge the need for further functional studies to elucidate their mechanistic role.

      Due to the scope and length constraints of the current manuscript, we have chosen to focus on the structural and interaction analyses provided by HiCapR. The functional validation of these homodimers and their impact on splicing will be pursued in subsequent studies, which we plan to initiate promptly. We believe that a dedicated follow-up study will allow for a more in-depth exploration of this complex and important aspect of HIV gene regulation.

      We are committed to advancing our understanding of the functional significance of these homodimers in the context of HIV splicing and will ensure that this line of investigation is thoroughly addressed in our future work.

      Thank you again for your valuable feedback. We look forward to contributing further to the field with our ongoing research.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife assessment

      “This work presents valuable data demonstrating that a camelid single-domain antibody can selectively inhibit a key glycolytic enzyme in trypanosomes via an allosteric mechanism. The claim that this information can be exploited for the design of novel chemotherapeutics is incomplete and limited by the modest effects on parasite growth, as well as the lack of evidence for cellular target engagement in vivo.”

      We agree with this assessment. In this re-worked version, we implemented the textual changes suggested by the reviewers and performed additional in silico work. The reviewers also presented valuable suggestions for additional experiments. However, we currently don’t have dedicated hands and funding for this project, which renders it impossible for us to perform additional “wet lab” experiments at this stage. We have thus not included new experimental “wet lab” data. Finally, the claim that our results may be exploited for the design of novel chemotherapeutics perhaps came across stronger than we intended to. We still believe our findings indicate a potential for such an endeavor, but this clearly requires further investigation and experimental evidence. We have softened this statement by removing it from the abstract and have edited the discussion to end as follows.

      “Based on the presented results, we propose that sdAb42 may pinpoint a site of vulnerability on trypanosomatid PYKs that could potentially be exploited for the design of novel chemotherapeutics. Indeed, antibodies (or fragments thereof) are valuable drug discovery tools. Antibodies (and camelid sdAbs especially) are known for their ability to "freeze out" specific conformations of highly dynamic antigens, thereby exposing target sites of interest, which could be exploited for rational drug design (the development of so-called "chemo-superiors", (Lawson, 2012; Khamrui et al., 2013; van Dongen et al., 2019)). While the design of a "chemo-superior" inspired on the sdAb42-mediated allosteric inhibition mechanism will require further investigation, the results presented here provide a foundation to fuel such an endeavour.”

      REVIEWER 1:

      Summary:

      The authors identified nanobodies that were specific for the trypanosomal enzyme pyruvate kinase in previous work seeking diagnostic tools. They have shown that a site involved in the allosteric regulation of the enzyme is targeted by the nanobody and using elegant structural approaches to pinpoint where binding occurs, opening the way to the design of small molecules that could also target this site.

      Strengths:

      The structural work shows the binding of a nanobody to a specific site on Trypanosoma congolense pyruvate kinase and provides a good explanation as to how binding inhibits enzyme activity. The authors go on to show that by expressing the nanobodies within the parasites they can get some inhibition of growth, which albeit rather weak, they provide a case on how this could point to targeting the same site with small molecules as potential trypanocidal drugs.

      Weaknesses:

      The impact on growth is rather marginal. Although explanations are offered on the reasons for that, including the high turnover rate of the expressed nanobody and the difficulty in achieving the high levels of inhibition of pyruvate kinase required to impact energy production sufficiently to kill parasites, this aspect of the work doesn't offer great support to developing small molecule inhibitors of the same site.

      Recommendations for authors:

      Generally, the paper is very well written and the figures and their legends are clear.

      Comment 1.1: I thought the Introduction could give more focus to the need for new drugs for veterinary trypanosomiasis. The reality is that with fexinidazole now available and acoziborole soon to be available, with <1,000 cases of human African trypanosomiasis in each of the last five years, the case for needing new drugs is difficult to make. For Animal trypanosomiasis, however, the need for novel drugs is much more pressing.

      We agree with this comment and have included an additional section in the Introduction’s second paragraph, which reads as follows.

      “Hence, there is a need for alternative compounds, preferably with novel modes of action and/or designed based on mechanistic insights of the target’s structure-function relationship (Field et al., 2017; De Rycker et al., 2018). This need is especially pressing for AAT, which strongly impedes sustainable livestock rearing in Sub-Saharan Africa. AAT results in drastic reductions of draft power, meat, and milk production by the infected animals (small and large ruminants), and its control relies mainly on vector control and chemotherapy, with only few drugs currently available. The lack of routine field diagnosis has resulted in the misuse of trypanocidal drugs, thereby accelerating the rise of parasite resistance and further exacerbating the problem (Richards et al., 2021). As such, AAT-inflicted annual losses are estimated at around $5 billion (and the necessity to invest another $30 million each year to control AAT through chemotherapy), thereby having a devastating impact on the socio-economic development of Sub-Saharan Africa (Fetene et al., 2021). In contrast, HAT is perceived as a minor threat as it has reached a post-elimination phase as a public health problem with less than 1,000 yearly documented cases (Franco et al., 2022). In addition, new and effective drugs for HAT treatment have recently become available (De Rycker et al., 2023). HAT control currently relies on case detection and treatment, and vector control (Büscher et al., 2017).”

      Comment 1.2: A few pedantic things can be tidied up too, for example on line 61 it is stated tsetse flies are part of the life cycle for all trypanosomes while some veterinary species e.g. T. evansi and some T.vivax strains use other biting flies for transmission. I'd also add in the Introduction that pyruvate kinase is not a glycosomal enzyme (it is covered in the legend to figure 1 but I think it is quite important to clarify in the Introduction too so as to assure readers aren't wondering if "intrabodies" can get targeted there.

      We agree with this comment and have included an additional section in the Introduction’s third paragraph to expand on the life cycles of African trypanosomes, which reads as follows.

      “African trypanosomes are extracellular parasites that have a bipartite life cycle involving insect vectors and mammals as hosts (Radwanska et al., 2018). Most HAT (T. brucei gambiense and T. b. rhodesiense) and AAT (T. b. brucei and T. congolense) causing trypanosomes are uniquely vectored by tsetse flies (Glossina spp.) and are confined to Sub-Saharan Africa. T. b. evansi and T. vivax (both causative agents of AAT) have expanded beyond the tsetse belt due to their ability to be mechanically transmitted by a variety of biting flies (Glossina, Stomoxys, and Tabanus spp.). Finally, T. b. equiperdum infects equids and represents an exception as it is transmitted directly from animal to animal through sexual contact.”

      The introduction now also explicitly mentions that pyruvate kinase is not a glycosomal enzyme.

      Comment 1.3: The introduction would also be a good place to include some more information on what is known about the allosteric effectors of pyruvate kinase in trypanosomes, and emphasize where gaps in knowledge exist too.

      We agree with this comment and have included an additional section in the Introduction’s third paragraph, which reads as follows.

      “Pyruvate kinase (PYK) represents another attractive glycolytic target. This non-glycosomal enzyme catalyses the last step of the glycolysis (the irreversible conversion of phosphoenolpyruvate (PEP) to pyruvate; Figure 1A). The importance of this reaction is two-fold: i) the generation of ATP through the transfer of a phosphoryl group from PEP to ADP and ii) the formation of pyruvate, a crucial metabolite of the central metabolism. Like most PYKs, trypanosomatid PYKs are homotetramers. The PYK monomer is a ∼55 kDa protein organized into four domains termed ’N’, ’A’, ’B’, and ’C’ (Figure 1B). The A domain constitutes the largest part of the PYK monomer and is characterized by an (𝛼/𝛽)8-TIM barrel fold that contains the active site. Together with the N-terminal domain, it is also involved in the formation of the PYK tetramer AA’ dimer interfaces. The B domain is known as the flexible ’lid’ domain that shields the active site during enzyme-mediated phosphotransfer. Finally, the C domain harbors the binding pocket for allosteric effectors and stabilizes the PYK tetramer by formation of CC’ dimer interfaces. Because of their role in ATP production and distribution of fluxes into different metabolic branches, the activity of trypanosomatid PYKs is tightly regulated through an allosteric mechanism known as the "rock and lock" model (Morgan et al., 2010, 2014; Pinto Torres et al., 2020). In this model (which is detailed in Figure 1C), the binding of substrates and/or effectors (and analogs thereof) to the active and effector sites, respectively, trigger a conformational change from the enzymatically inactive T state to the catalytically active R state. Known effector molecules for trypanosomatid PYKs are fructose 2,6-bisphosphate (F26BP), fructose 1,6-bisphosphate (F16BP) and sulfate (SO<sub>4</sub><sup>2-</sup>), with F26BP being the most potent one (van Schaftingen et al., 1985; Callens and Opperdoes, 1992; Ernest et al., 1994; Tulloch et al., 2008). Interestingly, trypanosomatid PYKs seem to be largely unresponsive to the allosteric regulation of enzyme activity by free amino acids (Callens et al., 1991), which contrasts with human PYKs (Chaneton et al., 2012; Yuan et al., 2018). Known trypanosomatid PYK inhibitors impair enzymatic activity through occupation of the PYK active site (Morgan et al., 2011).”

      In the Results, although I am not qualified to analyse the structural data in detail I am confident in the ability of the authors to do so.

      Comment 1.4: Differences in nanobody binding kinetics to the T. congolense enzyme when compared to T. brucei and Leishmania enzymes are attributed to the relatively few amino acid differences in those sites. It is desirable to test site-directed mutagenesis of those residues.

      This is a highly valuable suggestion from the reviewer. However, we currently don’t have dedicated hands and funding for this project, which renders it impossible for us to perform additional experiments at this stage.

      Comment 1.5: In the section on slow-binding inhibition kinetics (lines 194-220) I found it difficult to follow whether it was just the R<>T transition that slowed nanobody inhibition, or whether competition with effectors at the site would also impact on those inhibition kinetics. Can this be clarified?

      Since the sdAb42 epitope is located relatively far away from both active and effector sites (~20 and ~40 Å, respectively), it seems highly unlikely the observed “slow-binding inhibition” kinetics are the result of a competition between sdAb42 on one hand and substrates and/or effectors on the other for enzyme binding. Instead, given that sdAb42 selectively binds and locks the enzyme’s inactive T state, these data can be explained by the idea that sdAb42 can only bind to trypanosomatid PYKs after having undergone an R- to T-state transition. To clarify this matter, we slightly reformulated the discussion as indicated below. We also included a small discussion on the observation that there is a 400-fold difference between the Kd and the IC50.

      “Since the sdAb42 epitope is located relatively far away from both active and effector sites (~20 and ~40 Å, respectively), it seems highly unlikely that the observed “slow-binding inhibition” kinetics are the result of a direct competition between sdAb42 and substrates and/or effectors. Instead, given that sdAb42 selectively binds and locks the enzyme’s inactive T state, these data can be explained by the idea that sdAb42 can only bind to trypanosomatid PYKs after having undergone an R- to T-state transition. An additional observation in this context, is the 400-fold difference between the K<sub>D</sub> and IC<sub>50</sub> values. Although we currently do not have a mechanistic explanation, similar differences have been observed for the sdAb-mediated allosteric inhibition of other kinases (Singh et al., 2022).”

      For the intrabody expression work, the reference cited on line 230 actually points to a growing ability to genetically modify T. congolense. However, it is justifiable to work on T.brucei given the much wider availability and advanced status of the genetic tools.

      The growth inhibition data shown in Figure 7 is weak, albeit significant and the case is made as to why that might be.

      Comment 1.6: The authors do point to the fact that inhibiting other parts of the glycolytic pathway might be helpful in getting a better growth inhibitory effect. It would be useful, in this regard, to test the ability of the PFK inhibitors in the Macnae et al. paper in the intrabody expressing line, and possibly other inhibitors e.g. 2-deoxy-D-glucose to see if these combinations do have the desired impacts. Also, looking at the metabolome of the intrabody expressors under induction could also give some further insights into changes in flux (although perhaps not on its own given the weak effects on the growth seen).

      This is a highly valuable suggestion from the reviewer. However, we currently don’t have dedicated hands and funding for this project, which renders it impossible for us to perform additional experiments at this stage. We would like to point out that, in our experience, studying the effect of enzyme inhibition on the metabolome is usually only useful shortly after adding the onset of inhibition. The system adapts to the lowered flux and relevant changes are mostly transient. Since the induced expression of sdAb42 is almost certainly slow, we expect the metabolic changes will be minimal.

      REVIEWER 2:

      Summary:

      In this work, the authors show that the camelid single-chain antibody sdAb42 selectivity inhibits Trypanosome pyruvate kinase (PYK) but not human PYK. Through the determination of the crystal structure and biophysical experiments, the authors show that the nanobody binds to the inactive T-state of the enzyme, and in silico analysis shows that the binding site coincides with an allosteric hotspot, suggesting that nanobody binding may affect the enzyme active site. Binding to the T-state of the enzyme is further supported by non-linear inhibition kinetics. PYK is an important enzyme in the glycolytic pathway, and inhibition is likely to have an impact on organisms such a trypanosomes, that heavily rely on glycolysis for their energy production. The nanobody was generated against Trypanosoma congolense PYK, but for technical reasons the authors progressed to testing its impact on cell viability in Trypanosoma brucei brucei. First, they show that sdA42 is able to inhibit Tbb PYK, albeit with lower potency. Cell-based experiments next show that expression of sdA42 has a modest, and dose-dependent effect on the growth rate of Tbb. The authors conclude that their data indicates that targeting this allosteric site affects cell growth and is a valuable new option for the development of new chemotherapeutics for trypanosomatid diseases.

      Strengths:

      The work clearly shows that sdA42A inhibits Trypanosome and Leishmania PYK selectively, with no inhibition of the human orthologue. The crystal structure clearly identifies the binding site of the nanobody, and the accompanying analysis supports that the antibody acts as an allosteric inhibitor of PYK, by locking the enzyme in its apo state (T-state).

      Weaknesses:

      (1) The most impactful claim of this work is that sdAb42-mediated inhibition of PYK negatively affects parasite growth and that this presents an opportunity to develop novel chemotherapeutics for trypanosomatid diseases. For the following reasons I think this claim is not sufficiently supported:

      Comment 2.1: The authors do not provide evidence of target-engagement in cells, i.e. they do not show that sdA42A binds to, or inhibits, Tbb PYK in cells and/or do not provide a functional output consistent with PYK inhibition (e.g. effect on ATP production). Measuring the extent of target engagement and inhibition is important to draw conclusions from the modest effect on growth.

      The authors do not explore the selectivity of sdA42A in cells. Potentially sdA42A may cross-react with other proteins in cells, which would confound interpretation of the results.

      We understand the reviewer’s concern. While it is theoretically possible that sdAb42 may non-specifically (cross-)react with other proteins in the cell, this would be highly unlikely based on the following arguments. First, many studies have employed sdAbs as intrabodies and reported on specific sdAb-mediated effects (outstanding reviews on the topic are Cheloha et al. (PMID 32868455) and Soetens et al. (PMID 33322697)). Second, it has been demonstrated that selecting sdAbs from an immune library through phage display or “bacteriomatch” (a bacterial system similar to yeast two hybrid) yields highly similar results (Pellis et al., PMID 22583807), thereby indicating that sdAbs interact specifically with their target antigens in an intracellular environment. Third, we identified TcoPYK as the target for sdAb42 by employing sdAb42 as bait in a pull-down from a parasite whole cell lysate (Pinto Torres et al., PMID 29899344). The pull-down fractions were analysed by SDS-PAGE and we observed a clear prominent band, which was further analysed by mass spectrometry and revealed TcoPYK as the target with great certainty. Even though the affinity of sdAb42 for TbrPYK is lower, it still remains high (nM affinity) and we expect it to bind TbrPYK with high specificity.

      Regarding measuring the effect on ATP production, we would like to state that such experiments are not obvious. Instead of measuring ATP levels, one should measure ATP turnover as ATP levels may not necessarily be decreased. The latter was observed to be the case for the specific inhibition of trypanosomal PFK (Nare et al. PMID 36864883). The specific trypanosomal PFK inhibitor inhibits motility (and growth) of T. congolense IL3000 at concentrations that only slightly affect ATP levels. One could think of repeating the sdAb42 experiments in a T. congolense model. However, T. congolense BSF metabolism is more complicated than that of T. brucei BSF. First, the T. congolense glucose metabolic network is more expanded, allowing a lower glucose consumption rate to produce ATP and metabolites for growth. Second, pyruvate is not excreted but further metabolised, in part in the mitochondrion. Steketee et al. (PMID 34310651) have shown that T. congolense also takes up pyruvate from the medium. One can thus check if (increased) external pyruvate (partially) rescues the growth inhibition by sdAb42. It will not provide proof, but maybe an indication. As mentioned above, we are currently unable to perform such additional experiments due to lack of dedicated hands and funding.

      Comment 2.2: sdA42A only affects minor growth inhibition in Tbb. The growth defect is used as the main evidence to support targeting this site with chemotherapeutics, however based on the very modest effect on the parasites, one could reasonably claim that PYK is actually not a good drug target. The strongest effect on growth is seen for the high expressor clone in Figure 4a, however here the uninduced cells show an unusual profile, with a sudden increase in growth rate after 4 days, something that is not seen for any of the other control plots. This unexplained observation accentuates the growth difference between induced and uninduced, and the growth differences seen in all other experiments, including those with the highest expressors (clones 54 and 55) are much more modest. The loss of expression of sdA42A over time is presented as a reason for the limited effect, and used to further support the hypothesis that targeting the allosteric site is a suitable avenue for the development of new drugs. However, strong evidence for this is missing.

      We agree that the growth effect of sdAb42 expression is modest, and we have provided several explanations as to why this could be the case. In addition, as mentioned at the start of this rebuttal, the claim that our results may be exploited for the design of novel chemotherapeutics was perhaps expressed stronger than we intended to. We still believe our findings indicate a potential for such an endeavor, but this clearly requires further investigation and experimental evidence as mentioned by the reviewer.

      We, however, disagree that PYK would not be a good drug target. Its potential to serve as a drug target is related to its fundamentally important role in trypanosomal glycolysis and not to the features of sdAb42. Steketee et al. (PMID 34310651) have shown that glycolysis is essential for T. congolense BSF, despite a lower glycolytic flux than in T. brucei BSF. The T. congolense glucose metabolic network is more expanded, allowing a lower glucose consumption rate to produce ATP and metabolites for growth. Also here, PYK is thus almost certainly essential and from that perspective a good drug target.

      Comment 2.3: For chemotherapeutic interventions to be possible, a ligandable site is required. There is no analysis provided of the antibody binding site to indicate that small molecule binding is indeed feasible.

      We agree with the reviewer’s comment and have included APOP analysis on the TcoPYK T state crystal structure (see also reply to Comment 3.1). Briefly, APOP works by detecting pockets and then perturbing each pocket in the protein's elastic network (GNM) by adding stiffer springs between the surrounding residues. The pockets are scored and ranked based on the calculated shifts in the eigenvalues of the global GNM modes and their local hydrophobic densities, thereby also considering the pocket’s surface accessibility, which renders it suitable for the identification of allosteric (and druggable) pockets. The APOP analysis identifies pockets overlapping with the sdAb42 epitope as highly ranking allosteric ligand binding pockets. The data have been summarized in an additional supplementary figure (Figure 4 – figure supplement 1). The manuscript also contains details on the performed APOP analysis in the Materials and Methods section.

      Comment 2.4: The authors comment on the modest growth inhibition, and refer to the need to achieve over 88% reduction in Vmax of PYK to see a strong effect, something that may or may not be achieved in the cell-based model (no target-engagement or functional readout provided). The slow binding model and switch of species are also raised as potential explanations. While these may be plausible explanations, they are not tested which leaves us with limited evidence to support targeting the allosteric site on PYK.

      In our understanding of this remark, we believe it be related to Comments 2.1 and 2.2 and thus refer to our answers formulated above.

      Comment 2.5: The evidence to support an allosteric mechanism is derived from structural studies, including the in silico allosteric network predictions. Unfortunately, standard enzyme kinetics mode of inhibition studies are missing. Such studies could distinguish uncompetitive from non-competitive behaviour and strengthen the claim that sdAb42 locks the enzyme complex in the apo form.

      We agree with the referee that a thorough kinetic analysis could distinguish between uncompetitive (i.e., sdAb only binds to the enzyme if substrate is bound) or non-competitive (i.e., sdAb can bind to apo enzyme and substrate-bound enzyme) inhibition. In both cases, however, this would correspond to an allosteric mechanism of inhibition. Although such a thorough kinetic analysis would be interesting in its own right, we would like to argue that this type of very detailed kinetics is outside the scope of this paper. This is especially the case taking into account that this analysis could be complicated by the slow-onset inhibition behavior.

      Comment 2.6: As general comment, the graphical representation of the data could be improved in line with recent recommendations: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002128, https://elifesciences.org/inside-elife/5114d8e9/webinar-report-transforming-data-visualisation-to-improve-transparency-and-reproducibility.

      - Bar-charts for potency are ideally presented as dot plots, showing the individual data points, or box plots with datapoints shown.

      - Images in Figure 7 show significant heterogeneity of nanobody expression, but the extent of this can not be gleaned from Figure 7B. It would be much better to use box plots or violin plots for each cell line on this figure panel. The same applies to Figure 10.

      We thank the reviewer for these suggestions but have taken the decision not to act upon these as the other reviewers explicitly mentioned that our figures are very clear.

      Recommendations for authors:

      Please find below some minor comments:

      Comment 2.7: Line 24: "increasing number of drug failures": This does not really reflect the current situation for human African trypanosomiasis, with NECT treatment retaining efficacy, fexinidazole now being registered, and acoziborole progressing towards registration. It may be worth considering focusing the introduction more on Nagana, as all Trypanosome species used in the paper are animal infective, and the nanobody was discovered for T. congolense.

      We refer to our answer formulated in response to Comment 1.1.

      Comment 2.8: Line 55: "alarming number of reports describing ..." While resistance is a big problem, this mainly applies to malaria, bacterial and fungal diseases. For kinetoplastids, the number of reports describing resistance in the clinic is pretty limited. However, the drug discovery pipeline for these diseases is sparse, so I definitely agree there is a need to develop new compounds with differentiated mechanisms.

      We agree with the reviewer and have slightly adapted our wording here as follows.

      “Unfortunately, a number of reports describe treatment failure or parasite resistance to the currently available drugs (De Rycker et al., 2018).”

      Comment 2.9: This manuscript is about pyruvate kinase, but the enzyme is not properly introduced. I suggest a short paragraph introducing PYK at line 77 (without duplicating Figure 1), covering its role in glycolysis, the importance of pyruvate, any essentiality data from the literature, and any known inhibitors.

      We refer to our answer formulated in response to Comment 1.3.

      Comment 2.10: Figure 6: For the top insets it would be useful to somehow show the increasing antibody concentration, either by using a changing intensity or size for each line.

      We thank the reviewer for this suggestions, but decided not to act upon it as we found that the inclusion of this information in the figure made it “too crowded”, which is why we opted to provide this information in the figure legend.

      “Only a subset of the traces is shown for the sake of clarity. The following curves are shown (from bottom to top): TcoPYK (0.15 nM sdAb42, 500 nM sdAb42, 750 nM sdAb42, 1000 nM sdAb42, 1500 nM sdAb42, 2000 nM sdAb42, no enzyme control), LmePYK (5 nM sdAb42, 750 nM sdAb42, 1250 nM sdAb42, 1500 nM sdAb42, 2500 nM sdAb42, 3000 nM sdAb42, no enzyme control), and TbrPYK (1 nM sdAb42, 1000 nM sdAb42, 1750 nM sdAb42, 2000 nM sdAb42, 3500 nM sdAb42, 4000 nM sdAb42, no enzyme control).”

      Comment 2.11: You refer to the curves as biphasic, but they look like 1st order kinetics, and there are no clear 1st and 2nd phases (or at least they are not marked). It may be more appropriate to label these as non-linear.

      We agree that the term “biphasic” is potentially an over-simplification of the actual situation. What we mean is that the formation of product as a function of time ([P] versus [t] curve) is not linear at short time ranges but evolves from an initial “weakly inhibited” rate (v<sub>0</sub>) to a “strongly inhibited” steady-state rate (v<sub>ss</sub>). This conversion from v<sub>0</sub> to v<sub>ss</sub> indeed occurs in a fashion following single exponential behavior. With the term “biphasic” we thus meant a non-linear phase (before v<sub>ss</sub> is reached) followed by a linear phase (after v<sub>ss</sub> is reached). To avoid any confusion, we replaced the term “biphasic” by “non-linear”.

      Comment 2.12: IC50s - would be useful to provide a comparison with IC50s generated in the pre-incubation experiments - is the antibody less potent without pre-incubation? I could not find IC50s for the pre-incubation experiments shown in Figure 2.

      We determined an IC50 value for sdAb42 against TcoPYK under pre-incubation conditions, but initially decided not to include this into the manuscript. We agree with the reviewer that a comparison between IC50 values determined under pre- and post-incubation conditions would be of interest, and have therefore included the pre-incubation IC50 data for TcoPYK in Figure 2 (panel B). The data indeed show that sdAb42 far more efficiently inhibits an enzyme that is not continuously cycling between R and T states (IC50 values of 15 nM and 359 nM under pre- and post-incubation conditions, respectively). This is now discussed in the results section of the manuscript. We did not determine IC50 values for sdAb42 against TbrPYK and LmePYK under pre-incubation conditions, but suspect that a similar observation will be made upon comparing these values to IC50 under post-incubation conditions.

      REVIEWER 3:

      Summary:

      Out of the 20 Neglected Tropical Diseases (NTD) highlighted by the WHO, three are caused by members of the trypanosomatids, namely Leishmanaisis, Trypanosomiasis, and Chagas disease. Trypanosomal glycolytic enzymes including pyruvate kinase (PyK) have long been recognised as potential targets. In this important study, single-chain camelid antibodies have been developed as novel and potent inhibitors of PyK from the T, congolense. To gain structural insight into the mode of action, binding was further characterised by biophysical and structural methods, including crystal structure determination of the enzyme-nanobody complex. The results revealed a novel allosteric mechanism/pathway with significant potential for the future development of novel drugs targeting allosteric and/or cryptic binding sites.

      Strengths:

      This paper covers an important area of science towards the development of novel therapies for three of the Neglected Tropical Diseases. The manuscript is very clearly written with excellent graphics making it accessible to a wide readership beyond experts. Particular strengths are the wide range of experimental and computational techniques applied to an important biological problem. The use of nanobodies in all areas from biophysical binding experiments and X-ray crystallography to in-vivo studies is particularly impressive. This is likely to inspire researchers from many areas to consider the use of nanobodies in their fields.

      Weaknesses:

      There is no particular weakness, but I think the computational analysis of allostery, which basically relies on a single server could have been more detailed.

      Recommendations for authors:

      Overall an excellent paper, there are just a couple of points that the authors could consider, if time allows.

      Comment 3.1: As mentioned above the computational analysis of allostery appears to be based on a single server based on coordinates alone with no in-depth analysis. It would be extremely interesting to see if more sophisticated methods based on elastic network model and/or molecular dynamics simulation gave similar results. I realize that this would require quite a lot of work though.

      We agree with the reviewer’s comment and have complemented the perturbation analysis (previously presented in the manuscript) with dGNM and APOP analyses to identify allosteric communication pathways and allosteric binding pockets, respectively. dGNM, which is based on transfer entropy, allowing for a detailed characterization of the dynamic coupling and information transfer between residues. Meanwhile, APOP employs a perturbation-based approach to detect and rank allosteric pockets. The findings are in good agreement with the previously presented perturbation data and have been summarized in an additional supplementary figure (Figure 4 – figure supplement 1). The manuscript also contains details on the performed transfer entropy and APOP analyses in the Materials and Methods section.

      Comment 3.2: The figures are excellent and really help the reader - with the exception of the screenshots (Figure 8). Using pymol or chimera (or any other more expensive commercial package) would really help the reader and will not take much time.

      We agree with the referee that this is not the most beautiful figure. However, we find the quality and clarity of the figure to be adequate for its purpose (i.e., a supplemental figure).

      Comment 3.3: Finally, I would have liked to see at least the PDB validation files. This is a highly regarded and experienced team, nevertheless, the resolution is rather mediocre. As the crystal coordinates were used as input for the computational, any experimental inaccuracies will affect the computational results.

      We agree with the reviewer that we could have provided the validation report together with the submitted manuscript and we apologise for the inconvenience. The validation reports will be released together with the structures following final manuscript publication. Regarding the resolution of the crystal structures, we agree with the reviewer’s comment, but we obviously employed data sets from our best diffracting crystals and could not obtain a higher resolution despite our best efforts.

    1. Author response:

      eLife Assessment:

      This important study investigates the propensity of the intravacuolar pathogen, Leishmania, to scavenge lipids which it utilizes for its accelerated growth within macrophages. Although some of the data compellingly links increased lipid acquisition to parasite growth, data to support the underlying mechanism to describe the proposed model is incomplete. The study adds to other work that has implicated pathogen-derived processes in the selective recruitment of vesicles to the pathogen-containing vacuole, based on the content of the cargo.

      We appreciate the time and effort that Editor and Reviewers have provided to provide the assessment of our work (eLife: eLife-RP-RA-2024-102857). We thank them all for this assessment.

      Regarding some of the concerns raised by Reviewer 1, particularly the lack of data on NPC-1 knockdown, we would like to clarify that this information was included in our original submission (as elaborated in detail in the following section). Additionally, we acknowledge that one of the major concerns about the completeness of our work stems from Reviewer 1’s comments on the isolation and purity of the parasitophorous vacuole (PV). Reviewer 2 has also emphasized the importance of this experiment in strengthening the technical rigor of our study, and we fully agree with this recommendation. We acknowledge that this is a very appropriate suggestion by both the Reviewers and we will include this data in the subsequent revision of this work for revaluation of assessment. Also, ahead of a full revision of the paper, we would like to address the concerns raised by the reviewers outlining our revision plans.

      Public Reviews:

      Reviewer #1 (Public review):

      Although the use of antimony has been discontinued in India, the observation that there are Leishmania parasites that are resistant to antimony in circulation has been cited as evidence that these resistant parasites are now a distinct strain with properties that ensure their transmission and persistence. It is of interest to determine what are the properties that favor the retention of their drug resistance phenotype even in the absence of the selective pressure that would otherwise be conferred by the drug. The hypothesis that these authors set out to test is that these parasites have developed a new capacity to acquire and utilize lipids, especially cholesterol which affords them the capacity to grow robustly in infected hosts.

      We sincerely appreciate Reviewer 1's thoughtful and positive evaluation of our manuscript. We acknowledge that the reviewer has a few major concerns, and we would like to address them one by one in the following section of this initial response before submitting a full revision of our work.

      Major issues:

      (1) There are several experiments for which they do not provide sufficient details, but proceed to make significant conclusions.

      Experiments in section 5 are poorly described. They supposedly isolated PVs from infected cells. No details of their protocol for the isolation of PVs are provided. They reference a protocol for PV isolation that focused on the isolation of PVs after L. amazonensis infection. In the images of infection that they show, by 24 hrs, infected cells harbor a considerable number of parasites. Is it at the 24 hr time point that they recover PVs? What is the purity of PVs? The authors should provide evidence of the success of this protocol in their hands. Earlier, they mentioned that using imaging techniques, the PVs seem to have fused or interconnected somehow. Does this affect the capacity to recover PVs? If more membranes are recovered in the PV fraction, it may explain the higher cholesterol content.

      We would like to thank the reviewer for correctly pointing out lack of details regarding PV isolation and its purity. There are multiple questions raised by the reviewer and we will answer them one by one in a point wise manner:

      Firstly, “Is it at the 24 hr time point that they recover PVs?”

      In the ‘Methods’ section of the original submission (Line number-606-611), there is a separate section on “Parasitophorous vacuole (PV) Isolation and cholesterol measurement”, where it is clearly mentioned, “24Hrs LD infected KCs were lysed by passing through a 22-gauge syringe needle to release cellular contents. Parasitophorous vacuoles (PV) were then isolated using a previously outlined protocol [Ref: 73].” However, we do acknowledge further details might be useful to enrich this section, and hence we would like to include the following details in the revised manuscript, “10<sup>7</sup> KCs were seeded in a 100 mm plate and allowed to adhere for 24 hours. Following infection with Leishmania donovani (LD) for 24 hours, the infected KCs were harvested by gentle scraping and lysed through five successive passages through an insulin needle to ensure membrane disruption while preserving organelle integrity. The lysate was centrifuged at 200 × g for 10 minutes at 4°C to remove intact cells and large debris. The resulting supernatant was carefully collected and subjected to a discontinuous sucrose density gradient (60%, 40%, and 20%). The gradient was centrifuged at 700 × g for 25 minutes at 4°C to facilitate organelle separation. The interphase between the 40% and 60% sucrose layers, enriched with PVs, was carefully collected and subjected to a final centrifugation step at 12,000 × g for 25 minutes at 4°C. The supernatant was discarded, and the resulting pellet was enriched for purified parasitophorous vacuoles, suitable for downstream biochemical and molecular analyses.”

      Secondly, What is the purity of PVs? Earlier, they mentioned that using imaging techniques, the PVs seem to have fused or interconnected somehow. Does this affect the capacity to recover PVs? If more membranes are recovered in the PV fraction, it may explain the higher cholesterol content.

      We appreciate the reviewer for pointing this critical lack of data in the current version of the manuscript. We will be providing data on the purity of isolated fraction by performing western blot against PV and cytoplasmic fraction in the Revised manuscript. We admit, as rightly pointed out by the reviewer we need to access the purity of isolated PV in our experiment and we plan to show this is in the Revised manuscript along with a biochemical quantification of total PV membrane isolated under different experimental condition using Amplex Red kit (Invitrogen™ A12216) or similar other methods.

      (2) In section 6 they evaluate the mechanism of LDL uptake in macrophages. Several approaches and endocytic pathway inhibitors are employed. The authors must be aware that the role of cytochalasin D in the disruption of fluid phase endocytosis is controversial. Although they reference a study that suggests that cytochalasin D has no effect on fluid-phase endocytosis, other studies have found the opposite (doi: 10.1371/journal.pone.0058054). It wasn't readily evident what concentrations were used in their study. They should consider testing more than 1 concentration of the drug before they make their conclusions on their findings on fluid phase endocytosis.

      We thank the reviewer for this insightful comment and we apologise for missing out mentioning Cytochalasin D concentration. To clarify, LDL uptake by LD-R infected KCs is LDL-receptor independent as clearly shown in Section 6, Figure 4A, Figure S4A, Figure S4B i and Figure S4B ii in the Submitted manuscript. In (Figure 4F and Figure S4D) of the Submitted manuscript, as referred by the Reviewer, Cytochalasin D was used at a concentration of 2.5µg/ml. At this concentration, we did not observe any effect of Cytochalsin D on LDL-receptor independent fluid phase endocytosis as intracellular LD-R amastigotes was able to uptake LDL successfully and proliferate in infected Kupffer cells, unlike Latranculin-A (5µM) treatment which completely inhibited intracellular proliferation of LD-R amastigotes by blocking only receptor independent Fluid phase endocytosis (Movie 2A and 2B and Figure 4E in the Submitted manuscript). In fact, the study referred by the reviewer (doi: 10.1371/journal.pone.0058054), used a concentration of 4µg/ml Cytochalasin D which did affect both LDL-receptor dependent and also receptor independent endocytosis in bone marrow derived macrophages. We would also like to clarify that in this work during our preliminary experiments we have also tested higher concentration Cytochalasin-D (5µg/ml). However, even at this higher concentration there were no significant effect of Cytochalasin-D on LD-R induced LDL-receptor independent fluid phase endocytosis as observed from intracellular LD-R amastigote count represented in Author response image 1. Thus, we strongly believe that Cytochalasin D does not have any impact on LD-R induced fluid phase endocytosis even at higher concentration. We will include this in the discussion section of the revised manuscript to clear out any confusion that readers might have, and also concentration of all the inhibitors used in the study will be mentioned in the Result section, as well as in the revised Figure legends.

      Author response image 1.

      A. Giemsa-stained images illustrating the impact of concentrations of CYT-D (2.5 and 5 µg/ml) on LD-R-infected Kupffer cells. Black arrow showing intracellular amastigotes. Scale bar 10µM. B. Graphical representation depicting the effect of varying concentrations of CYT-D on the intracellular growth of LD-R. ‘ns’ depicts no significant change.

      (3) In Figure 5 they present a blot that shows increased Lamp1 expression from as early as 4 hrs after infection with LD-R and by 12 hrs after infection of both LD-S and LD-R. Increased Lamp1 expression after Leishmania infection has not been reported by others. By what mechanism do they suggest is causing such a rapid increase (at 4hrs post-infection) in Lamp-1 protein? As they report, their RNA seq data did not show an increase in LAMP1 transcription (lines 432 - 434).

      We would like to express our gratitude to the reviewer for highlighting the novelty of this observation. Indeed, to the best of our knowledge, no similar findings have been reported previously in primary macrophages infected with Leishmania donovani (LD). Firstly, we would like to point out, as stated in the Methods section (lines 562–566) of the Submitted manuscript: "Flow-sorted metacyclic LD promastigotes were used at a MOI of 1:10 (with variations of 1:5 and 1:20 in some cases) for 4 hours, which was considered the 0th point of infection. Macrophages were subsequently washed to remove any extracellular loosely attached parasites and incubated further as per experimental requirements.” This indicates that our actual study points correspond to approximately the 8th hour post-infection”. We just wanted to clarify this to prevent any potential confusion.

      Now regarding LAMP1 expression, although we could not find any previous reports of its expression in LD infected primary macrophages, we would like to mention that a previous report (doi.org/10.1128/mBio.01464-20), has shown a similar punctuated LAMP-1 upregulation (as observed by us in Figure 5A i of the Submitted manuscript) in response to leishmania infection in non-phagocytic fibroblast. It is tempting to speculate that increased LAMP-1 expression observed in response to LD-R infected macrophages might be due to increased lysosomal biogenesis, required for degrading increased endocytosed-LDL into bioavailable cholesterol.  However, since no change in LAMP-1 expression in RNA seq data (Figure 6, of the Submitted manuscript), we can only speculate that this is happening due to some post transcriptional or post translational modifications. But further work will definitely require to investigate this mechanism in details which is beyond the scope of this work. That is why, in the Submitted manuscript, (Line 432-435), we have discussed this, “Although available RNAseq analysis (Figure 6) did not support this increased expression of lamp-1 in the transcript level, it did reflect a notable upregulation of vesicular fusion protein (VSP) vamp8 and stx1a in response to LD-R-infection. LD infection can regulate LAMP-1 expression, and the role of VSPs in LDL-vesicle fusion with LD-R-PV is worthy of further investigation.”

      However, we agree with the reviewer that this might not be enough for the clarification. Hence in the revised manuscript we plan to update this part as follows, “Although available RNAseq analysis (Figure 6) did not support this increased expression of lamp-1 in the transcript level, it did reflect a notable upregulation of vesicular fusion protein (VSP) vamp8 and stx1a in response to LD-R-infection. How, LD infection can regulate LAMP-1 expression, and the role of VSPs in LDL-vesicle fusion with LD-R-PV is worthy of further investigation. It is possible and has been earlier reported that LD infection can regulate host proteins expression through post transcriptional and post translational modifications (doi.org/10.1111/pim.12156, doi.org/10.3389/fmicb.2017.00314, doi: 10.3389/fimmu.2023.1287539). It is tempting to speculate that LD-R amastigote might be promoting an increased lysosomal biogenesis through any such mechanism to increase supply of bioavailable cholesterol through action of lysosomal acid hydrolases on LDL.”

      (4) In Figure 6, amongst several assays, they reported on studies where SPC-1 is knocked down in PECs. They failed to provide any evidence of the success of the knockdown, but nonetheless showed greater LD-R after NPC-1 was knocked down. They should provide more details of such experiments.

      Although we do understand the concern raised by the reviewer, this statement in question is factually incorrect. We would like to point out that in Figure 6 F i, of the Submitted manuscript, we have demonstrated decreased NPC-1 staining following transfection with NPC-1-specific siRNA, whereas no such reduction was observed with scrambled RNA. Similar immunofluorescence data confirming LDL-receptor knockdown has also been provided in Figure S4B i of the Submitted manuscript. However, we acknowledge that the reviewer may be referring to the lack of quantitative validation of the knockdown via Western blot. We would like to clarify although, we already had this data, but we did not include it to avoid duplication to reduce the data density of the manuscript. But as suggested by the reviewer, we will be including western blot for both NPC-1 and LDL-receptor knock down in the revised manuscript as represented in Author response image 2. Additionally, as suggested by the reviewer, we also noticed lack of details in Methods section of the submitted manuscript, concerning siRNA mediated Knock down (KD). Therefore, we plan to include more details in the revised manuscript, which will read as, “For all siRNA transfections, Lipofectamine® RNAiMAX Reagent (Life Technologies, 13778100) specifically designed for knockdown assays in primary cells was used according to the manufacturer's instructions with slight modifiction. PECs were seeded into 24-well plates at a density of 1x10<sup>5</sup> per well, and incubated at 37°C with 5% CO2. The transfection complex, comprising (1µl Lipofectamine® RNAiMAX and 50µl Opti MEM) and (1 µl siRNA and 50µl Opti MEM) mixed together directly added to the incubated PECs. Gene silencing was checked by IFA and by Western blot as mentioned previously”.

      Author response image 2.

      SiRNA-mediated gene knockdown analysis. (A-i, A-ii) Representative immunofluorescence microscopy image and corresponding Western blot analysis demonstrating the knockdown efficiency of NPC1 following SiRNA-mediated gene silencing, scale bar 10µm. (B-i, B-ii) Immunofluorescence image and Western blot confirming LDLr knockdown upon SiRNA treatment. Scrambled RNA (ScRNA) was used as a negative control, while Small Interfering RNA (SiRNA) specifically targeted NPC1 and LDLr transcripts, scale bar 10µm. TR-1 and TR-2 represent independent experimental trials. β-Actin was used as an endogenous loading control for Western blot normalization.

      Minor issues

      (1) There is an implication that parasite replication occurs well before 24hrs post-infection? Studies on Leishmania parasite replication have reported on the commencement of replication after 24hrs post-infection of macrophages (PMCID: PMC9642900). Is this dramatic increase in parasite numbers that they observed due to early parasite replication?

      We thank the reviewer for this insightful comment and appreciate the opportunity to clarify our findings. Indeed, as rightly assumed by the Reviewer, as our data suggest, and we also believe that this increase intracellular amastigotes number is a consequence of early replication of Leishmania donovani.  As already mentioned in response to Point number 3 raised by Reviewer 1, we would again like to highlight that in the Methods section (lines 562–566), it is clearly stated: "Flow-sorted metacyclic LD promastigotes were used at a MOI of 1:10 (with variations of 1:5 and 1:20 in some cases) for 4 hours, which was considered the 0th point of infection. Macrophages were subsequently washed to remove any extracellular loosely attached parasites and incubated further as per experimental requirements.” This effectively means that our actual study points correspond to approximately the 8th and 28th hours post-infection and we just want to mention it to avoid any confusion.

      Now, regarding specific concern, the study referred by the reviewer on the commencement of replication after 24hrs, was conducted on Leishmania major, which may differ significantly from Leishmania donovani owing to its species and strain-specific characteristics.  In fact, doubling time of Leishmania donovani (LD) has been previously reported to be approximately 11.4 hours (doi: 10.1111/j.1550-7408. 1990.tb01147.x).  Moreover, multiple studies have indicated an exponential increase in intracellular LD amastigote number (more than two-fold increase) by 24hrs post infection. However, by 48hrs post-infection, the replication rate appeared to slow down, with amastigote numbers not increasing (doubling) proportionally (doi:10.1128/AAC.01196-07, doi.org/10.1016/j.ijpara.2011.07.013). We also have a similar observation for both infected PEC and KC as depicted in Figure 1Ci and Figure S1Ci in the Submitted manuscript) along with Author response image 3. Hence it was an informed decision from our side to focus on 24 hours’ time point to perform the analysis on intracellular proliferation.

      Author response image 3.

      Graph representing number of intracellular LD-R (MHOM/IN/2009/BHU575/0) parasite burden at different time points post-infection. *** signifies p value < 0.0001, * signifies p value < 0.05.

      (2) Several of the fluorescence images in the paper are difficult to see. It would be helpful if a blown-up (higher magnification image of images in Figure 1 (especially D) for example) is presented.

      We apologise for the inconvenience. Although we have provided Zoomed images for several Figures in the Submitted manuscript, like Figure 4, Figure 5, Figure 6 and Figure 8. However, this was not always doable for all the figures (like for Figure 1D), due to lack of space and Figure arrangements requirements. However, to accommodate Reviewer’s request we would like to provide a blown-up image for Figure 1D as represented in Author response image 4 in the Revised version. If the reviewer similar representation for any other particular Figures, we will be happy to perform a similar presentation.

      Author response image 4.

      Three-Dimensional morphometric representation of Parasitophorous Vacuoles (PVs) in Leishmania infected Kupffer Cells at 24 Hours Post-Infection: Confocal 3D reconstruction illustrating the spatial distribution of parasitophorous vacuoles (PVs) in Kupffer cells (KCs) infected for 24 hours. ATP6V0D2, a lysosomal vacuolar ATPase subunit, is visualized in magenta, while the nucleus is depicted in cyan. The final panel highlights PV structural grooves outlined in red solid lines, with intracellular Leishmania donovani (LD) amastigotes indicated by white arrows. Higher magnification of Figure 1D further emphasizes the increased abundance of PVs in LD-R infected cells, suggesting enhanced intracellular replication and adaptation mechanisms of drug-resistant strains. Scale bar 5µM. Both yellow and magenta solid line box represents the same area of the image.

      (3) The times at which they choose to evaluate their infections seem arbitrary. It is not clear why they stopped analysis of their KC infections at 24 hrs. As mentioned above, several studies have shown that this is when intracellular amastigotes start replicating. They should consider extending their analyses to 48 or 72 hrs post-infection. Also, they stop in vitro infection of Apoe-/- mice at 11 days. Why? No explanation is given for why only 1 point after infection.

      Reviewer has raised two independent concerns and we would like to address them individually.

      Firstly, “The times at which they choose to evaluate their infections seem arbitrary. It is not clear why they stopped analysis of their KC infections at 24 hrs. As mentioned above, several studies have shown that this is when intracellular amastigotes start replicating. They should consider extending their analyses to 48 or 72 hrs post-infection.”

      We have already provided a detail justification for time point selection in our response to Reviewer Minor Comment 1. As mentioned already we observed a significant and sharp rise in the number of intracellular amastigotes between 4 and 24Hrs post-infection (Author response image 4), with replication rate appeared to be not increaseing proportionally after that. This early stage of rapid replication of LD amastigotes, therefore likely coincides with a critical period of lipid acquisition by intracellular amastigotes (Movie 2A and 2B and Figure 4E in the submitted manuscript) and thus 24hrs infected KC was specifically selected. In this regard, we would also like to add that at 72hrs post-infection, we noticed a notable number of infected Kupffer cells began detaching from the wells with extracellular amastigotes probably egressing out from the infected KCs. This phenomenon potentially reflects the severe impact of prolonged infection on Kupffer cell viability and adhesion properties as shown in Author response image 5 and Author Response Video 1. This point further influenced our decision to conclude all infection studies in Kupffer cells by the 48Hrs post-infection, which necessitate to complete the infection time point at 24 Hrs, for allowing treatment of Amp-B for another 24 Hrs (Figure 8, and Figure S5, in the Submitted manuscript). We acknowledge that we should have been possibly more clear on our selection of time points and as the Reviewer have suggested we plan to include this information in the revised manuscript for clear understanding of the reader.

      Author response image 5.

      Representative images of Kupffer cells infected with Leishmania donovani at 72Hrs post-infection showing a significant morphological changes. Infected cells exhibit a rounded morphology and progressive detachment. Scale bar 10µm.

      Secondly “Also, they stop in vitro infection of Apoe-/- mice at 11 days. Why? No explanation is given for why only 1 point after infection.”

      We apologize for not providing an explanation regarding the selection of the 11-day time point for Apoe-/- experiments (Figure 2 of the Submitted manuscript). Our rationale for this choice is based on both previous literature and the specific objectives of our study. Previous report suggests that Leishmania donovani infection in Apoe-/- mice triggers a heightened inflammatory response at approximately six weeks’ post-infection compared to C57BL/6 mice, leading to more efficient parasite clearance. This is owing to unique membrane composition of Apoe-/- which rectifies leishmania mediated defective antigen presentation at a later stage of infection (DOI 10.1194/jlr.M026914). Additionally, previous studies (doi: 10.1128/AAC.47.5.1529-1535.2003) have also indicated that Leishmania donovani infection is well-established in vivo within 6 to 11 days post-infection in murine models. Given that in this experiment we particularly aimed to assess the early infection status (parasite load) in diet-induced hypercholesterolemic mice, we would like to argue that the selection of the 11-day time point was intentional and well-aligned with our study objectives as this time point within this window are optimal for capturing initial parasite burden depending on initial lipid utilization, before host-driven immune clearance mechanisms could significantly alter infection dynamics. We will include this explanation in the Revised manuscript as suggested by the Reviewer.

      Reviewer #2 (Public review):

      Summary:

      This study by Pradhan et al. offers critical insights into the mechanisms by which antimony-resistant Leishmania donovani (LD-R) parasites alter host cell lipid metabolism to facilitate their own growth and, in the process, acquire resistance to amphotericin B therapy. The authors illustrate that LD-R parasites enhance LDL uptake via fluid-phase endocytosis, resulting in the accumulation of neutral lipids in the form of lipid droplets that surround the intracellular amastigotes within the parasitophorous vacuoles (PV) that support their development and contribute to amphotericin B treatment resistance. The evidence provided by the authors supporting the main conclusions is compelling, presenting rigorous controls and multiple complementary approaches. The work represents an important advance in understanding how intracellular parasites can modify host metabolism to support their survival and escape drug treatment.

      We would like to sincerely thank the reviewer for appreciating our work and find the evidence compelling to address the issue of emergence of drug resistance in infection with intracellular protozoan pathogens. Before we submit a full revision of the paper, we would like to provide a primary response addressing the concerns of the reviewer.

      Strengths:

      (1) The study utilizes clinical isolates of antimony-resistant L. donovani and provides interesting mechanistic information regarding the increased LD-R isolate virulence and emerging amphotericin B resistance.

      (2) The authors have used a comprehensive experimental approach to provide a link between antimony-resistant isolates, lipid metabolism, parasite virulence, and amphotericin B resistance. They have combined the following approaches:

      (a) In vivo infection models involving BL/6 and Apoe-/- mice.

      (b) Ex-vivo infection models using primary Kupffer cells (KC) and peritoneal exudate macrophages (PEC) as physiologically relevant host cells.

      (c) Various complementary techniques to ascertain lipid metabolism including GC-MS, Raman spectroscopy, microscopy.

      (d) Applications of genetic and pharmacological tools to show the uptake and utilization of host lipids by the infected macrophage resident L. donovani amastigotes.

      (3) The outcome of this study has clear clinical significance. Additionally, the authors have supported their work by including patient data showing a clear clinical significance and correlation between serum lipid profiles and treatment outcomes.

      (4) The present study effectively connects the basic cellular biology of host-pathogen interactions with clinical observations of drug resistance.

      (5) Major findings in the study are well-supported by the data:

      (a) Intracellular LD-R parasites induce fluid-phase endocytosis of LDL independent of LDL receptor (LDLr).

      (b) Enhanced fusion of LDL-containing vesicles with parasitophorous vacuoles (PV) containing LD-R parasites both within infected KCs and PECs cells.

      (c) Intracellular cholesterol transporter NPC1-mediated cholesterol efflux from parasitophorous vacuoles is suppressed by the LD-R parasites within infected cells.

      (d) Selective exclusion of inflammatory ox-LDL through MSR1 downregulation.

      (e) Accumulation of neutral lipid droplets contributing to amphotericin B resistance.

      Weaknesses:

      The weaknesses are minor:

      (1) The authors do not show how they ascertain that they have a purified fraction of the PV post-density gradient centrifugation.

      (2) The study could have benefited from a more detailed analysis of how lipid droplets physically interfere with amphotericin B access to parasites.

      We have addressed both these concerns as our preliminary response in details in subsequent “Recommendations for the Authors section” before we submit a complete Revised manuscript,

      Impact and significance:

      This work makes several fundamental advances:

      (1) The authors were able to show the link between antimony resistance and enhanced parasite proliferation.

      (2) They were also able to reveal how parasites can modify host cell metabolism to support their growth while avoiding inflammation.

      (3) They were able to show a certain mechanistic basis for emerging amphotericin B resistance.

      (4) They suggest therapeutic strategies combining lipid droplet inhibitors with current drugs.

      Recommendations for the authors:

      Reviewer #2 (Recommendations for the authors):

      (1) Experimental suggestions:

      a) The authors could have provided a more detailed analysis of lipid droplet composition. This is a critically missing piece in this nice study.

      We completely agree with the reviewer on this, a more detailed analysis of lipid droplets composition, dynamics of its formation and mechanism of lipid transfer to amastigotes residing within the PV would be worthy of further investigation.  To answer the reviewers, we are already conducting investigation in this direction and have very promising initial results which we are willing to share with the reviewer as unpublished data if requested. Since, we plan to address these questions independently, we hope reviewer will understand our hesitation to include these data into the present work which is already immensely data dense. We sincerely believe existence of lipid droplet contact sites with the PV along with the specific lipid type transfer to amastigotes and its mechanism requires special attention and could stand out as an independent work by itself.

      b) The macrophages (PEC, KC) could have been treated with latex beads as a control, which would indicate that cholesterol and lipids are indeed utilized by the Leishmania parasitophorous vacuole (PV) and essential for its survival and proliferation.

      We thank the reviewer for this nice suggestion, which we believe will further strengthen the conclusion of this work. This has also been suggested by Reviewer 1 and we are planning to conduct this experiment and will include this data in the revised version of this manuscript.

      c) HMGCoA reductase is an important enzyme for the mevalonate pathway and cholesterol synthesis. The authors have not commented on this enzyme in either host or parasite. Additionally, western blots of these enzymes along with SREBP2 could have been performed.

      We appreciate the concern and do see the point why reviewer is suggesting this. We would like to mention that regarding HMGCoA we already do have real time qPCR data which perfectly aligns with our RNAseq data (Figure 6 Ai, in the Submitted manuscript), showing significant downregulation specifically in LD-R infected KC as compared to uninfected control. We are including this data as Author response image 6.  However, we did not proceed with checking the level of HMGCoA at the protein level as we noticed several previous reports have suggested that HMGCoA remains under transcriptional control of SERBP2(doi.org/10.1016/j.cmet.2011.03.005,doi: 10.1194/jlr.C066712,doi:10.1194/jlr.RA119000201), which acts the master regulator of mevalonate pathway and cholesterol synthesis (doi.org/10.1161/ATVBAHA.122.317320).  However, as suggested by the Reviewer, we will perform this experiment and will update the Revised manuscript with the expression data on HMGCoA probably in the Supplementary section

      Author response image 6.

      qPCR Analysis of HMGCR Expression Following Leishmania donovani Infection: Quantitative PCR analysis showing the relative expression of hmgcr (3-hydroxy-3-methylglutaryl-CoA reductase) in Kupffer cells after 24 hours of Leishmania donovani (LD) infection compared to uninfected control cells. Gene expression levels are normalized to β-actin as an internal control, and fold change is represented relative to the uninfected condition.

      d) The authors should discuss the expression pattern of any enzyme of the mevalonate pathway that they have found to be dysregulated in the transcript data.

      As per the reviewer’s suggestion, we have already looked into the RNA seq data and observed that apart from hmgcr, hmgcs (_3-hydroxy-3-methylglutaryl-CoA synthase), another key enzyme in the mevalonate pathway, is significantly downregulated in host PECs in response to LD-R infection compared to the LD-S infection.  We will update this in the Discussion section of the Revised manuscript, which will read as “Further analysis of RNA sequencing data revealed a significant downregulation of _hmgcs (3-hydroxy-3-methylglutaryl-CoA synthase) in LD-R infected PECs as compared to LD-S infecton. HMGCS which catalyzes the condensation of acetyl-CoA with acetoacetyl-CoA to form 3-hydroxy-3-methylglutaryl-CoA (HMG-CoA), which serves as an intermediate in both cholesterol biosynthesis and ketogenesis. The downregulation of hmgcs further supports our observation that LD-R-infected PECs preferentially rely on endocytosed low-density lipoprotein (LDL)-derived cholesterol rather than de novo synthesized cholesterol for their metabolic needs.”

      e) The authors have followed a previously published protocol by Real F (reference 73) to enrich for parasitophorous vacuole (PV). However, they do not show how they ascertain that they have a purified fraction of the PV post-density gradient centrifugation. The authors should at least show Western blot data for LAMP1 for different fractions of density gradient from which they enriched the PV.

      As we previously stated in our response to Reviewer 1, the Revised manuscript will include a detailed analysis of purity for different fractions during PV isolation. We sincerely appreciate the reviewer for highlighting this important concern and for suggesting an approach to conduct the experiment. We believe this experiment is crucial and will further reinforce the conclusions of our study.

      (2) Presentation improvements:

      a) Add a clear timeline for infection experiments.

      Sure. We will be including a schematic of Timelines in the revised figures 2 and 7

      b) Provide more details on patient sample collection and analysis.

      We plan to include more details on the sample collection in the Method section of the Revised manuscript as follows, “Blood samples were collected from a total of 22 individuals spanning a diverse age range (8 to 70 years) by RMRI, Bihar, India. Among these, nine samples were obtained from healthy individuals residing in endemic regions to serve as controls. Serum was isolated from each blood sample through centrifugation, and the lipid profile was subsequently analysed using a specialized diagnostic kit (Coral Clinical System) following the manufacturer's protocol.”

      c) Consider reorganizing figures to better separate mechanistic and clinical findings.

      We would like to thank the reviewer for this suggestion. However, we feel that the arrangement of the Figures as presented in the Original Submission is really helping a smooth flow of the story and hence, we would not want to disturb that. However, having said that, if the reviewer has specific suggestion regarding rearrangement of any particular figure, we will be happy to consider that.

      (3) Technical clarifications needed:

      a) Specify exact concentrations used for inhibitors.

      We apologise for this unwanted and unnecessary mistake. Please note we will clearly mention the concentration of all the inhibitors used in this study in Result section and in Revised Figure legends. The revised section will read as, “Finally, we infected the KCs with GFP expressing LD-R for 4Hrs, washed and allowed the infection to proceed in presence of fluorescent red-LDL and Latrunculin-A ( 5µM), a compound  which specifically inhibits fluid phase endocytosis by inducing actin depolymerization [41]. Real-time fluorescence tracking demonstrated that Latrunculin-A treatment not only prevented the uptake of fluorescent red-LDL but also severely impacted intracellular proliferation of LD-R amastigotes (Movie 2A and 2B and Figure 4E). In contrast, treatment with Cytochalasin-D (2.5µg/ml), which alters cellular F-actin organization but does not affect fluid phase endocytosis”

      b) Include more details on image analysis methods.

      Please note that in specific sections like in Line numbers 574-579, 653-658, 1047-1049 of the Submitted manuscript, we have put special attention in describing the Image analysis process. However, we agree that in some particular cases more details will be appreciated by the reader. Hence we will be including an additional section of Image Analysis in the Methods section of the revised manuscript. This section will read as, “Image processing and analysis were conducted using Fiji (ImageJ). For optimal visualization, Giemsa-stained macrophages (MΦs) were represented in grayscale to enhance contrast and structural clarity. To improve the distinction of different fluorescent signals, pseudo-colors were assigned to fluorescence images, ensuring better differentiation between various cellular components. For colocalization analysis (Figures 3, 5, 6, and S2), we utilized the RGB profile plot plugin in ImageJ, which allows for the precise assessment of signal overlap by generating fluorescence intensity profiles across selected regions of interest. This approach provided quantitative insights into the spatial relationship between labeled molecules within infected cells. Additionally, for analyzing the distribution of cofilin in Figure 4, the ImageJ surface plot plugin was employed. This tool enabled three-dimensional visualization of fluorescence intensity variations, facilitating a more detailed examination of cofilin localization and its potential reorganization in response to infection.”

      c) Clarify statistical analysis procedures.

      Response: We have already provided a dedicated section of Statistical Analysis in the Methods section and also have also shown the groups being compared to determine the statistical analysis in the Figure and in the Figure Legends of the Submitted manuscript. Furthermore, we plan to add additional clarification regarding the statistical analysis performed Revised manuscript. For example, in the Revised manuscript this section will read as, “All statistical analyses were performed using GraphPad Prism 8 on raw datasets to ensure robust and reproducible results. For datasets involving comparisons across multiple conditions, one-way or two-way analysis of variance (ANOVA) was conducted, followed by Tukey’s post hoc test to assess pairwise differences while controlling for multiple comparisons. A 95% confidence interval (CI) was applied to determine the statistical reliability of the observed differences. For non-parametric comparisons across multiple groups, Wilcoxon rank-sum tests were employed, maintaining a 95% confidence interval, which is particularly useful for analysing skewed data distributions. In cases where only two groups were compared, Student’s t-test was used to determine statistical significance, ensuring an accurate assessment of mean differences. All quantitative data are represented as mean ± standard error of the mean (SEM) to illustrate variability within experimental replicates. Statistical significance was determined at P ≤ 0.05. Notation for significance levels: *P ≤ 0.05; **P ≤ 0.001; ***P ≤ 0.0001.”

      (4) Minor corrections:

      a) Methods section could benefit from more details on Raman spectroscopy analysis.

      We agree with this suggestion of the Reviewer. For providing more clarity we will incorporate additional details in the Methodology for the Raman section of the Revised manuscript. The updated section will read as follows in the revised manuscript. “For confocal Raman spectroscopy, spectral data were acquired from individual cells at 1000× magnification using a 100 × 100 μm scanning area, following previously established specifications. After spectral acquisition, distinct Raman shifts corresponding to specific biomolecular signatures were extracted for further analysis. These included: Cholesterol (535–545 cm⁻¹), Nuclear components (780–790 cm⁻¹), Lipid structures (1262–1272 cm⁻¹), Fatty acids (1436–1446 cm⁻¹) Following spectral extraction, pseudo-color mapping was applied to highlight the spatial distribution of each biomolecular component within the cell. These processed spectral images are presented in Figure 3D1, where the first four panels illustrate the individual biomolecular distributions. A merged composite image was then generated to visualize the co-localization of these biomolecules within the cellular microenvironment, with the final panel specifically representing the spatial distribution of key biomolecules.”

      b) In the methods section line 609, page 14, the authors cite Real F protocol as reference 73 for PV enrichment. However, in the very next section on GC-MS analysis (lines 615-616, page 15), they state they have used reference 74 for PV enrichment. Can they explain why a discrepancy in PV isolation references this? Reference 74 does not mention anything related to PV isolation.

      We would like to sincerely apologise for this confusion which probably raised from our writing of this section. We would like to confirm that our PV isolation protocol is based on the published work of Real F protocol (reference 73). However, in the next section of the submitted manuscript, GC-MS analysis was described and that was performed based on protocol referenced in 74. In the Revised manuscript, we will avoid this confusion and made correction by putting the references in the proper places. Revised section will read as,

      “GC-MS analysis of LD-S and LD-R-PV

      Following a 24Hrs infection period, KCs were harvested, washed with phosphate-buffered saline (PBS), and pelleted. Subsequent to this, PV isolation was carried out using the previously described method [73]. The resulting parasitophorous vacuole (PV) pellet was processed for sterol isolation for GC_MS analysis following a previously established protocol [74], with slight modification. Briefly, the PV pellet was resuspended in 20 ml of dichloromethane:methanol (2:1, vol/vol) and incubated at 4°C for 24hours. After centrifugation (11,000 g, 1 hour, 4°C), the supernatant was checked through thin layer chromatography (TLC) and subsequently evaporated under vacuum. The residue and pellet were saponified with 30% potassium hydroxide (KOH) in methanol at 80°C for 2 hours. Sterols were extracted with n-hexane, evaporated, and dissolved in dichloromethane. A portion of the clear yellow sterol solution was treated with N, O-bis(trimethylsilyl)trifluoroacetamide (BSTFA) and heated at 80°C for 1 hour to form trimethylsilyl (TMS) ethers. Gas chromatography/mass spectrometry (GC/MS) analysis was performed using a Varian model 3400 chromatograph equipped with DB5 columns (methyl-phenylsiloxane ratio, 95/5; dimensions, 30 m by 0.25 mm). Helium was used as the gas carrier (1 ml/min). The column temperature was maintained at 270°C, with the injector and detector set at 300°C. A linear gradient from 150 to 180°C at 10°C/min was used for methyl esters, with MS conditions set at 280°C, 70 eV, and 2.2 kV.”

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This work by Ding et al uses agent-based simulations to explore the role of the structure of molecular motor myosin filaments in force generation in cytoskeletal structures. The focus of the study is on disordered actin bundles which can occur in the cell cytoskeleton and have also been investigated with in vitro purified protein experiments.

      Strengths:

      The key finding is that cooperative effects between multiple myosin filaments can enhance both total force and the efficiency of force generation (force per myosin). These trends were possible to obtain only because the detailed structure of the motor filaments with multiple heads is represented in the model.

      We appreciate your comments about the strength of our study.

      Weaknesses:

      It is not clearly described what scientific/biological questions about cellular force production the work answers. There should be more discussion of how their simulation results compare with existing experiments or can be tested in future experiments.

      Thank you for the comment. First, our study explains why non-muscle myosin II in stress fibers shows focal distributions rather than uniform distributions; if they stay closely, they can generate much larger forces in the stress fibers via the cooperative overlap. Our study also predicts a difference between bipolar structures (found in skeletal muscle myosins and non-muscle myosins) and side polar structures (found in smooth muscle myosins) in terms of the likelihood of the cooperative overlap. As shown below, myosin filaments with the bipolar structure can add up their forces better than those with the side polar structure when their overlap level is the same. We will add discussion about these in the revised manuscript.

      Author response image 1.

      As the reviewer noticed, our results were briefly compared with prior observations in Ref. 4 (Thoresen et al., Biophys J, 2013) where different myosin isoforms were used for in vitro actin bundles. We will add more quantitative comparisons between the in vitro study and our results.

      In addition, at the end of the conclusion section, we suggested future experiments that can be used for verifying our results. In particular, experiments with synthetic myosin filaments with tunable geometry seem to be suitable for verifying our computational predictions and observations.

      The model assumptions and scientific context need to be described better.

      We apologize for the insufficient descriptions about the model. We will revise those parts to better explain model assumptions and scientific context.

      The network contractility seems to be a mere appendix to the bundle contractility which is presented in much more detail.

      We included some cases run with the two-dimensional network in this study to prove the generality of our conclusions. We included minimal preliminary results in this study because we are currently working on a follow-up study with network structures. I hope that the reviewer would understand our intention and situation.

      Reviewer #2 (Public review):

      Summary:

      In this study, the authors use a mechanical model to investigate how the geometry and deformations of myosin II filaments influence their force generation. They introduce a force generation efficiency that is defined as the ratio of the total generated force and the maximal force that the motors can generate. By changing the architecture of the myosin II filaments, they study the force generation efficiency in different systems: two filaments, a disorganized bundle, and a 2D network. In the simple two-filament systems, they found that in the presence of actin cross-linking proteins motors cannot add up their force because of steric hindrances. In the disorganized bundle, the authors identified a critical overlap of motors for cooperative force generation. This overlap is also influenced by the arrangement of the motor on the filaments and influenced by the length of the bare zone between the motor heads.

      Strengths:

      The strength of the study is the identification of organizational principles in myosin II filaments that influence force generation. It provides a complementary mechanistic perspective on the operation of these motor filaments. The force generation efficiency and the cooperative overlap number are quantitative ways to characterize the force generation of molecular motors in clusters and between filaments. These quantities and their conceptual implications are most likely also applicable in other systems.

      Thank you for the comments about the strength of our study.

      Weaknesses:

      The detailed model that the authors present relies on over 20 numerical parameters that are listed in the supplement. Because of this vast amount of parameters, it is not clear how general the findings are. On the other hand, it was not obvious how specific the model is to myosin II, meaning how well it can describe experimental findings or make measurable predictions. The model seems to be quantitative, but the interpretation and connection to real experiments are rather qualitative in my point of view.

      As the reviewer mentioned, all agent-based computational models for simulating the actin cytoskeleton are inevitably involved with such a large number of parameters. Some of the parameter values are not known well, so we have tuned our parameter values carefully by comparing our results with experimental observations in our previous studies since 2009. 

      We were aware of the importance of rigorous representation of unbinding and walking rates of myosin motors, so we implemented the parallel cluster model, which can predict those rates with consideration of the mechanochemical rates of myosin II, into our model. Thus, we are convincing that our motors represent myosin II.

      In our manuscript, our results were compared with prior observations in Ref. 4 (Thoresen et al., Biophys J, 2013) several times. In particular, larger force generation with more myosin heads per thick filament was consistent between the experiment and our simulations.

      Our study can make various predictions. First, our study explains why non-muscle myosin II in stress fibers shows focal distributions rather than uniform distributions; if they stay closely, they can generate much larger forces in the stress fibers via the cooperative overlap. Our study also predicts a difference between bipolar structures (found in skeletal muscle myosins and non-muscle myosins) and side polar structures (found in smooth muscle myosins) in terms of the likelihood of the cooperative overlap. As shown in Author response image 1, myosin filaments with the bipolar structure can add up their forces better than those with the side polar structure when their overlap level is the same. We will add discussion about these in the revised manuscript.

      We will add more discussion about these in the revised manuscript.

      It was often difficult for me to follow what parameters were changed and what parameters were set to what numerical values when inspecting the curve shown in the figures. The manuscript could be more specific by explicitly giving numbers. For example, in the caption for Figure 6, instead of saying "is varied by changing the number of motor arms, the bare zone length, the spacing between motor arms", the authors could be more specific and give the ranges: ""is varied by changing the number of motor arms form ... to .., the bare zone length from .. to..., and the spacing between motor arms from .. to ..".

      This unspecificity is also reflected in the text: "We ran simulations with a variation in either L<sub>sp</sub> or L<sub>bz</sub>" What is the range of this variation? "When L<sub>M</sub> was similar" similar to what? "despite different N<sub>M</sub>." What are the different values for N<sub>M</sub>? These are only a few examples that show that the text could be way more specific and quantitative instead of qualitative descriptions.

      We appreciate the comment. We will specify the range of the variation in each parameter in the revised manuscript.

      In the text, after equation (2) the authors discuss assumptions about the binding of the motor to the actin filament. I think these model-related assumptions and explanations should be discussed not in the results section but rather in the "model overview" section.

      Thank you for pointing this out. We will reorganize the text in the revised manuscript.

      The lines with different colors in Figure 2A are not explained. What systems and parameters do they represent?

      The different colors used in Fig. 2A were used for distinguishing 20 cases. We will add explanation about the colors in the figure caption in the revised manuscript.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Reviewer #2 (Recommendations for the authors):

      While the authors have responded to most of the comments, a number of issues remain, most of which pertain to imprecise writing, as previously mentioned.

      In the second revision of our manuscript, we tried our best to precise our writing.

      For example, at high concentrations of PRG-GEF, the authors repeatedly state that RhoA is inhibited (including in the summary). While this may be functionally valid, it is imprecise. RhoA is activated (not inhibited), but its ability to promote contractility is impaired, presumably as a consequence of sequestration of the active GTPase by the PH domain of PRG-GEF. To put a finer point on this, the activity of RhoA•GTP is to bind to proteins that selectively bind active RhoA. One such protein the PH domain of PRG. In the case where PRG is overexpressed, RhoA•GTP binds to PRG. Due to the high concentrations of PRG in some cells, this outcompetes the ability of RhoA•GTP to bind other effectors such as formins or ROCK. However, there no strong evidence that RhoA is inhibited. The only hint of such evidence is a reduction in the biosensor for active RhoA, but this too is likely outcompeted by the overexpressed active GEF. There does not appear to be any disagreement about the mechanism, but rather a semantic difference.

      We thank Reviewer #2 for emphasizing this semantic concern, which indeed requires clarification. We agree that RhoA is not chemically inactivated; rather, the protein remains active but is functionally sequestered. Our use of the term “inhibition” was intended to describe functional inhibition, consistent with the definition of inhibition as the act of reducing, preventing, or blocking a process, activity, or function. However, we recognize that this terminology could be interpreted as imprecise. To address this, we have clarified the text by explicitly referring to "functional inhibition of RhoA signaling" where appropriate, or by rewording to terms such as "competitive inhibition of RhoA effector binding" to more accurately reflect the mechanism.

      Overall, the manuscript is written in a conversational style, not with the precision expected of a scientific manuscript.

      We acknowledge Reviewer #2’s comment regarding the style of our manuscript. While our manuscript adopts a somewhat conversational tone, this was a deliberate choice. We believe this style helps engage the reader and facilitates understanding of our reasoning, guided by the philosophy that science is conducted by humans and should be communicated in a way that resonates with them. That said, we fully agree that this approach should not compromise scientific precision. In response to this feedback, we have revised the manuscript to ensure greater clarity and precision while maintaining the approachable style we have chosen.

      To exemplify this, I provide an alternative phrasing of one such paragraph.

      Lines 51-62:

      Here, contrarily to previous optogenetic approaches, we report a serendipitous discovery where the optogenetic recruitment at the plasma membrane of GEFs of RhoA triggers both protrusion and retraction in the same cell type, polarizing the cell in opposite directions. In particular, one GEF of RhoA, PDZ-RhoGEF (PRG), also known as ARHGEF11, was most efficient in eliciting both phenotypes. We show that the outcome of the optogenetic perturbation can be predicted by the basal GEF concentration prior to activation. At high concentration, we demonstrate that Cdc42 is activated together with an inhibition of RhoA by the GEF leading to a cell protrusion. Thanks to the prediction of a minimal mathematical model, we can induce both protrusion and retraction in the same cell by modulating the frequency of light pulses. Our ability to control both phenotypes with a single protein on timescales of second provides a clear and causal demonstration of the multiplexing capacity of signaling circuits.

      Here, we report that the phenotypic consequences of plasma membrane recruitment of a guanine nucleotide exchange factor (GEF), PDZ-RhoGEF (PRG, aka ARHGEF11) depends on the level of expression and degree of recruitment of the GEF. At low concentrations, recruitment of PRG induces cell retraction, consistent with the expected function of a GEF for RhoA. However, at high concentrations, Cdc42 is activated, leading to cell protrusion. A minimal mathematical predicts, and experimental observations confirm, that the extent of recruitment determines the consequences of GEF recruitment. The ability of a single GEF to induce disparate outcomes demonstrates the multiplexing capacity of signaling circuits.

      We thank Reviewer #2 for providing an alternative phrasing for lines 51–62. We appreciate the effort to enhance clarity and precision in this key section of the manuscript. While we agree with many aspects of the suggested revision and have incorporated several elements to improve the text, we have also retained aspects of our original phrasing that align with the overall tone and structure of the manuscript. Specifically, we have ensured that the balance between precision and accessibility is maintained while integrating the reviewer's suggestions. We hope that the revised text now addresses the concerns raised.

      Key points to correct throughout the manuscript are:

      -  overexpression of PRG does not "inhibit" RhoA.

      -  retraction and protrusion are distinct phenotypes, they are not opposite phenotypes. One results from RhoA activation, the other results from Cdc42 activation.

      Regarding the term “inhibition,” we agree with the reviewer’s point and have addressed this in our earlier comment.

      Regarding the terminology of "opposite phenotypes," we believe this description is valid. While protrusion and retraction arise from distinct signaling pathways (Cdc42 activation and RhoA activation, respectively), we describe them as opposite phenotypes because they represent mutually exclusive cellular behaviors. A cell cannot protrude and retract at the same location simultaneously; instead, these behaviors represent opposing ends of the dynamic spectrum of cell morphology.

      Here are some other places where editing would improve the manuscript (a noncomprehensive list).

      We went through the whole manuscript to improve the scientific precision according to Reviewer #2 comment on the terminology “inhibition”.

      line 15 "inhibition of RhoA by the PH domain of the GEF at high concentrations."

      We modified the wording: “sequestration of active RhoA by the GEF PH domain at high concentrations”

      line 51 "Here, contrarily to previous optogenetic approaches"

      We removed “contrarily to previous optogenetic approaches"

      line 141 "We next wonder what could differ in the activated cells that lead to the two opposite phenotypes." (the state of mind of the authors is not relevant)

      As explained earlier, we made the choice to keep our writing style.

      line 185 "Very surprised by this ability of one protein to trigger opposite phenotypes"

      As explained earlier, we made the choice to keep our writing style.

      lines 206 ff "As our optogenetic tool prevented us from using FRET biosensors because of spectral overlap, we turned to a relocation biosensor that binds RhoA in its GTP form. This highly sensitive biosensor is based on the multimeric TdTomato, whose spectrum overlaps with the RFPt fluorescent protein used for quantifying optoPRG recruitment. We thus designed a new optoPRG with iRFP, which could trigger both phenotypes *but was harder to transiently express* (?? what does this have to do with the spectral overlap), giving rise to a majority of retracting phenotype. *Looking at the RhoA biosensor*, we saw very different responses for both phenotypes (Figure 3G-I). "

      We have clarified.

      lines 231ff "RhoA activity shows a very different behavior: it first decays, and then rises. It seems that, adding to the well-known activation of RhoA, PRG DH-PH can also negatively regulate RhoA activity." again, RhoA activity may appear to decay, but this is a limitation of the measurements. RhoA is likely activated to the GTP-bound form. PRG is not negatively regulating RhoA activity. An activity that prevents nucleotide exchange by RhoA or accelerates its hydrolysis would constitute negative regulation of RhoA.

      We modified the wording to clarify the sentence.

      The attempts to quantify the degree of overexpression, though rough, should be included in the version of record. It is not clear how that estimate was generated.

      The estimate of absolute concentration (switch at 200nM) was obtained by comparing fluorescent intensities of purified RFPt and cells under a spinning disk microscope while keeping the exact same acquisition settings. The whole procedure will be described in a manuscript in preparation, focused on Rac1 GEFs.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary and Strengths:

      The ability of Wolbachia to be transmitted horizontally during parasitoid wasp infections is supported by phylogenetic data here and elsewhere. Experimental analyses have shown evidence of wasp-to-wasp transmission during coinfection (eg Huigins et al), host to wasp transmission (eg Heath et al), and mechanical ('dirty needle') transmission from host to host (Ahmed et al). To my knowledge this manuscript provides the first experimental evidence of wasp to host transmission. Given the strong phylogenetic pattern of host-parasitoid Wolbachia sharing, this may be of general importance in explaining the distribution of Wolbachia across arthropods. This is of interest as Wolbachia is extremely common in the natural world and influences many aspects of host biology.

      Weaknesses:

      The first observation of the manuscript is that the Wolbachia strains in hosts are more closely related to those in their parasitoids. This has been reported on multiple occasions before, dating back to the late 1990s. The introduction cites five such papers (the observation is made in other studies too that could be cited) but then dismisses them by stating "However, without quantitative tests, this observation could simply reflect a bias in research focus." As these studies include carefully collected datasets that were analysed appropriately, I felt this claim of novelty was rather strong. It is unclear why downloading every sequence in GenBank avoids any perceived biases, when presumably the authors are reanalysing the data in these papers.

      Thank you for bringing this to our attention. In this study, we downloaded all wsp sequences from GenBank and conducted a systematic analysis. We acknowledge that there could still be a bias in research focus, but a systematic analysis, compared to a limited dataset, may reduce this bias. We agree with the reviewer's point, and we have revised this statement to make it more accurate. Now the new sentence reads: "However, there is still a lack of systematic statistical analyses to support this hypothesis." (Lines 69–70 in the revised manuscript)

      I do not doubt the observation that host-parasitoid pairs tend to share related Wolbachia, as it is corroborated by other studies, the effect size is large, and the case study of whitefly is clearcut. It is also novel to do this analysis on such a large dataset. However, the statistical analysis used is incorrect as the observations are pseudo-replicated due to phylogenetic non-independence. When analysing comparative data like this it is essential to correct for the confounding effects of related species tending to be similar due to common ancestry. In this case, it is well-known that this is an issue as it is a repeated observation that related hosts are infected by related Wolbachia. However, the authors treat every pairwise combination of species (nearly a million pairs) as an independent observation. Addressing this issue is made more complex because there are both the host and symbiont trees to consider. The additional analysis in lines 123-124 (including shuffling species pairs) does not explicitly address this issue.

      We agree with your point about the non-independence of data due to phylogenetic relationships. In the analysis of species traits, a conventional phylogenetic correction assumes that traits follow a Brownian motion model (Felsenstein, 1985). The variance of the trait values for a species i is given by:

      Var[Yi]=σ2Ti,

      Where Ti represents the time from the root to the tip for species i. Consequently, the covariance between traits of species i and j is:

      Cov[Yij,Yj]=σ<sup>2</sup>Tii,

      where Tij is the time from the root to the most recent common ancestor (MRCA) of species i and j. Linear model analysis incorporates the covariance matrix to correct for the effects of non-independence. Mathematically, this method is equivalent to the independent contrasts approach (Felsenstein, 1985).

      In our analysis, we treat the minimum interspecific wsp distance between two species as a trait for the species pair (i, j). Similarly, for any two pairs of species (i, j) and (k, l), we postulate that the covariance between their traits is given by:

      Cov[Y<sub>ij</sub>,Y<sub>kl</sub>]=σ2⋅(T<sub>ik</sub>+T<sub>jl</sub>),

      where Tik denotes the time from the root to the MRCA of species i and k, and Tjl represents the time from the root to the MRCA of species j and l. This covariance matrix is then incorporated into our linear model analysis to account for the effects of phylogenetic non-independence.

      However, when extending trait analysis to pairs of species, the computational demands increase substantially. For instance, with a dataset of 1,377 species, forming all possible pairs yields 947,376 unique species combinations. Consequently, constructing a covariance matrix for these pairs would necessitate storing 897,521,285,376 entries, a requirement that far exceeds the memory capabilities of standard computing systems.

      To address this, we randomly sampled 1,000 pairs from the total of 947,376 species pairs within the 'Others' category, thereby reducing the computational load without compromising the representativeness of our analysis. Ultimately, even after accounting for phylogenetic correction using covariance, the effect of parasitism remains highly significant (p < 0.0001).

      We have added a “Phylogenetic correction” section to Materials and Methods (Lines 392–405 in the revised manuscript). The corresponding results are described on lines 120–121 and in supplementary Note 1. The data and scripts for this analysis are available at https://doi.org/10.6084/m9.figshare.24718119.

      REFERENCE

      Felsenstein J, 1985. Phylogenies and the comparative method. The American Naturalist, 125(1), 1-15.

      The sharing of Wolbachia between whitefly and their parasitoids is very striking, although this has been reported before (eg the authors recently published a paper entitled "Diversity and Phylogenetic Analyses Reveal Horizontal Transmission of Endosymbionts Between Whiteflies and Their Parasitoids"). In Lines 154-164 it is suggested that from the tree the direction of transfer between host and parasitoid can be inferred from the data. This is not obvious to me given the poor resolution of the tree due to low sequence divergence. There are established statistical approaches to test the direction of trait changes on a tree that could have been used (a common approach is to use the software BEAST).

      We thank the reviewer for this constructive feedback on our interpretation of Wolbachia transfer between whiteflies and their parasitoids. Inspired by the reviewer's comments, we have now incorporated a trait-based approach, using the taxonomic order of the source species of the wsp gene as a discrete trait for ancestral state reconstruction on the wsp tree. The estimated ancestral trait state for one clade, which clusters wsp sequences from whiteflies and parasitoids, is Hymenoptera, suggesting that within this clade, the direction of Wolbachia transfer may have been from parasitoids to hosts. Conversely, in another clade characterized by the ancestral trait state of Hemiptera, the inferred direction of transfer appears to be from hosts to parasitoids. We have added a “Ancestral state reconstruction” section to Materials and Methods (Lines 406–412 in the revised manuscript). The corresponding results are described on lines 159–163 and 167–168. The data and script for this analysis is available at https://doi.org/10.6084/m9.figshare.24718119.

      Reviewer #2 (Public Review):

      The paper by Yan et al. aims to provide evidence for horizontal transmission of the intracellular bacterial symbiont Wolbachia from parasitoid wasps to their whitefly hosts. In my opinion, the paper in its current form consists of major flaws.

      Weaknesses:

      The dogma in the field is that although horizontal transmission events of Wolbachia occur, in most systems they are so rare that the chances of observing them in the lab are very slim.

      For the idea of bacteria moving from a parasitoid to its host, the authors have rightfully cited the paper by Hughes, et al. (2001), which presents the main arguments against the possibility of documenting such transmissions. Thus, if the authors want to provide data that contradict the large volume of evidence showing the opposite, they should present a very strong case.

      In my opinion, the paper fails to provide such concrete evidence. Moreover, it seems the work presented does not meet the basic scientific standards.

      We are grateful for your critical perspective on our work. Nonetheless, we are confident in the credibility of our findings regarding the horizontal transmission of Wolbachia from En. formosa to B. tabaci. Our study has documented this phenomenon through phylogenetic tree analyses, and we have further substantiated our observations with rigorous experiments in both cages and petri dishes. The horizontal transfer of Wolbachia was confirmed via PCR, with the wsp sequences in B. tabaci showing complete concordance with those in En. formosa. Additionally, we utilized FISH, vertical transmission experiments, and phenotypic assays to demonstrate that the transferred Wolbachia could be vertically transmitted and induce significant fitness cost in B. tabaci. All experiments were conducted with strict negative controls and a sufficient number of replicates to ensure reliability, thereby meeting basic scientific standards. The collective evidence we present points to a definitive case of Wolbachia transmission from the parasitoid En. formosa to the whitefly B. tabaci.

      My main reservations are:

      - I think the distribution pattern of bacteria stained by the probes in the FISH pictures presented in Figure 4 looks very much like Portiera, the primary symbiont found in the bacterium of all whitefly species. In order to make a strong case, the authors need to include Portiera probes along with the Wolbachia ones.

      We thank you for your critical evaluation regarding the specificity of FISH in our study. We assure the reliability of our FISH results based on several reasons.

      (1) We implemented rigorous negative controls which exhibited no detectable signal, thereby affirming the specificity of our hybridization. (2) The central region of the whitefly nymphs is a typical oviposition site for En. formosa. Post-parasitism, we observed FISH signals around the introduced parasitoid eggs, distinct from bacteriocyte cells which are rich in endosymbionts including Portiera (Fig 3e-f). This observation supports the high specificity of our FISH method. (3) In the G3 whiteflies, we detected the presence of Wolbachia in bacteriocytes in nymphs and at the posterior end of eggs in adult females (Fig. 4). This distribution pattern aligns with previously reported localizations of Wolbachia in B. tabaci (Shi et al., 2016; Skaljac et al., 2013). Furthermore, the distribution of Wolbachia in the whiteflies does indeed exhibit some overlap with that of Portiera (Skaljac et al., 2013; Bing et al., 2014). 4) The primers used in our FISH assays have been widely cited (Heddi et al., 1999) and validated in studies on B. tabaci and other systems (Guo et al., 2018; Hegde et al., 2024; Krafsur et al., 2020; Rasgon et al., 2006; Uribe-Alvarez et al., 2019; Zhao et al., 2013).

      Taking all these points into consideration, we stand by the reliability of our FISH results.

      REFERENCES

      Bing XL, Xia WQ, Gui JD, et al., 2014. Diversity and evolution of the Wolbachia endosymbionts of Bemisia (Hemiptera: Aleyrodidae) whiteflies. Ecol Evol, 4(13):2714-37.

      Guo Y, Hoffmann AA, Xu XQ, et al., 2018. Wolbachia-induced apoptosis associated with increased fecundity in Laodelphax striatellus (Hemiptera: Delphacidae). Insect Mol Biol, 27:796-807.

      Heddi A, Grenier AM, Khatchadourian C, Charles H, Nardon P, 1999. Four intracellular genomes direct weevil biology: nuclear, mitochondrial, principal endosymbiont, and Wolbachia. Proc Natl Acad Sci USA, 96:6814-6819.

      Hegde S, Marriott AE, Pionnier N, et al., 2024. Combinations of the azaquinazoline anti-Wolbachia agent, AWZ1066S, with benzimidazole anthelmintics synergise to mediate sub-seven-day sterilising and curative efficacies in experimental models of filariasis. Front Microbiol, 15:1346068.

      Krafsur AM, Ghosh A, Brelsfoard CL, 2020. Phenotypic response of Wolbachia pipientis in a cell-free medium. Microorganisms, 8.

      Rasgon JL, Gamston CE, Ren X, 2006. Survival of Wolbachia pipientis in cell-free medium. Appl Environ Microbiol, 72:6934-6937.

      Shi P, He Z, Li S, et al., 2016. Wolbachia has two different localization patterns in whitefly Bemisia tabaci AsiaII7 species. PLoS One, 11: e0162558.

      Skaljac M, Zanić K, Hrnčić S, et al., 2013. Diversity and localization of bacterial symbionts in three whitefly species (Hemiptera: Aleyrodidae) from the east coast of the Adriatic Sea. Bull Entomol Res, 103(1):48-59.

      Uribe-Alvarez C, Chiquete-Félix N, Morales-García L, et al., 2019. Wolbachia pipientis grows in Saccharomyces cerevisiae evoking early death of the host and deregulation of mitochondrial metabolism. MicrobiologyOpen, 8: e00675.

      Zhao DX, Zhang XF, Chen DS, Zhang YK, Hong XY, 2013. Wolbachia-host interactions: Host mating patterns affect Wolbachia density dynamics. PLoS One, 8: e66373.

      - If I understand the methods correctly, the phylogeny presented in Figure 2a is supposed to be based on a wide search for Wolbachia wsp gene done on the NCBI dataset (p. 348). However, when I checked the origin of some of the sequences used in the tree to show the similarity of Wolbachia between Bemisia tabaci and its parasitoids, I found that most of them were deposited by the authors themselves in the course of the current study (I could not find this mentioned in the text), or originated in a couple of papers that in my opinion should not have been published to begin with.

      We appreciate your meticulous examination of the sources for our sequence data. All the sequences included in our phylogenetic analysis were indeed downloaded from the NCBI database as of July 2023. The sequences used to illustrate the similarity of Wolbachia between B. tabaci and its parasitoids include those from our previously published study (Qi et al., 2019), which were sequenced from field samples. Additionally, some sequences were also obtained from other laboratories (Ahmed et al., 2009; Baldo et al., 2006; Van Meer et al., 1999). We acknowledge that in our prior research (Qi et al., 2019), the sequences were directly submitted to NCBI and, regrettably, we did not update the corresponding publication information after the article were published. It is not uncommon for sequences on NCBI, with some never being followed by a published paper (e.g., FJ710487- FJ710511 and JF426137-JF426149), or not having their associated publication details updated post-publication (for instance, sequences MH918776-MH918794 from Qi et al., 2019, and KF017873-KF017878 from Fattah-Hosseini et al., 2018). We recognize that this practice can lead to confusion and apologize for the oversight in our work.

      REFERENCES

      Ahmed MZ, Shatters RG, Ren SX, Jin GH, Mandour NS, Qiu BL, 2009. Genetic distinctions among the Mediterranean and Chinese populations of Bemisia tabaci Q biotype and their endosymbiont Wolbachia populations. J Appl Entomol, 133:733-741.

      Baldo L, Dunning Hotopp JC, Jolley KA, et al., 2006. Multilocus sequence typing system for the endosymbiont Wolbachia pipientis. Appl Environ Microbiol. 72(11):7098-110.

      Fattah-Hosseini S, Karimi J, Allahyari H, 2014. Molecular characterization of Iranian Encarsia formosa Gahan populations with natural incidence of Wolbachia infection. J Entomol Res Soc, 20(1):85–100.

      Qi LD, Sun JT, Hong XY, Li YX, 2019. Diversity and phylogenetic analyses reveal horizontal transmission of endosymbionts between whiteflies and their parasitoids. J Econ Entomol, 112(2):894-905.

      Van Meer MM, Witteveldt J, Stouthamer R, 1999. Phylogeny of the arthropod endosymbiont Wolbachia based on the wsp gene. Insect Mol Biol, 8(3):399-408.

      - The authors fail to discuss or even acknowledge a number of published studies that specifically show no horizontal transmission, such as the one claimed to be detected in the study presented.

      Thank you for bringing this to our attention. We have made corresponding modifications to the discussion section (Lines 256271 in the revised manuscript) and have discussed the published studies that report no evidence of horizontal transmission (Lines 260263 in the revised manuscript). The added sentences read: “Experimental confirmations of Wolbachia horizontal transfer remain relatively rare, with only a limited number of documented cases (24, 27, 37, 38). Additionally, some experiments have found no evidence of horizontal transmission of Wolbachia (39-42).” (Lines 260263 in the revised manuscript)

      Reviewer #3 (Public Review):

      This is a very ordinary research paper. The horizontal of endosymbionts, including Wolbachia, Rickettsia etc. has been reported in detail in the last 10 years, and parasitoid vectored as well as plant vectored horizontal transmission is the mainstream of research. For example, Ahmed et al. 2013 PLoS One, 2015 PLoS Pathogens, Chiel et al. 2014 Enviromental Entomology, Ahmed et al. 2016 BMC Evolution Biology, Qi et al. 2019 JEE, Liu et al. 2023 Frontiers in Cellular and Infection Microbiology, all of these reported the parasitoid vectored horizontal transmission of endosymbiont. While Caspi-Fluger et al. 2012 Proc Roy Soc B, Chrostek et al. 2017 Frontiers in Microbiology, Li et al. 2017 ISME Journal, Li et al. 2017 FEMS, Shi et al. 2024 mBio, all of these reported the plant vectored horizontal transmission of endosymbiont. For the effects of endosymbiont on the biology of the host, Ahmed et al. 2015 PLoS Pathogens explained the effects in detail.

      Thank you for the insightful comments and for highlighting the relevant literature in the field of horizontal transmission of endosymbionts, including Wolbachia and Rickettsia. After careful consideration of the studies mentioned in the commences, we believe that our work presents significant novel contributions to the field. 1) Regarding the parasitoid-mediated horizontal transmission of Wolbachia, most of the cited articles, such as Ahmed et al. 2013 in PLoS One and Ahmed et al. 2016 in BMC Evolutionary Biology, propose hypotheses but do not provide definitive evidence. The transmission of Wolbachia within the whitefly cryptic species complex (Ahmed et al. 2013) or between moths and butterflies (Ahmed et al. 2016) could be mediated by parasitoids, plants, or other unknown pathways. 2) Chiel et al. 2014 in Environmental Entomology reported “no evidence for horizontal transmission of Wolbachia between and within trophic levels” in their study system. 3) The literature you mentioned about Rickettsia, rather than Wolbachia, indirectly reflects the relative scarcity of evidence for Wolbachia horizontal transmission. For example, the evidence for plant-mediated transmission of Wolbachia remains isolated, with Li et al. 2017 in the ISME Journal being one of the few reports supporting this mode of transmission. 4) While the effects of endosymbionts on their hosts are not the central focus of our study, the effects of transgenerational Wolbachia on whiteflies are primarily demonstrated to confirm the infection of Wolbachia into whiteflies. Furthermore, the effects we report of Wolbachia on whiteflies are notably different from those reported by Ahmed et al. 2015 in PLoS Pathogens, likely due to different whitefly species and Wolbachia strains. 6) More importantly, our study reveals a mechanism of parasitoid-mediated horizontal transmission of Wolbachia that is distinct from the mechanical transmission suggested by Ahmed et al. 2015 in PLoS Pathogens. Their study implies transmission primarily through dirty needle, without Wolbachia infection of the parasitoid, suggesting host-to-host transmission at the same trophic level, where parasitoids serve as phoretic vectors. In contrast, our findings demonstrate transmission from parasitoids to hosts through unsuccessful parasitism, which represents cross-trophic level transmission. To our knowledge, this is the first experimental evidence that Wolbachia can be transmitted from parasitoids to hosts. We believe these clarifications and the novel insights provided by our research contribute valuable knowledge to the field.

      REFERENCES

      Ahmed MZ, De Barro PJ, Ren SX, Greeff JM, Qiu BL, 2013. Evidence for horizontal transmission of secondary endosymbionts in the Bemisia tabaci cryptic species complex. PLoS One, 8(1):e53084.

      Ahmed MZ, Li SJ, Xue X, Yin XJ, Ren SX, Jiggins FM, Greeff JM, Qiu BL, 2015. The intracellular bacterium Wolbachia uses parasitoid wasps as phoretic vectors for efficient horizontal transmission. PLoS Pathog, 10(2):e1004672.

      Ahmed MZ, Breinholt JW, Kawahara AY, 2016. Evidence for common horizontal transmission of Wolbachia among butterflies and moths. BMC Evol Biol, 16(1):118.

      Caspi-Fluger A, Inbar M, Mozes-Daube N, Katzir N, Portnoy V, Belausov E, Hunter MS, Zchori-Fein E, 2012. Horizontal transmission of the insect symbiont Rickettsia is plant-mediated. Proc Biol Sci, 279(1734):1791-6.

      Chiel E, Kelly SE, Harris AM, Gebiola M, Li X, Zchori-Fein E, Hunter MS, 2014. Characteristics, phenotype, and transmission of Wolbachia in the sweet potato whitefly, Bemisia tabaci (Hemiptera: Aleyrodidae), and its parasitoid Eretmocerus sp. nr. emiratus (Hymenoptera: Aphelinidae). Environ Entomol, 43(2):353-62.

      Chrostek E, Pelz-Stelinski K, Hurst GDD, Hughes GL, 2017. Horizontal transmission of intracellular insect symbionts via plants. Front Microbiol, 8:2237.

      Li SJ, Ahmed MZ, Lv N, Shi PQ, Wang XM, Huang JL, Qiu BL, 2017. Plant-mediated horizontal transmission of Wolbachia between whiteflies. ISME J, 11(4):1019-1028.

      Li YH, Ahmed MZ, Li SJ, Lv N, Shi PQ, Chen XS, Qiu BL, 2017. Plant-mediated horizontal transmission of Rickettsia endosymbiont between different whitefly species. FEMS Microbiol Ecol, 93(12).

      Liu Y, He ZQ, Wen Q, Peng J, Zhou YT, Mandour N, McKenzie CL, Ahmed MZ, Qiu BL, 2023. Parasitoid-mediated horizontal transmission of Rickettsia between whiteflies. Front Cell Infect Microbiol, 12:1077494.

      Qi LD, Sun JT, Hong XY, Li YX, 2019. Diversity and phylogenetic analyses reveal horizontal transmission of endosymbionts between whiteflies and their parasitoids. J Econ Entomol, 112(2):894-905.

      Shi PQ, Wang L, Chen XY, Wang K, Wu QJ, Turlings TCJ, Zhang PJ, Qiu BL, 2024. Rickettsia transmission from whitefly to plants benefits herbivore insects but is detrimental to fungal and viral pathogens. mBio, 15(3):e0244823.

      Weaknesses:

      In the current study, the authors downloaded the MLST or wsp genes from a public database and analyzed the data using other methods, and I think the authors may not be familiar with the research progress in the field of insect symbiont transmission, and the current stage of this manuscript lacking sufficient novelty.

      We appreciate your critical perspective on our study. However, we respectfully disagree with the viewpoint that our manuscript lacks sufficient novelty.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      The data and scripts from the experimental section of the paper are not made publicly available. This would be good practice. It may well be a requirement for this journal too, but I have not read the journal policy on this matter.

      Thank you for the kind reminder, we have uploaded the data and scripts to the public database at https://doi.org/10.6084/m9.figshare.24718119.

      • Line 16 should read 'intertrophic' not 'intertropical'.

      Corrected.

      • Line 50 should not say 'the most infectious' as this is an incorrect use of the word 'infectious'. Maybe 'common'? Should also add something like 'likely' here.

      Corrected. The new sentence reads “Together, these characteristics make Wolbachia likely the most common microbe on Earth in terms of the number of species it infects (7, 8).” (Lines 47–49 in the revised manuscript).

      • Line 54 These references are all about mosquito disease vectors, not pests. More generally, in this paragraph, the research interest in Wolbachia relates overwhelmingly to blocking arbovirus transmission and not controlling pest populations.

      To enhance consistency with our statements, we have revised the supporting references as follows:

      X. Zheng et al., "Combined incompatible and sterile insect techniques eliminate mosquitoes," Nature 572, 56-61 (2019).

      A. A. Hoffmann et al., "Wolbachia establishment in Aedes populations to suppress dengue transmission," Nature 476, 454-457 (2011).

      J. T. Gong, T. P. Li, M. K. Wang, X. Y. Hong, "Prospects of Wolbachia in agricultural Pest Control," Current Opinion in Insect Science 57, 101039 (2023).J. T. Gong et al., "Stable integration of plant-virus-inhibiting Wolbachia into planthoppers for rice protection," Current Biology 30, 4837-4845.e4835 (2020).

      Regarding the content of the articles:

      Zheng et al. (2019) detail the successful suppression of wild mosquito populations through the release of male mosquitoes artificially infected with Wolbachia.

      Gong et al. (2020) present the potential of releasing Wolbachia-infected brown planthoppers to inhibit plant viruses and control pest populations.

      Gong et al. (2023) provide a comprehensive review on the application and future of Wolbachia in managing agricultural pests.

      • Line 60-61. This sentence seems poorly supported by theory or data. I suggest it is deleted. Why should CI cause extinction, and why would it have a major effect on genetic diversity beyond mtDNA?

      We have deleted the statements about extinction or genetic diversity. Now the sentence reads “It may also spread to nontarget organisms, potentially disrupting their population dynamics.” (Lines 57–58 in the revised manuscript)

      • Line 66. Reword to make clear these routes are not an exhaustive list.

      We have reworded these sentences. The new sentences now read “Similar to other symbionts, Wolbachia host shifts may occur through three main routes: parasitism, predation, and shared plant or other food sources (17). However, it is important to note that these are not the only routes through which transmission may occur, and the specific contributions of each to the overall process of host shift are not yet fully understood.” (Lines 62–66 in the revised manuscript).

      • Line 77-79. This could do with mentioning studies of parasitoid-to-host transmission like Ahmedd et al given that it is common knowledge that insects commonly survive parasitoid attacks.

      We have added sentences acknowledging the common occurrence of insects surviving parasitoid attacks and referenced and described the Ahmed et al. 2015 study. The added sentences read:

      “However, it is common in nature for hosts to survive parasitoid attacks (27-29). For example, whiteflies can survive after attacks of Eretmocerus parasitoids (27). These parasitoids can act as phoretic vectors, facilitating the spread of Wolbachia within whitefly populations through the contamination of their mouthparts and ovipositors with Wolbachia during the probing process (27).” (Lines 77–82 in the revised manuscript).

      • Line 173. Mention that there are three replicates of each cage. In Figures 2C and D, it is better to show each replicate as a separate line to see how consistent they are.

      In accordance with the reviewer's suggestion, we have included a statement highlighting the replication of our experiments: “Notably, each cage setup was replicated three times to ensure experimental rigor.” (Lines 179–180 in the revised manuscript).

      Regarding Figures 2C and D, we have revised the figures to display each replicate as a separate line, as suggested. However, we have encountered a visual clutter that may detract from the clarity of the figures. Additionally, in Figure C, the three black lines, all representing zero values, do not allow for the distinction of individual trends. Therefore, we recommend retaining the original figure format. In accordance with eLife's data policy, we have also provided the source data for all figures, ensuring that readers can access to the detailed data, thus balancing the need for visual simplicity with the provision of comprehensive data.

      Author response image 1.

      • The GloBI database is central to the phylogenetic analysis and it would be helpful to have a few words in the results stating where this information comes from.

      The revised sentence now reads: “To investigate potential horizontal transmission of Wolbachia, we retrieved 4685 wsp sequences from the NCBI database, and species interaction relationships were extracted from the GloBI database (for details, see Methods and Materials).” (Lines 94–96 in the revised manuscript).

      Reviewer #3 (Recommendations For The Authors):

      To improve the quality of this manuscript, I have some questions and suggestions.

      Introduction:

      Line 41-42, I don't agree with this statement, as mentioned above, the ways of insect symbiont transmission have been studied in the last 10 years.

      According to the reviewer’s suggestion, we have deleted this statement.

      Line 75-76, Again, the statement is not correct, many studies have clearly revealed and confirmed that Wolbachia CAN be transferred from parasitoid to their insect hosts including whitefly Bemisia tabaci.

      Thank you for your insightful comments. After careful consideration of the studies you have mentioned above, none of these articles provided definitive evidence supporting the transfer of Wolbachia from parasitoids to their insect hosts. A closely related study is Ahmed et al. (2015) in PLoS Pathogens. This article demonstrates that parasitoid wasps can act as phoretic vectors mediating the transmission of Wolbachia between whiteflies. However, Wolbachia did not infect the parasitoid wasps themselves. Therefore, this study does not provide evidence for intertrophic transmission of Wolbachia from parasitoids to their hosts. To avoid confusion, we have cited the Ahmed et al. (2015) reference following this statement and described its findings accordingly. (Lines 88-92 in revised manuscript).

      Results:

      Line 133-134, Ahmed et al. 2016 BMC Evolution Biology, clearly revealed and confirmed the "common horizontal transmission of Wolbachia between butterflies and moths".

      We thank you for guiding us to the relevant study. Ahmed et al. 2016 BMC Evolution Biology suggested common horizontal transmission of Wolbachia between butterflies and moths and proposed that this horizontal transmission might be caused by parasitoid wasps. Here, we present the potential Wolbachia transfer between Trichogramma and their lepidopteran hosts (Lines 135–136 in revised manuscript). Integrating the results from Ahmed et al. 2016, our result also suggests that Trichogramma wasps may be the vectors for horizontal transmission of Wolbachia among lepidopteran hosts. We have discussed this point in the discussion section and cited Ahmed et al. 2016 BMC Evolution Biology (Lines 239–246 in revised manuscript).

      Line 176-177, as we know Wolbachia in Encarsia formosa is a strain of parthenogenesis, why did it reduce the female ratio of whitefly progeny after it was transmitted to whitefly B. tabaci, it needs a convincing explanation.

      Wolbachia induces parthenogenesis in En. formosa. However, we observed that Wolbachia from En. formosa failed to induce parthenogenesis in B. tabaci, possibly due to the requirement for host gene compatibility. Additionally, we noted a reduced female ratio in B. tabaci infected with En. formosa Wolbachia. We speculate that this might result from the burden imposed by En. formosa Wolbachia on the new host, potentially reducing fertilization success rates and indirectly leading to a decrease in the female ratio. Similarly, we observed a decline in female fecundity, egg hatching rate, and immature survival rate in B. tabaci infected with En. formosa Wolbachia. The mechanisms underlying these fitness costs remain unclear and warrant further in-depth research.

      Line 189-190, do the authors have convincing evidence that the 60Gy irradiation only has effects on the reproduction of En. formosa, but does not have any negative effects on the activity of Wolbachia? I think there may be.

      We observed that after irradiation, the titer of Wolbachia within En. formosa significantly decreased (Fig S3). We agree that the irradiation may cause other negative effects on Wolbachia which is worth of close investigation. However, even with a significant reduction in Wolbachia titer, irradiation increased the infection rate of Wolbachia in surviving B. tabaci after wasp attacks (Fig 3C). We speculate that this may be due to irradiation of En. formosa increasing the rate of parasitic failure. While the full extent of the effects of irradiation on Wolbachia is not yet clear in our experiments, it does not alter our conclusion that Wolbachia can be transmitted from En. formosa to whitefly hosts through failed parasitism.

      Discussion:

      Line 289-290, I don't understand, why the authors think from parasitoid Eretmocerus to whitefly, and from Trichogramma to moth, are the same trophic level, they are indeed two different trophic levels.

      Thank you for your feedback. We have conducted a thorough search but were unable to locate the specific statement you are referring to. If there has been any ambiguity in our manuscript that has led to confusion, we sincerely apologize for any misunderstanding it may have caused. We agree with your perspective and have always considered the parasitoid Eretmocerus and whitefly, as well as Trichogramma and moth, to be at different trophic levels. However, in the context of specific references, such as Ahmed et al. 2015 in PLoS Pathogens, we believe that Wolbachia is transmitted within the same trophic level without infecting the parasitoid Eretmocerus, merely serving as a phoretic vector to facilitate the spread of Wolbachia among whitefly hosts. Similarly, in the case of Huigens et al. 2000 in Nature, Wolbachia uses lepidopteran hosts as vectors to promote its transmission among Trichogramma without the need to infect the lepidopteran hosts themselves.

      Materials and Methods

      Line 348, what is tblastn?

      We have corrected tblastn to TBLASTN. We are grateful to the reviewer for pointing this out. Here, we utilized TBLASTN instead of BLASTN, to avoid missing the rapidly evolving wsp sequences. Because alignment at the protein level is generally more sensitive than at the nucleotide level. TBLASTN is a bioinformatics tool within the BLAST (Basic Local Alignment Search Tool) suite used for comparing a protein query sequence against a nucleotide database. Specifically, TBLASTN aligns a given protein sequence with nucleotide sequences in a database by translating the nucleotide sequences into all possible protein sequences (considering different reading frames) and comparing them to the query protein sequence.

      Line 383, how was the Wolbachia-free line of B. tabaci established, by antibiotics? If so, how do we ensure the antibiotic does not have any negative to other symbionts in whitefly B. tabaci?

      The Wolbachia-free line of B. tabaci was collected from field, without the treatment of antibiotics. We have made revisions in the Materials and Methods section to clarify this, stating, "An iso-female line of B. tabaci, which is naturally Wolbachia-free and has not been treated with antibiotics, was established." (Lines 417–418 in the revised manuscript)

      Line 419-421 as I mentioned before, the irradiation may have negative effects on Wolbachia too, so change the biology of both Encarsia and whitefly host.

      We observed that after irradiation, the titer of Wolbachia within En. formosa significantly decreased (Fig S3). However, even with a significant reduction in Wolbachia titer, irradiation increased the infection rate of Wolbachia in surviving B. tabaci after wasp attacks (Fig 3C). We speculate that this may be due to irradiation of En. formosa increasing the rate of parasitic failure. While the full extent of the effects of irradiation on Wolbachia is not yet clear in our experiments, it does not alter our conclusion that Wolbachia can be transmitted from En. formosa to whitefly hosts through failed parasitism.

      Line 452-453, From egg to eclosion, it needs about 21 days to understand suitable temperature and other conditions, during this period, the egg and nymphs can not move, so how to keep the cut-leaf fresh enough in a Petri dish for 21 days?

      We apologize for not clearly describing the materials and methods. By using wet cotton to wrap the end of petiole of the leaf, we can keep the leaves fresh for up to a month. We have included this detail in the materials and methods to enhance the reproducibility of the experiment. “A single irradiated wasp was subsequently introduced into a Petri dish, which contained a tomato leaf infested with Wolbachia-free third or fourth instar whitefly nymphs, and wet cotton was used to wrap the end of the leaf petiole to keep the leaf fresh.” (Lines 455–458 in the revised manuscript)

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Ghone et al show that HIV-1 Vif causes a pseudo-metaphase arrest rather than a G2 arrest. The metaphase arrest correlates with misregulation of the kinetochore which could be explained by the loss of phosphatase functions that determine chromosome-microtubule interactions.

      Strengths:

      The single-cell imaging using different reporters of cell cycle progression is very elegant and the quantitation is convincing. The authors clearly show that what others have characterized as a G2 arrest by flow cytometry is somewhat later in metaphase and correlates with kinetochore misregulation.

      We sincerely appreciate the reviewer recognizing the quality and precision of our study, particularly our use of long-term live cell imaging combined with single-cell resolution analysis.

      Weaknesses:

      (1) The major problem with the paper is trying to connect what is observed in tumor cell lines with actual infections in primary T cells. While all of the descriptive work in cell lines is convincing, none of these cells are relevant targets and tumor cells have different cell death and cell cycle regulation than primary T cells. Thus, while Vif might well do all of the things described in the manuscript, it is a stretch to connect any of it to what happens in vivo.

      We fully agree with this point. It is indeed technically challenging to perform 48-120 hours of live-cell imaging at high magnification at short intervals using primary T cells because of their non-adherent nature. We also agree that Vif’s functions in pseudo-metaphase arrest and the consequent induction of cell death, observed in cancer cells (e.g., Cal51, HeLa, and MDA-MB-231 cell lines) or normal non-transformed epithelial cells (e.g., the RPE1 cell line), may differ in T cells. Further studies and refined approaches will be required to address this important question. We have revised the manuscript to include a discussion of this issue in the section of Limitation of this study.

      (2) Line 109 and elsewhere. The ability of Vif to cause cell cycle arrest and bind PP2A subunits is not a completely conserved feature. Rather, it is quite variable in different HIV-1 strains. (e.g. https://doi.org/10.1016/j.bbrc.2020.04.123 and https://elifesciences.org/articles/53036). Therefore, it is necessary for the authors to quite clearly use strain designations in the manuscript rather than a generic "Vif", and to more clearly describe the viruses being used.

      Thank you for raising this important point. We utilized the NL4-3 strain in our study and have revised the manuscript to specify this detail. While this study uncovered part of the mechanism by which Vif modulates phosphatase regulation during mitosis, further research is required to elucidate the full mechanism, particularly how this degradation induces a robust pseudo-metaphase arrest.

      (3) Figure 5: This figure shows disruption of PP2A-B56 at the kinetochores. However, is this specific to the kinetochores? Since Vif has been described to more broadly degrade PP2A-B56, could this not be a result of a more general decrease in PP2A activity throughout the cell?

      Thank you for highlighting this critical point. PP2A is a major serine/threonine phosphatase that regulates numerous essential cell cycle processes. To the best of our knowledge, Vif selectively targets the degradation of the B56 family of PP2A regulatory subunits, without affecting other three B-type subunits or the catalytic core of PP2A itself. During early mitosis, all five members of the B56 family (B56α, B56β, B56γ, B56δ, and B56ε) accumulate at kinetochores and centromeres, where they play critical roles in chromosome alignment. Many PP2A-B56 substrates are also localized to kinetochores and chromosomes during mitosis. Depletion of specific B56 isoforms or introduction of phosphorylation-deficient mutants of PP2A-B56 substrates at kinetochores has been shown to result in mitotic defects, underscoring the crucial roles of PP2A-B56 in regulating kinetochore, centromere, and chromosomal functions during mitosis. Interestingly, we observed no significant cell cycle arrest during G1, S, or G2 phases in Vif-expressing cells. While PP2A-B56 likely has important roles outside of mitosis, Vif-mediated degradation of PP2A-B56 appears to selectively disrupt its mitotic functions, particularly at the kinetochore. This finding highlights a targeted mechanism by which Vif interferes with PP2A-B56-mediated regulation of mitotic processes. However, further experiments are required to elucidate the precise mechanisms underlying Vif's inhibition of the specific mitotic roles of PP2A-B56.

      Reviewer #2 (Public review):

      Summary

      The authors characterize the cell-cycle arrest induced by HIV-1 Vif in infected cells. They show this arrest is not at G2/M as previously thought but during metaphase. They show that the metaphase plate forms normally but progression to anaphase is massively delayed, and chromosome segregation is dysregulated in a manner consistent with impaired assembly of microtubules at the kinetochore. This correlates with the lack of recruitment of B56-subunits of PP2 phosphatase which are known degradation targets of Vif, suggesting that this weakens and unbalances the microtubule-mediated forces on the separating chromosomes.

      Strengths

      The authors present a very well-performed set of quantitative live cell imaging experiments that convincingly show a difference between Vif and Vpr-mediated cell cycle arrests. Through an in-depth characterization of the Vif-mediated block in metaphase, they make a strong case for this phenotype being tied to the degradation of PP2-B56 by Vif. Furthermore, it is important that they have performed most of these experiments with virally infected cells, meaning that their observations are observable at relevant viral expression levels of Vif.

      We appreciate the reviewer’s recognition of the importance and significance of our study.

      Weaknesses

      Experimentally there is very little to criticize with respect to the cellular systems used. Data from 10.1016/j.bbrc.2020.04.123 has identified selective mutants that fail to degrade B56 while maintaining A3G degradation by Cul5, and it would be nice to confirm that such a mutant behaves like the delta-Vif virus when examining metaphase, but selective ablation of B56 during mitosis to mimic Vif is would expect to be very challenging and beyond the scope.

      Thank you for your valuable suggestion. As also highlighted by Reviewer #1, it is true that certain variants of Vif, as discussed in 10.1016/j.bbrc.2020.04.123, differentially impact B56 degradation. Notably, some variants degrade A3G without inducing cell cycle arrest. We agree that investigating whether Vif's effects on B56 are directly linked to the mitotic arrest phenotype is an important direction for future research. Equipped with our advanced imaging tools, we are now preparing to extend our studies to include Vif variants from additional HIV-1 subtypes, including primary isolates. As you rightly pointed out, depletion of B56 is expected to be challenging as the B56 family comprises multiple isoforms, each with distinct and partially redundant roles in mitosis, particularly in microtubule assembly and spindle assembly checkpoint regulation. The functions of PP2A-B56 in mitosis are well-documented compared to the relatively new studies on Vif’s role in PP2A-B56 degradation. In human cells, the B56 family comprises 5 isoforms (B56α, B56β, B56γ, B56δ, and B56ε). While all B56 isoforms localize to kinetochores or centromeres during early mitosis, the reasons for their slightly different localization patterns (to either kinetochores or centromeres) remain unclear (Vallardi et al., eLife, 2019). Notably, these isoforms exhibit functional redundancy; thus, the depletion of any single isoform does not result in severe mitotic defects (Foley et al., Nature Cell Biology, 2011; Neumann et al., Nature, 2010). Supporting this redundancy, the overexpression of a single isoform (tested only B56α and B56γ) can rescue kinetochore function when all other isoforms are depleted (Foley et al., Nature Cell Biology, 2011; Vallardi et al., eLife, 2019). This complexity poses significant challenges to modulating the relative levels of individual B56 isoforms experimentally. While these specific experiments are beyond the current scope of our study, we remain committed to advancing our understanding of the mechanisms driving Vif-induced pseudo-metaphase arrest. Your suggestion aligns with our ongoing efforts, and we will consider these experiments as we further explore this fascinating area.

      Where I would raise some criticism is in the relevance of these observations to the replication and pathogenesis of the virus itself, which the authors do not address or discuss. Firstly, despite clear data that both Vpr and Vif can lead to a cell cycle arrest in cycling cells, it has never been particularly clear why the virus does this. While I would agree with the authors that Vif results in the metaphase arrest through targeting B56-PP2A, this may not be the reason WHY the virus targets one of the cell's major phosphatases, but rather a knock-on effect of doing so. I appreciate that this is beyond the scope of the study, but it is something I feel should be discussed rather than the narrow mechanistic points made in the discussion. Secondly, the authors suggest that this activity of Vif is a major cause of apoptosis in infected cells and perhaps CD4+ T cell depletion in vivo. It would be good to quantify how much apoptosis is Vif-dependent in infected primary human CD4+ T cells rather than transformed tumor cells, and whether this correlates with the Vif-mediated induction of a pseudometaphase.

      Thank you for highlighting this important point. We completely agree that the full scope of Vif’s bi-functional roles, in both degrading the APOBEC3 family, which is essential for HIV-1 infection, and inducing cell cycle arrest, is not yet fully understood. The connection between Vif’s role in cell cycle arrest and the HIV-1 life cycle remains unclear. One possible explanation, as discussed in our study, is that Vif-induced pseudo-metaphase arrest may contribute to cell death, suggesting that Vif could play a role in the reduction of CD4+ T cells. Alternatively, Vif’s impact on cell cycle arrest, or its disruption of phosphatase activity, could facilitate HIV-1 virus production. However, further experiments, especially using primary human CD4+ T cells with similar approaches as in this study, are essential to gain deeper insights. This discussion has been included in the Limitations section of our study.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) The first paragraph of the Introduction is not necessary and anyway is quite outdated about the current state of HIV pathogenesis. Likewise, the discussion implies that HIV pathogenesis is due to virally-induced cell death, which is also outdated by more than a decade of work demonstrating that chronic immune activation is the driver of CD4 cell decline rather than direct cytotoxicity due to viral proteins.

      We have revised the first paragraph of the Introduction.

      (2) Line 134. I do not know what are Cal51 cells, and why they are being used for an HIV study here. Some rationale for being the cell of choice for this study should be included.

      Thank you for this suggestion. We have revised the text to clearly articulate the rationale for selecting the Cal51 cell line in this study. Briefly, this study focuses on the robust mitotic arrest induced by Vif. To capture this phenomenon, long-term live-cell imaging was required with a range of 48–120 hours, with imaging intervals of 6–12 minutes and 3–4 z-stacks per time point. These parameters presented considerable technical challenges. The Cal51 cell line was chosen as it has been genetically engineered by the CRISPR-Cas9 method to express mScarlet-tagged Histone H2B and mNeonGreen-tagged Tubulin, enabling extended live-cell imaging. Furthermore, the Cal51 cell line exhibits wild-type p53 expression and maintains a stable near-diploid karyotype, making it an ideal model for studying cell cycle progression.

      (3) A description of the viruses being used is necessary. Although the authors cite a previous paper, the names in that paper do not exactly match the names used here. I presume that is the NL4.3 strain?

      Thank you for raising this important point. We utilized the B type HIV-1 NL4-3 strain in our study and have revised the manuscript to specify this detail.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Reviews):

      Summary:

      This study examines to what extent this phenomenon varies based on the visibility of the saccade target. Visibility is defined as the contrast level of the target with respect to the noise background, and it is related to the signal-to-noise ratio of the target. A more visible target facilitates the oculomotor behavior planning and execution, however, as speculated by the authors, it can also benefit foveal prediction even if the foveal stimulus visibility is maintained constant. Remarkably, the authors show that presenting a highly visible saccade target is beneficial for foveal vision as the detection of stimuli with an orientation similar to that of the saccade target is improved, the lower the saccade target visibility, the less prominent the effect.  

      Strengths:

      The results are convincing and the research methodology is technically sound.

      Weaknesses:

      Discussion on how this phenomenon may unfold in natural viewing conditions when the foveal and saccade target stimuli are complex and are constituted by different visual properties is lacking. Some speculations regarding feedforward vs feedback neural processing involved in the phenomenon and the speed of the feedforward signal in relation to the visibility of the target, are not well justified and not clearly supported by the data.

      We thank the reviewer for their comment. In general, we tried to address conceptual points only briefly in this Research Advance if we had discussed them in depth in our main article which this advance will be linked to (Kroell & Rolfs, 2022: https://elifesciences.org/articles/78106). However, the reviews showed us that this rendered our theoretical reasoning in the current manuscript appear incomplete. In the revised Discussion section, we have elaborated on several conceptual questions. In particular, we expand on the transferability of our findings to natural viewing conditions:

      “Foveal prediction in natural visual environments

      As noted above, human observers typically move their eyes towards the most conspicuous objects in their environment (‘t Hart, Schmidt, Roth, & Einhäuser, 2013). Foveal prediction seems to benefit from this strategy as the strength of the predicted signal increases with the conspicuity of the eye movement target. Nonetheless, natural visual environments as well as naturalistic viewing behavior pose several challenges for the foveal prediction mechanism (see Kroell & Rolfs, 2022, for an initial discussion). 

      First, naturalistic saccade target stimuli will likely exhibit complex shapes and, more often than not, will include feature conjunctions rather than isolated features. Previous findings suggest that the foveal feedback mechanism is capable of operating at this level of complexity: High-level peripheral information such as the category of novel, rendered objects (Williams et al., 2008) has been successfully decoded from activation in foveal retinotopic cortex. If, indeed, temporal objectspecific areas such as area TE send feedback, the foveal prediction mechanism may even be specialized for the transfer of complex visual properties.

      Second, foveal input will often be of high contrast in natural visual environments. If fed-back predictive signals can influence foveal perception in the presence of high-contrast feedforward input remains to be established. In our main investigation (Kroell & Rolfs, 2022; Figure 2B) as well as in previous studies (Hanning & Deubel, 2022b), pre-saccadic foveal detection performance decreased markedly in the course of saccade preparation, presumably because visuospatial attention gradually shifted towards the saccade target and away from the foveal location. This presaccadic decrease in foveal sensitivity may boost the relative weight of fed-back signals by attenuating the conspicuity of high-contrast feedforward input. In other words, the strength of feedforward input to the fovea is reduced gradually across saccade preparation. At the same time, the strength of the fed-back predictive signal should profit from the high contrast of naturalistic saccade targets.

      Third, while foveal and peripheral information was congruent on 50% of all ‘probe present’ trials in our investigation, peripheral and foveal features will often be weakly correlated or even uncorrelated in natural environments (see Samonds, Geisler, & Priebe, 2018). Again, the presaccadic attenuation of foveal feedforward processing may allow fed-back peripheral signals to influence perception even if they are uncorrelated with foveal information. Moreover, in piloting variations of our paradigm, we observed that the subjective impression of perceiving the saccade target at the pre-saccadic foveal location is most pronounced if the foveal noise region is replaced with a black Gaussian blob at certain time points before saccade onset (unpublished phenomenological accounts). In consequence, fed-back signals do not seem to require correlated feedforward input to influence perception. Quantitative evidence, however, remains to be established.

      Lastly, pre-saccadic foveal input is likely less relevant during natural viewing behavior than it is in our task. It is possible that this task-induced prioritization of the foveal location facilitated the emergence of congruency effects. In a previous experiment (Kroell & Rolfs, 2022; Figure 1D), however, the perceptual probe could appear anywhere on a horizontal axis of 9 dva length around the fixation location. Despite this spatial unpredictability, congruency effects peaked at the presaccadic foveal location, even after peripheral baseline performances had been raised to a foveal level through an adaptive increase in probe opacity. On a similar note, the orientation of the saccade target is irrelevant to the behavioral task in our design, mirroring naturalistic situations: The eye movement can be planned and executed based on local contrast variations alone, and observers are never required to report on the orientation of the peripheral target stimulus. Ultimately, however, an influence of task demands on visual processing can only be fully excluded through techniques that provide a direct readout of perceptual contents without requiring overt responses. In psychophysical investigations, a prediction of saccade target motion may be read out from observers’ eye velocities (Kroell, Mitchell, & Rolfs, 2023; Kwon, Rolfs, & Mitchell, 2019). In electroencephalographic (EEG) and electrophysiological studies, foveal predictions should manifest in early visually evoked potentials (e.g., Creel, 2019) and increased firing rates of featureselective foveal neurons in early visual areas, respectively. In conclusion, previous findings (Williams et al., 2008), the assumed properties of the neuronal feedback mechanism (Williams et al., 2008; Bullier, 2001) and characteristics of our current and previous experimental paradigms collectively suggest that foveal feature predictions are likely to transfer to naturalistic environments and viewing situations. Experimental evidence remains to be established.”

      We have furthermore modified the Abstract to emphasize the connection of the current manuscript to the main article.

      With respect to the reviewer’s point that “speculations regarding feedforward vs feedback neural processing involved in the phenomenon and the speed of the feedforward signal in relation to the visibility of the target, are not well justified”: 

      Again, we understand that we should have elaborated on our theoretical reasoning in this Research Advance. The assumption that our initial findings rely on neuronal feedback to foveal retinotopic cortex is derived from Williams et al.’s (2008) seminal findings: In an fMRI study, the category of peripherally presented objects could be decoded from voxels in foveal retinotopic cortex, suggesting that peripheral visual information was available to neurons with strictly foveal receptive fields. We extended these findings to saccade preparation, suggesting that feedback from higher-order, non-retinotopically organized visual areas may transmit information without the requirement of efference copies (see Kroell, 2023; Dissertation; https://doi.org/10.18452/27204, pp. 54-59): Irrespective of the vector of the upcoming saccade, the features of the attended saccade target would invariably be relayed to foveal retinotopic cortex. Ultimately, only anatomical and functional studies in non-human primates can conclusively establish the role of feedback connections in the observed foveal prediction effects. At present, however, this parsimonious model could account for all of our current and previous findings, that is, a temporally, spatially and feature-specific anticipation of saccade target properties in the presaccadic center of gaze. Nonetheless, we are open to considering any other mechanism that may account for our findings, and have integrated the explanation provided by the reviewer into the paragraph on potential thalamic mechanisms (see the reviewer’s Major Point 1).

      Concerning the point that the “some speculations regarding feedforward vs feedback neural processing […] and the speed of the feedforward signal in relation to the visibility of the target are not well justified and not clearly supported by the data”: 

      Theoretical considerations on the impact of peripheral target contrast on feedforward processing speed were a main motivation for the current study. We apologize if our theoretical reasoning was incomplete and have added additional references and elaborations to the Introduction: 

      “In particular, neuronal response latencies decrease systematically as the contrast of visual input increases. While this phenomenon is reliably observed at varying stages of the visual processing hierarchy—such as the lateral geniculate nucleus (Lee, Elepfandt, & Virsu, 1981b), primary visual cortex (e.g., Albrecht, 1995; Carandini & Heeger, 1994; Carandini, Heeger, & Movshon, 1997; Carandini, Heeger, & Senn, 2002), and anterior superior temporal sulcus (STSa; Oram, Xiao, Dritschel, & Payne, 2002; van Rossum, van der Meer, Xiao, & Oram, 2008)—influences of contrast on neuronal response latency are particularly pronounced in higher-order visual areas: A doubling of stimulus contrast has been shown to decrease the latency of V1 neurons by 8 ms, compared to a reduction of 33 ms in area STSa (Oram et al., 2002; van Rossum et al., 2008). Assuming that the peripheral target is processed in a bottom-up fashion until it reaches higher-order object processing areas, the time point at which peripheral signals are available for feedback should be dictated by the temporal dynamics of visual feedforward processing.”

      Concerning the interpretation of the observed time courses, and regarding the reviewer’s Major points 3 & 6, we substantially revised the Results and Discussion section. In brief, we deemphasized the claim/interpretation of faster enhancement with increasing target opacity and instead focus on describing the oscillatory pattern mentioned by the reviewer. We provide a more temporally resolved pre-saccadic time course using a moving-window analysis and discuss all suggested and further alternative explanations (i.e., saccade-locked perceptual or attentional oscillations, longer signal accumulation intervals for low-contrast information, oscillatory nature of feedback signaling). Details and full revised paragraphs are provided in the response to this reviewer’s Major points 3 & 6.

      Unfortunately, there is no line numbering in the manuscript version I downloaded so I cannot refer to the specific lines of text here.

      We apologize for the inconvenience and have added line numbers.

      Major:

      (1) The authors speculate that the phenomenon of pre-saccadic foveal prediction arises from feedback connections from higher-order visual areas, which relay relevant saccade target features to the foveal retinotopic cortex. These feedback signals are then presumably combined with feedforward foveal input to the early visual cortex and facilitate the detection of target-congruent features at the center of gaze. This interpretation is sensible, however, it may not be the only plausible scenario. The thalamus receives copies of feedforward and feedback connections between all visual areas and is a likely candidate hub for combining information across visual space. In this latter case, the phenomenon of pre-saccadic foveal prediction may not arise from feedback from higher-order visual areas, but rather from a combination of signals occurring at the level of the thalamus. The authors should either acknowledge this possibility and the fact that this phenomenon is not necessarily the result of a feedback loop, or they should explain their rationale for excluding this scenario.

      We thank the reviewer for their highly thoughtful suggestion, and for alerting us to relevant literature. We have added the following paragraph to the Discussion section. In brief, we discuss the thalamic pulvinar as either an intermediate modulatory region or as the final receiver of the fed-back signal. Yet, we assume that—to solve the combinatorial issue associated with a transfer of feature information before saccades with any possible direction and amplitude—the contribution of non-retinotopic, higherorder object processing areas is likely required. 

      “Neural implementation of foveal prediction

      Based on the body of our findings as well as previous literature, we suggested a parsimonious feedback mechanism to underly the observed effects: the preparation of a saccadic eye movement, and the concomitant shift of pre-saccadic attention (e.g., Kowler, Anderson, Dosher, & Blaser, 1995; Deubel & Schneider, 1996), selects the peripheral target stimulus among competing information. Higher-order visual areas feed selected feature input back to early retinotopic areas— specifically, to neurons with foveal receptive fields. Fed-back feature information combines with congruent, foveal feedforward input, resulting in the enhancement effects we observe. Especially in the context of active vision, this feedback mechanism is appealing as it resolves a combinatorial issue associated with feature-specific information transfer before saccades. Consider a simplified case in which, right before a saccadic eye movement, the activation of a feature-selective neuron that encodes a certain retinal location is transferred to a neuron within the same brain area that will encode said retinal location after saccade landing. For this mechanism to function for any possible saccade direction and amplitude, most neurons would need to be connected to most other neurons (or, in a simplified version, to neurons with foveal receptive fields) in a given brain area. Assuming an information transmission via feedback rather than horizontal connections significantly reduces this dimensionality: Higher-order visual areas that encode object properties (largely) detached from retinotopic or spatiotopic reference frames selectively transfer feature information to neurons with foveal receptive fields, irrespective of the vector of the upcoming saccade. This parsimonious mechanism would have shortcomings. In particular, foveal feedback should become less effective during saccade sequences where several peripheral targets are simultaneously attended. Feature information at both attended target locations may be fed back in temporal succession or weighted and erroneously combined into a single fed-back signal. In most cases, however, foveal feedback may reasonably achieve what established transsaccadic mechanisms struggle to explain: An anticipation of the features of a single saccade target—which typically constitutes the currently most relevant object in the visual field—in foveal vision. 

      While direct feedback connections from higher-order to early visual areas would constitute the most straightforward implementation, it is conceivable that feedback signals are relayed through and modulated by subcortical areas. In particular, the thalamic pulvinar has been identified as a connection hub for visual processing that receives copies of feedforward and feedback connections from different visual areas and may even combine information across visual space (Cortes, Ladret, Abbas-Farishta, & Casanova, 2024). In the case of foveal prediction, thalamic neurons may receive fed-back signals from higher-order areas and enhance those signals before passing them on to cortical neurons with foveal receptive fields. Perhaps, a modification of foveal activation within the thalamic pulvinar itself is sufficient to influence perception. To the best of our understanding, however, the fed-back signal must originate in non-retinotopic, higher-order object processing areas to reduce the number of necessary neuronal connections.”

      (2) The results presented are very compelling. I wonder to which extent they generalize to situations in which the foveal input and the peripheral input are more heterogenous (e.g., faces or complex objects composed of many different features, orientations, and other visual properties). I think the current research raises a number of interesting questions. In general, it would be important for the readers to elaborate more on how the mechanism of pre-saccadic foveal prediction may play out in normal viewing conditions or in conditions in which the foveal input is completely irrelevant to the task.

      We agree and have reiterated this point in the current manuscript (see our first reply to “Weaknesses”). We also explicitly refer to Kroell & Rolfs (2022) for an extensive initial discussion of this question.

      (3) On page 10 the authors state that their data suggest that foveal enhancement emerges in earlier stages of saccade preparation as target opacity increases. However, this is not clear from the figures, when performance is locked to saccade onset (Fig 3 C), for the highest opacity targets performance seems to oscillate, however, the authors do not comment on that. There is literature showing how saccades can reset perceptual oscillations, and maybe what is observed here is just a stronger performance oscillation when the saccade target is more visible. Why would performance drop systematically 75 ms before saccade onset and then increase again 25 ms before the onset? Can the authors elaborate more on this?

      In response to this comment, we inspected the pre-saccadic time course of enhancement effects in a more temporally resolved fashion and, indeed, observed pronounced oscillations for the two higher target opacity conditions (see Results): 

      “Especially at higher target opacities, the temporal development of foveal enhancement appears to exhibit an oscillatory pattern. To inspect this incidental observation in a more temporally resolved fashion, we determined mean enhancement values in a boxcar window of 50 ms duration sliding along all saccade-locked probe offset time points (step size = 10 ms; x-axis values in Figure 4 indicate the latest time point in a certain window). We then fitted 6th order polynomials (with no constraints on parameters) to the resulting time courses and compared the fitted values against zero using bootstrapping (see Methods). The average foveal enhancement across target opacities reached significance starting 115 ms before saccade onset (gray curve in Figure 4; all ps < .046). For every individual target opacity condition, we observed significant enhancement immediately before saccade onset, although only very briefly for the lowest opacity (-2–0 ms for 25%; -39–0 ms for 39%, -106–0 ms for 59% &  -13–0 ms for 90%; all ps < .050; yellow to dark red curves in Figure 4). Especially for the higher two target opacities, we observed a local maximum preceding eye movement onset by approximately 80 ms. Interestingly, assuming a peak in enhancement in approximately 80 ms intervals (i.e., at x-axis values of -80 and 0 ms in Figure 4) would correspond to an oscillation frequency of 12.5 Hz. In contrast to rapid feedforward processing, feedback signaling is associated with neural oscillations in the alpha and beta range (i.e., between 7 and 30 Hz; Bastos et al., 2015; Jensen, Bonnefond, Marshall, & Tiesinga, 2015; van Kerkoerle et al., 2015).”

      We had observed an oscillatory pattern in multiple previous investigations, and in both Hit Rates to foveal orientation content and reflexive gaze velocities in response to peripheral motion information. So far, we have been unsure how to explain it. The literature on thalamic visual processing mentioned by the reviewer alerted us to the oscillatory nature of feedback signaling itself. Interestingly, the temporal frequency range of feedback oscillations includes the frequency of ~12.5 Hz observed in our data. We have included this and alternative explanations in the Discussion section (see below). Throughout, we highlight that we are aware that our analysis approach is purely descriptive and that the potential explanations we give are speculative.

      “Moreover, foveal congruency effects appear to exhibit an oscillatory pattern, with peaks in a medium saccade preparation stage (~80 ms before the eye movement) and immediately before saccade onset. We have noticed this pattern in several investigations with substantially different visual stimuli and behavioral readouts. For instance, using a full-screen dot motion paradigm, we observed a pre-saccadic, small-gain ocular following response to coherent motion in the saccade target region (Kroell, Rolfs, & Mitchell, 2023, conference abstract; Kroell, 2023, dissertation). Predictive ocular following first reached significance ~125 ms before the eye movement, then decreased and subsequently ramped up again ~25 ms before saccade onset. Several explanatory mechanisms appear conceivable. Unlike rapid feedforward processing, feedback propagation has been shown to follow an oscillatory rhythm in the alpha and beta range, that is, between 7 and 30 Hz (Bastos et al., 2015; Jensen, Bonnefond, Marshall, & Tiesinga, 2015; van Kerkoerle, et al., 2015). In our case, it is possible that the object-processing areas that send feedback to retinotopic visual cortex do so at a temporal frequency of ~12.5 Hz. At higher stimulus contrasts, feedforward signals may be fed back instantaneously and without the need for signal accumulation in feedbackgenerating areas. The resulting perceptual time courses may reflect innate temporal feedback properties most veridically. Alternatively, the initial enhancement peak may be related to the sudden onset of the saccade target stimulus and not to movement preparation itself. In this case, the initial peak should become particularly apparent if enhancement is aligned to the onset of the target stimulus. Yet, Figure 3 and Figure 4 suggest more prominent oscillations in saccade-locked time courses. In accordance with this, perceptual and attentional processes have been shown to exhibit oscillatory modulations that are phase-locked to action onset (e.g., Tomassini, Spinelli, Jacono, Sandini, & Morrone, 2015; Hogendoorn, 2016; Wutz, Muschter, van Koningsbruggen, Weisz, & Melcher, 2016; Benedetto & Morrone, 2017; Tomassini, Ambrogioni, Medendorp, & Maris, 2017; Benedetto, Morrone & Tomassini, 2019). Whether the oscillatory pattern of foveal enhancement, as well as its increased prominence at higher target contrasts, relies on innate temporal properties of feedback signaling, signal accumulation, saccade-locked oscillatory modulations of feedforward processing or attention, or a combination of these factors, one conclusion remains: task-induced cognitive influences suggested to underlie the considerable variability in temporal characteristics of foveal feedback during passive fixation (e.g., Fan et al., 2016; Weldon et al., 2016; 2020) are not the only possible explanation. Low-level target properties such as its luminance contrast modulate the resulting time course and should be equally considered, at least in our paradigm.”

      In the revised Abstract, we removed our claim on an earlier emergence of enhancement at higher opacities and have added this summary instead:

      “Second, the time course of foveal enhancement appeared to show an oscillatory pattern that was particularly pronounced at higher target opacities. Interestingly, the temporal frequency of these oscillations corresponded to the frequency range typically associated with neural feedback signaling.”

      (4) What was the average difference in latency between short and long latencies? It would be good to report it in the main text.

      We apologize for the oversight. The difference was 61 ms, with latencies of md = 247±18 ms for short- and md = 308±18 ms for long-latency saccades. We have added this information to the main text.

      (5) From the saccade latency graphs in Figure S1 it seems there is some variability in the latency of saccades across subjects, I wonder if there is a correlation between saccade latency and the magnitude of the foveal prediction effect across subjects.

      We had inspected a connection between saccade latency and congruency in our first investigation (Kroell & Rolfs, 2022; not reported) and observed that participants with lower latencies tended to show more enhancement, albeit non-significantly. Likewise, we observed a non-significant negative correlation between the median saccade latency and the mean foveal prediction effect (across opacities and time points) in the current investigation, r \= -0.22, p \= .572. While our study involved a small number of observers (n = 9), the analysis approach illustrated in Figure 2 A-C instead makes use of the large number of trials collected per participant (mean n = 2841 trials per observer) and demonstrates a reliable influence of saccade latency on an individual-observer level.

      (6) Page 14, the authors state that their findings suggest that the feedforward processing of the peripheral saccade target is accelerated when it is presented at high contrast. I find this a bit too speculative, both in terms of assuming that there is a feedforward vs a feedback process (see my point 1) and in terms of speculating that the feedforward process is accelerated as I do not see a clear hint of this in the data (see my point 3) and it is a bit of a stretch to speculate on delays or accelerations of neural processing. It is possible that the feedforward signal is always delivered at the same speed but it is weaker in one case and the effect needs more time to build up.

      We fully agree and hope to have addressed the reviewer’s arguments in the sections preceding this point. We included the reviewer’s last sentence in the Discussion section as well: 

      “Alternatively, or in addition, it is conceivable that weaker feedforward signals require a longer accumulation interval before the feedback process can be initiated.”

      Minor:

      (1) I think the description of the linear mixed-effects model can go in the supplemental methods, if possible, and its results can be briefly mentioned in the text.

      In previous work, we have been asked to move linear mixed-effects model descriptions from supplemental to main method (or even results) sections for clarity. We have followed this suggestion ever since and, due to the relevance of the models for the interpretation of the presented results, would like to keep their description in the methods section.

      (2) This is just a minor point, but I would suggest using a different word instead of opacity (maybe visibility?).

      We had gone back and forth on this. We decided to use the term ‘conspicuity’ when we discuss our findings conceptually and the term ‘opacity’ when we refer to the experimental manipulation (since we directly manipulate the transparency, i.e., 1-opacity, of the target patch against the background). To compute the slopes in Figures 2 and 5, we ordered observers’ performances by the linearly spaced opacity conditions. Since the term ‘opacity’ is closest to both the experimental manipulation and the variable entered into analysis, we would like to adhere to this terminology. However, we have added an explicit note to the end of our introduction to avoid confusion: 

      “Throughout the paper, we use the term ‘opacity’ when we refer to the experimental manipulation (that is, a variation of the transparency, i.e., 1-opacity of the target patch against the background noise) and the term ‘conspicuity’ when we discuss our findings conceptually.”

      Reviewer #2 (Public Review):

      Summary:

      In this manuscript, the authors ran a dual task. Subjects monitored a peripheral location for a target onset (to generate a saccade to), and they also monitored a foveal location for a foveal probe. The foveal probe could be congruent or incongruent with the orientation of the peripheral target. In this study, the authors manipulated the conspicuity of the peripheral target, and they saw changes in performance in the foveal task. However, the changes were somewhat counterintuitive.

      Strengths:

      The authors use solid analysis methods and careful experimental design.

      Weaknesses:

      I have some issues with the interpretation of the results, as explained below. In general, I feel that a lot of effects are being explained by attention and target-probe onset asynchrony etc, but this seems to be against the idea put forth by the authors of "foveal prediction for visual continuity across saccades". Why would foveal prediction be so dependent on such other processes? This needs to be better clarified and justified.

      We address the described weaknesses in the respective sections below. In general, as we point out in response to Reviewer 1 as well, the current submission is a Research Advance article meant to supplement our main article (Kroell & Rolfs, 2022, https://doi.org/10.7554/eLife.78106). To comply with the eLife recommendations for Research Advance submissions, we addressed conceptual points only briefly, especially if they had been explained in detail in our main article. To make the nature and format of the current submission as explicit as possible, and to emphasize its connection to our previous work, we refer to the submission format in our abstract and introduction now.

      Specifics:

      The explanation of decreased hit rates with increased peripheral target opacity is not convincing. The authors suggest that higher contrast stimuli in the periphery attract attention. But, then, why are the foveal results occurring earlier (as per the later descriptions in the manuscript)? And, more importantly, why would foveal prediction need to be weaker with stronger pre-saccadic attention to the periphery? What is the function of foveal prediction? What of the other interpretation that could be invoked in general for this type of task used by the authors: that the dual task is challenging and that subjects somehow misattribute what they saw in the peripheral task when planning the saccade. i.e. foveal hit rates are misperceptions of the peripheral target. When the peripheral target is easier to see, then the foveal hit rate drops.

      We will address these comments one by one:

      The authors suggest that higher contrast stimuli in the periphery attract attention. But, then, why are the foveal results occurring earlier (as per the later descriptions in the manuscript)?

      We consider these observations to rely on separate processes. Already in the main publication (Kroell & Rolfs, 2022), we had observed a continuous decrease of target-congruent and target-incongruent foveal Hit Rates (HRs) during saccade preparation, and suggested that this decrease (similarly observed in Hanning & Deubel, 2022b is likely caused by the pre-saccadic shift of visuospatial attention to the target. In other words, as attentional resources shift towards the periphery, foveal detection performance is hampered, irrespective of peripheral and foveal feature (in-)congruency. In the current investigation, we again observed a pronounced pre-saccadic decrease of foveal HRs, irrespective of foveal probe orientation. Our argument that high-contrast peripheral saccade targets attract more attention relies on the clear observation that this decrease becomes more pronounced as the contrast of the saccade target increases. To the best of our judgment and experience with doing the task ourselves, this interpretation appears very conceivable. We explain this rationale in the Abstract and the Results sections of the manuscript (see below).

      Our hypotheses and interpretations concerning the time course of foveal prediction refer to the difference between target-congruent and target-incongruent foveal HRs (i.e., to predictive foveal feature enhancement). Irrespective of the general, feature-unspecific decrease of foveal detection performances, we had hypothesized that the peripheral target is processed faster if it exhibits a high contrast. This assumption is based on temporal processing properties of many visual neurons that we have expanded on in our revision: 

      “In particular, neuronal response latencies decrease systematically as the contrast of visual input increases. While this phenomenon is reliably observed at varying stages of the visual processing hierarchy—such as the lateral geniculate nucleus (Lee et al., 1981b), primary visual cortex (e.g., Albrecht, 1995; Carandini et al., 1997, 2002; Carandini and Heeger, 1994), and anterior superior temporal sulcus (STSa; Oram et al., 2002; van Rossum et al., 2008)— influences of contrast on neuronal response latency are particularly pronounced in higher-order visual areas: A doubling of stimulus contrast has been shown to decrease the latency of V1 neurons by 8 ms, compared to a reduction of 33 ms in area STSa (Oram et al., 2002; van Rossum et al., 2008). Assuming that the peripheral target is processed in a bottom-up fashion until it reaches higher-order object processing areas, the time point at which peripheral signals are available for feedback should be dictated by the temporal dynamics of visual feedforward processing.”

      Of note, both reviewers asked us to explore the oscillatory nature of the difference between targetcongruent and target-incongruent HRs. We will post our changes in response to the reviewer’s remark below.

      And, more importantly, why would foveal prediction need to be weaker with stronger pre-saccadic attention to the periphery?

      We hope that our previous reply has cleared up that the opposite is true: In general, and irrespective of the feature congruency of target and foveal probe, foveal HRs decrease as target contrast increases. As we have stated in our Abstract and Results, “foveal Hit Rates for target-congruent and incongruent probes decreased as target opacity increased, presumably since attention was increasingly drawn to the target the more salient it became. Crucially, foveal enhancement defined as the difference between congruent and incongruent Hit Rates increased with opacity”. This finding did not appear counterintuitive to us and was, in fact pre-registered as a main hypothesis (see https://osf.io/wceba). 

      We are unsure if this goes beyond the reviewer’s concern but we, in fact, speculate in the revised Discussion section as well as in our original eLife article that the overall, feature-unspecific decrease in foveal detection performances may aid feature-specific foveal prediction: 

      “This pre-saccadic decrease in foveal sensitivity may boost the relative weight of fed-back signals by attenuating the conspicuity of high-contrast feedforward input. In other words, the strength of feedforward input to the fovea is reduced gradually across saccade preparation. At the same time, the strength of the fed-back predictive signal should profit from the high contrast of naturalistic saccade targets.”

      What is the function of foveal prediction?

      Please refer to the section ‘What is the function of foveal prediction?’ in our main article. We have pasted this paragraph below for the reviewer’s convenience. 

      “What is the function of foveal prediction?

      As stated above, previous investigations on foveal feedback required observers to make peripheral discrimination judgments. We, in contrast, did not ask observers to generate a perceptual judgment on the orientation of the saccade target. Instead, detecting the target was necessary to perform the oculomotor task. While the identification of local contrast changes would have sufficed to direct the eye movement, the orientation of the target enhanced foveal processing of congruent orientations. The automatic nature of foveal enhancement showcases that perceptual and oculomotor processing are tightly intertwined in active visual settings: planning an eye movement appears to prioritize the features of its target; commencing the processing of these features before the eye movement is executed may accelerate post- saccadic target identification and ultimately provide a head start for corrective gaze behavior (Deubel et al., 1982; Ohl and Kliegl, 2016; Tian et al., 2013).”

      What of the other interpretation that could be invoked in general for this type of task used by the authors: that the dual task is challenging and that subjects somehow misattribute what they saw in the peripheral task when planning the saccade. i.e. foveal hit rates are misperceptions of the peripheral target. When the peripheral target is easier to see, then the foveal hit rate drops.

      Alternative explanations in general: In our main article, we ruled out—either through direct experimentation or by considering relevant properties of our findings—the following alternative explanations: i) spatially global feature-based attention to the target orientation, ii) a multiplicative combination of spatial and feature-based attention, and iii) shifts of decision criterion. While dual tasks (i.e., simultaneous oculomotor planning and perceptual detection) are standard in psychophysical investigations of active vision, we acknowledge the potential influence of an explicit foveal task in the revised manuscript, and in response to both reviewers: 

      “Lastly, pre-saccadic foveal input is likely less relevant during natural viewing behavior than it is in our task. It is possible that this task-induced prioritization of the foveal location facilitated the emergence of congruency effects. In a previous experiment (Kroell & Rolfs, 2022; Figure 2D), the perceptual probe could appear anywhere on a horizontal axis of 9 dva length around the screen center. Despite this spatial unpredictability, however, congruency effects peaked at the pre-saccadic foveal location, even after peripheral baseline performances had been raised to a foveal level through an adaptive increase in probe opacity. Ultimately, an influence of task demands on visual processing can only be fully excluded through techniques that provide a direct readout of perceptual contents without requiring keyboard responses. In psychophysical investigations, a prediction of saccade target motion may be read out from observers’ eye velocities (Kroell, Mitchell, & Rolfs, 2023; Kwon, Rolfs, & Mitchell, 2019). In electroencephalographic (EEG) and neurophysiological studies, foveal predictions should manifest in early visual evoked potentials (e.g., Creel, 2019) and increased firing rates of feature-selective foveal neurons in early visual areas, respectively.”

      Difficulty of the task: Concerning the perceptual detection task, every experimental session was preceded by an adaptive staircase procedure that adjusted the transparancy of the foveal probe—and, thus, task difficulty—depending on the respective observer’s performance (see Methods for details). Concerning the oculomotor task, observers were able to perform accurate saccades with typical movement latencies for all target opacity conditions (see Results, Supplements & Figure S1). In general, we are unsure how high task difficulty could produce a feature-, temporally and spatially specific enhancement of both filtered and incidental target-congruent foveal orientation information. In fact, a main finding of our current submission is that foveal HRs decrease as the target becomes easier to see and the oculomotor task thus becomes easier to perform.

      Perceptual confusion of target and probe stimulus: We observe a specific increase in HRs for foveal probes that exhibit the same orientation as the peripheral saccade target. Just like in our main article, a response is defined as a ‘Hit’ if a foveal probe is presented and the observer generates a ‘present’ judgment. To our understanding, the suggestion that a confusion of target and probe stimuli may account for these effects necessarily implies that this confusion hinges on the congruency between peripheral and foveal feature inputs. In other words, peripheral and foveal signals should be more readily “confused” if they exhibit similar features. We assume that peripheral feature information is fed back to neurons with foveal receptive field and combines with feature-congruent feedforward input. Whether this combination of signals can be described as low-level perceptual “confusion” likely depends on individual linguistic judgments (it would certainly be a novel description of feedback-feedforward interactions). Perhaps a defining difference between the reviewer’s concern and our assumed mechanism is the spatial specificity of the resulting congruency effects. We suggest that only neurons with foveal receptive fields receive feature information via feedback. And indeed, we demonstrate a clear spatial specificity of congruency effects around the pre-saccadic foveal location, even after parafoveal performances had been raised to a foveal level by an adaptive increase in probe opacity (see Kroell & Rolfs, 2022; Figure 2C & Figure 3). In other words, observers’ perception is altered in their pre-saccadic center of gaze while the target is presented peripherally. We struggle to conceive a

      scenario in which a confusion of signals should be feature-specific as well as specific to an interaction between peripheral and foveal signals without being meaningful at the same time. If the reviewer is referring to confusions on the response or decision level, we would like to point them towards the Discussion section ‘Can our findings be explained by established mechanisms other than foveal prediction?’ in our main article. In this paragraph, we provide detailed arguments for a dissociation between our findings and shifts in decision criterion that would exceed the scope of a Research Advance. 

      When the peripheral target is easier to see, then the foveal hit rate drops.

      We agree. Target-congruent and incongruent foveal HRs decreased as the contrast of the probe increased. However, and as we stated in response to the reviewer’s first comment, the difference between target-congruent and target-incongruent foveal HRs (and, thus, foveal enhancement of the target orientation) increased with peripheral target contrast.

      The analyses of Fig. 3C appear to be overly convoluted. They also imply an acknowledgment by the authors that target-probe temporal difference matters. Doesn't this already negate the idea that the foveal effects are associated with the saccade generation process itself? If the effect is related to target onset, how is it interpreted as related to a foveal prediction that is associated with the saccade itself? 

      We indeed conducted analyses that can reveal an influence of target presentation duration at probe onset, the saccade preparation stage at probe offset, as well as a combination of both factors. The fact that target presentation duration may have an influence on foveal prediction would not negate a simultanous influence of saccade preparation and vice versa. In the main article, we directly investigated the influence of saccade preparation on foveal enhancement by introducing a passive fixation condition (Kroell & Rolfs, 2022; Figure 5). At identical target-probe offset durations, pre-saccadic foveal enhancement was significantly more pronounced and accelerated compared to enhancement during passive fixation. We have added a purely saccade-locked time course (uncorrected by targetprobe interval) to our Results section and to Figure 3 (second row). We still believe that the target-locked, saccade-locked and combined analysis are informative for future investigations and would like to present them all for completeness.

      Also, the oscillatory nature of the effect in Fig. 3C for 59% and 90% opacity is quite confusing and not addressed. The authors simply state that enhancement occurs earlier before the saccade for higher contrasts. But, this is not entirely true. The enhancement emerges then disappears and then emerges again leading up to the saccade. Why would foveal prediction do that?

      In response to this comment and a suggestion by Reviewer 1, we inspected the pre-saccadic time course of enhancement effects in a more temporally resolved fashion and, indeed, observed pronounced oscillations for the two higher target opacity conditions (see Results): 

      “Especially at higher target opacities, the temporal development of foveal enhancement appears to exhibit an oscillatory pattern. To inspect this incidental observation in a more temporally resolved fashion, we determined mean enhancement values in a boxcar window of 50 ms duration sliding along all saccade-locked probe offset time points (step size = 10 ms; x-axis values in Figure 4 indicate the latest time point in a certain window). We then fitted 6th order polynomials to the resulting time courses and compared the fitted values against zero using bootstrapping (see Methods). The average foveal enhancement across target opacities reached significance starting 115 ms before saccade onset (gray curve in Figure 4; all ps < .046). For every individual target opacity condition, we observed significant enhancement immediately before saccade onset, although only very briefly for the lowest opacity (-2–0 ms for 25%; -39–0 ms for 39%, -106–0 ms for 59% &  -13–0 ms for 90%; all ps < .050; yellow to dark red curves in Figure 4). Especially for the higher two target opacities, we observed a local maximum preceding eye movement onset by approximately 80 ms. Interestingly, assuming a peak in enhancement in approximately 80 ms intervals (i.e., at x-axis values of -80 and 0 ms in Figure 4) would correspond to an oscillation frequency of 12.5 Hz. In contrast to rapid feedforward processing, feedback signaling is associated with neural oscillations in the alpha and beta range (i.e., between 7 and 30 Hz; Bastos et al., 2015; Jensen, Bonnefond, Marshall, & Tiesinga, 2015; van Kerkoerle et al., 2015).”

      We had observed an oscillatory pattern in multiple previous investigations, and in both Hit Rates to foveal orientation content and reflexive gaze velocities in response to peripheral motion information. So far, we have been unsure how to explain it. The literature on thalamic visual processing mentioned by the reviewer alerted us to the oscillatory nature of feedback signaling itself. Interestingly, the temporal frequency range of feedback oscillations includes the frequency of ~12.5 Hz observed in our data. We have included this and alternative explanations in the Discussion section (see below). We are aware, and acknowledge in the manuscript, that our analysis approach is purely descriptive, and that the potential explanations we give are speculative. 

      “Moreover, foveal congruency effects appeared to exhibit an oscillatory pattern, with peaks in a medium saccade preparation stage (~80 ms before the eye movement) and immediately before saccade onset. We have noticed this pattern in several investigations with substantially different visual stimuli and behavioral readouts. For instance, using a full-screen dot motion paradigm, we observed a pre-saccadic, small-gain ocular following response to coherent motion in the saccade target region (Kroell, Rolfs, & Mitchell, 2023, conference abstract; Kroell, 2023, dissertation). Predictive ocular following first reached significance ~125 ms before the eye movement, then decreased and subsequently ramped up again ~25 ms before saccade onset. Several explanatory mechanisms appear conceivable. Unlike rapid feedforward processing, feedback propagation has been shown to follow an oscillatory rhythm in the alpha and beta range, that is, between 7 and 30 Hz (Bastos et al., 2015; Jensen, Bonnefond, Marshall, & Tiesinga, 2015; van Kerkoerle, et al., 2015). In our case, it is possible that the object-processing areas that send feedback to retinotopic visual cortex do so at a temporal frequency of ~12.5 Hz. At higher stimulus contrasts, feedforward signals may be fed back instantaneously and without the need for signal accumulation in feedback-generating areas. The resulting perceptual time courses may reflect innate temporal feedback properties most veridically. Alternatively, the initial enhancement peak may be related to the sudden onset of the saccade target stimulus and not to movement preparation itself. In this case, the initial peak should become particularly apparent if enhancement is aligned to the onset of the target stimulus. Yet, Figure 3 and Figure 4 suggest more prominent oscillations in saccade-locked time courses. In accordance with this, perceptual and attention processes have been shown to exhibit oscillatory modulations that are phase-locked to action onset (e.g., Tomassini, Spinelli, Jacono, Sandini, & Morrone, 2015; Hogendoorn, 2016; Wutz, Muschter, van Koningsbruggen, Weisz, & Melcher, 2016; Benedetto & Morrone, 2017; Tomassini, Ambrogioni, Medendorp, & Maris, 2017; Benedetto, Morrone & Tomassini, 2019). Whether the oscillatory pattern of foveal enhancement, as well as its increased prominence at higher target contrasts, relies on innate temporal properties of feedback  signaling, signal accumulation, saccade-locked oscillatory modulations of feedforward processing or attention, or a combination of these factors, one conclusion remains: task-induced cognitive influences suggested to underlie the considerable variability in temporal characteristics of foveal feedback during passive fixation (e.g., Fan et al., 2016; Weldon et al., 2016; 2020) are not the only possible explanation. Low-level target properties such as its luminance contrast modulate the resulting time course and should be equally considered, at least in our paradigm.”

      The interpretation of Fig. 4 is also confusing. Doesn't the longer latency already account for the lapse in attention, such that visual continuity can proceed normally now that the saccade is actually eventually made? In all results, it seems that the effects are all related to the dual nature of the task and/or attention, rather than to the act of making the saccade itself. Why should visual continuity (when a saccade is actually made, whether with short or long latency) have different "fidelity"? And, isn't this disruptive to the whole idea of visual continuity in the first place?

      We are unsure if we grasp the unifying concern behind these remarks. For the reviewer’s point on the dual-task nature of our paradigm, please consider our answer above. Perhaps it is important to note that we do not (and would never) claim that foveal prediction is the only mechanism underlying visual continuity. We believe that multiple mechanisms, including but not limited to pre-saccadic shifts of attention, predictive remapping of attention pointers and the perception of intra-saccadic signals interact and jointly contribute to visual continuity. It appears highly conceivable that, like most processes in biological systems, motor and perceptual performances are subject to fluctuations. We argue that saccade latencies as well as the magnitude of foveal prediction constitute read-outs of these variations. We also suggest that those read-outs are innately correlated beyond their common moderator of, perhaps, attentional state; we have previously presented clear evidence for a link between eye movement preparation and foveal prediciton (Kroell & Rolfs, 2022; Figure 2). To the best of our judgment, we consider it reasonable that the effectiveness of movement-contingent perceptual processes varies with the effectiveness (in programming or execution) of the very movement motivating them. We present evidence for this assumption in our submission. We would also like to make clear that we do not assume our vision to fail entirely, even if every single well-known mechanism of visual continuity were to break down at once. Upon saccade landing, the visual system receives reliable visual input. Nonetheless, the visual system has undeniably developed mechanisms to optimize this process. We believe foveal prediciton to rank among them.

      Small question: is it just me or does the data in general seem to be too excessively smoothed?

      We did not apply any smoothing to either the analysis or visualization of our data in the initial manuscript.

      Every observer completed a large number of trials (mean n = 2841 trials per observer; total trial number > 25,500), which likely contributes to the clarity of our data. To inspect the oscillatory pattern of enhancement in a more temporally resolved fashion (in response to the reviewer’s point above), we applied a moving window analysis in this revision. Due to overlapping window borders, this analysis introduces a certain degree of smoothing. Nonetheless, data patterns are comparable to the time course with only few non-overlapping time bins (Figure 3B; second row). In general, we have described all steps of our analysis routine extensively in the Methods section and will make our data publicly available upon publication of the Reviewed Preprint. 

      General comment: it is important to include line numbers in manuscripts, to help reviewers point to specific parts of the text when writing their comments. Otherwise, the peer review process is rendered unnecessarily complicated for the reviewers.

      We apologize and have added line numbers.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this work, the authors investigate the functional difference between the most commonly expressed form of PTH, and a novel point mutation in PTH identified in a patient with chronic hypocalcemia and hyperphosphatemia. The value of this mutant form of PTH as a potential anabolic agent for bone is investigated alongside PTH(1-84), which is a current anabolic therapy. The authors have achieved the aims of the study. Their conclusion that this suggests a "new path of therapeutic PTH analog development" seems unfounded; the benefit of this PTH variant is not clear, but the work is still interesting.

      The work does not identify why the patient with this mutation has hypocalcemia and hyperphosphatemia; this was not the goal of the study, but the data is useful for helping to understand it.

      Thank you for your valuable feedback. In this study, we confirmed that <sup>R25C</sup>PTH can form a dimer, and our in vivo experiments in the mouse model demonstrated that dimeric <sup>R25C</sup>PTH can stimulate bone formation similarly to normal PTH. Furthermore, patients with the <sup>R25C</sup>PTH mutation, who have been exposed to high levels of this variant over an extended period, were reported to have high bone mineral density. Based on these observations, we hypothesized that dimeric <sup>R25C</sup>PTH might have potential as a new therapeutic PTH analog, particularly as a bone anabolic agent. However, we acknowledge that it is premature to make definitive claims regarding its therapeutic utility. Thus, we are currently conducting follow-up research to further investigate the subsignaling pathway changes induced by dimeric <sup>R25C</sup>PTH and their impact on bone metabolism.

      Moreover, to fully understand the patient’s symptoms, it is crucial to determine the form in which <sup>R25C</sup>PTH exists in vivo. While our in vitro experiments demonstrated that <sup>R25C</sup>PTH is secreted primarily in its dimeric form, we do not yet know whether this dimeric structure is maintained in vivo. We are actively conducting experiments to analyze the circulating form of <sup>R25C</sup>PTH in patients through blood sample collection (Andersen et al., 2022; Lee et al., 2015). Should the mutation predominantly exist in its monomeric form in vivo, this would align with clinical findings reported by Lee et al. (2015), which could help explain the patient’s hypocalcemia and hyperphosphatemia. However, if <sup>R25C</sup>PTH primarily exists in its dimeric form, additional research will be necessary to uncover the underlying mechanisms. Based on our experimental results, the dimeric <sup>R25C</sup>PTH exhibits a reduced binding affinity to PTH1R compared to the monomeric form. Furthermore, our in vitro experiments revealed that dimeric <sup>R25C</sup>PTH induces lower levels of cAMP production upon PTH1R activation. Accordingly, we can assume that this reduction in receptor signaling is likely to account for the impaired regulation of calcium and phosphate in patients with the mutation. However, despite this diminished signaling in calcium and phosphate homeostasis, dimeric <sup>R25C</sup>PTH was still capable of promoting bone formation at levels comparable to wild-type PTH. This apparent paradox warrants further investigation, and we are actively pursuing studies to elucidate how the dimeric form exerts its effects on bone metabolism.

      References

      Andersen, S. L., Frederiksen, A. L., Rasmussen, A. B., Madsen, M., & Christensen, A. R. (2022). Homozygous missense variant of PTH (c.166C>T, p.(Arg56Cys)) as the cause of familial isolated hypoparathyroidism in a three-year-old child. J Pediatr Endocrinol Metab, 35(5), 691-694. https://doi.org/10.1515/jpem-2021-0752

      Lee, S., Mannstadt, M., Guo, J., Kim, S. M., Yi, H. S., Khatri, A., Dean, T., Okazaki, M., Gardella, T. J., & Juppner, H. (2015). A Homozygous [Cys25]PTH(1-84) Mutation That Impairs PTH/PTHrP Receptor Activation Defines a Novel Form of Hypoparathyroidism. J Bone Miner Res, 30(10), 1803-1813. https://doi.org/10.1002/jbmr.2532

      Strengths:

      The work is novel, as it describes the function of a novel, naturally occurring, variant of PTH in terms of its ability to dimerise, to lead to cAMP activation, to increase serum calcium, and its pharmacological action compared to normal PTH.

      Weaknesses:

      (1) The use of very young, 10 week old, mice as a model of postmenopausal osteoporosis remains a limitation of this study, but this is now quite clearly described as a limitation, including justifying the use of the primary spongiosa as a measurement site.

      We appreciate the reviewer’s comment.

      (2) Methods have been clarified. It is still necessary to properly define the micro-CT threshold in mm HA/cc^3. I think it might be acat about 200mg HA/cc^3 in this study.

      Thank you for your insightful comment. To address this, we utilized hydroxyapatite (HA) phantom with HA content ranging from 0 to 1200 mg/cm<sup>3</sup>, with calibration points at 0, 50, 200, 800, 1000, and 1200 mg CaHA/cm<sup>3</sup>, to measure grayscale values via µ-CT. Based on these measurements, the trabecular bone BMD in our study was determined to range from 100 to 200 mg/cm<sup>3</sup>.

      Author response image 1.

      (3) The apparent contradiction between the cortical thickness data (where there is no difference between the two PTH formulations) and the mechanical testing data (where there is a difference) remains unresolved. It is still not clear whether there is a material defect in the bone, which can be partially assessed by reporting the 3-point bending test, corrected for the diameters of the bone (i.e. as stress / strain curves).

      Thank you for your comment. First, we ensured that the bones sampled during the experiment showed no defects, and we carefully separated the femur bones from the mice to preserve their integrity. In the 3-point bending test, PTH treatment significantly increased the maximum load of the femur bone compared to the OVX-control group. Additionally, the maximum load in the PTH treatment group was significantly greater than that observed in the PTH dimer group. Furthermore, structural factors influencing bone strength, such as the perosteal perimeter and the endocortical bone perimeter, were also increased in the PTH treatment group compared to the PTH dimer group (data only for reviewer).

      Author response image 2.

      (4) It is also puzzling that both dimeric and monomeric PTH lead to a reduction in total bone area (cross sectional area?). This would suggest a reduction in bone growth. This should be discussed in the work.

      In our experiment, the data showed an increase in cortical bone area in the PTH treatment group, but not in the PTH dimer treatment group. However, both dimeric and monomeric PTH treatments resulted in a reduction in total tissue area. We added revised sentence in page 13 line 317 and page 14 line 333 as follows:

      “In addition, the data showed an increase in cortical bone area (Ct.Ar) in the PTH treatment group but not in the PTH dimer treatment group. However, both dimeric and monomeric PTH treatments reduced total tissue area (Tt.Ar), suggesting potential effects on bone growth in the width of mice or humans.”

      “This study has several limitations. First, it is urgently necessary to determine whether dimeric <sup>R25C</sup>PTH is present in human patient serum. Second, TRAP staining showed an inhibitory effect of PTH treatment on the primary spongiosa area. However, the secondary spongiosa, which more accurately reflects bone remodeling (55), was not examined due to the barely detectable bone in this area in OVX-induced osteoporosis mouse models. Third, it is unclear whether similar bone phenotypes exist between human <sup>R25C</sup>PTH patients and dimeric <sup>R25C</sup>PTH-treated mice, particularly regarding low bone strength. Although the dimeric <sup>R25C</sup>PTH-treated group showed higher cortical BMD compared to WT-Sham or PTH groups, there was no difference in bone strength compared to the osteoporotic mouse model. Fourth, our study demonstrated that PTH or <sup>R25C</sup>PTH treatment decreased circumferential length, which could affect bone growth in width. However, whether this phenotype is also observed in patients treated with PTH or <sup>R25C</sup>PTH remains uncertain.”

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      The study presents important findings on inositol-requiring enzyme (IRE1α) inhibition on diet-induced obesity (overnutrition) and insulin resistance where IRE1α inhibition enhances thermogenesis and reduces the metabolically active and M1-like macrophages in adipose tissue. The evidence supporting the conclusions is convincing but can be enhanced with information/data on the validity, specificity, selectivity, and toxicity of the IRE1α inhibitor and supported with more detail on the mechanisms by which adipose tissue macrophages influence adipocyte metabolism. The work will be of interest to cell biologists and biochemists working in metabolism, insulin resistance, and inflammation.

      We thank the editors for the assessment and appreciation of our findings in this study. In the revision, we have added the information on the validity, selectivity and toxicity of IRE1α inhibitor. In addition, we also discussed the likelihood that suppression of metabolically activated proinflammatory macrophage population in adipose tissue on the reversal of adipose remodeling and thermogenesis. In the revision, we have improved the manuscript significantly throughout the text and figures following the recommends by the reviewers.

      Public Reviews:

      Reviewer #1 (Public review):

      First, the authors confirm the up-regulation of the main genes involved in the three branches of the Unfolded Protein Response (UPR) system in diet-induced obese mice in AT, observations that have been extensively reported before. Not surprisingly, IRE1a inhibition with STF led to an amelioration of the obesity and insulin resistance of the animals. Moreover, non-alcoholic fatty liver disease was also improved by the treatment. More novel are their results in terms of thermogenesis and energy expenditure, where IRE1a seems to act via activation of brown AT. Finally, mice treated with STF exhibited significantly fewer metabolically active and M1-like macrophages in the AT compared to those under vehicle conditions. Overall, the authors conclude that targeting IRE1a has therapeutical potential for treating obesity and insulin resistance.

      The study has some strengths, such as the detailed characterization of the effect of STF in different fat depots and a thorough analysis of macrophage populations. However, the lack of novelty in the findings somewhat limits the study´s impact on the field.

      We thank the reviewer for the appreciation of our findings and the comments about the novelty. Regarding the novelty, we would emphasize several novelties presented in this manuscript. First, as the reviewer correctly pointed out, we discovered that IRE1 inhibition by STF activates brown AT and promotes thermogenesis and that IRE1 inhibition not only significantly attenuated the newly discovered CD9+ ATMs and the “M1-like” CD11c+ ATMs but also diminished the M2 ATMs for the first time. These discoveries are very important and novel. In obesity, it was originally proposed that ATM undergoes M1/M2 polarization from an anti-inflammatory M2 to a classical pro-inflammatory M1 state. It was further reported that IRE1 deletion improves thermogenesis by boosting M2 population which then synthesize and secrete catecholamines to promote thermogenesis. It is now known that M2 macrophages do not synthesize catecholamines or promote thermogenesis. In this study, we discovered that IRE1 inhibition doesn’t increase (but instead decrease) the M2 population and that IRE1 inhibition promotes thermogenesis likely by suppressing pro-inflammatory macrophage populations including the M1-like ATMs and most importantly the newly identified metabolically active macrophages, given that ATM inflammation has been reported to suppress thermogenesis. Second, this study presented the first characterization of relationship between the more classical M1-like ATMs and the newly discovered metabolically active ATMs, showing that the CD11c+ M1-like ATMs are largely overlapping with but yet non-identical to CD9+ ATMs in the eWAT under HFD. Third, although upregulation of ER stress response genes in the adipose tissues of diet-induced obese mice have been extensively reported, it doesn’t necessarily mean that targeting IRE1a or ER stress can reverse existing insulin resistance and obesity. It is not uncommon that a therapy doesn’t yield the desired effect as expected. For instance, amyloid plaques are a hallmark of Alzheimer's disease (AD), interventions that prevent or reverse beta amyloid deposition have been expected to prevent progression or even reverse cognitive impairment in AD patients. However, clinical trials on such therapies have been disappointing. In essence, experimental demonstration of effectiveness or feasibility for any potential therapeutic targets is a first step for any future clinical implementation.

      Reviewer #2 (Public review):

      The manuscript by Wu et al demonstrated that IRE1a inhibition mitigated insulin resistance and other comorbidities through increased energy expenditure in DIO mice. In this reviewer's opinion, this timely study has high significance in the field of metabolism research for the following reasons.

      (1) The authors' findings are significant and may offer a new therapeutic target to treat metabolic diseases, including diabetes, obesity, NAFLD, etc.

      (2) The authors carefully profiled the ATMs and examined the changes in gene expression after STF treatment.

      (3) The authors presented evidence collected from both systemic indirect calorimetry and individual tissue gene expression to support the notion of increased energy expenditure.

      Overall, the authors have presented sufficient background in a clear and logically organized structure, clearly stated the key question to be addressed, used the appropriate methodology, produced significant and innovative main findings, and made a justified conclusion.

      We thank the reviewer for the appreciation of our work.

      Reviewer #3 (Public review):

      Summary:

      The manuscript by Wu D. et al. explores an innovative approach to immunometabolism and obesity by investigating the potential of targeting macrophage Inositol-requiring enzyme 1α (IRE1α) in cases of overnutrition. Their findings suggest that pharmacological inhibition of IRE1α could influence key aspects such as adipose tissue inflammation, insulin resistance, and thermogenesis. Notable discoveries include the identification of High-Fat Diet (HFD)-induced CD9+ Trem2+ macrophages and the reversal of metabolically active macrophages' activity with IRE1α inhibition using STF. These insights could significantly impact future obesity treatments.

      Strengths:

      The study's key strengths lie in its identification of specific macrophage subsets and the demonstration that inhibiting IRE1α can reverse the activity of these macrophages. This provides a potential new avenue for developing obesity treatments and contributes valuable knowledge to the field.

      Weaknesses:

      The research lacks an in-depth exploration of the broader metabolic mechanisms involved in controlling diet-induced obesity (DIO). Addressing this gap would strengthen the understanding of how targeting IRE1α might fit into the larger metabolic landscape.

      Impact and Utility:

      The findings have the potential to advance the field of obesity treatment by offering a novel target for intervention. However, further research is needed to fully elucidate the metabolic pathways involved and to confirm the long-term efficacy and safety of this approach. The methods and data presented are useful, but additional context and exploration are required for broader application and understanding.

      We thank the reviewer for the appreciation of strengths in our manuscript. In particular, we appreciate the reviewer’s recommendation on the exploration of broader metabolic landscape, such as the effect of IRE1 inhibition on non-adipose tissue macrophages and metabolism. We agree that achieving these will certainly broaden the therapeutic potential of IRE1 inhibition to larger metabolic disorders and we will pursue these explorations in future studies.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      A list of recommendations for the authors is presented below:

      (1) Please, update the literature review to include more recent studies relevant to the topic.

      We thank the reviewer’s suggestions. We have added more references from recent studies.

      (2) Please, provide a detailed explanation of how STF functions, including potential off-target effects or issues related to specificity.

      We thank the reviewer’s suggestions. STF is a small-molecule inhibitor designed to selectively inhibit the RNase activity of IRE1a. Once IRE1a is activated (e.g., in obesity), its RNase domain initiates the unconventional splicing of the transcription factor X-box binding protein 1 (XBP1) mRNA and the Regulated IRE1-Dependent Decay (RIDD) of microRNAs, which is detrimental if prolonged. IRE1a RNase inhibitors including STF engage the RNase-active site of IRE1a with high affinity and specificity by exploiting a shallow complementary pocket through pi-stacking interactions with His910 and Phe889 and an essential Schiff base interaction between the aldehyde moiety of the inhibitor and the side chain amino group of Lys907 (Sanches et al., NComm 2014, PMID: 25164867). This specific and high affinity binding blocks the IRE1a RNase activity, preventing the splicing of XBP1 mRNA and RIDD. As IRE1a has been shown to be activated in multiple tissues under various pathological conditions and to be responsible for the progression of the pathological conditions, inhibition of IRE1a by pharmacological agents including STF has the great potential for the treatment of various pathological disorders. Several studies have reported that STF shows no overt toxicity when administered systemically (Madhavan, Aparajita, et al.2022, PMID 35105890; Herlea-Pana et al., 2021, PMID 34675883; Papandreou et al., 2011, PMID 21081713; Tufanli et al., 2017, PMID 28137856).

      (3) Lines 263-266 require a reference.

      We thank the reviewer’s suggestion. A reference has been added.

      (4) Stromal vascular fraction (SVF) also contains a significant amount of preadipocytes and stem cells, not only macrophages, which might affect the conclusions reached by the authors.

      We thank the reviewer’s comments. It is true that SVF consists of multiple cell types, including endothelial cells, macrophages, preadipocytes, and various stem cell populations. In HFD-induced obesity, adipose tissue undergoes significant remodeling, and the percentage of macrophages in the SVF of obese adipose tissue increases significantly relative to other cell types. In our studies, SVFs from adipose tissues of obese mice were isolated, cultured, and treated with STF for overnight.  We observed that IRE1 RNase activity in SVFs was inhibited by STF treatment, and that ATM population and the expression of pro-inflammatory genes were downregulated by STF. Given the short-term treatment, the parsimonious interpretation of the data would be that STF directly acts on ATMs.  However, we note that the possibility that the effect of STF on other cell types might influence the ATM and inflammatory gene expression can’t be totally ruled out. As such, we have modified our conclusion from “these results indicate that STF acts directly on ATMs to regulate inflammation” to “these results indicate that STF likely acts directly on ATMs to regulate inflammation”.

      (5) Figures 1A and G: It is common practice to present the XBP1s/XBP1u ratio; consider using this standard measure.

      We thank the reviewer’s comments. Regarding the XBP1 mRNA splicing, we see both ways of presentation in publications. There are quite a number of papers, for instance, PMID25018104, 2014, Cell; PMID23086298, 2012, NCB, that used the XBP1s/ (XBP1s+XBP1u) ratio. We preferred this way of presentation as it shows the ratio of spliced XBP1 (XBP1s) relative to the total XBP1 mRNA (XBP1s+XBP1u).

      (6) Figure 1F: please indicate the type of AKT phosphorylation assessed.

      We thank the reviewer’s comments. We have added Ser473 as the phosphorylation site at in both figure legend and figure.

      (7) Figures 2E-H: please clearly indicate the specific fat depots analyzed in each figure.

      We thank the reviewer’s comments. We have added the information in the figure legends and figures.

      (8) Figures 1I and 3A, and Supplementary Figures 6D-E: please include a quantification analysis of the images presented.

      We thank the reviewer’s suggestion. We have added the quantifications of the images.

      (9) In Figure 3D the image corresponding to the merge for the STF condition is a duplication of the control, please correct this.

      We thank the reviewer for pointing this out. We have replaced it with the correct image.

      (10) Figures 4B-F: please provide individual data points in the graphs to show variability and sample distribution.

      We thank the reviewer’s suggestion. We have re-plotted the graphs in Fig. 4B-F with the individual data points.

      (11) Figure 4I: it is rather unusual to have such a strong signal of UCP1 in ND conditions, please explain.

      We thank the reviewer for the comment. We wish to point out that the images were taken from BAT slides. UCP1 is expected to show strong staining in BAT under DN condition, which as expected is weakened under HFD condition. STF treatment was able to correct the HFD-induced weakening of UCP1 staining in BAT.

      (12) Supplementary Figures 2C-D: please provide representative images for better clarity and interpretation.

      We thank the reviewer for the comment. The representative images for Supplementary Figures 2C-D were actually shown in Figures 2C and F. Supplementary Figures 2C-D were the mere quantification for adipocyte areas for Figures 2C and F.

      (13) Supplementary Table 3 is repeated, please remove.

      We thank the reviewer for the comment. We have deleted this repetition.

      Reviewer #2 (Recommendations for the authors):

      The manuscript can be further strengthened with more clarification on the following points.

      (1) The use of IRE1a pharmacological inhibitor STF-083010 (STF) needs to be validated. How was the dose determined? Were there any dose-dependent studies? Under the current dosing regimen, what are the specificity, selectivity, and toxicity of STF? Also, were the serine/threonine kinase and RNase activities measured in the adipocytes and ATMs of the animals dosed with the compound? What's the PK data?

      We thank the reviewer for the comments. In the animal study, we used STF 10 mg/kg for intraperitoneal injection. This dose was adopted from several recent studies (Madhavan, Aparajita, et al.2022, PMID 35105890; Herlea-Pana et al., 2021, PMID 34675883; Papandreou et al., 2011, PMID 21081713; Tufanli et al., 2017, PMID 28137856), in which STF treatment showed beneficial effect in their respective disease models. STF didn’t compromise cell viability or induce any other toxicity at the dose or concentration used in these studies (Papandreou I, et al., 2011; Upton JP, et al., 2012; Lerner AG, et al., 2012; Kemp KL, et al., 2013; Cross BC, et al., 2012). In our study, we didn’t observe any apparent toxicity on mice at this dose. Importantly, we did observe that STF inhibited IRE1 RNase activity in adipose tissues (F1G, S1D) and ATMs (F6Q, S8C, G, I) of the animals at this dose. As the IRE1 inhibitors including STF has been extensively examined and shown to have no effect on the kinase function of IRE1 (Cross et al., 2012, PMID: 22315414; Tufanli et al., 2017, PMID 28137856), we didn’t perform the assay on Ire1 kinase activity. Additionally, as the chemical has been administered into several animal models, with significant beneficial effects, one would assume decent pharmacokinetic parameters being achieved with the current dose. It would be important and necessary to have systematic PK studies in the future if clinical trials are to be considered.

      (2) The statistical method for individual panels in each figure needs to be specified.

      We thank the reviewer for the suggestion. We have specified the statistical method in the figure legends.

      (3) In Figure 1E, there's no difference in fasting insulin levels, though a difference was detected after the glucose load. This suggests an effect on insulin secretion but not insulin sensitivity.

      We thank the reviewer for the comments. The insulin levels are still different between Veh and STF groups at fasting, just not reaching statistically significant. Under glucose stimulation, the insulin levels all showed the same trend, which is, the STF group is lower than the Veh group. Even if the fasting insulin levels showed no difference between the two groups regardless of glucose stimulation, the fact that the blood glucose levels at all the time points are lower in STF group than Veh group (Fig. 1C) indicates that insulin sensitivity is improved. In our study, the insulin levels were lower in STF group, but the blood glucose levels were still lowered by STF, further strengthening the notion that STF treatment improves insulin sensitivity. This is indeed further corroborated by the ITT results (Fig. 1D).

      (4) Figure 2 and S2A did not show a decrease in BW but rather BW gain. The statement (line 308) needs to be edited. As a result of this, the relative fat mass measurement (% of BW) needs to be presented in addition to Figure 2B.

      We thank the reviewer for the comments/suggestions. As shown in Figs. 2A and S2A, we observed a slight decrease in body weight (~2g reduction) in STF-treated mice while Veh group increased body weight by ~3.5g, at the end of 4 weeks of treatment. As shown in Fig. 2B, this difference in body weight between Veh and STF groups was primarily due to a reduction in fat tissue. In the revision, we also added the percentages of fat and lean masses over total body weight in Supplemental Fig. 2B, which show the similar trend.

      (5) The measurement of blood lipid levels in Figure 3F-H is informative. More importantly, hepatic lipid content needs to be measured.

      We thank the reviewer for and agree on the comments. As this study is more focused on the insulin resistance and adipose tissue remodeling, we didn’t go deep into the comorbidities beyond the reported observations. It will be interesting to explore the effects of IRE1 inhibition on the obesity/insulin resistance comorbidities including hepatic lipid content measurement in future study.

      Minor corrections:

      (1) Line 261: "(spliced".

      Done. We have corrected it.

      (2) Line 334: spell out "PEPCK".

      We have added the full name “Phosphoenolpyruvate carboxykinase”. Thanks!

      (3) Line 478: please rephrase.

      We thank the reviewer for the comment. We have rephrased the sentence as following: “These results reveal that STF treatment suppresses the adipose tissue inflammation and the accumulation of pro-inflammatory ATM with augmenting (suppressing instead) M2-like ATMs.”

      (4) Figure 4L: "pGC1-a".

      We thank the reviewer for pointing this out. We have corrected the name.

      (5) Figure 4O: missing Y-axis label.

      We have added the label. Thanks!

      Reviewer #3 (Recommendations for the authors):

      The observations presented by Wu D. et al. in the manuscript are potentially interesting and relevant. The current study seeks to build upon previous findings, specifically from the work titled, "Silencing IRE1α using myeloid-specific cre suppresses alternative activation of macrophages and impairs energy expenditure in obesity." By using a pharmacological inhibitor to modulate IRE1α activity in adipose tissue macrophages (ATMs), the authors aim to develop therapeutics that could significantly impact the treatment of obesity and metabolic disease.

      The authors have performed some satisfactory experiments related to liver steatosis. However, the manuscript would benefit from a more comprehensive exploration of the mechanisms by which ATMs influence adipocyte metabolism, particularly in epididymal white adipose tissue (eWAT). In particular, the study should investigate how adiposity and lipid droplet size change in response to alterations in lipolysis and adipogenesis, as this could provide insights into how these processes contribute to the amelioration of the obesity phenotype.

      Several issues should be addressed to strengthen the manuscript and make the study more convincing. Below are specific comments and recommendations:

      Major:

      (1) The indirect calorimetric data should be normalized for dependent variables such as body weight, lean mass, and fat mass+ lean mass to accurately interpret the results. The results for 24-hour energy expenditure should be included in Figure 4B-F to provide a more comprehensive analysis. It is recommended to plot bar graphs with all individual data points for the energy expenditure (EE) results shown in Figure 4B-F, to offer a clearer and more detailed presentation of the data (Figure 4B-F).

      We thank the reviewer for the comments. Data analysis on the indirect calorimetric studies has evolved over the years. One common practice was/is to normalize the data by body weight. However, this approach was deemed improper some years ago (Tschop et al Nature Methods 2012, PMID: 22205519). Tschop paper also pointed out the shortcomings associated with normalization by lean mass. Instead, it concludes that “generalized linear model is the most appropriate statistical approach to accommodate discrete (genotype) and continuous (body mass) traits, rather than using a simple division by BW or lean BW”. In our study, we used CalR, an improved generalized linear model (which includes ANOVA and ANCOVA) (Mina et al Cell Metabolism 2018, PMID: 30017358) for all our energy expenditure data analysis (shown in Fig. 4A-E). In the revision, we also included data analysis normalized by BW (Fig. S2F-H’), which actually shows even wider difference between Veh and STF groups than the data shown in Fig. 4A-F. As STF decreased the fat mass and had little effect on lean mass, the difference would be more drastic for normalization with fat mass and with fat mass+ lean mass than the data shown in Fig. 4A-E and would be similar to the data shown in Fig. 4A-E for normalization with lean mass. In addition, we replotted the graphs in Fig. 4B, D, F-H with the individual data points.

      (2) At the thermoneutral point (30{degree sign}C), the study could benefit from testing the indirect calorimetric models of human energy physiology. Future studies could also explore this to evaluate the implications for drug development.

      We agree with the reviewer on the comments. In the future study, it will be very informative to investigate the effects of STF under thermoneutral conditions, which could provide more consistent data on how drugs affect metabolic processes in humans, improving translational research.

      (3) The current study missed the opportunity to investigate the effects of STF on non-adipose tissue (non-AT) resident macrophage populations, such as those in bone marrow or lymph-node macrophages. Understanding how STF modulates macrophage metabolism in these contexts would be valuable.

      We thank the reviewer for and agree on the comments. As this study is more focused on the insulin resistance and adipose tissue remodeling, we were mostly restricted to adipose tissue macrophage populations. In the future, it would be interesting to investigate the effect of STF on macrophages in other non-adipose tissues, which will provide a more comprehensive understanding of STF's effects on immune cell metabolism, which could inform its application in various therapeutic areas.

      (4) The study should explore how STF influences the expression of CD9, Trem2, (positive subpopulations), and the secretion of pro-inflammatory cytokines by macrophages, particularly in response to LPS and IFNγ activation in stromal vascular fraction (SVF) cells and bone marrow-derived macrophages (BM-Macrophages).

      We appreciate the reviewer for the comments. Under obesity, the ATM does not undergo the classical M1/M2 polarization; instead, both M1-like/pro-inflammatory macrophages and M2 macrophages increase drastically in obesity. It will be interesting to investigate the effects of STF on the newly identified CD9- and Trem2-positive macrophage subpopulations in SVF and bone marrow macrophages in response to LPS and IFNγ stimulation in the future, although these studies might not faithfully reflect the changes in adipose tissue under obesity as these stressors typically induce classical M1/M2 polarization.

      (5) Additional macrophage gating is necessary better to understand adipose tissue macrophage (ATM) inflammation. Specifically, CD11c−MHC2 low macrophages represent a newly identified inflammatory and dynamic subset in murine adipose tissue. These ATMs accumulate rapidly after ten days of a high-fat diet (HFD) and should increase further with prolonged HFD. For this study, CD11c−MHC2 low ATMs could be subdivided for flow cytometry analysis based on their MHC2 expression, distinguishing them from CD11c−MHC2 high ATMs. All macrophage subtypes categorized here can be studied for metabolic health using seahorse analysis as well.

      We appreciate the reviewer for the comments. It will be interesting to investigate the effects of STF on the newly identified CD11c−MHC2 low macrophage subpopulation in the future. Future studies certainly can include metabolic analysis with Seahorse which can corroborate the energy metabolism at the cellular level with organismal thermogenesis. 

      (6) All flow cytometry histograms - are they showing mean fluorescence intensity or cell# per population? Please specify. All flow cytometry dot plots - It would be helpful for readers to see populations plotted as bar graphs next to respective flow plots, as opposed to being shown as supplemental tables. Additionally, labeling dot plots with the parent population from which cells were gated on would also help readers understand faster what we're looking at.

      We appreciate the reviewer for the comments. In flow cytometry histograms, we used “normalized to mode”. The mode is often used to compare the distribution of fluorescence intensity between different samples. It focuses on the shape of the distribution (with a max of 100%) rather than the absolute cell counts, which helps remove variations caused by different cell numbers or sample sizes, making it easier to compare populations based on fluorescence intensity. When normalizing to the mode, the highest peak in the histogram is scaled to 100%, and all other values are scaled relative to that peak. This allows for easy comparison of multiple histograms, even if the total number of cells (or events) differs between samples.

      (7) The results appear to confuse the actual sample size and p-value. Please carefully review the statistical analyses to ensure that biological replicates are accurately represented. Additionally, include p-values alongside fold change data in the text for clarity represented.

      We appreciate the reviewer for the comments. We have rechecked the statistical analyses confirming that the biological replicates are now properly represented. The exact number of biological replicates for each experiment is now clearly specified in both the methods section and figure legends.

      (8) To further validate the findings, consider using Seahorse analysis at the cellular level in future experiments. This could confirm indirect calorimetric data and thermogenesis responses to cold stimulation.

      We appreciate the reviewer for the comments. Yes, Seahorse analysis at the cellular level will be conducted in future experiments.

      (9) Please ensure the use of person-first language, avoiding labels or adjectives that define individuals based on a condition or characteristic.

      We appreciate the reviewer for the comments. We have changed the descriptions by using person-first language.

      (10) The manuscript does not demonstrate how STF inhibition of IRE1α in ATM, specifically through CD9 and Trem2, controls diet-induced obesity. This aspect should be further elucidated.

      We appreciate the reviewer for the comment. In this study, we observed that STF inhibits IRE1α RNase activity in SVF and in sorted ATMs as well as in adipose tissue. The improvement in diet-induced obesity can be attributable to IRE1α inhibition in both adipocytes and macrophages as shown previously by myeloid and adipocyte-specific knockouts of IRE1α. To conclude whether the IRE1α in CD9- and/or Trem2-positive ATMs controls diet-induced obesity, genetic means would be needed to generate CD9- and/or Trem2-positive ATMs-specific deletion of IRE1α, which will be technically challenging at this moment as there is no CD9 or Trem2-specific Cre lines available.

      Minor:

      (1) Line 43-44: Update terminology to "MASLD" instead of "NAFLD."

      We thank the reviewer for pointing these out. We have changed the terminology in the revision.

      (2) Line 58-59: Add a reference for the mentioned text.

      We thank the reviewer for the comment. Added a reference in the text in the revision.

      (3) Was the antibody used to detect CD9 and Trem2 validated for FACS and other analyses?

      We thank the reviewer for the comment. In our studies, we determined CD9 and Trem2 expression through flow cytometry and immunostaining staining. In flow experiment, CD9 and Trem2 were acquired from Biolegend: PE/Dazzle™ 594 anti-mouse CD9 (BioLegend Cat# 124821, RRID:AB_2800601); APC-conjugated Trem2 (R&D Systems Cat# FAB17291N, RRID:AB_3646995), which were validated for FACS. For immunostaining: CD9  (Abcam Cat# ab223052, RRID:AB_2922392). and Trem2 (R&D Systems Cat# MAB17291, RRID:AB_2208679).

      (4) Studies were limited to male mice; this should be noted in the title and discussed as a limitation.

      We thank the reviewer for the comment. We have modified the wording in the revision.

      (5) Ensure all reagents are fully described with preparation details and identifiable numbers for reproducibility and/or submit the FACS protocol to any protocol archives.

      We thank the reviewer for the suggestions. Yes, we have modified the wording in the revision.

      (6) Provide the correct version numbers for all software used (FlowJo, Prism, etc.).

      We thank the reviewer for the suggestions. We have provided the correct version numbers for softwares for FlowJo and Prism.

      (7) Specify section size (µm) and blocking agent used for eWAT immunofluorescence (Line 207).

      We thank the reviewer for the suggestions. We have added this information.

      (8) Add gene accession numbers to Supplementary Table 3.

      We thank the reviewer for the suggestions. We have added this information.

      (9) Figure 2: Clarify HFD and treatment timelines with a schematic diagram.

      We thank the reviewer for the suggestions. We have added a schematic diagram in Supplemental Figure 1C.

      (10) For histology analysis, the minimum combined data from triplicate images is shown in Figure 2C-2H. For Figures 2E and H, provide complete methods for histology analysis.

      We thank the reviewer for the comments. For the histology analysis shown in Figures 2C–2H, we used a minimum of three mice per treatment group. For each mouse, 3–5 images were taken for analysis. All histology analyses were conducted using ImageJ for image quantification, and the data were processed and organized using Excel and Graphpad.

      (11) Figure 3D Macrophage markers F4/80 stained differently in Figure 5B; to avoid false positive staining, show isotype control to confirm actual staining. For eWAT immunofluorescence (Figures 3D, 5B, 6E)., counterstaining is needed in addition to macrophages, such as for adipocytes-perilipin, and phalloidin for total cells.

      We thank the reviewer for the comments. Yes, Figures 3D macrophage marker F4/80 stained is differently from that of Figure 5B, as they are in different tissues, with Figure 3D in liver samples while Figure 5B in adipose tissues. In the liver, subsets of macrophages are known as Kupffer cells. Kupffer cells have distinct morphology and behavior compared to other tissue-resident macrophages. When stained with F4/80 in the liver, the pattern may reflect the specialized role of Kupffer cells, typically showing a more diffuse or localized staining around blood vessels and sinusoids. In adipose tissue, macrophages tend to accumulate around dead or dying adipocytes, forming what is known as "crown-like structures" (CLS). The F4/80 staining in adipose tissue shows a more clustered pattern, particularly around areas of fat tissue undergoing remodeling or inflammation. In adipose tissue, you can still see clear, defined cells even without counterstaining like perilipin, and importantly, adipocytes are generally way larger than macrophages in size. Yes, we agree that if with counterstaining it would enhance the accuracy. In the future study, we will use perilipin staining to make it easier to differentiate adipocytes from other structures and provide stronger data.

      (12) Insert scale bars in the original images for Figures 3D, 4I, 4M, 5B, 6E, S3B, S6D-E, and S7A-B. All images added a scale bar not inserted while acquiring the image or using imaging software.

      We thank the reviewer for the suggestions. The resolution for the scale bars in the images obtained during acquisition, somehow, isn’t sufficient enough to be clearly visible and requires the enlargement of the images to be seen clearly. In the revision, we have manually added the scale bars for clarity.

      (13) Figure 5E: Please label X-axis as F4/80.

      We thank the reviewer for pointing this out. The label has been added in the revision.

      (14) Figure 5F: It is specified in the legend that cells were gated on F4/80+CD11b+CD11c+, but there is a CD11c- population shown in the histogram...How is this population appearing if all cells should be CD11c+?

      We thank the reviewer for pointing this out. We gated against CD11c in F4/80+CD11b+ population. As such, we have corrected the description in the legend.

      (15) Figure 5G: What is the F4/80+CD11b+CD11c-CD206- population gated in quadrants?

      We thank the reviewer for the comment. The F4/80+CD11b+CD11c-CD206- population was shown in Figure 5G on the lower left side, with the percentages being 15.7% for ND, 5.54% for Veh-HFD, and 26% for STF-HFD.

      (16) Figure 6J: Flow cytometry gates seem slightly misplaced and the sample appears to be overcompensated - were FMOs included in this experiment to establish proper gates? If so, please include.

      We thank the reviewer for the comment. In the study, we did include Fluorescence Minus One (FMO) control in the experiment to establish proper gating. We have included this information in the methods section.

      (17) Table 1-3: Indicate the number of replicates (n=) used in all tables.

      We thank the reviewer for the suggestion. We have provided the specific number of mice used in the study within the figure legends.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1:

      The analysis of the dormancy rates is interesting and offers some intriguing questions related to the higher dormancy rate found for the L2 isolates and lower for the L3 ones. It will be interesting in the future to expand the data generated in this advanced in vitro plaAorm to in vivo studies.

      Indeed, an increased dormancy propensity of L2 isolates was previously reported in broth culture and associated to specific genetic polymorphisms. The opposite phenotype observed in the L3 isolates is indeed particularly intriguing and was not described to date. Hence, we fully agree that it would be very interesting to find out whether these phenotypes are also observed in vivo.

      The authors propose that ‘strains exhibiting greater proliferative capacity are more prone to induce macrophage apoptosis, thereby contributing to the extent of the granulomatous response.’ It would be interesting to know what happens if the macrophage apoptotic response is blocked.

      This is an interesting suggestion that would deserve a dedicated comprehensive investigation covering other cell death pathways. Even though the trend is significant, the correlation coefficient is rather low in this interaction, which looks a fortiori due to substantial inter-host variability in the apoptotic propensity of macrophages from individual donors to a given strain. In addition, such blocking experiments may require performing isolated macrophage infections that would fall outside of the scope of this study, or considering the extent and the contribution of the apoptosis of other cell subsets. 

      In contrast to macrophage apoptosis, T cell activation correlated with less replicative bacteria. Are these two findings related, ie, are the granulomas showing more (apoptotic) macrophages the ones with a lower percentage of activated T cells? This would shed light on what distinguishes granulomas that are protective from those that support bacterial growth. 

      Indeed, a significant negative correlation between macrophage apoptosis induction and T cell activation can be observed, specifically with activated CD4 T cells expressing CD38 (rS \= -0.36, p < 0.05) or CD69 (rS = -0.40, p < 0.01). We have added this additional result in the manuscript text (line 217).

      It would also be interesting to know the functional impact of blocking early CXCL9 or IL1b on the outcome of granulomatous response/bacteria growth.

      We have performed the suggested early blocking experiments and added the expected negative effect on granuloma formation upon neutralization of IL-1b (current Fig. 6E) in the revised version of the manuscript, and furthermore discussed the null effect on bacterial growth of the treatment with an anti-CXCL-9 specific antibody (current Fig. 6H).

      The authors acknowledge the absence of neutrophils in this model. However, this could be discussed in more detail, as neutrophils play an important part in TB pathogenesis as shown in different models of infection and human TB. 

      We concur and have expanded the importance of neutrophils in TB pathogenesis (including references) in the discussion section (line 260). 

      Related to neutrophils and TB pathogenesis, another important player is type I IFN. The multiplex assay used included IFN-alpha, was this molecule detected? If so, was there any difference in the levels of type I IFN detected among the different infections?

      We agree and that is why we had originally included IFN-α in our screen. However, this cytokine remained under the limit of quantification at both studied time points, preventing us to draw conclusions on the effect of Mtb strain diversity on the secretion of type I IFNs in in vitro granulomas.

      Reviewer #2:

      In Figure 1b/c, it is not clear what comparisons are being made to give the p-value annotations.

      In Figure 2a/b, it is not clear what comparisons are being made to give the p-value annotations.

      In Figure 3a, again it is not clear what comparisons are being made to give the p-value annotation.

      The p-values formerly present on the upper le] corner of the panels were resulting from either Friedman (Figures 1C, 2A and 3A) or Kruskal-Wallis (Figures 1B and 2B) tests and indicated whether there was a significant difference between the analyzed groups overall. To avoid confusion, those values have been removed to only leave the post-test comparison between specific groups.  

      In the results narrative related to Figure 1 (lines 93-103), the authors refer to lineage heterogeneity without providing any objective quantification of this - I suggest they do so, by providing variance or standard deviations. 

      Thank you very much for this relevant suggestion, we have now included the coefficients of variation as a quantitative measure of the within-lineage heterogeneity in the manuscript (line 97). 

      I also suggest the authors explain what the data points actually represent in this figure - do I assume each data point = cfu from a well of 'granuloma'? Are they all from the same donor PBMC? What is the sample N for each lineage? If the data are not from the same donor PBMC, I think more informative to present the results of paired statistical analyses, stratified by donor cells. In addition, the authors should include a summary table of the demographic characteristics of the donors (at least sex, ethnicity, and age). If the data are derived from a single donor, I'd advocate providing data from at least one further donor.

      In the new supplementary figure requested by Reviewer 3 Figure 1—figure supplement 1 (actual CFU data on days 1 and 8 p.i. used to calculate the growth rate) it is now indicated that bacterial load was quantified as CFU per well.

      Regarding the number of donors used, as stated in the Material and Methods section (current line 418) and depicted by the four different shapes used when data are grouped by individual infecting strain, all figures in our manuscript have been generated using PBMCs from 4 independent donors. For greater clarity, “n = 4” has now been included in the figure legends. Regarding the statistical analyses, paired statistical analyses stratified by donor were already performed in the original version of the manuscript whenever appropriate. 

      As stated in the methods section, the buffy coats used for PBMC isolation are anonymized so demographic data are unavailable.

      The premise of the analysis in Figure tic and the results narrative ("This finding suggests that an increased ability to enter dormancy is not necessarily associated with a more pronounced growth phenotype", line 132) is not clear to me. Why would increased dormancy relate to increased growth in the same context? I suggest this analysis be removed.

      We apologize for the confusion in our original statement. We now rephrased it as “This finding suggests that an increased tendency to remain in a metabolically active state is not necessarily associated with a more pronounced growth phenotype”.

      In Figure 3b, I think it may be more informative if the data points from the same donor were linked. Likewise in Figure 3c, I'd like to see a donor-paired statistical analysis.

      For all figures, the choice of using individual symbols to identify data points from the same donor but not connecting lines was made to provide a neater image. Nevertheless, we have now modified the figure linking the data points from the same donor. The statistical analysis performed is always donor-paired whenever appropriate. 

      The casual inference suggested in the results narrative between ‘macrophage apoptosis’ and granulomatous response line 173-175) is not tested directly by the experiment – I suggest the authors exclude this statement.

      Fair point, the statement has been removed.

      To what extent have the authors considered whether variation in T cell responses between lineages may be confounded by variation in Mtb reactive T cell frequencies in donor PBMC. Can this be disentangled at all? This should be acknowledged as a potential limitation of the study.

      We did characterize the presence of mycobacterial antigen-specific reactive T cells in the PBMCs from the investigated donors. To do so, we performed in vitro stimulations with purified protein derivative (PPD) or an ESAT-6/CFP-10 peptide pool and quantified the frequency of IFN-γ-positive CD4 T cells by flow cytometry. The percentage of IFN-γg-positive CD4 T cells recalled by PPD stimulation ranged from 0.02% to 0.13%, while no ESAT6/CFP-10 reactive T cells were detected. As such, we can akest that the PBMC donors never encountered Mtb even though some levels of memory recalled by PPD may be due to cross-reactivity with BCG or pre-exposure to non-tuberculous mycobacteria. We have now added a panel in Figure 5—figure supplement 2 representing the frequency of mycobacteria-specific CD4 T cells and, as suggested, discussed the impact on the extent of the T cell responses observed in granulomas in the revised version of the manuscript.  Nevertheless, the observed MTBC strain-specific trends are consistent across the donors, as depicted in Figure 5B and Figure 5—figure supplement 2A-B.

      Moreover, the experimental design does not really test cause and effect for the relationship between T cell proliferation/activation and bacterial growth. What is the impact of T-cell depletion from PBMC on bacterial growth?

      The increased TB susceptibility of HIV patients demonstrated that T cells play a critical part in the control of Mtb infection. We agree and did envisage such a depletion experiment. However, depleting T cells from PBMCs would imply removing up to 70% of the cells present in the specimen, which would lead to a situation from which results cannot be compared to the original sample and therefore would not be interpretable. 

      Reviewer #3:

      Data presentation:

      - In Figure 1 (replication rate), actual cumulative CFU means from each strain for both days 1 and 8 with statistical analysis should be presented as panels in this figure.

      Agreed. We are providing the requested representation of the data and the corresponding paired statistical analysis as supplementary material Figure 1—figure supplement 1.

      - In Figure 2 (dormancy), a panel comparing the mean number of bacteria that are single positive for either Auramine-O, Nile Red, or are double positive should be included for each strain, with statistical analysis. Representative photomicrographs of phenotypes from the staining should also be included. Electron microscopy could be conducted to compare the presence of intermediate lipid inclusions within organoidbound mycobacteria.

      As requested, percentages of single stained as well as double positive bacilli in each sample are now represented in Figure 2—figure supplement 1. In addition, we have now also followed the request and included a photomicrograph picturing representative Mtb staining phenotypes. Lastly, it would certainly be very elegant to visualize the presence of Mtb lipid inclusions within cellular aggregates by electron microscopy. However, we do not currently have the means for such investigations and the implementation of such a protocol under BSL3 conditions appears unrealistic in the context of this study.  

      - In Figure 3 (granulomatous response), the number, circularity, and size of immune aggregates are presented as "granuloma score" in which the mean ratio of size to circularity is divided by the number of inclusions. To their credit, in Supplementary Figure 2, the authors provide the data in a straighAorward manner. However, the granuloma score metric is reduced as the number of observed "granulomas" increases, which is counterintuitive. Additionally, circularity is not a definitive aspect of human granulomas (Wells et al., Am J Respir Crit Care Med, 2021, PMID: 34015247). I am skeptical that the "granuloma score" is an accurate predictor granulomatous inflammation. Is there precedent for this metric in the literature? If so, a reference should be provided. A high magnification inset of 1 representative granuloma from each strain should be included in Figure 3A.

      As requested, insets of a representative average granuloma for each strain have been included in Figure 3A. The formulation of the “granuloma score” has no precedent and cannot be referenced. By doing so, we meant to integrate within one single parameter the visual differences represented in the current Figure 3— figure supplement 2. We intentionally sought to assign the highest score to the massive aggregation that some strains may promote unlike some that trigger several small, dispersed and diffused aggregates.

      - In Figure 4 (macrophage apoptosis), a panel showing the percentage of dual Annexin V and 7-AAD positive cells should be included to provide the reader with the relative scope of ongoing apoptotic vs necrotic/secondary necrotic death in the model. If the data is readily available, including a control of uninfected PBMCs would also allow the reader to evaluate donor-dependent differences of in vitro cell death at baseline.

      No significant differences were observed in the percentage of dual Annexin V- and 7-AAD-positive macrophages (necrosis/secondary necrosis) between the MTBC strains at this time-point. Nevertheless, we have disclosed this result in the revised manuscript as Figure 4—figure supplement 2.

      - In Figures 5 and 6 (lymphocyte activation and soluble mediator secretion), panels showing unscaled data should be included. Panels depicting the unscaled immunoassay protein readings (pg/mL) by strain for CXCL9, granzyme B, and TNF with statistical analysis should be included in Figure 6.

      As requested, unscaled lymphocyte activation and soluble mediator data have been included as Figure 5— figure supplement 2 and Figure 6—figure supplement 1, respectively (replacing former supplementary figures 5 and 7). In addition, updated Figure 6G panel now depicts correlation analysis with the unscaled cytokine concentrations.

      The DosR-regulon:

      The authors hypothesize that differences in the prevalence of the dormancy metrics (acid-fastness or lipid inclusion prevalence, are due to strain-specific increases in expression of the DosR regulon within the model's hypoxic conditions (lines 107-114, 126-127). The claim that their model is equipped to evaluate dosR-dependent mycobacterial phenotypes was also previously proposed (Arbués et el., 2021) and should be tested. A comparison of the dosR-dependent gene expression of each strain in PBMC aggregates and broth culture by qRT-PCR would test this idea at a very basic level.

      We agree. Actually, a similar request was made during the revision of our first in vitro granuloma study for which such qPCR data were generated and presented in Fig. 1 D (PMID: 32069329). In addition, the work of Kapoor et al., who originally developed the in vitro granuloma model also demonstrated the induction of most of the DosR regulated genes by qPCR (PMID: 23308269). We trust that the reviewer will agree that this does not need to be repeated.

      The modern Beijing lineage strain L2C:

      The authors claim (Line 101-102) that the results of Figure 1 "confirm the higher virulence propensities of strains from modern lineages". From the data presented, it appears that strain L2C (Modern-Beijing) dominates the modern vs ancestral and inter/intra-lineage phenotypes of replication, dormancy, and apoptosis. Are significant differences between modern and ancestral lineages or between strains simply a facet of the distinct profile of L2C? Do the statistical differences disappear when the L2C group is excluded?

      Indeed, among the modern lineages’ isolates, L2C exhibits a hypervirulent profile in terms of bacterial replication. However, the difference between modern and ancestral strains remains statistically significant when L2C is excluded from the analysis (p = 0.002). That is also the case when we analyze the proportion of dormant bacteria. Exclusion of L2C strain results in a Kruskal-Wallis overall p = 0.005, and p = 0.0002 when we compare L2 vs. L3. Lastly, regarding the percentage of apoptotic macrophages, if we use L2B (instead of L2C) to compare, the difference is still significant vs. L1A (p = 0.008) although there is no longer a trend for L2A (p = 0.1).

      "Dormancy":

      Dormancy is definitively a non-replicative state, where bacterial growth is absent. The authors' findings and claims appear to be incompatible with that definition, which they acknowledge (Lines 130-135). The lack of correlation between growth and dormancy in their model is supported with reference to Figure 2C, a Spearman's analysis of dormancy ratio with growth rate (inclusive of all strains under consideration). The figure supports a model where "dormancy" and "growth rate" are disjunct but also appears to show high "dormancy" accompanying increasing "growth" in the L2C group. How are strains able to grow if they are in a non-replicative state? Are the "growth rate" assays actually measures of survival? Are there different rates of infectivity? Are the bacteria growing cellularly in the serum-rich ECM, etc. etc? We need to see the hard CFU and Nile Red, and Auramine-O data to contextualize these findings. Alternatively, could the accumulation of inclusions in the model not be a reliable dormancy metric (Fines et al., BioRxiv [Preprint], 2023, PMID: 37609245)?

      We fully agree. The Nile red profiles are always relative and only depict the proportion of the population that has entered a dormant state. Nevertheless, dormancy can be dynamic and bacteria may swi]ly resuscitate in that model. Furthermore, and as depicted in Figure 2—figure supplement 1, despite showing an increased tendency to enter a dormant-like state, a considerable population of lineage 2 bacilli still remains metabolically active and in a replicative state. The referred preprint is very interesting and we will follow it up closely.

      Specificity of responses to PBMC aggregation:

      The authors claim that their results "reveal a broad spectrum of granulomatous responses" (Line 73) but do not show any aggregation specificity of PBMC responses beyond the model's intrinsic metrics of area and circularity. To establish that their phenotypes such as lymphocyte activation, cytokine release, cell death, or mycobacterial acid-fastness/lipid inclusion prevalence, are aspects of the granulomatous response the authors could infect PBMCs from the same donors with the same strains and perform the same assays using established Mtb-PBMC models in which the cells do not aggregate. This would answer many important questions, for example, does the rate of macrophage infection account for variability in apoptosis percentage? Phagocytosis assay and quantification of stained intracellular mycobacteria within recently infected PBMCs could be conducted to determine if phenotypes are an aspect of granulomatous aggregation or due to strain-specific differences in cellintrinsic macrophage immunity. It would also be very informative to know what percentage of PBMCs and mycobacteria are granuloma-bound in the ECM.

      We are not aware of Mtb-PBMC models in which the cells do not aggregate. We previously compared PBMC infection models in the presence or absence of the collagen matrix and cells also spontaneously coalesced around infection foci (PMID: 34603299). Regarding the last point, the melting step of the collagen matrix requires enzymatic digestion and pipetting that dislocate the aggregates. Accordingly, we cannot distinguish the bacteria that would remain within the matrix compared to those replicating within cellular aggregates. However, we did resolve this question by demonstrating that the bacteria were not able to grow in the absence of cells in this culture condition (Supplementary material, PMID: 34603299)

      Minor recommendations

      - The term TNF-a should be replaced with TNF throughout the manuscript.

      We acknowledge that the term TNF-a can be interchangeable with TNF. However, we chose to use the TNFα terminology to differentiate it from lymphotoxin α, which is also referred to as TNF-β.

      - The authors cite studies conducted in murine and NHP models to support the claim that "understanding of immune protective traits in TB remains insufficient and yet dominated by data from mouse and non-human primate studies" (Lines 63-64) but ignore an abundance of data from other in vivo and in vitro models that have provided numerous valuable insights in the field of TB immunology. This line should be revised or omired.

      For us, the term “dominate” implies that these models are widely used, not that they are the only ones. Other models indeed provided additional relevant data. We are citing the lung-on-chip model of McKinney’lab and the in vitro granuloma model of Elkigton’s lab (line 66). We would be very happy to include more references upon further specifications even though we cannot build an extensive review here.

      - The authors claim that their model "encompasses, with the exception of neutrophils, all immune cell types involved in TB" (Lines 67-68). To support this claim, they should provide additional references or data demonstrating that the PBMC aggregates include, eosinophils, mast cells, dendritic cells, yolk-sac-derived alveolar macrophages, and Langhan's giant cells.

      With the aim of providing a more accurate and detailed information regarding the cell types present in the model, the sentence has been reformulated as: “The model encompasses all PBMC-derived cell types involved in TB immune responses, but lacks granulocytes (i.e. neutrophils, eosinophils, basophils and mast cells)” (line 260). Noteworthy, the presence of multinucleated giant cells was reported in Kapoor’s paper describing the in vitro granuloma model for the first time (PMID: 23308269).

      -  As an additional note, the title can be improved and made more broadly accessible by revising the use of the acronyms CXCL9, granzyme B, and TNF-α.

      To render the title more broadly accessible we propose to replace the listed acronyms by “soluble immune mediators”, but we remain opened to more appropriate and specific suggestions.

      Answers to the reviewers’ public comments

      Reviewer #1:

      First of all, we would like to thank the reviewers for their feedback and suggestions to improve our manuscript. To strengthen the findings of our study, we have performed and added results from IL-1b and CXCL9 blocking experiments evaluating the impact on the granulomatous response and bacterial load, respectively. In the revised version of the manuscript, while we discuss the null effect on bacterial growth of the treatment with an anti-CXCL-9 antibody and the potential reason behind it, we are now reporting a negative effect on the magnitude of granuloma formation upon neutralization of IL-1b that the correlation analysis had initially suggested.

      Reviewer #2:

      The revised version of our manuscript incorporates now all the points detailed in the private answers to the reviewer, including clarifications on the statistical tests performed, additional supplementary materials to transparently disclose the raw data behind the normalization approach, as well as flow cytometry data on the immune memory status of the blood donors. In addition, and as stated in the answer to reviewer #1, to test causal relationship between some host and pathogen traits, we have now performed and provided data and interpretation of IL-1b and CXCL9 blocking experiments.

      Reviewer #3:

      We are thankful and concur with these constructive comments and insights. We have now consistently revisited the statistics in the figures to improve clarity and included new supplementary figures reporting the raw data that were missing in the initial version of the manuscript. In addition, and as mentioned in the answers to reviewers #1 and #2, we have now performed and added IL-1β and CXCL9 blocking experiments to test causal relationship between specific host and pathogen traits. In particular, we are now reporting a negative effect on the magnitude of granuloma formation upon neutralization of IL-1β that the correlation analysis had initially suggested.

      More specifically, regarding the point that our method for bacterial collection calls into question whether all Mtb plated for CFU assay resided within granulomatous aggregates, we previously reported that Mtb growth strictly required the presence of human cells in our culture conditions (Supplementary material, Arbués et al, 2021, PMID: 34603299). In the presence of cells, our microscopy read-out does allow us to observe extra-cellular growth if infections are carried on beyond an 8-day limit, which we applied in the current study to exclude this particular caveat. 

      Concerning the apparently conflicting observation that those strains displaying an increased tendency to enter a dormant-like state are the ones exhibiting the highest replication rates, we would like to point out that a considerable population of bacilli still remains metabolically active and in a replicative state. For instance, and as depicted in Figure 2—figure supplement 1, despite showing an increased tendency to enter a dormant-like state, a considerable population of lineage 2 bacilli does remain metabolically active. Moreover, dormancy can be dynamic and bacteria may swi]ly resuscitate.

      Regarding the mentioned limitations of our study that we have discussed in the revised version of our manuscript, we fully concur that PBMC-based in vitro granuloma models lack tissue structure as well as some important stromal and immune cellular players. Nevertheless, we and others demonstrated the particular relevance of the 3-dimensional infection approach within a matrix of collagen and fibronectin by providing mechanistical insights into Mtb resuscitation previously associated to treatment with various immunomodulatory drugs (Arbués et al., 2020, PMID: 32069329; Tezera et al., 2020, PMID: 32091388).

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This manuscript describes the impact of modulating signaling by a key regulatory enzyme, Dual Leucine Zipper Kinase (DLK), on hippocampal neurons. The results are interesting and will be important for scientists interested in synapse formation, axon specification, and cell death. The methods and interpretation of the data are solid, but the study can be further strengthened with some additional studies and controls.

      We greatly appreciate the thorough review and thoughtful suggestions from the reviewers and editors on our original manuscript. We provide point-to-point response below.  We added new studies on P10 mice and controls as suggested, and made revision of figures and texts for clarification. The revised manuscript includes three new supplemental figures; major text revision is copied under response.

      Reviewer #1 (Public Review):

      Summary:

      In this work, Ritchie and colleagues explore functional consequences of neuronal over-expression or deletion of the MAP3K DLK that their labs and others have strongly implicated in both axon degeneration, neuronal cell death, and axon regeneration. Their recent work in eLife (Li, 2021) showed that inducible over-expression of DLK (or the related LZK) induces neuronal death in the cerebellum. Here, they extend this work to show that inducible over-expression in Vglut1+ neurons also kills excitatory neurons in hippocampal CA1, but not CA3. They complement this very interesting finding with translatomics to quantify genes whose mRNAs are differentially translated in the context of DLK over-expression or knockout, the latter manipulation having little to no effect on the phenotypes measured. The authors note that several genes and pathways are differentially regulated according to whether DLK is over-expressed or knocked out. They note DLK-dependent changes in genes related to synaptic function and the cytoskeleton and ultimately relate this in cultured neurons to findings that DLK over-expression negatively impacts synapse number and changes microtubules and neurites, though with a less obvious correlation.

      Strengths:

      This work represents a conceptual advance in defining DLK-dependent changes in translation. Moreover, the finding that DLK may differentially impact neuronal death will become the basis for future studies exploring whether DLK contributes to differential neuronal susceptibility to death, which is a broadly important topic.

      We thank the reviewer for the comments on the value of our work.

      Weaknesses:

      This seems like two works in parallel that the authors have not yet connected. First is that DLK affects the translation of an interesting set of genes, and second, that DLK(OE) kills some neurons, disrupts their synapses, and affects neurite growth in culture.

      Specific questions:

      (1) Is DLK effectively knocked out? The authors reference the floxxed allele in their 2016 work (PMID: 27511108), however, the methods of this paper say that the mouse will be characterized in a future publication. Has this ever been published? The major concern is that here the authors show that Cre-mediated deletion results in a smaller molecular weight protein and the maintenance of mRNA levels.

      We apologize for out-of-date citation of the DLK(cKO)<sup>fl/fl</sup> mice.  The DLK(cKO)<sup>fl/fl</sup> mice have been published in (Li et al., 2021; Saikia et al., 2022); excision of the flox-ed exon was verified using several Cre drivers (Pv-Cre, AAV-Cre, and VGlut1-Cre in this study).  The flox-ed exon contains the initiation ATG and 148 amino acids.  By western blot analysis using antibodies against C-terminal peptides of DLK on cerebellar extracts (in Li et al., 2021) and hippocampal extracts (this study), the full-length DLK protein was significantly reduced (Fig 1A-B); DLK is expressed in other hippocampal cells, in addition to glutamatergic neurons, explaining remaining full-length DLK detected. 

      Our Ribo-seq of VGlut1-Cre; DLK(cKO)<sup>fl/fl</sup> detected remaining Dlk mRNAs lacking the floxed exon (Fig.S1C), which has several candidate ATG at amino acid 223 and after (Fig.S1C1). We detected a very faint band for smaller molecular weight proteins on western blots, only when the membrane was exposed under 5X longer exposure using Pico PLUS Chemiluminescent Substrate (Thermo Scientific, 34580) and a Licor Odyssey XF Imager (revised Fig. S1B). This smaller molecular weight protein might be produced using any candidate ATGs, but would represent an N-terminal truncated DLK protein lacking the ATP binding site and ~1/4 of the kinase domain, i.e. not a functional kinase. 

      The revised manuscript has updated citation for DLK(cKO)<sup>fl/fl</sup>. Revised Fig.S1B includes images of a western blot under normal exposure vs longer exposure of western blots using anti-DLK antibodies. New Fig.S1C1 shows effects of floxed exon on DLK.

      (2) Why does DLK(OE) not kill CA3 neurons? The phenomenon is clear but there is no link to gene expression changes. In fact, the highlighted transcript in this work, Stmn4, changes in a DLK-dependent manner in CA3.

      We agree that this is a very interesting question not answered by our gene expression analysis.  While we verified Stmn4 expression levels to correlate to the levels of DLK, we do not think that increased Stmn4 per se in DLK(iOE) is a major factor accounting for CA1 death vs CA3 survival. Several published studies have also reported regulation of Stmn4 mRNAs in other cell types, in the contexts of cell death (Watkins et al., 2013; Le Pichon et al., 2017) and axon regeneration and cytoskeleton disruption (Asghari Adib et al., 2024; DeVault et al., 2024; Hu et al., 2019;  Shin et al., 2019). As Stmns have significant expression and function redundancy, conventional knockdown or overexpression of individual Stmn generally does not lead to detectable effects on cellular function. As CA3 neurons are widely known for their dense connections and show resilience to NMDA-mediated neurotoxicity (Sammons et al., 2024; Vornov et al., 1991), we speculate that the differential vulnerability of CA1 and CA3 under DLK(iOE) is a reflection of both the intrinsic property, such as gene expression, and also their circuit connection. 

      In the revised manuscript, we have included following statement on pg 18:

      ‘While our data does not pinpoint the molecular changes explaining why CA3 would show less vulnerability to increased DLK, we may speculate that DLK(iOE) induced signal transduction amplification may differ in CA1 vs CA3. CA1 genes appear to be more strongly regulated than CA3 genes, consistent with our observation that increased c-Jun expression in CA1 is greater than that in CA3. Other parallel molecular factors may also contribute to resilience of CA3 neurons to DLK(iOE), such as HSP70 chaperones, different JNK isoforms, and phosphatases, some of which showed differential expression in our RiboTag analysis of DLK(iOE) vs WT (shown in File S2. WT vs DLK(iOE) DEGs). Together with other genes that show dependency on DLK, the DLK and Jun regulatory network contributes to the regional differences in hippocampal neuronal vulnerability under pathological conditions.’

      Further we state in ‘Limitation of our study’ on pg 20:

      ‘Our analysis also does not directly address why CA3 neurons are less vulnerable to increased DLK expression. Future studies using cell-type specific RiboTag profiling and other methods at a refined time window will be required to address how DLK dependent signaling interacts with other networks underlying hippocampal regional neuron vulnerability to pathological insults.’

      We hope our data will stimulate continued interests for testable hypothesis in future studies.

      (3) Why are whole hippocampi analyzed to IP ribosome-associated mRNAs? The authors nicely show a differential effect of DLK on CA1 vs CA3, but then - at least according to their methods ¬- lyse whole hippocampi to perform IP/sequencing. Their data are therefore a mix of cells where DLK does and does not change cell death. The key issue is whether DLK does/does not have an effect based on the expression changes it drives.

      At the time of planning the Ribo-Tag experiment several years ago, we focused on the hippocampal glutamatergic neurons. Due to technical difficulty in micro-dissecting individual hippocampal regions from this early timepoint, we opted to use whole hippocampi to isolate ribosome-associated mRNAs. We agree with the reviewer that it is important to sort out DLK-dependent general gene expression changes vs those specific to a particular cell type where DLK impacts its survival. With emerging CA1, CA3 and other cell-type specific Cre drivers and advanced RNAseq technology, we hope that our work will stimulate broad interest in these questions in future studies. 

      In the revised manuscript, we have included new analysis comparing our Vglut1-RiboTag profiling (P15) with CamK2-RiboTag (for CA1) and Grik4-RiboTag (for CA3) (P42) published in Traunmüller et al., 2023 (GSE209870). We find that >80% of the top ranked genes in their CamK2-RiboTag (for CA1) and Girk4-RiboTag (for CA3) were detected in our VGlut1-RiboTag (revised methods and Supplemental Excel File S3). CA1-enriched genes tended to be expressed higher in DLK(cKO), compared to control, whereas CA3-enriched genes showed less significant correlation to DLK expression levels. Additionally, many genes known to specify CA1 fate do not show significant downregulation in DLK(iOE). This analysis, along with other data in our manuscript, is consistent with an idea that DLK does not regulate neuronal fate.

      In the revised manuscript, we presented this additional analysis in Fig. S6K-L, and expanded text description on page 9:

      ‘Additionally, we compared our Vglut1-RiboTag datasets with CamK2-RiboTag and Grik4-RiboTag datasets from 6-week-old wild type mice reported by (Traunmüller et al., 2023; GSE209870). We defined a list of genes enriched in CamK2-expressing CA1 neurons relative to Grik4-expressing CA3 neurons (CA1 genes), and those enriched in Grik4-expressing CA3 neurons (CA3 genes) (File S3). When compared with the entire list of Vglut1-RiboTag profiling in our control and DLK(cKO), we found CA1 genes tended to be expressed more in DLK(cKO) mice, compared to control (Fig.S6K), while CA3 genes showed a slight enrichment in control though the trend was less significant, and were less clustered towards one genotype (Fig.S6L). Moreover, many CA1 genes related to cell-type specification, such as FoxP1, Satb2, Wfs1, Gpr161, Adcy8, Ndst3, Chrna5, Ldb2, Ptpru, and Ntm, did not show significant downregulation when DLK was overexpressed. These observations imply that DLK likely specifically down-regulates CA1 genes both under normal conditions and when overexpressed, with a stronger effect on CA1 genes, compared to CA3 genes. Overall, the informatic analysis suggests that decreased expression of CA1 enriched genes may contribute to CA1 neuron vulnerability to elevated DLK, although it is also possible that the observed down-regulation of these genes is a secondary effect associated with CA1 neuron degeneration’.

      (4) Is the subtle decrease in synapse number (Basson/Homer co-loc.) in the DLK (OE) simply a function of neurons (and their synapses, presumably) having died? At the P15 time point that the authors choose because cell death is minimal, there is still a ~25% reduction in CA1 thickness (Figure 2B), which is larger than the ~15% change in synapses (Figure 5H) they describe.

      We thank reviewer for the question. To address this, we have analyzed synapses in the CA1 region at P10 in DLK(iOE) mice when there was no detectable loss of neurons. At P10, we did not detect significant changes in Bassoon, Homer1, or colocalized puncta in CA1 (Fig.S11A-F). In P15 DLK(iOE) mice, Homer1 puncta were slightly smaller (Fig.5L) and showed a significant decrease in CA1 SR (Fig.5I).

      In the revised manuscript we have also redone our statistical analysis of synapses, using mice rather than ROIs (revised Fig. 5), as recommended by R3. We also analyzed synapses in CA3, and found no significant differences in P10 or P15 (Fig.S12).  We would interpret the data to mean that the effects of DLK(OE) on synapses in CA1 may represent an early step in neuronal death. We hope that future studies will shed clarity on this question.

      Reviewer #2 (Public Review):

      This manuscript describes the impact of deleting or enhancing the expression of the neuronal-specific kinase DLK in glutamatergic hippocampal neurons using clever genetic strategies, which demonstrates that DLK deletion had minimal effects while overexpression resulted in neurodegeneration in vivo. To determine the molecular mechanisms underlying this effect, ribotag mice were used to determine changes in active translation which identified Jun and STMN4 as DLK-dependent genes that may contribute to this effect. Finally, experiments in cultured neurons were conducted to better understand the in vivo effects. These experiments demonstrated that DLK overexpression resulted in morphological and synaptic abnormalities.

      Strengths:

      This study provides interesting new insights into the role of DLK in the normal function of hippocampal neurons. Specifically, the study identifies:

      (1) CA1 vs CA3 hippocampal neurons have differing sensitivity to increased DLK signaling.

      (2) DLK-dependent signaling in these neurons is similar to but distinct from the downstream factors identified in other cell types, highlighted by the identification of STMN4 as a downstream signal.

      (3) DLK overexpression in hippocampal neurons results in signaling that is similar to that induced by neuronal injury.

      The study also provides confirmatory evidence that supports previously published work through orthogonal methods, which adds additional confidence to our understanding of DLK signaling in neurons. Taken together, this is a useful addition to our understanding of DLK function.

      We thank the reviewer for careful reading and positive comments.

      Weaknesses:

      There are a few weaknesses that limit the impact of this manuscript, most of which are pointed out by the authors in the discussion. Namely:

      (1) It is difficult to distinguish whether the changes in the translatome identified by the authors are DLK-dependent transcriptional changes, DLK-dependent post-transcriptional changes or secondary gene expression changes that occur as a result of the neurodegeneration that occurs in vivo. Additional expression analysis at earlier time points could be one method to address this concern.

      We appreciate the reviewer’s comment, and have performed new analysis on c-Jun and p-c-Jun levels in CA1, CA3, and DG in P10 DLK(OE) mice. Our data suggest that in CA3 elevations in p-c-Jun and c-Jun occur separately from cell death in a DLK-dependent manner, though the high elevation of both p-c-Jun and c-Jun in CA1 correlates with cell death.

      The data is presented in revised Fig.S7A,B, and described in revised text on pg 9-10:

      ‘In control mice, glutamatergic neurons in CA1 had low but detectable c-Jun immunostaining at P10 and P15, but reduced intensity at P60; those in CA3 showed an overall low level of c-Jun immunostaining at P10, P15 and P60; and those in DG showed a low level of c-Jun immunostaining at P10 and P15, and an increased intensity at P60 (Fig.S7A,C,E). In Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice at P10 when no discernable neuron degeneration was seen in any regions of hippocampus, only CA3 neurons showed a significant increase of immunostaining intensity of c-Jun, compared to control (Fig.S7A). In P15 mice, we observed further increased immunostaining intensity of c-Jun in CA1, CA3, and DG, with the strongest increase (~4-fold) in CA1, compared to age-matched control mice (Fig.S7C). The overall increased c-Jun staining is consistent with RiboTag analysis.’

      Also, on pg.10:

      In Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice, we observed increased p-c-Jun positive nuclei in CA1 at P10, and strong increase in CA1 (~10-fold), CA3 (~6-fold), and DG (~8-fold) at P15 (Fig.S7B,D).

      (2) Related to the above, it is difficult to conclusively determine from the current data whether the changes in synaptic proteins observed in vivo are a secondary result of neuronal degeneration or a primary impact on synapse formation. The in vitro studies suggest this has the potential to be a primary effect, though the difference in experimental paradigm makes it impossible to determine whether the same mechanisms are present in vitro and in vivo.

      We appreciate the comment, which is related to R1 point 4. We have performed further analysis and revised the text on pg.12 with the following text:

      ‘To assess effects of DLK overexpression on synapses, we immunostained hippocampal sections from both P10 and P15, with age-matched littermate controls. Quantification of Bassoon and Homer1 immunostaining revealed no significant differences in CA1 SR and CA3 SR and SL in P10 mice of _<_i>Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> and control (Fig.S11A-F, S12A-J). In P15, Bassoon density and size in CA1 SR were comparable in both mice (Fig 5G, H, K), while Homer1 density and size were reduced in DLK(iOE) (Fig.5G,I, L). Overall synapse number in CA1 SR was similar in DLK(iOE) and control mice (Fig.5J). Similar analysis on CA3 SR and SL detected no significant difference from control (Fig.S12M-V).’

      We would interpret the data to mean that the effects of DLK(OE) on synapses in CA1 may represent an early step in neuronal death. We hope that future studies will shed clarity on this question.

      Additionally, to address whether the same mechanisms are present in vitro, we have performed further analysis on cultured hippocampal neurons. As described in the Methods, we made hippocampal neuron cultures from P1 pups of the following crosses:

      For control: Vglut1<sup>Cre/+</sup> X Rosa26<sup>tdT/+</sup> 

      For DLKcKO: Vglut1<sup>Cre/+</sup>;DLK(cKO)<sup>fl/fl</sup>  X Vglut1<sup>Cre/+</sup>;DLK(cKO)<sup>fl/fl</sup>;Rosa26<sup>tdT/+</sup> 

      For DLKiOE: H11-DLK<sup>iOE/iOE</sup> X Vglut1<sup>Cre/+</sup>;Rosa26<sup>tdT/+</sup> 

      Dissociated cells from a given litter were pooled into the same culture. Because there were different proportions of neurons with our genotype of interest in each culture, it is not simple to know whether DLK was causing significant cell death.

      On pg 13, we stated our observation:

      ‘We did not notice an obvious effect of DLK(iOE) or DLK(cKO) on neuron density in cultures at DIV2. To assess neuronal type distribution in our cultures, we immunostained DIV14 neurons with antibodies for Satb2, as a CA1 marker (Nielsen et al., 2010), and Prox1, as a marker of DG neurons (Iwano et al., 2012). We did not observe significant differences in the proportion of cells labeled with each marker in DLK(cKO) or DLK(iOE) cultures (Fig.S13E). These data are consistent with the idea that DLK signaling does not have a strong role in neuron-type specification both in vivo and in vitro’.

      (3) The phenotype of DLK cKO mice is very subtle (consistent with previous reports) and while the outcome of increased DLK levels is interesting, the relevance to physiological DLK signaling is less clear. What does seem possible is that increased DLK may phenocopy other neuronal injuries but there are no real comparisons to directly address this in the manuscript. It would be helpful for the authors to provide this analysis as well as a table with all of the translational changes along with fold changes.

      Thank you for the suggestion. The fold changes of genes showing significantly altered expression in DLK(cKO) and DLK(iOE) are provided in the excel files (Supplementary excel File S1 WT vs DLK(cKO) DEGs and File S2. WT vs DLK(iOE) DEGs, highlighted columns B and F).  

      On pg 6, we revised the text as following to include comparison of DLK levels in other physiological conditions and our mice:

      ‘Several studies have reported that DLK protein levels increase under a variety of conditions, including optic nerve crush (Watkins et al., 2013), NGF withdrawal (~2 fold) (Huntwork-Rodriguez et al., 2013; Larhammar et al., 2017), and sciatic nerve injury (Larhammar et al., 2017). Induced human neurons show increased DLK abundance about ~4 fold in response to ApoE4 treatment (Huang et al., 2019). Increased expression of DLK can lead to its activation through dimerization and autophosphorylation (Nihalani et al., 2000)’.

      And,

      ‘Additional analysis at the mRNA level (supplemental excel, File S2. WT vs DLK(iOE) DEGs) and at the protein level (Fig.S8E) suggest that the increase in DLK abundance was around 3 times the control level. The localization patterns of DLK protein appeared to vary depending on region of hippocampus and age of animals in both control and Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice (Fig.S3C).’

      In Discussion, we state (pg. 16): ‘The levels of DLK in our DLK(iOE) mice model appear comparable to those reported under traumatic injury and chronic stress.’

      (4) For the in vivo experiments, it is unclear whether multiple sections from each animal were quantified for each condition. More information here would be helpful and it is important that any quantification takes multiple sections from each animal into account to account for natural variability.

      We apologize this was unclear in the original manuscript.

      In the revised methods, under Confocal imaging and quantification (pg 33), we stated: “For brain tissue, three sections per mouse were imaged with a minimum of three mice per genotype for data analysis.”

      In revised figure legends, we made it clear that multiple sections from each animal have been used for quantification in all instances, i.e. “Each dot represents averaged thickness from 3 sections per mouse, N≥4 mice/genotype per timepoint.” 

      In Fig.1F-H: “Each dot represents averaged intensity from 3 sections per mouse”

      In Fig.S3B “Data points represent individual mice, averages taken across 3 sections per mouse”

      Reviewer #3 (Public Review):

      Dr Jin and colleagues revisit DLK and its established multifactorial roles in neuronal development, axonal injury, and neurodegeneration. The ambitious aim here is to understand the DLK-dependent gene network in the brain and, to pursue this, they explore the role of DLK in hippocampal glutamatergic neurons using conditional knockout and induced overexpression mice. They produce evidence that dorsal CA1 and dentate gyrus neurons are vulnerable to elevated expression of DLK, while CA3 neurons appear unaffected. Then they identify the DLK-dependent translatome featured by conserved molecular signatures and cell-type specificity. Their evidence suggests that increased DLK signaling is associated with possible STMN4 disruptions to microtubules, among else. They also produce evidence on cultured hippocampal neurons showing that expression levels of DLK are associated with changes in neurite outgrowth, axon specification, and synapse formation. They posit that downstream translational events related to DLK signaling in hippocampal glutamatergic neurons are a generalizable paradigm for understanding neurodegenerative diseases.

      Strengths

      This is an interesting paper based on a lot of work and a high number of diverse experiments that point to the pervasive roles of DLK in the development of select glutamatergic hippocampal neurons. One should applaud the authors for their work in constructing sophisticated molecular cre-lox tools and their expert Ribotag analysis, as well as technical skill and scholarly treatment of the literature. I am somewhat more skeptical of interpretations and conclusions on spatial anatomical selectivity without stereological approaches and also going directly from (extremely complex) Ribotag profiling patterns to relevance based on immunohistochemistry and no additional interventions to manipulate (e.g. by knocking down or blocking) their top Ribotag profile hits. Also, it seems to this reviewer that major developmental claims in the paper are based on gene translational profiling dependent on DLK expression, not DLK activation, despite some evidence in the paper that there is a correlation between the two. Therefore, observed patterns and correlations may or may not be physiologically or pathologically relevant. Generalizability to neurodegenerative diseases is an overreach not justified by the scope, approach, and findings of the paper.

      We thank the reviewer for the encouraging and constructive comments on the manuscript.

      Weaknesses and Suggestions:

      The authors state that the rationale for the translatomic studies is to "to gain molecular understanding of gene expression associated with DLK in glutamatergic neurons" and to characterize the "DLK-dependent molecular and cellular network", However, a problem with the experimental design is the selection of an anatomical region at a time point featured by active neurodegeneration. Therefore, it is not straightforward that the differentially expressed genes or pathways caused by DLK overexpression changes could be due to processes related to neurodegeneration. Indeed, the authors find enrichment of signals related to pathways involved in extracellular matrix organization, apoptosis, unfolded protein responses, the complement cascade, DNA damage responses, and depletion of signals related to mitochondrial electron transport, etc., all of which could be the consequence of neurodegeneration regardless of cause. A more appropriate design to discover DLK-dependent pathways might be to look at a region and/or a time point that is not confounded by neurodegeneration.

      We appreciate reviewer’s comment. We included our thoughts in ‘Limitation of the study’ (pg 20):

      ‘Future studies using cell-type specific RiboTag profiling and other methods at a refined time window will be required to address how DLK dependent signaling interacts with other networks underlying hippocampal regional neuron vulnerability to pathological insults.’

      In a related vein, the authors ask "if the differentially expressed genes associated with DLK(iOE) might show correlation to neuronal vulnerability" and, to answer this question, they select the set of differentially expressed genes after DLK overexpression and assess their expression patterns in various regions under normal conditions. It looks to me that this selection is already confounded by neurodegeneration which could be the cause for their downregulation. Therefore, such gene profiles may not be directly linked to neuronal vulnerability. A similar issue also relates to the conclusion that "...the enrichment of DLK-dependent translation of genes in CA1 suggests that the decreased expression of these genes may contribute to CA1 neuron vulnerability to elevated DLK".

      We agree with the reviewer’s concern that it is difficult to separate neurodegenerative consequences from changes caused by DLK solely based on our translatomics studies on P15 DLK(iOE) mice.  As responded to reviewer 1 (point 4) and reviewer 2 (point 1), we have included new analysis of P10 mice (Fig.S7A,B) when neurons did not show detectable sign of degeneration.

      We consider several lines of evidence supporting that some differentially expressed genes in DLK(iOE) vs control may likely be specific for increased DLK signaling.

      First, the genes identified in DLK(iOE) vs control represent a small set of genes (260), which is comparable to other DLK dependent datasets (Asghari Adib et al., 2024) but shows cell-type specificity.

      Second, our analysis using rank-rank hypergeometric overlap (RRHO) detects a significant correlation between upregulated genes from DLK(iOE) vs downregulated genes in DLK(cKO), and vice versa, suggesting that expression of a similar set of genes is depended on DLK (Fig.3C, S6C-E). Consistently, GO term analysis using the list of genes coordinately regulated by DLK, derived from our RRHO analysis, leads to identification of similar GO terms related to up- and downregulated genes as using DLK(iOE)-RiboTag data alone. SynGO analysis of DLK(iOE) regulated genes and DLK(cKO) regulated genes also identified similar synaptic processes regulated by significantly regulated genes (Fig.3F and S6J).  

      Third, we performed additional analysis comparing our Vglut1-RiboTag dataset with CamK2-RiboTag and Grik4-RiboTag datasets from 6-week-old wild type mice reported by (Traunmüller et al., 2023; GSE209870). We observed >80% overlap among the top ranked genes (revised Methods). We described this analysis on pg 9 and Fig. S6K-L (and Supplemental Excel File S3):

      ‘Additionally, we compared our Vglut1-RiboTag datasets with CamK2-RiboTag and Grik4-RiboTag datasets from 6-week-old wild type mice reported by (Traunmüller et al., 2023; GSE209870). We defined a list of genes enriched in CamK2-expressing CA1 neurons relative to Grik4-expressing CA3 neurons (CA1 genes), and those enriched in Grik4-expressing CA3 neurons (CA3 genes) (File S3). When compared with the entire list of Vglut1-RiboTag profiling in our control and DLK(cKO), we found CA1 genes tended to be expressed more in DLK(cKO) mice, compared to control (Fig.S6K), while CA3 genes showed a slight enrichment in control though the trend was less significant, and were less clustered towards one genotype (Fig.S6L). Moreover, many CA1 genes related to cell-type specification, such as FoxP1, Satb2, Wfs1, Gpr161, Adcy8, Ndst3, Chrna5, Ldb2, Ptpru, and Ntm, did not show significant downregulation when DLK was overexpressed. These observations imply that DLK likely specifically down-regulates CA1 genes both under normal conditions and when overexpressed, with a stronger effect on CA1 genes, compared to CA3 genes. Overall, the informatic analysis suggests that decreased expression of CA1 enriched genes may contribute to CA1 neuron vulnerability to elevated DLK, although it is also possible that the observed down-regulation of these genes is a secondary effect associated with CA1 neuron degeneration.’

      To understand the role and relevance of the DLK overexpression model, there should be a discussion of to what extent it corresponds to endogenous levels of DLK expression or DLK-MAPK pathway activation under baseline or pathological conditions.

      We appreciate the suggestion, which is similar to R2 point 3. We have revised the text and discussion to include how DLK levels may be altered in other physiological conditions vs our mice.

      Pg. 6: ‘Several studies have reported that DLK protein levels increase under a variety of conditions, including optic nerve crush (Watkins et al., 2013), NGF withdrawal (~2 fold) (Huntwork-Rodriguez et al., 2013; Larhammar et al., 2017), and sciatic nerve injury (Larhammar et al., 2017). Induced human neurons show increased DLK abundance about ~4 fold in response to ApoE4 treatment (Huang et al., 2019). Increased expression of DLK can lead to its activation through dimerization and autophosphorylation (Nihalani et al., 2000)’.

      And,

      ‘Additional analysis at the mRNA level (supplemental excel, File S2. WT vs DLK(iOE) DEGs) and at the protein level (Fig.S8E) suggest that the increase in DLK abundance was around 3 times the control level. The localization patterns of DLK protein appeared to vary depending on region of hippocampus and age of animals in both control and Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice (Fig.S3C).’

      In Discussion (pg. 16): ‘The levels of DLK in our DLK(iOE) mice model appear comparable to those reported under traumatic injury and chronic stress.’

      The authors posit that "dorsal CA1 neurons are vulnerable to elevated DLK expression, while neurons in CA3 appear largely resistant to DLK overexpression". This statement assumes that DLK expression levels start at a similar baseline among regions. Do the authors have any such data? Ideally, they should show whether DLK expression and p-c-Jun (as a marker of downstream DLK signaling) are the same or different across regions in both WT and overexpression mice. For example, what are the DLK/p-c-Jun expression levels in regions other than CA1 in Supplementary Figures 2-3 and how do they compare with each other? Normalization to baseline for each region does not allow such a comparison. Also, in Supplementary Figure 6, analyses and comparisons between regions are done at a time point when degeneration has already started. Ideally, these should be done at P10.

      We thank the reviewer for raising these points. In the revised manuscript we have included protein expression analysis of DLK (Fig S3), c-Jun, and p-c-Jun at P10 (Fig. S7).

      We provided a quantification of DLK immunostaining intensity in CA1 and CA3 in Fig.S3D,E and find roughly comparable levels between regions.

      Pg. 6: ‘Additional analysis at the mRNA level (supplemental excel, File S2. WT vs DLK(iOE) DEGs) and at the protein level (Fig.S8E) suggest that the increase in DLK abundance was around 3 times the control level. The localization patterns of DLK protein appeared to vary depending on region of hippocampus and age of animals in both control and Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice (Fig.S3C).’

      We provided our quantifications without normalization to baseline in each region for c-Jun and p-c-Jun, and revised the text accordingly:

      Pg. 9-10: ‘In control mice, glutamatergic neurons in CA1 had low but detectable c-Jun immunostaining at P10 and P15, but reduced intensity at P60; those in CA3 showed an overall low level of c-Jun immunostaining at P10, P15 and P60; and those in DG showed a low level of c-Jun immunostaining at P10 and P15, and an increased intensity at P60 (Fig.S7A,C,E). In Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice at P10 when no discernable neuron degeneration was seen in any regions of hippocampus, only CA3 neurons showed a significant increase of immunostaining intensity of c-Jun, compared to control (Fig.S7A). In P15 mice, we observed further increased immunostaining intensity of c-Jun in CA1, CA3, and DG, with the strongest increase (~4-fold) in CA1, compared to age-matched control mice (Fig.S7C). The overall increased c-Jun staining is consistent with RiboTag analysis’.

      Pg. 10: ‘In Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice, we observed increased p-c-Jun positive nuclei in CA1 at P10, and strong increase in CA1 (~10-fold), CA3 (~6-fold), and DG (~8-fold) at P15 (Fig.S7B,D).

      Illustration of proposed selective changes in hippocampal sector volume needs to be very carefully prepared in view of the substantial claims on selective vulnerability. In 2A under P15 and especially P60, it is difficult to see the difference - this needs lower magnification and a lot of care that anteroposterior levels are identical because hippocampal sector anatomy and volumes of sectors vary from level to level. One wonders if the cortex shrinks, too. This is important.

      Thank you for raising the point. We have provided images to view the anteroposterior level in Fig.S2A-C. We have noticed cortex in DLK(OE) mice to become thinner, along with expansion of ventricles in some animals at later timepoints (Fig.S2C).

      One cannot be sure that there is selective death of hippocampal sectors with DLK overexpression versus, say, rearrangement of hippocampal architecture. One may need stereological analysis, otherwise this substantial claim appears overinterpreted.

      We appreciate the comment.

      In the revised manuscript, we included a new supplemental figure (Fig. S2) showing lower magnification images of coronal sections, and used cautionary wording, such as ‘CA3 is less vulnerable, compared to CA1’, to minimize the impression of over-interpretation.  By NeuN staining, at P10, P15, P60, we did not observe detectable difference in overall hippocampus architecture, apart from noted cell death of CA1 and DG and associated thinning of each of the layers. At 46 weeks, some animals showed differences in the overall shape of dorsal hippocampus, though this appeared to reflect a disproportionately large CA3 region compared to other regions (Fig S2). Increased GFAP staining (Fig.S5A-C) was detected in CA1 but not in CA3, and microglia by IBA1 staining (Fig.S5E) also displayed less reactivity in CA3, compared to CA1. Thus, based on NeuN staining, GFAP staining, IBA1 staining and analysis of the differentially regulated genes, we infer that the effect of DLK(iOE) in CA1 is different than the effect on CA3.

      Is the GFAP excess reflective of neuroinflammation? What do microglial markers show? The presence of neuroinflammation does not bode well with apoptosis. Speaking of which, TUNEL in one cell in Supplementary Figure 4E is not strong evidence of a more widespread apoptotic event in CA1.

      We have included staining data for the microglia marker IBA1. Both GFAP and IBA1 showed evidence of reactivity particularly in the CA1 region (S5A-E), supporting the differential vulnerability in different regions, though whether cell death is primarily due to apoptosis is unclear.

      We agree that our data of sparse TUNEL staining at P15 (Fig S5F,G) do not rule out whether other mechanisms of cell death may also occur.  We have included this in our limitations (pg.20) “While we find evidence for apoptosis, other forms of cell death may also occur.”

      In several places in the paper (as illustrated in Figure 4B, Supplementary Figure 2B, etc.): the unit of biological observation in animal models is typically not a cell, but an organism, in which averaged measures are generated. This is a significant methodological problem because it is not easy to sample neurons without involving stereological methods. With the approach taken here, there is a risk that significance may be overblown.

      We appreciate the reviewer’s point. We used same region for quantification of RNAscope, genotype-blind when possible. We revised the graphs to show mean values for individual mice in Fig.4B, 4C, and Fig.S3B (previously Fig.S2B).

      Other Comments and Questions:

      Supplementary Figure 9: The authors state that data points are shown for individual ROIs - ideally, they should also show averages for biological replicates. Can the authors confirm that statistical analyses are based on biological replicates (mice) and not ROIs?

      We have revised the graphs to show averages from individual mice in Fig.5B-D, F5E-F (previously Fig.S9G-I), Fig.5H-J, and Fig.5K-L (previously Fig.S9J-L)  and Fig.S10B,C,E,F (previously Fig.S9B,C, E,F). The statistical analyses are based on biological replicates of mice.

      For in vitro experiments, what is the effect of DLK overexpression on neuronal viability and density? Could these variables confound effects on synaptogenesis/synapse maturation?

      As described in the Methods, we made hippocampal neuron cultures from P1 pups of the following crosses:

      For control: Vglut1<sup>Cre/+</sup> X Rosa26<sup>tdT/+</sup> 

      For DLKcKO: Vglut1<sup>Cre/+</sup>;DLK(cKO)<sup>fl/fl</sup>  X Vglut1<sup>Cre/+</sup>;DLK(cKO)<sup>fl/fl</sup>;Rosa26<sup>tdT/+</sup> 

      For DLKiOE: H11-DLK<sup>iOE/iOE</sup> X Vglut1<sup>Cre/+</sup>;Rosa26<sup>tdT/+</sup> 

      Dissociated cells from a given litter were pooled into the same culture. Because there were different proportions of neurons with our genotype of interest in each culture, it is not simple to know whether DLK was causing significant cell death.

      On pg 13, we stated our observation:

      ‘We did not notice an obvious effect of DLK(iOE) or DLK(cKO) on neuron density in cultures at DIV2. To assess neuronal type distribution in our cultures, we immunostained DIV14 neurons with antibodies for Satb2, as a CA1 marker (Nielsen et al., 2010), and Prox1, as a marker of DG neurons (Iwano et al., 2012). We did not observe significant differences in the proportion of cells labeled with each marker in DLK(cKO) or DLK(iOE) cultures (Fig.S13E). These data are consistent with the idea that DLK signaling does not have a strong role in neuron-type specification both in vivo and in vitro’.

      We cannot rule out whether variable factors in our cultures may confound effects on synaptogenesis/synapse maturation, and would hope future studies will shed clarity.

      Correlations between c-jun expression and phosphorylation are extremely important and need to be carefully and convincingly documented. I am a bit concerned about Supplementary Figure 6 images, especially 6B-CA1 (no difference between control and KO, too small images) and 6D (no p-c-Jun expression at all anywhere in the hippocampus at P15?).

      At P10, P15, and P60 we stained for p-c-Jun using the Rabbit monoclonal p-c-Jun (Ser73) (D47G9) antibody from Cell Signaling (cat# 3270) at a 1:200 dilution and imaged using an LSM800 confocal microscope with a 20x objective. We observed p-c-Jun to be quite low generally in control animals. We have replaced the images in Fig.S7F (previously S6D), and adjusted the brightness/contrast to enable better visualization of the low signal in Fig.S7B,D,F (previously Fig.S6B,D).

      We revised our text to present the data carefully as stated above:

      Pg. 9-10: ‘In control mice, glutamatergic neurons in CA1 had low but detectable c-Jun immunostaining at P10 and P15, but reduced intensity at P60; those in CA3 showed an overall low level of c-Jun immunostaining at P10, P15 and P60; and those in DG showed a low level of c-Jun immunostaining at P10 and P15, and an increased intensity at P60 (Fig.S7A,C,E). In Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice at P10 when no discernable neuron degeneration was seen in any regions of hippocampus, only CA3 neurons showed a significant increase of immunostaining intensity of c-Jun, compared to control (Fig.S7A). In P15 mice, we observed further increased immunostaining intensity of c-Jun in CA1, CA3, and DG, with the strongest increase (~4-fold) in CA1, compared to age-matched control mice (Fig.S7C). The overall increased c-Jun staining is consistent with RiboTag analysis’.

      Pg. 10: ‘In Vglut1<sup>Cre/+</sup>;H11-DLK<sup>iOE/+</sup> mice, we observed increased p-c-Jun positive nuclei in CA1 at P10, and strong increase in CA1 (~10-fold), CA3 (~6-fold), and DG (~8-fold) at P15 (Fig.S7B,D).

      Recommendations for the authors:

      Several major and minor reservations were raised. The major issues are the need for more information about the over-expression of DLK and a need to extrapolate to an in vivo condition with DLK. A considerable amount of useful information is presented with some very nicely done experiments but it is not yet a coherent or integrated story. The lack of impact of DLK overexpression in some neurons is perhaps the most impactful observation of the study and would be great to have more information around the differential transcriptional/signaling response in these cell types. There is also a need for more experimental details and to address several questions about the mouse genetic and translatome analysis. They are valid concerns that require attention by the authors.

      We thank the editors and reviewers for their thoughtful evaluation and suggestions.  We hope that the editors and reviewers find that the new data and text changes in our revised manuscript, along with above point-to-point response, have addressed the concerns and strengthened our findings.

      Minor points:

      (1)The authors state that deletion of DLK has no effect on CA1 at 1yr, however, the image of CA1 in Figure S1D shows substantially fewer NeuN+ neurons. Is this a representative field of view?

      We have re-examined images, and observed no effect on hippocampal morphology at 1 yr. We now included representative images in the revised Fig S1D.

      (2) Is the DLK protein section staining in Figure 2C a real signal? The staining looks like speckles and is purely somatic. Axonal staining is widely expected based on the literature and the authors' own work. There should be a specificity control.

      To our knowledge, axonal staining of DLK reported in the literature is mostly based on cultured DRG neurons. In addition to the reported axonal localization, DLK is present in the cell soma, near the golgi (Hirai et al., 2002), and in the post-synaptic density (Pozniak et al., 2013).

      In the revised manuscript, we addressed this point by including controls with no primary antibody, and using an antibody against the closely related kinase, LZK. These additional data are shown in (Fig.S3C,D) (previously Fig.S2C), supporting that DLK protein staining represents real signal.  At P10 and P15, DLK immunostaining around CA3 showed axonal staining of the mossy fibers, as well as in the soma and dendritic layers (Fig.S3C,D). A similar pattern was also seen in primary cultured neurons (Fig 6A).

      (3) The protein expression of DLK in the transgenic overexpressor (Figure S7C) looks, to the resolution of this blot, to be at least 50kD heavier than 'WT' DLK. Can the authors explain this discrepancy?

      The Cre-induced DLK(iOE) transgene has T2A and tdTomato in-frame to C-terminus of DLK. It is known that T2A ‘self-cleavage’ is often incomplete. DLK-T2A-tdTomato would be about 50 kD bigger than WT DLK. We now include the transgene design in revised Fig S1D, and also stated in figure legend of Fig.S8C (previously S7C) that ‘Larger molecular weight band of DLK in Vglut1<sup>Cre/+</sup>;H11-DLKiOE/+ would match the predicted molecular weight of DLK-T2A-tdTomato if T2A-peptide induced ‘self-cleavage’ due to ribosomal skipping is ineffective (Fig.S1D).’

      (4) Expression changes in DLK affect various aspects of neurites in CA1 cultures (Figure 6), and changes in DLK also modestly affect STMN4 (and 2, perhaps indirectly) levels (Figure S7C), but there is no indication that DLK acts via STMN4 to cause these changes. It is not clear what to make of these data. Of note, Stmn4 levels change in response to DLK in CA3, without DLK affecting cell death in this region.

      We appreciate and agree with the comment. Other studies (Asghari Adib et al., 2024; DeVault et al., 2024; Hu et al., 2019; Larhammar et al., 2017; Le Pichon et al., 2017; Shin et al., 2019; Watkins et al., 2013) reported expression changes in Stmn4 mRNAs in other cell types and cellular contexts, which appeared to depend on DLK. Hippocampal neurons express multiple Stmns (Fig.S8A). While we present our analysis on the effects of DLK dosage on Stmn4, and also Stmn2, we do not think that DLK-induced changes of Stmn4 expression per se is a major factor underlying CA1 cell death vs CA3 survival.

      In the revised manuscript, we addressed this point in ‘Limitation of our study’ (pg 20):

      ‘Additional experiments will be needed to elucidate in vivo roles of STMN4 and its interaction with other STMNs’.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review): 

      The role of enteric glial cells in regulating intestinal mucosal functions at a steady state has been a matter of debate in recent years. Enteric glial cell heterogeneity and related methodological differences likely underlie the contrasting findings obtained by different laboratories. Here, Prochera and colleagues used Plp1-CreERT2 driver mice to deplete the majority of enteric glia from the gut. They found that glial loss has very limited effects on the transcriptome of gut cells 11 days after tamoxifen treatment (used to induce DTA expression), and by extension - more specifically, has only minimal impact on cells of the intestinal mucosa. Interestingly, in the colon (where Paneth cells are not present) they did observe transcriptomic changes related to Paneth cell biology. Although no overt gene expression alterations were found in the small intestine - also not in Paneth cells - morphological, ultrastructural, and functional changes were detected in the Paneth cells of enteric glia-depleted mice. In addition, and possibly related to Paneth cell dysfunction, enteric glia-depleted mice also show alterations in intestinal microbiota composition. 

      In their analyses of enteric glia from existing single-cell transcriptomic data sets, it is stated that these come from 'non-diseased' humans. However, the data on the small intestine is obtained from children with functional gastrointestinal disorders (Zheng 2023). Data on colonic enteric glia was obtained from colorectal cancer patients (Lee 2020). Although here the cells were isolated from non-malignant regions, saying that the large intestines of these patients are nondiseased is probably an overstatement. 

      In the Zheng et al. dataset, “functional GI disorders” refers to biopsies from children that do not have any histopathologic evidence of digestive disease. The children do, however, have at least one GI symptom that prompted a diagnostic endoscopy with biopsies, leading to the designation of “functional” disorder. Given that diagnostic endoscopies are invasive procedures that necessitate anesthesia, obtaining biopsies from asymptomatic children without any clinical indication would not be allowable per most institutional review boards, leading the authors of that study to use these samples as a control group. We had thus used the “non-diseased” label to encompass these samples as well as those from the unaffected regions of large intestine from colorectal cancer patients. We now recognize, however, that this label could be misleading, so we have revised the Results and Figure Legends to more accurately reflect details of control tissue origin for this and the Lee et al. (2020) datasets. Per the reviewer’s suggestion, we have removed the term “non-diseased”.

      Another existing dataset including human mucosal enteric glia of healthy subjects is presented in Smillie et al (2019). It would be interesting to see how the current findings relate to the data from Smillie et al. 

      Per the reviewer’s suggestion, we have now added an analysis of the Smillie et al. dataset in Supp. Fig. 1B. This dataset derives from colonic mucosal biopsies from 12 healthy adults (8480 stromal cells) and 18 adults with ulcerative colitis (10,245 stromal cells from inflamed bowel segments and 13,147 from uninflamed), all between the ages of 20-77 years. These data show that SOX10, PLP1, and S100B are selectively expressed within the putative glial cluster from colonic mucosa of both healthy adults and individuals with ulcerative colitis, whereas GFAP is not detected (Supp. Fig. 1B). These observations are consistent with our observations from the two other human datasets already included in our manuscript in Fig. 1 and Supp. Fig. 1.

      The time between enteric glia depletion and analyses (mouse sacrifice) must be a crucial determinant of the type of effects, and the timing thereof. In the current study 11 days after tamoxifen treatment was chosen as the time point for analyses, which is consistent with earlier work by the lab using the same model (Rao et al 2017). What would happen when they wait longer than 11 days after tamoxifen treatment? Data, not necessarily for all parameters, on later time points would strengthen the manuscript significantly. 

      This is an excellent question, particularly given the longer-lived nature of Paneth cells relative to other epithelial cell types. As detailed in our previous study, Cre<sup>+</sup> mice in the Plp1CreER-DTA model are well-appearing and indistinguishable from their Cre-negative control littermates through 11dpt. Unfortunately, a limitation of the model is that beyond 11dpt, Cre<sup>+</sup> mice become anorexic, lose body weight, and have signs of neurologic debility such as hindlimb weakness and uncoordinated gait. These deficits are overt by 14dpt and likely due to targeting Plp1<sup>+</sup> glia outside the gut, such as Schwann cells and oligodendrocytes (as described in another study which used a similar model to study demyelination in the central nervous system, PMID: 20851998). Given these CNS effects and that starvation is well known to affect Paneth cell phenotypes (PMIDs: 1167179, 21986443), we elected not to examine timepoints beyond 11dpt. Technological advances that enable more selective cell depletion will allow study of chronic effects of enteric glial loss in the future.

      The authors found transcriptional dysregulation related to Paneth cell biology in the colon, where Paneth cells are normally not present. Given the bulk RNA sequencing approach, the cellular identity in which this shift is taking place cannot be determined. However, it would be useful if the authors could speculate on which colonic cell type they reckon this is happening in.

      Per the reviewer’s suggestion, we have added a paragraph to the Discussion addressing one plausible hypothesis to explain this observation. Paneth-like cells have been described in the large intestine and are known, particularly in humans, to express markers typical of Paneth cells, such as lysozyme and defensins (PMID: 27573849, 31753849). These cells could represent the source of the Paneth cell-like transcriptional signature observed in our model. Alternatively, ectopic expression of Paneth cell-associated genes in the colon has been documented in certain pathological conditions, such as colorectal cancer models (e.g., PMID: 15059925), where changes in the local microenvironment appear to trigger activation of Paneth cell genes. Similar, yet unidentified changes in our model could potentially underlie the transcriptional dysregulation related to Paneth cell biology observed here.

      On the other hand, enteric glia depletion was found to affect Paneth cells structurally and functionally in the small intestine, where transcriptional changes were initially not identified. Only when performing GSEA with the in silico help of cell type-specific gene profiles, differences in Paneth cell transcriptional programs in the small intestine were uncovered. A comment on this discrepancy would be helpful, especially for the non-bioinformatician readers among us. 

      Standard differential gene expression analysis (DEG) of the effects of glial loss revealed significant differences only in the colon, and even then, only a handful of genes were changed. These changes were not accompanied by corresponding changes at the protein level, at least as detectable by IHC. In the small intestine, there were no significant differences by standard DEG thresholds. Unlike DEG, gene set enrichment analyses (GSEA), provides a significance value based on whether there is a higher than chance number of genes that are changing in a uniform direction without consideration for the significance of the magnitude of change. Therefore, the GSEA detected that a significant number of genes in the curated Paneth cell gene list exhibited a positive fold change difference in the bulk RNA sequencing data. This prompted us to examine Paneth cells and other epithelial cell types in more detail by IHC, functional and ultrastructural analyses, which all converged on the observation that Paneth cells were relatively selectively disrupted in the epithelium of glial depleted mice.

      From looking at Figure 3B it is clear that Paneth cells are not the only epithelial cell type affected (after less stringent in silico analyses) by enteric glial cell depletion. Although the authors show that this does not translate into ultrastructural or numerical changes of most of these cell types, this makes one wonder how specific the enteric glia - Paneth cell link is. Besides possible indirect crosstalk (via neurons), it is not clear if enteric glia more closely associate with Paneth cells as compared to these other cell types. Immunofluorescence stainings of some of these cells in the Plp1-GFP mice would be informative here. 

      Enteric glia have long been reported to closely associate with crypts, the sites of residence for Paneth cells and intestinal stem cells (PMID: 7043279, 16423922). Consistent with these reports, our observations from Plp1-eGFP mice confirm that enteric glia often appose the entire base of small intestinal crypts (see Author response image 1 below). Given this reproducible observation, we did not pursue histological quantification to compare preferential glial apposition to specific epithelial cell types. Enteric glia have been reported to form close associations with enteroendocrine cells as well (PMID: 24587096), which is not surprising because these cells are highly innervated; however, our analyses did not reveal changes in the abundance and morphology of these cells or other epithelial cell types.

      Author response image 1.

      (A) Immunohistochemical staining of a small intestinal cross-section from a Vil1<sup>Cre</sup>Rosa26<sup>tdTomato/+</sup> Plp1<sup>eGFP</sup> transgenic mouse in which enteric glia are labeled with green fluorescent protein (GFP) and intestinal epithelial cells are labeled with tdTomato. (B) Mucosal glia closely associate with epithelial cells in intestinal crypts. Scale bar – 20µm.

      The authors mention IL-22 as a possible link, but do Paneth cells express receptors for transmitters commonly released by enteric glia? Maybe they can have a look at putative cell-cell interactions by mapping ligand-receptor pairs in the scRNAseq datasets they used. 

      Beyond IL-22R, it is established that Paneth cells express receptors for secreted WNT proteins, which enteric glia have been shown to express (PMID: 34727519). This interaction could potentially be involved in glial regulation of Paneth cells, but mice lacking glia do not exhibit the same phenotypes as mouse models with disrupted WNT signaling. For example, animals lacking the WNT receptor Frizzled-5 in Paneth cells have mislocalization of Paneth cells to the villi (PMID: 15778706), which we do not readily observe in Plp1CreER-DTA mice. Furthermore, while mucosal enteric glia have been proposed as a source of WNT ligands, this role has been specifically attributed to GFAP+ cells, which may or may not be glia in the mucosa. Moreover, several other cell types in the mucosa around crypts have also been identified as significant sources of WNT ligands (PMID: 16083717, 22922422). We have now added these ideas to the Discussion.

      Per the reviewer’s suggestion to use bioinformatics to explore other potential ligand-receptor pairings that might underlie glial regulation of Paneth cells, we conducted a CellPhoneDB analysis focused on these two cell types with a collaborator. This analysis highlighted a handful of potential ligand-receptor interactions, but none of these pathways could be clearly linked to the observed Paneth cell phenotype. Furthermore, virtually all the candidate interactions were not specific to glia, with the candidate ligands expressed by many other more abundant cell types in the mucosa. For these reasons, we decided not to include this analysis in the revised manuscript. 

      Previously the authors showed that enteric glia regulation of intestinal motility is sex-dependent (Rao et al 2017). While enteric glia depletion caused dysmotility in female mice, it did not affect motility in males. For this reason, most experiments in the current study were conducted in male mice only. However, for the experiments focusing on the effect of enteric glia depletion on hostmicrobiome interactions and intestinal microbiota composition both male and female mice were used. In Figure 8A male and female mice are distinctly depicted but this was not done for Figure 8C. Separate characterization of the microbiome of male and female mice would have helped to figure out how much intestinal dysmotility (in females) contributes to the effect on gut microbial composition. This is an important exercise to confirm that the effect on the microbiome is indeed a consequence of altered Paneth cell function, as suggested by the authors (in the results and discussion, and in the abstract). 

      In our microbiome analysis, we initially analyzed males and females separately but did not observe significant differences between the two sexes. Thus, we merged the data to increase the statistical power of the genotype comparisons. It was an oversight on our part to not label the datapoints by sex as we did for the other data in the manuscript. We have now revised the figures related to microbiome characterization (Fig. 5D-E and Supp. Fig. 8C) to indicate the sexes of the mice used. Stratifying the data by sex within-sample revealed no major sex-specific differences in microbiome diversity or enriched/depleted biomarkers in the core genotype-dependent observations.

      In this context, it would also be interesting to compare the bulk sequencing data after enteric glia depletion between female and male mice. 

      Our bulk sequencing analysis of the effects of glial loss was conducted in male mice only in order to assess the effects independent of colonic dysmotility, a phenotype observed only in female Plp1CreER-DTA animals (PMID: 28711628). Given that we found rather muted transcriptional changes in male mice, we chose not to perform subsequent transcriptional analyses in female mice, further reasoning that any changes identified would most likely be attributable to dysmotility rather than direct glial effects. Future studies focusing on sex differences in the small intestine, where motility in the Plp1CreER-DTA model is unaffected by glial loss, could provide additional insights, especially in light of the recently reported sex differences in the gene expression and activity levels of enteric glia in the myenteric plexus (PMID: 34593632, 38895433).

      Reviewer #1 (Recommendations For The Authors): 

      - Intro 2nd paragraph: please add to the sentence: "They found no major defects in epithelial properties AT STEADY STATE (or during homeostasis). 

      Revised as suggested.

      - There seems to be a word missing in the 2nd sentence of paragraph 2 on page 4. "... but xxx consistent...". 

      Reviewed and there were no missing words.

      - In the 2nd paragraph on page 8, when discussing GFAP expression in IBD patients, a reference is missing. Also, here it should be GFAP, not Gfap (in italics). 

      Revised as suggested.

      Reviewer #2 (Public Review): 

      This is an excellent and timely study from the Rao lab investigating the interactions of enteric glia with the intestinal epithelium. Two early studies in the late 1990s and early 2000s had previously suggested that enteric glia play a pivotal role in control of the intestinal epithelial barrier, as their ablation using mouse models resulted in severe and fatal intestinal inflammation. However, it was later identified that these inflammatory effects could have been an indirect product of the transgenic mouse models used, rather than due to the depletion of enteric glia. In previous studies from this lab, the authors had identified expression of PLP1 in enteric glia, and its use in CRE driver lines to label and ablate enteric glia. 

      In the current paper, the authors carefully examine the role of enteric glia by first identifying that PLP1-creERT2 is the most useful driver to direct enteric glial ablation, in terms of the number of glial cells targeted, their proximity to the intestinal epithelium, and the relevance for human studies (GFAP expression is rather limited in human samples in comparison). They examined gene expression changes in different regions of the intestine using bulk RNA-seq following ablation of enteric glia by driving expression of diphtheria toxin A (PLP1-creERT2;Rosa26-DTA). Alterations in gene expression were observed in different regions of the gut, with specific effects in different regions. Interestingly, while there were gene expression changes in the epithelium, there were limited changes to the proportions of different epithelial cell types identified using immunohistochemistry in control vs glial-ablated mice. The authors then focused on the investigation of Paneth cells in the ileum, identifying changes in the ultrastructural morphology and lysozyme activity. In addition, they identified alterations in gut microbiome diversity. As Paneth cells secrete antimicrobial peptides, the authors conclude that the changes in gut microbiome are due to enteric glia-mediated impacts on Paneth cell activity. 

      Overall, the study is excellent and delves into the different possible mechanisms of action, including the investigation of changes in enteric cholinergic neurons innervating the intestinal crypts. The use of different CRE drivers to target enteric glial cells has led to varying results in the past, and the authors should be commended on how they address this in the Discussion. 

      We thank the reviewer for this positive feedback.

      Reviewer #2 (Recommendations For The Authors): 

      I have a few minor comments: 

      Changes in bacterial diversity - the authors make a very compelling case that changes in the proportions of various intestinal microbiome species were impacted by the change in Paneth cell secretions resulting from the depletion of enteric glia. Another potential mechanism of action could be alterations in gut motility resulting from loss of enteric glia. It appears that faecal samples were collected from both male and female mice, and hence changes in colonic motility could be involved. This should be addressed in the Results and Discussion. 

      We agree with the reviewer that GI dysmotility could influence microbial composition. To address this, we initially analyzed microbiome data separately for male and female mice, because only female Plp1CreER-Rosa26DTA exhibit dysmotility. We found no significant sex-specific differences in microbiome composition, however, which suggested to us that dysmotility was unlikely to be the primary driver of the observed microbial changes. Based on these findings, we opted to combine data from male and female mice in our final microbiome analysis. We have now revised the Results, Discussion, and Methods sections to clarify this.

      Supplementary Figure 2: it would be helpful to include some labels of landmarks on the tissues, and arrows pointing to immunoreactive cells. 

      We have added labels and arrows to images in Supplementary Figure 2 per the reviewer’s suggestion.   

      Figure 4B: It's hard to tell the difference in ultrastructural morphology of the Paneth cells between Cre- and Cre+ mice in the EM images. Heterogeneous granules (PG) seem to be labelled in cells from both genotypes of mice. Some outlines of cells or arrows pointing to errant granules would be helpful. 

      We have added arrows indicated errant granules to images in Figure 4 per the reviewer’s suggestion.   

      Reviewer #3 (Public Review): 

      In this study, Prochera, et al. identify PLP1+ cells as the glia that most closely interact with the gut epithelium and show that genetic depletion of these PLP1+ glia in mice does not have major effects on the intestinal transcriptome or the cellular composition of the epithelium. Enteric glial loss, however, causes dysregulation of Paneth cell gene expression that is associated with morphological disruption of Paneth cells, diminished lysozyme secretion, and altered gut microbial composition. 

      Overall, the authors need to first prove whether the Plp1CreER Rosa26DTA/+ mice system is viable. 

      In previous work, we discovered that the gene Plp1 is broadly expressed by enteric glia and, within the mouse intestine, is quite specific to glial cells (PMID: 26119414). We characterized the Plp1CreER mouse line as a genetic tool in detail in this initial study. Then in a subsequent manuscript, we used Plp1CreER-DTA mice to genetically deplete enteric glia and study the consequences on epithelial barrier integrity, crypt cell proliferation, enteric neuronal health and gastrointestinal motility (PMID: 28711628). In this second study, we performed extensive validation of the Plp1CreER-DTA mouse model including detailed quantification of glial depletion in the small and large intestines across the myenteric, intramuscular and mucosa compartments by immunohistochemical (IHC) staining of whole tissue segments to sample thousands of cells. We found that the majority of S100B<sup>+</sup>enteric glia were depleted within 5 days in both sexes, including more than 88% loss of mucosal glia, and that this loss was stable at 3 subsequent timepoints (7, 9 and 14 days post-tamoxifen induction of Cre activity). Glial loss was further confirmed by IHC for GFAP in the myenteric plexus, and by ultrastructural analysis of the small intestine to ensure cell depletion rather than simply loss of marker expression. Our group was the first to use this model to study enteric glia, and since then similar models and our key observations have been replicated by other groups (PMID: 33282743, 34550727). Thus, we consider this model to be well established.

      Also, most experimental systems have been evaluated by immunohistochemistry, scRNAseq, and electron microscopy, but need quantitative statistical processing. 

      RNA-sequencing and microbiome analyses are inherently quantitative (Figures 1A-B, Supp. Figure 1, Figure 2, Supp. Figure 4A, Figure 3A-B, Supp. Figure 5, Figure 5, and Supp. Figure 8C). Virtually all our other observations are also supported by quantitative analysis including analysis of mucosal glial markers (Fig. 1C-D), validation of Paneth cell transcript expression in the colon (Supp. Fig. 4B), measurement of epithelial cell type composition (Figure 3C, D), assessment of crypt innervation (Supp. Fig. 7E), and measurement of bacteria-to-crypt distance (Supp. Fig. 8A-B). The only observation that was not quantified was that of morphological abnormalities of Paneth cells. Given the inherently low sampling rate of EM studies, we felt that functional assays (explant secretion assays, effects on microbial composition) would be more meaningful for interrogation of a potential Paneth cell phenotype and thus elected to focus our quantitative analyses on those functional assays rather than further histological measurements. 

      In addition, the value of the paper would be enhanced if the significance of why the phenotype appeared in the large intestine rather than the small intestine when PLP1 is deficient for Paneth cells is clarified. 

      Please see detailed response to Reviewer 1 that addresses this comment and the corresponding addition to the Discussion.

      Major Weaknesses: 

      (1)  Supplementary Figure 2; Cannot be evaluated without quantification. 

      Supplemental Figure 2 shows qualitative IHC observations that were highly reproducible across all the subjects indicated for each marker and align well with the quantitative transcriptional data from human subjects shown in Figure 1 and Supplemental Figure 1. The DAB staining in Supplemental Figure 2 could theoretically be quantified by staining intensity or counting cell number but we felt this would be arbitrary and difficult to achieve in a meaningful way with a single chromogen. The DAB reaction is associated with a non-linear relationship between amount of an antigen and staining intensity, especially at higher levels (PMID: 16978204, 19575836), because it is not a direct conjugate and relies upon an enzymatic reaction. The amplification step required for DAB staining using Horseradish Peroxidase (HRP) introduces variability, particularly with cytoplasmic markers and in complex tissue structures like the plexuses, where proteins are distributed throughout the glial network. Counting cell number also would not lead to fair comparisons between markers because while SOX10 shows a clear nuclear signal suitable for quantification, the other markers are all membrane or cytoplasmic proteins, making accurate counting nearly impossible in dense ganglia. Finally, quantifying cell number in 5-micron paraffin sections which have major differences in sampling from one subject to another in terms of presence of ganglia and ganglia size, would also make this prone to inaccuracy. Given these limitations and the robust qualitative data we have shown that aligns completely with the quantitative transcriptional analyses, we respectfully disagree with the reviewer’s comment.

      (2) Figure 2A; Is Plp1CreER Rosa26DTA/+ mice system established correctly? S100B immunohistology picture is not clear. A similar study is needed for female Plp1CreER Rosa26DTA/+ mice. What is the justification for setting 5 dpt, 11 dpt? Any consideration of changes to organs other than the intestine? Wouldn't it be clearer to introduce Organoid technology? 

      Please see the detailed response to first comment. The Plp1CreER- DTA mouse model is well-established and there are detailed experimental justifications for the 5 and 11dpt timepoints as well as the focus on male mice for RNA-sequencing analyses. As described in our previous work (PMID: 28711628), Plp1<sup>+</sup> cells throughout the animal would be affected, including Schwann cells and oligodendrocytes, which is why we limit our analyses to the first 11dpt, when there are fewer confounding variables. The S100B immunohistology picture in Figure 2A was intended to be a schematic graphical representation of the paradigm of glial loss, not a data figure. Extensive validation of glial loss in this model was shown in our previous study. To improve clarity, we have now enlarged the picture for the reader.

      Regarding the suggestion to use organoid technology, standard intestinal epithelial organoids do not incorporate any elements of the enteric nervous system (ENS), which is the focus of this study. Some groups have made heroic efforts to incorporate ENS components into intestinal organoids by introducing neural crest progenitor cells and grafting the hybrid organoids under the renal capsule in mice (example PMID: 27869805); but these studies are still limited, and it remains unclear how much the preparations reflect functional, natively innervated intestine. Our ex vivo explant assay preserves native ENS-epithelial interactions, providing a more effective model for studying the relationship between enteric glia and Paneth cells.

      (3) Figure 2B; Need an explanation for the 5 genes that were altered in the colon. Five genes should be evaluated by RT-qPCR. Why was there a lack of change in the duodenum and ileum? 

      While RT-qPCR validation of differentially expressed genes was once common practice, especially with microarray data, there is now robust evidence for strong correlations between RNA sequencing (RNAseq) results and RT-qPCR measurements of gene expression (PMID: 26208977, 28484260). Notably Rajkumar et al. (PMID: 26208977) demonstrated that RNAseq analyzed using DESeq2 (a method which we employed in our study), yields highly accurate results. They reported a 0% false positive rate and a 100% positive predictive value for DESeq2, rendering additional RT-qPCR validation redundant. We only performed RT-qPCR analysis of colonic Lyz1 expression because our IHC analyses failed to show any ectopic expression of the protein in the colons of Cre<sup>+</sup> mice (Supp. Figure 4D) and we wished to validate the gene expression change seen by RNAseq in an independent cohort to be absolutely sure. Per the detailed response to Reviewer 1, we do not have a mechanistic explanation for why there is selective transcriptional induction of Paneth cell-related genes in the colon upon glial depletion. We have elaborated on this in the revised Discussion.  

      (4) Supplementary Figure 3; Top 3 genes should be evaluated by RT-qPCR.  

      Given that none of the changes included in Supplementary Figure 3 for the duodenum or ileum reach the standard threshold for statistical significance and in view of the findings by Rajkumar, et al. (2015) described above, we don’t believe that evaluating expression of these genes by RT-qPCR would be informative in interpreting these negative results. 

      (5) Supplementary Figure 4B, C, and D; Why not show analysis in the small intestine? 

      We chose to focus on the colon for this analysis because this was the only region of the intestine that exhibited statistically significant differences in transcriptional profiles as assessed by DEG.

      (6) Supplementary Figure 4D; Cannot be evaluated without quantification. 

      As shown in the representative images, no LYZ1 or DEFA5 signal was detected in the colons of Cre<sup>-</sup> or Cre<sup>+</sup> mice (n=3 mice per genotype; >100 crypts/mouse assessed), though it was readily detectable in the ileums of both genotypes.  We have now added the number of crypts assessed to the figure legend.

      (7) Figure 3D; Cannot be evaluated without quantification. 

      Please see Fig. 3C for quantification of each cell type marker shown in Figure 3D. 

      (8) Supplementary Figure 5B and C; Top 3 genes should be evaluated by RT-qPCR. 

      Please see detailed explanation to comments #3 and #4 above. 

      (9) Supplementary Figure 6; Top 3 genes should be evaluated by RT-qPCR. 

      This comment was likely made in error because Supplementary Fig. 6 does not show any gene expression data. 

      (10) Figure 4A; Cannot be evaluated without quantification. 

      We appreciate the reviewer’s comment here and strived very hard to add quantification of the Paneth cell granule phenotype seen by light microscopy to our study. IHC for LYZ1 is typically the gold standard for assessment of Paneth cell granules by light microscopy. In our hands, however, we encountered persistent issues with IHC for this protein. While it very reproducibly detected Paneth cells with sufficient specificity to enable quantification of number of immunoreactive cells (as shown in Figure 3C), it did not enable quantification of granule morphology because it consistently exhibited diffuse staining throughout the cell (see Author response image 2 below). This appearance persisted regardless of extensive titration of fixation parameters (time, temperature, fixative supplier, 10% NBF vs 4% PFA), tissue preparation (fixed as intact tubes versus “swiss-rolls”), permeabilization conditions, operator, antibody used, and other variables. Upon subsequently surveying the literature, it seems that similar diffuse staining patterns for LYZ1 have been observed by numerous other groups and this may simply be an experimental limitation.

      Author response image 2.

      Representative IHC images showing LYZ1 staining optimization. Ileal tissues from 8-10-week-old mice were prepared as either 'swiss-rolls' (A-D) or tubes (E-F) and fixed using different protocols: 10% neutral buffered formalin (NBF) from Epredia (#5710-LP) (A-B, E), 10% NBF from G-Biosciences (#786-1057) (C-D), or 4% paraformaldehyde (PFA) from VWR (#100503-917) (F). Fixations were conducted at room temperature (A, C) or at 4°C (B, D-F). Diffuse cytoplasmic LYZ1 staining is observed within Paneth cells, regardless of conditions of tissue preparation.  

      As an alternative approach to detecting Paneth cell granules, we tried UEA-I lectin staining. This labeling approach was sufficient to reveal qualitative differences in Paneth granule morphology in Cre<sup>+</sup> mice, as shown in Fig. 4A. However, the transient nature of this lectin labeling made it very difficult to systematically quantify granule morphology in a blinded manner, as we did for our other analyses. Given these persistent challenges, we decided to present qualitative data on morphology by two orthogonal approaches (UEA-I staining by light microscopy and ultrastructure by EM) and focus on functional read-outs for quantitative analyses (explant secretion assays and microbiome analyses). In aggregate, we feel that these data provide robust and complementary evidence of the observed phenotype from independent experimental approaches.

      (11) Figure 4D; Cannot be evaluated without quantification. 

      This comment was likely made in error because there is no Figure 4D. 

      (12) Additional experiments on in vivo infection systems comparing Plp1CreER Rosa26DTA/+ mice and controls would be great. 

      We agree that in vivo infection experiments would be very interesting to pursue, particularly given the potential role of Paneth cells in innate immunity. These studies are beyond the scope of the current manuscript, but we hope to report on them in the future.

      Reviewer #3 (Recommendations For The Authors)

      Patients with inflammatory bowel disease (IBD); UC or CD. 

      Revised per reviewer suggestion.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      The article by Piersma et al. aims to reduce the complex process of NK cell licensing to the action of a single inhibitory receptor for MHC class I. This is achieved using a mouse strain lacking all of the Ly49 receptors expressed by NK cells and inserting the Ly49a gene into the Ncr1 locus, leading to expression on the majority of NK cells.

      Strengths:

      The mouse model used represents a precise deletion of all NK-expressed genes within the Ly49 cluster. The re-introduction of the Ly49a gene into the Ncr1 locus allows expression by most NK cells. Convincing effects of Ly49a expression on in vitro activation and in vivo killing assay are shown.

      Weaknesses:

      The choice of Ly49a provides a clear picture of H-2D<sup>d</sup> recognition by this Ly49. It would be valuable to perform additional studies investigating Ly49c and Ly49i receptors for H-2b. This is of interest because there are reports indicating that Ly49c may not be a functional receptor in B6 mice due to strong cis interactions.

      We agree with the reviewer that it will be important to extend our findings to H-2b haplotypes with individual cognate Ly49 receptors (Ly49C and Ly49I). While these experiments are subject of our ongoing studies, they are beyond the scope of the current manuscript considering the significant time, effort and cost to generate these new Ly49C and Ly49I knockin mice.

      This work generates an excellent mouse model for the study of NK cell licensing by inhibitory Ly49s that will be useful for the community. It provides a platform whereby the functional activity of a single Ly49 can be assessed.

      Reviewer #2 (Public review):

      Piersma et al. continue to work on deciphering the role and function of Ly49 NK cell receptors. This manuscript shows that a single inhibitory Ly49 receptor is sufficient to license NK cells and eliminate MHC-I-deficient target cells in mice. In short, they refined the mouse model ∆Ly49-1 (Parikh et al., 2020) into the Ly49KO model in which all Ly49 genes are disrupted. Using this model, they confirmed that NK cells from Ly49KO mice cannot be licensed, produce lower levels of IFN-gamma, and cannot reject MHC-I-deficient cells. To study the effect of a single Ly49 receptor in the function of NK cells, the authors backcrossed Ly49KO mice to H-2D<sup>d</sup> transgenic KODO (D8-KODO) Ly49A knock-in mice in which a single inhibitory Ly49A receptor that recognizes H-2D<sup>d</sup> ligands is expressed. By doing so, they demonstrate that a single inhibitory Ly49 receptor expressed by all NK cells is sufficient for licensing and missing-self killing.

      While the results of the study are largely consistent with the conclusions, it is important to address some discrepancies. For instance, in the title of Figure 1, the authors state that NK cells in Ly49KO mice compared to WT mice have a less mature phenotype , which is not consistent with the corresponding text in the Results section (lines 170-171) that states there is no difference in maturation. These differences are not evident in Figure 1, panel D. It is crucial to acknowledge these inconsistencies to ensure a comprehensive understanding of the research findings.

      We thank the reviewer for pointing this out. We have corrected the figure legend title to: “Mice generated to lack all NK-related Ly49 molecules using CRISPR have NK cells that display alterations in select surface molecules.”

      In the legend of Figure 2. the text related to panel C indicates the use of dyes to label the splenocytes, and CFSE, CTV, and CTFR were mentioned. However, only CTV and CTFR are shown on the plots and mentioned in the corresponding text in the Results section. Similarly, in the legend of Figure 4, which is related to panel C, the authors write that splenocytes were differentially labeled with CFSE and CTV as indicated; however, in Figure 4, C and the Results section text, there is no mention of CFSE.

      We thank the reviewer to point out these inconsistencies. We did label target cells with CFSE to distinguish them from host cells, to clarify we have done the following:

      We have removed CFSE from figure legends of Figure 2 and 4.

      We included the following on CFSE labeling in the Materials and Methods section: “Target splenocytes were additionally labeled with CFSE to identify transferred target splenocytes from host cells.”

      The authors should clarify why they assume that KLRG1 expression is influenced by the expression of inhibitory Ly49 receptors and not by manipulations on chromosome 6, where the genes for both KLRG1 and Ly49 receptors are located.

      The effect on KLRG1 expression in phenocopied in the Ly49A KI mice (on a Ly49 KO background). The Ly49A KI allele is encoded by the Ncr1 locus, which is located on chromosome 7 and not by chromosome 6 where KLRG1 is located, thus excluding involvement of cis-regulatory elements encoded by the Ly49 locus on chromosome 6. 

      We have clarified this in the discussion section (lines 350-358):

      “The Ly49 gene family as well as Klrg1 is located within the NKC on chromosome 6 (Yokoyama and Plougastel, 2003) ….  expression of only Ly49A, encoded in the Ncr1 locus located on chromosome 7, in Ly49KO mice on a H-2D<sup>d</sup> background restored KLRG1 expression”

      However, a better explanation for the possible influence of other inhibitory NK cell receptors still needs to be included. In the study by Zhang et al. (doi: 10.1038/s41467-019-13032-5 the authors showed the synergized regulation of NK cell education by the NKG2A receptor and the specific Ly49 family members. Although in this study, Piersma and colleagues show the control of MHC-I deficient cells by Ly49A+ NKG2A-NK cells in Figure 4., this receptor is not mentioned in the Results or in the Discussion section, so its role in this story needs to be clarified. Therefore, the reader would benefit from more information regarding NKG2A receptor and NKG2A+/- populations in their results.

      We agree with the reviewer that it is important to describe our results in the context of other inhibitory receptors. To clarify the role of NKG2A and potentially other inhibitory receptors we have made the following improvements to our manuscript:

      We discuss the role of NKG2A in the discussion section, which now include (lines 259-266):

      “While our results did not interrogate licensing by inhibitory receptors outside of the Ly49 receptor family, such as has been reported for NKG2A (Anfossi et al., 2006; Zhang et al., 2019), they do demonstrate that expression of Ly49A without other Ly49 family members can mediate NK cell licensing. Moreover, we found that Ly49 receptors are required and sufficient for missing-self rejection under steady-state conditions. However, these observations do not rule out involvement of other inhibitory receptors under specific inflammatory conditions. For example, NKG2A contributes to rejection of missing-self targets in poly(I:C)-treated mice (Zhang et al., 2019).”

      We also added the following to the result section (lines 179-182):

      NKG2A has been implicated in NK cell licensing by the non-classical MHC-I molecule Qa1 (Anfossi et al., 2006), to eliminate potential confounding effects by this interaction, effector functions of NKG2A- NK cells were evaluated as described before (Bern et al., 2017).

      Reviewer #3 (Public review):

      Summary:

      In this study, Piersma et al. successfully generated a mouse model with all Ly49n et al., 2017 genes knocked out, resulting in the complete absence of Ly49 receptor expression on the cell surface. The absence of Ly49 expression led to the loss of NK cell education/licensing and consequently, a failure in responsiveness against missing-self target cells. The experimental work and findings are partially overlapping with the previous work by Zhang et al. (2019), who also performed knockout of the entire Ly49 locus in mice and demonstrated that loss of NK responsiveness was due to the removal of inhibitory, and not activating Ly49 genes. The authors demonstrate the restoration of NK cell licensing by knocking in a single Ly49 gene, Ly49A, in a mouse expressing the H-2D<sup>d</sup> ligand for this receptor, which is a novel and important finding.

      Strengths:

      The authors established a novel mouse model enabling them to have a clean and thorough study on the function of Ly49 on NK cell licensing. Also, by knocking in a single Ly49, they were able to investigate the function of a given Ly49 receptor excluding the "contamination" of co-expression of any other Ly49 genes. Their idea and method were novel though the mouse model was somehow genetically similar to a previous study. The experiment design and data interpretation were logically clear and the evidence was solid.

      Weaknesses:

      The paper is very poorly written and confusing. The authors should be more accurate in the usage of terminology, provide more details on experimental procedures, and revise much of the text to improve clarity and coherence. A thorough revision aiming to clarify the paper would be helpful.

      We regret that the manuscript was confusing to the reviewer. We have made thorough revisions to the different sections, which we hope will improve the clarity of the manuscript.

      We have made changes to all sections of the manuscript, including the title. These revisions include improved clarity on description of NK cell licensing and consistent usage throughout the manuscript, per the reviewer recommendations. We hope that all our improvements help the clarity of the manuscript.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      I was confused by lines 262-270 in the discussion. The data from Hanke et al. is presented as contradictory to the observation that Ly49s bind more efficiently to H2-Kb than -Db, but they showed that Ly49c/i did not bind Kb-deficient cells, supporting the preferred binding to Kb.

      We have clarified this issue and the paragraph now reads: “This is further supported by early studies using Ly49 transfectants binding to Con A blasts showing that Ly49C and Ly49I can bind to H-2D<sup>b</sup>-deficient but not H-2K<sup>b</sup>-deficient cells (Hanke et al., 1999), despite the caveat of testing binding to cells overexpressing Ly49s in these studies.”

      Reviewer #2 (Recommendations for the authors):

      The authors' conclusion that one type of inhibitory Ly49 receptor expressed on NK cells is sufficient for successful licensing and rejection of missing self-cells is a significant step forward. However, it would be beneficial to complement this with additional data. For instance, exploring the role of a single inhibitory Ly49 receptor responsible for licensing in a mouse model with a different haplotype (e.g. Ly49C or Ly49I on H-2b MHC I haplotype in C57BL/6J mice) could provide valuable insights and open new avenues for research in the field.

      We agree with the reviewer that it will be important to extend our findings to additional MHC-I haplotypes with single cognate Ly49 receptors. While these experiments are subject of our ongoing studies, they are beyond the scope of the current manuscript considering the significant effort, time, and cost to generate these new Ly49C and Ly49I knockin mice.

      Reviewer #3 (Recommendations for the authors):

      Specific issues that should be addressed are as follows:

      (1) The title of the paper: "Expression of a single inhibitory Ly49 receptor is sufficient to license NK cells for effector functions" is ambiguous. When I first read the title, I thought the authors meant that only a single Ly49 molecule on the NK cell surface was necessary to induce licensing. It might be better to replace "single inhibitory receptor" with "single member of Ly49 receptor family".

      We have changed the title to: “Expression of a single inhibitory member of the Ly49 receptor family is sufficient to license NK cells for effector functions”

      (2) In the abstract, introduction, and results, the authors distinguish "licensing" and "rejection of missing-self targets" as two distinct phenomena. An example includes Abstract, lines 51-53: "Herein, we showed mice lacking expression of all Ly49s were unable to reject missing-self target cells in vivo, were defective in NK cell licensing, and displayed lower KLRG1 on the surface of NK cells". Similarly, the title of the second subsection of the Results states: "Ly49-deficient NK cells are defective in licensing and rejection of cognate MHC-I deficient target cells" (line 176). In these instances, it seems that by "licensing", they mean only response to plate-bound anti-NK1.1 stimulation and not a response to missing-self targets. Alternatively, in the first paragraph of the Discussion, it sounds as if licensing includes both anti-NK1.1 and missing-self responses (lines 258-260): "...NK cells were fully licensed in terms of their functional phenotype, including the capacity to be activated by an activation receptor in vitro and efficient rejection of MHC-I deficient target cells in vivo". Please define the terms and use the terms consistently throughout the paper.

      We were the first to describe the term licensing and have defined this as acquisition of NK cell functional competence by self-MHC molecules (Kim et al., 2005), which is characterized by increased NK cell effector functions to activating signals. Thus, licensed NK cells are prevented from attacking normal MHC-I<sup>+</sup> cells by the same self-MHC-I-specific receptor that conferred licensing, while unlicensed NK cells without appropriate Ly49 receptors are functionally incompetent.

      To clarify we made changes throughout the manuscript including the following:

      Lines 91-101:

      “In addition to effector function in missing-self, Ly49 receptors that recognize their cognate MHC-I ligands are involved in licensing or education of NK cells to acquire functional competence. NK cell licensing is characterized by potent effector functions including IFNγ production and degranulation in response to activation receptor stimulation (Elliott et al., 2010; Kim et al., 2005). Like missing-self recognition, inhibitory Ly49s require SHP-1 for NK cell licensing which interacts with the ITIM-motif encoded in the cytosolic tail of inhibitory Ly49s (Bern et al., 2017; Kim et al., 2005; Viant et al., 2014). Moreover, lower expression of SHP-1, particularly within the immunological synapse, is associated with licensed NK cells (Schmied et al., 2023; Wu et al., 2021). Thus, inhibitory Ly49s have a second function that licenses NK cells to self-MHC-I thereby generating functionally competent NK cells but it has not been possible to exclude contributions from other co-expressed Ly49s.”

      Lines 268-271 (previously 258-260):

      “Yet the NK cells were fully licensed in terms of IFNγ production and degranulation in vitro and efficiently rejected MHC-I deficient target cells in vivo. Thus, a single Ly49 receptor is capable to confer the licensed phenotype and missing-self rejection in vitro and in vivo.”

      Lines 309-312:

      “In conclusion, these data show that expression of a single inhibitory Ly49 receptor is necessary and sufficient to license NK cells and mediate missing self-rejection under steady state conditions in vivo.”

      (3) Introduction, lines 76-79. Please provide the C57BL/6 MHC-I genotype. It is difficult to follow the text here without this information. In general, please provide information to help the reader who may not be working in this precise field.

      We thank the reviewer for pointing this out. We have included this and the lines now read: “For example, in the C57BL/6 background, Ly49C and Ly49I can recognize H-2<sup>b</sup> MHC-I molecules that include H-2K<sup>b</sup> and H-2D<sup>b</sup>, while Ly49A and Ly49G cannot recognize H-2<sup>b</sup> molecules and instead they recognize H-2<sup>d</sup> alleles.”

      (4) Introduction, lines 85-97. Please use commas: "...the MHC-I specificities of other Ly49s have been primarily studied with MHC tetramers containing human b2m, which is not recognized by Ly49A, on cells overexpressing Ly49s" in order to clarify the sentence.

      Commas have been added as suggested by the reviewer.

      (5) Introduction, lines 91-101. The whole paragraph starting with the following sentence does not make sense and should be re-written. "In addition to effector function in missing-self, when inhibitory Ly49 receptors recognize their cognate MHC-I ligands in vivo, they license or educate NK cells for potent effector functions including IFNγ production and degranulation in response to activation receptor stimulation".

      We regret that this paragraph was not clear to the reviewer. We have changed this paragraph to:

      “In addition to effector function in missing-self, Ly49 receptors that recognize their cognate MHC-I ligands are involved in licensing or education of NK cells to acquire functional competence. NK cell licensing is characterized by potent effector functions including IFNγ production and degranulation in response to activation receptor stimulation (Elliott et al., 2010; Kim et al., 2005). Like missing-self recognition, inhibitory Ly49s require SHP-1 for NK cell licensing which interacts with the ITIM-motif encoded in the cytosolic tail of inhibitory Ly49s (Bern et al., 2017; Kim et al., 2005; Viant et al., 2014). Moreover, lower expression of SHP-1, particularly within the immunological synapse, is associated with licensed NK cells (Schmied et al., 2023; Wu et al., 2021). Thus, inhibitory Ly49s have a second function that licenses NK cells to self-MHC-I thereby generating functionally competent NK cells but it has not been possible to exclude contributions from other co-expressed Ly49s.”

      (6) Results, line 181. Please edit: "...MHC-I-deficient H-2K<sup>b</sup> x H-2D<sup>b</sup> deficient (KODO) mice".

      This sentence now reads “... NK cells from H-2K<sup>b</sup> and H-2D<sup>b</sup> double deficient (KODO) mice”

      (7) Results, line 192. Please re-word the following phrase: "missing-self is dominated by H-2K<sup>b</sup> in the C57BL/6 background", as it is unclear. Do you mean that H-2K<sup>b</sup> is protected from lysis as opposed to H-2D<sup>b</sup>?

      We thank the reviewer for pointing this out, line 192 now reads: “..missing-self recognition in the C57BL/6 background depends on the absence of H-2K<sup>b</sup> rather than H-2D<sup>b</sup>.”

      (8) Please briefly describe the Ncr1-Ly49A knockin procedure so that the reader understands the link between NKp46 and Ly49A expression without going to the earlier paper. Also, it needs to be mentioned that Ncr1 is the gene encoding NKp46.

      Lines 201-205 now read: “To investigate the potential of a single inhibitory Ly49 receptor on mediating NK cell licensing and missing-self rejection, the Ly49KO mice were backcrossed to H-2D<sup>d</sup> transgenic KODO (D8-KODO) Ly49A KI mice that express Klra1 cDNA encoding the inhibitory Ly49A receptor in the Ncr1 locus encoding NKp46 and its cognate ligand H-2D<sup>d</sup> but not any other classical MHC-I molecules (Parikh et al., 2020).

      In the materials and Methods section, the following has been added (lines 324-326):

      “In Ly49A KI mice the stop codon of Ncr1 encoding NKp46 is replaced with a P2A peptide-cleavage site upstream of the Ly49A cDNA, while maintaining the 3’ untranslated region.”

      (9) Figure 4C, legend. There is no CFSE staining in this experiment. Please correct.

      We did label target cells with CFSE to distinguish them from host cells, to clarify we have done the following:

      We have removed CFSE from figure legends of Figure 2 and 4.

      We included the following on CFSE labeling in the Materials and Methods section (lines 377-379): “Target splenocytes were additionally labeled with CFSE to identify transferred target splenocytes from host cells.”

      (10) Discussion, lines 262-270. This paragraph sounds as if data by Hanke et al. does not agree with the data presented in the paper. On the contrary, Hanke et al. demonstrate that Ly49C and Ly49I detectably bind to H-2K<sup>b</sup>, but poorly to H-2D<sup>b</sup>, supporting observations shown in Figure 2C.

      We have clarified this issue and the paragraph now reads: “This is further supported by early studies using Ly49 transfectants binding to Con A blasts showing that Ly49C and Ly49I can bind to H-2D<sup>b</sup>-deficient but not H-2K<sup>b</sup>-deficient cells (Hanke et al., 1999), despite the caveat of testing binding to cells overexpressing Ly49s in these studies.”

    1. Author response:

      The following is the authors’ response to the current reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Zanetti et al use biophysical and cellular assays to investigate the interaction of the birnavirus VP3 protein with the early endosome lipid PI3P. The major novel finding is that association of the VP3 protein with an anionic lipid (PI3P) appears to be important for viral replication, as evidenced through a cellular assay on FFUs.

      Strengths:

      Support previously published claims that VP3 associates with early endosome membrane, potentially through binding to PI3P. The finding that mutating a single residue (R200) critically affects early endosome binding and that the same mutation also inhibits viral replication suggests a very important role for this binding in the viral life cycle.

      Weaknesses:

      The manuscript is relatively narrowly focused: the specifics of the bi-molecular interaction between the VP3 of an unusual avian virus and a host cell lipid (PIP3). Further, the affinity of this interaction is low and its specificity relative to other PIPs is not tested, leading to questions about whether VP3-PI3P binding is relevant.

      Regarding the manuscript’s focus, we challenge the notion that studying a single bi-molecular interaction makes the scope of the paper overly narrow. This interaction—between VP3 and PI3P—plays a critical role in the replication of the birnavirus, which is the central theme of our work. Moreover, identifying and understanding such distinct interactions is a fundamental aspect of molecular virology, as they shed light on the precise mechanisms that viruses exploit to hijack the host cell machinery. Consequently, far from being narrowly focused, we believe our work contributes to the broader understanding of host-pathogen interactions.

      As for the low affinity of the VP3-PI3P interaction, we argue that this is not a limitation but rather a biologically relevant feature. As discussed in the manuscript, the moderate strength of this interaction is likely critical for regulating the turnover rate of VP3/endosomal PI3P complexes, which in turn could optimize viral replication efficiency. A stronger affinity might trap VP3 on the endosomal membrane, whereas weaker interactions might reduce its ability to efficiently target PI3P. Thus, the observed affinity may reflect a fine-tuned balance that supports the viral life cycle.

      With regard to specificity, we emphasize that in the context of the paper, we refer to biological specificity, which is not necessarily the same as chemical specificity. The binding of PI3P to early endosomes is “biologically” preconditioned by the distribution of PI3P within the cell. PI3P is predominantly localized in endosomal membranes, which “biologically precludes” interference from other PIPs due to their distinct cellular distributions. Moreover, while early endosomes also contain other anionic lipids, our work demonstrates that among these, PI3P plays a distinctive role in VP3 binding. This highlights its functional relevance in the context of early endosome dynamics.

      Reviewer #3 (Public review):

      Summary:

      Infectious bursal disease virus (IBDV) is a birnavirus and an important avian pathogen. Interestingly, IBDV appears to be a unique dsRNA virus that uses early endosomes for RNA replication that is more common for +ssRNA viruses such as for example SARS-CoV-2. This work builds on previous studies showing that IBDV VP3 interacts with PIP3 during virus replication. The authors provide further biophysical evidence for the interaction and map the interacting domain on VP3.

      Strengths:

      Detailed characterization of the interaction between VP3 and PIP3 identified R200D mutation as critical for the interaction. Cryo-EM data show that VP3 leads to membrane deformation.

      We thank the reviewer for the feedback.


      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Zanetti et al. use biophysical and cellular assays to investigate the interaction of the birnavirus VP3 protein with the early endosome lipid PI3P. The major novel finding is that the association of the VP3 protein with an anionic lipid (PI3P) appears to be important for viral replication, as evidenced through a cellular assay on FFUs.

      Strengths:

      Supports previously published claims that VP3 may associate with early endosomes and bind to PI3P-containing membranes. The claim that mutating a single residue (R<sub>200</sub>) critically affects early endosome binding and that the same mutation also inhibits viral replication suggests a very important role for this binding in the viral life cycle.

      Weaknesses:

      The manuscript is relatively narrowly focused: one bimolecular interaction between a host cell lipid and one protein of an unusual avian virus (VP3-PI3P). Aspects of this interaction have been described previously. Additional data would strengthen claims about the specificity and some technical issues should be addressed. Many of the core claims would benefit from additional experimental support to improve consistency.

      Indeed, our group has previously described aspects of the VP3-PI3P interaction, as indicated in lines 100-105 from the manuscript. In this manuscript, however, we present biochemical and biophysical details that have not been reported before about how VP3 connects with early endosomes, showing that it interacts directly with the PI3P. Additionally, we have now identified a critical residue in VP3—the R<sub>200</sub>—for binding to PI3P and its key role in the viral life cycle. Furthermore, the molecular dynamics simulations helped us come up with a mechanism for VP3 to connect with PI3P in early endosomes. This constitutes a big step forward in our understanding of how these "non-canonical" viruses replicate.

      We have now incorporated new experimental and simulation data; and have carefully revised the manuscript in accordance with the reviewers’ recommendations. We are confident that these improvements have further strengthened the manuscript.

      Reviewer #2 (Public Review):

      Summary:

      Birnavirus replication factories form alongside early endosomes (EEs) in the host cell cytoplasm. Previous work from the Delgui lab has shown that the VP3 protein of the birnavirus strain infectious bursal disease virus (IBDV) interacts with phosphatidylinositol-3-phosphate (PI3P) within the EE membrane (Gimenez et al., 2018, 2020). Here, Zanetti et al. extend this previous work by biochemically mapping the specific determinants within IBDV VP3 that are required for PI3P binding in vitro, and they employ in silico simulations to propose a biophysical model for VP3-PI3P interactions.

      Strengths:

      The manuscript is generally well-written, and much of the data is rigorous and solid. The results provide deep knowledge into how birnaviruses might nucleate factories in association with EEs. The combination of approaches (biochemical, imaging, and computational) employed to investigate VP3-PI3P interactions is deemed a strength.

      Weaknesses:

      (1) Concerns about the sources, sizes, and amounts of recombinant proteins used for co-flotation: Figures 1A, 1B, 1G, and 4A show the results of co-flotation experiments in which recombinant proteins (control His-FYVE v. either full length or mutant His VP3) were either found to be associated with membranes (top) or non-associated (bottom). However, in some experiments, the total amounts of protein in the top + bottom fractions do not appear to be consistent in control v. experimental conditions. For instance, the Figure 4A western blot of His-2xFYVE following co-flotation with PI3P+ membranes shows almost no detectable protein in either top or bottom fractions.

      Liposome-based methods, such as the co-flotation assay, are well-established and widely regarded as the preferred approach for studying protein-phosphoinositide interactions. However, this approach is rather qualitative, as density gradient separation reveals whether the protein is located in the top fractions (bound to liposomes) or the bottom fractions (unbound). Our quantifications aim to demonstrate differences in the bound fraction between liposome populations with and without PI3P. Given the setting of the co-flotation assays, each protein-liposome system [2xFYVE-PI3P(-), 2xFYVE-PI3P(+), VP3-PI3P(-), or VP3-PI3P(+)] is assessed separately, and even if the experimental conditions are homogeneous, it is not surprising to observe differences in the protein level between different experiments. Indeed, the revised version of the manuscript includes membranes with more similar band intensities, as depicted in the new versions of Figures 1 and 4.

      Reading the paper, it was difficult to understand which source of protein was used for each experiment (i.e., E. coli or baculovirus-expressed), and this information is contradicted in several places (see lines 358-359 v. 383-384). Also, both the control protein and the His-VP3-FL proteins show up as several bands in the western blots, but they don't appear to be consistent with the sizes of the proteins stated on lines 383-384. For example, line 383 states that His-VP3-FL is ~43 kDa, but the blots show triplet bands that are all below the 35 kDa marker (Figures 1B and 1G). Mass spectrometry information is shown in the supplemental data (describing the different bands for His-VP3-FL) but this is not mentioned in the actual manuscript, causing confusion. Finally, the results appear to differ throughout the paper (see Figures 1B v. 1G and 1A v. 4A).

      Thank you for pointing out these potentially confusing points in the previous version of the manuscript. Indeed, we were able to produce recombinant VP3 from the two sources: Baculovirus and Escherichia coli. Initially, we opted for the baculovirus system, based on evidence from previous studies showing that it was suitable for ectopic expression of VP3. Subsequently, we successfully produced VP3 using Escherichia coli. On the other side, the fusion proteins His-2xFYVE and GST-2xFYVE were only produced in the prokaryotic system, also following previous reported evidence. We confirmed that VP3, produced in either system, exhibited similar behavior in our co-flotation and bio-layer interferometry (BLI) assays. However, the results of co-flotation and BLI assays shown in Figs. 1 and 4 were performed using the His-VP3 FL, His-VP3 FL R<sub>200</sub>D and His-VP3 FL DCt fusion proteins produced from the corresponding baculoviruses. We have clarified this in the revised version of our manuscript. Please, see lines 430-432.

      Additionally, we have made clear that the His-VP3 FL protein purification yielded four distinct bands, and we confirmed their VP3 identity through mass spectrometry in the revised version of the manuscript. Please, see lines 123-124.

      Finally, we replaced membranes for Figs. 4A and 1G (left panel) with those with more similar band intensities. Please, see the new version of Figures 1 and 4.

      (2) Possible "other" effects of the R<sub>200</sub>D mutation on the VP3 protein. The authors performed mutagenesis to identify which residues within patch 2 on VP3 are important for association with PI3P. They found that a VP3 mutant with an engineered R<sub>200</sub>D change (i) did not associate with PI3P membranes in co-floatation assays, and (ii) did not co-localize with EE markers in transfected cells. Moreover, this mutation resulted in the loss of IBDV viability in reverse genetics studies. The authors interpret these results to indicate that this residue is important for "mediating VP3-PI3P interaction" (line 211) and that this interaction is essential for viral replication. However, it seems possible that this mutation abrogated other aspects of VP3 function (e.g., dimerization or other protein/RNA interactions) aside from or in addition to PI3P binding. Such possibilities are not mentioned by the authors.

      The arginine amino acid at position 200 of VP3 is not located in any of the protein regions associated with its other known functions: VP3 has a dimerization domain located in the second helical domain, where different amino acids across the three helices form a total of 81 interprotomeric close contacts; however, R<sub>200</sub> is not involved in these contacts (Structure. 2008 Jan;16(1):29-37, doi:10.1016/j.str.2007.10.023); VP3 has an oligomerization domain mapped within the 42 C-terminal residues of the polypeptide, i.e., the segment of the protein composed by the residues at positions 216-257 (J Virol. 2003 Jun;77(11):6438–6449, doi: 10.1128/jvi.77.11.6438-6449.2003); VP3’s ability to bind RNA is facilitated by a region of positively-charged amino acids, identified as P1, which includes K<sub>99</sub>, R<sub>102</sub>, K<sub>105</sub>, and K<sub>106</sub> (PLoS One. 2012;7(9):e45957, doi: 10.1371/journal.pone.0045957). Furthermore, our findings indicate that the R<sub>200</sub>D mutant retains a folding pattern similar to the wild-type protein, as shown in Figure 4B. All these lead us to conclude that the loss of replication capacity of R<sub>200</sub>D viruses results from impaired, or even loss of, VP3-PI3P interaction.

      We agree with the reviewer that this is an important point and have accordingly addressed it in the Discussion section of the revised manuscript. Please, see lines 333-346.

      (3) Interpretations from computational simulations. The authors performed computational simulations on the VP3 structure to infer how the protein might interact with membranes. Such computational approaches are powerful hypothesis-generating tools. However, additional biochemical evidence beyond what is presented would be required to support the authors' claims that they "unveiled a two-stage modular mechanism" for VP3-PI3P interactions (see lines 55-59). Moreover, given the biochemical data presented for R<sub>200</sub>D VP3, it was surprising that the authors did not perform computational simulations on this mutant. The inclusion of such an experiment would help tie together the in vitro and in silico data and strengthen the manuscript.

      We acknowledge that the wording used in the previous version of the manuscript may have overstated the "unveiling" of the two-stage binding mechanism of VP3. Our intention was to propose a potential mechanism, that is consistent both with the biophysical experiments and the molecular simulations. In the revised version of the manuscript, we have tempered these claims and framed them more appropriately.

      Regarding the simulations for the R<sub>200</sub>D VP3 mutant, these simulations were indeed performed and included in the original manuscript as part of Figure S14 in the Supplementary Information. However, we realize that this was not sufficiently emphasized in the main text, an oversight on our part. We have now revised the manuscript to highlight these results more clearly.

      Additionally, to further strengthen the connection between experimental and simulation trends, we have now included a new figure in the Supplementary Information (Figure S15). This figure depicts the binding energy of VP3 ΔNt and two of its mutants, VP3 ΔNt R<sub>200</sub>D and VP3 ΔNt P2 Mut, as a function of salt concentration. The results show that as the number of positively charged residues in VP3 is systematically reduced, the binding of the protein to the membrane becomes weaker. The effect is more pronounced at lower salt concentrations, which highlights the weight of electrostatic forces on the adsorption of VP3 on negatively charged membranes. Please, see Supplementary Information (Figure S15).

      Reviewer #3 (Public Review):

      Summary:

      Infectious bursal disease virus (IBDV) is a birnavirus and an important avian pathogen. Interestingly, IBDV appears to be a unique dsRNA virus that uses early endosomes for RNA replication that is more common for +ssRNA viruses such as for example SARS-CoV-2.

      This work builds on previous studies showing that IBDV VP3 interacts with PIP3 during virus replication. The authors provide further biophysical evidence for the interaction and map the interacting domain on VP3.

      Strengths:

      Detailed characterization of the interaction between VP3 and PIP3 identified R<sub>200</sub>D mutation as critical for the interaction. Cryo-EM data show that VP3 leads to membrane deformation.

      Weaknesses:

      The work does not directly show that the identified R<sub>200</sub> residues are directly involved in VP3-early endosome recruitment during infection. The majority of work is done with transfected VP3 protein (or in vitro) and not in virus-infected cells. Additional controls such as the use of PIP3 antagonizing drugs in infected cells together with a colocalization study of VP3 with early endosomes would strengthen the study.

      In addition, it would be advisable to include a control for cryo-EM using liposomes that do not contain PIP3 but are incubated with HIS-VP3-FL. This would allow ruling out any unspecific binding that might not be detected on WB.

      The authors also do not propose how their findings could be translated into drug development that could be applied to protect poultry during an outbreak. The title of the manuscript is broad and would improve with rewording so that it captures what the authors achieved.

      In previous works from our group, we demonstrated the crucial role of the VP3 P2 region in targeting the early endosomal membranes and for viral replication, including the use of PI3K inhibitors to deplete PI3P, showing that both the control RFP-2xFYVE and VP3 lost their ability to associate with the early endosomal membranes and reduces the production of an infective viral progeny (J Virol. 2018 May 14;92(11):e01964-17, doi: 10.1128/jvi.01964-17; J Virol. 2021 Feb 24;95(6):e02313-20, doi: 10.1128/jvi.02313-20). In the present work, to further characterize the role of R<sub>200</sub> in binding to early endosomes and for viral replication, we show that: i) the transfected VP3 R<sub>200</sub>D protein loses the ability to bind to early endosomes in immunofluorescence assays (Figure 2E and Figure 3); ii) the recombinant His-VP3 FL R<sub>200</sub>D protein loses the ability to bind to liposomes PI3P(+) in co-flotation assays (Figure 4A); and, iii) the mutant virus R<sub>200</sub>D loses replication capacity (Figure 4C).

      Regarding the cryo-electron microscopy observation, we verified that there is no binding of gold particles to liposomes PI3P(-) when they are incubated solely with the gold-particle reagent, or when they are pre-incubated with the gold-particle reagent with either His-2xFYVE or His-VP3 FL. We have incorporated a new panel in Figure 1C showing a representative image of these results. Please, see lines 143-144 in the revised version of our manuscript and our revised version of Figure 1C.

      We have replaced the title of the manuscript by a more specific one. Thus, our current is " On the Role of VP3-PI3P Interaction in Birnavirus Endosomal Membrane Targeting".

      Regarding the question of how our findings could be translated into drug development, indeed, VP3-PI3P binding constitutes a good potential target for drugs that counteract infectious bursal disease. However, we did not mention this idea in the manuscript, first because it is somewhat speculative and second because infected farms do not implement any specific treatment. The control is based on vaccination.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Critical issues to address:

      (1) The citations in the important paragraph on lines 101-5 are not identifiable. These references are described as showing that VP3 is associated with EEs via P2 and PI3P, which is basically what this paper also shows. The significant advance here is unclear.

      We apologize for this mistake. These citations are identifiable in the revised version of the manuscript (lines 100-105). As mentioned before, in this manuscript we present biochemical and biophysical details that have not been reported before about how VP3 connects with early endosomes, showing that it interacts directly with the PI3P. Additionally, we have now identified a critical residue in VP3 P2—the R<sub>200</sub>—for binding to PI3P and its key role in the viral life cycle. Furthermore, the molecular dynamics simulations helped us come up with a mechanism for VP3 to connect with PI3P in early endosomes. This constitutes a big step forward in our understanding of how these "non-canonical" viruses replicate.

      (2) Even if all the claims were to be clearly supported through major revamping, authors should make the significance of knowing that this protein binds to early endosomes through PI3P more clear?

      Thank you for the recommendation, which aligns with a similar suggestion from Reviewer #2. In response, we have revised the significance paragraph to emphasize the mechanistic aspects of our findings. Please refer to lines 62–67 in the revised manuscript.

      (3) Flotation assay shows binding, but this is not quantitative. An estimate of a Kd would be useful. BLI experiments suggest that half of the binding disappears at 0.5 mM, implying a very low binding affinity.

      We agree with the reviewer that our biophysical and molecular simulation results suggest a specific but weak interaction of VP3 with PI3P bearing membranes. Indeed, our previous version of the manuscript already contained a paragraph in this regard. Please, see lines 323-332 in the revised version of the manuscript.

      From a biological point of view, a low binding affinity of VP3 for the endosomes may constitute an advantage for the virus, in the sense that its traffic through the endosomes may be short lived during its infectious cycle. Indeed, VP3 has been demonstrated to be a "multifunctional" protein involved in several processes of the viral cycle (detailed in lines 84-90), and in our laboratory we have shown that the Golgi complex and the endoplasmic reticulum are organelles where further viral maturation occurs. Taking all of this into account, a high binding affinity of VP3 for endosomes could result in the protein becoming trapped on the endosomal membrane, potentially hindering the progression of the viral infection within the host cell.

      (4) There are some major internal inconsistencies in the data: Figure 1B quantifies VP3-FL T/B ratio ~4 (which appears inconsistent with the image shown, as the T lanes are much lighter than the B) whereas apparently the same experiment in Figure 1G shows it to be ~0.6. With the error bars shown, these results would appear dramatically different from each other, despite supposedly measuring the same thing. The same issue with the FYVE domain between Figures 1A and 4A.

      We appreciate the reviewer’s comment, as it made us aware of an error in Figure 1B. There, the mean value for the VP3-FL Ts/B ratio is 3.0786 for liposomes PI3P(+) and 0.4553 for liposomes PI3P(-) (Please, see the new bar graph on Figure 1B). This may have occurred because, due to the significance of these experiments, we performed multiple rounds of quantification in search of the most suitable procedure for our observations, leading to a mix-up of data sets. Anyway, it’s possible that these corrected values still seem inconsistent given that T lanes are much lighter than the B for VP3-FL in the image shown. Flotation assays are quite labor-intensive and, at least in our experience, yield fairly variable results in terms of quantification. To illustrate this point, the following image shows the three experiments conducted for Figure 1B, where it is clear that, despite producing visually distinct images, all three yielded the same qualitative observation. For Figure 1B, we chose to present the results from experiment #2. However, all three experiments contributed to a Ts/B ratio of 3.0786 for His-VP3 FL, which may account for the apparent inconsistency when focusing solely on the image in Figure 1B.

      Author response image 1.

      We acknowledge that, at first glance, some inconsistencies may appear in the results, and we have thoroughly discussed the best approach for quantification. However, we believe the observations are robust in terms of reproducibility and reliable, as the VP3-PI3P interaction was consistently validated by comparison with liposomes lacking PI3P, where no binding was observed.

      (5) Comparison of PA (or PI) to PI3P at the same molar concentration is inappropriate because PI3P has at least double charge. The more interesting question about specificity would be whether PI45P2 (or even better PI35P2) binds or not. Without this comparison, no claim to specificity can be made.

      For us, "specificity" refers to the requirement of a phosphoinositide in the endosomal membrane for VP3 binding. Phosphoinositides have a conspicuous distribution among cellular compartments, and knowing that VP3 associates with early endosomes, our specificity assays aimed to demonstrate that PI3P is strictly required for the binding of VP3. To validate this, we used PI (lacking the phosphate group) and PA (lacking the inositol group) despite their similar charges. In spite of the potential chemical interactions between VP3 and various phosphoinositides, our experimental results suggest that the virus specifically targets endosomal membranes by binding to PI3P, a phosphoinositide present only in early endosomes.

      That said, we agree with the reviewer’s point and consider adequate to smooth our specificity claim in the manuscript as follows: “We observed that His-VP3 FL bound to liposomes PI3P(+), but not to liposomes PA or PI, reinforcing the notion that a phosphoinositide is required since neither a single negative charge nor an inositol ring are sufficient to promote VP3 binding to liposomes (SI Appendix, Fig. S2)” (Lines 136-139).

      (6) In the EM images, many of the gold beads are inside the vesicles. How do they cross the membranes?

      They do not cross the membrane. Our EM images are two-dimensional projections, meaning that the gold particles located on top or beneath the plane appear to be inside the liposome.

      (7) Images in Figure 2D are very low quality and do not show the claimed difference between any of the mutants. All red signal looks basically cytosolic in all images. It is not clear what criteria were used for the quantification in Figure 2E. The same issue is in Figure 2E, where no red WT puncta are observable at all. Consistently, there is minimal colocalization in the quantification in Figure S3, which appears to show no significant differences between any of the mutants, in direct contradiction to the claim in the manuscript.

      We apologize for the poor quality of panels in Figures 2D and 2E. Unfortunately, this was due to the PDF conversion of the original files. Please, check the high-quality version of Figure 2. As suggested by reviewers #2 and #3, we have incorporated zoomed panels, which help the reader to better see the differences in distribution.

      As mentioned in the legend to Figure 2, the quantification in Figure 2D was performed by calculating the percentage of cells with punctuated fluorescent red signal (showing VP3 distribution) for each protein. The data were then normalized to the P2 WT protein, which is the VP3 wild type.

      Figure S3 certainly shows a tendency which positively correlates with the results shown in Figure 3, where we used FYVE to detect PI3P on endosomes and observed significantly less co-localization when VP3 bears its P2 region all reversed or lacks the R<sub>200</sub>

      (8) The only significant differences in colocalization are in Figure 3B, whose images look rather dramatically different from the rest of the manuscript, leading to some concern about repeatability. Also, it is unclear how colocalization is quantified, but this number typically cannot be above 1. Finally, it is unclear what is being colocalized here: with three fluorescent components, there are 3 possible binary colocalizations and an additional ternary colocalization.

      We thank the reviewer for pointing out those aspects related to Figure 3. The experiments performed for Figure 3B were conducted by a collaborator abroad handling the purified GST-2xFYVE, which recognizes endogenous PI3P, while the rest of the cell biology experiments were conducted in our laboratory in Argentina. This is why they are aesthetically different. We have made an effort in homogenizing the way they look for the revised version of the manuscript. Please, see the new version of Figure 3.

      For quantification of the co-localization of VP3 and EGFP-2xFYVE (Figure 3A), the Manders M2 coefficient was calculated out of approximately 30 cells per construct and experiment. The M2 coefficient, which reflects co-localization of signals, is defined as the ratio of the total intensities of magenta image pixels for which the intensity in the blue channel is above zero to the total intensity in the magenta channel. JACoP plugin was utilized to determine M2. For VP3 puncta co-distributing with EEA1 and GST-FYVE (Figure 3B), the number of puncta co-distributing for the three signals was manually determined out of approximately 40 cells per construct and experiment per 200 µm². We understand that Manders or Pearson coefficients, typically ranging between 0 and 1, is the most commonly used method to quantify co-localizing immunofluorescent signals; however, this “manual” method has been used and validated in previous published manuscripts [Figures 3 and 7 from (Morel et al., 2013); Figure 7 in (Khaldoun et al., 2014); and Figure 4 in (Boukhalfa et al., 2021)].

      (9) SegA/B plasmids are not introduced, and it is not clear what these are or how this assay is meant to work. Where are the foci forming units in the images of Figure 4C? How does this inform on replication? Again, this assay is not quantitative, which is essential here: does the R<sub>200</sub> mutant completely kill activity (whatever that is here)? Or reduce it somewhat?

      We apologize for the missing information. Segments A and B are basically the components of the IBDV reverse genetics system. For their construction, we used a modification of the system described by Qi and coworkers (Qi et al., 2007), in which the full length sequences of the IBDV RNA segments A and B, flanked by a hammerhead ribozyme at the 5’-end and the hepatitis delta ribozyme at the 3’-end, were expressed under the control of an RNA polymerase II promoter within the plasmids pCAGEN.Hmz.SegA.Hdz (SegA) and pCAGEN.Hmz.SegB.Hdz (SegB). For this specific experiment we generated a third plasmid, pCAGEN.Hmz.SegA.R<sub>200</sub>D.Hdz (SegA.R<sub>200</sub>D), harboring a mutant version of segment A cDNA containing the R<sub>200</sub>D substitution. Then, QM7 cells were transfected with the plasmids SegA, SegB or Seg.R<sub>200</sub>D alone (as controls) or with a mixture of plasmids SegA+SegB (wild type situation) or SegA.R<sub>200</sub>D+SegB (mutant situation). At 8 h post transfection (p.t.), when the new viruses have been able to assemble starting from the two segments of RNA, the cells were recovered and re-plated onto fresh non-transfected cells for revealing the presence (or not) of infective viruses. At 72 h post-plating, the generation of foci forming units (FFUs) was revealed by Coomassie staining. As expected, single-transfections of SegA, SegB or Seg.R<sub>200</sub>D did not produce FFUs and, as shown in Figure 4C, the transfection of SegA+SegB produced detectable FFUs (the three circles in the upper panel) while no FFUs (the three circles in the lower panel) were detected after the transfection of SegA.R<sub>200</sub>D+SegB (Figure 4C). This system is quantitative, since the FFUs detected 72 h post-plating are quantifiable by simply counting the FFUs. However, since no FFUs were detected after the transfection of SegA.R<sub>200</sub>D+SegB, evidenced by a complete monolayer of cells stained blue, we did not find any sense in quantifying. In turn, this drastic observation indicates that viruses bearing the VP3 R<sub>200</sub>D mutation lose their replication ability (is “dead”), demonstrating its crucial role in the infectious cycle.

      We agree with the reviewer that a better explanation was needed in the manuscript, so we have incorporated a paragraph in the results section of our revised version of the manuscript (lines 209-219).

      (10) Why pH 8 for simulation?

      The Molecular Theory calculations were performed at pH 8 for consistency with the experimental conditions used in our biophysical assays. These biophysical experiments were also performed at pH 8, following the conditions established in the original study where VP3 was first purified for crystallization (DOI: 10.1016/j.str.2007.10.023).

      (11) There is minimal evidence for the sequential binding model described in the abstract. The simulations do not resolve this model, nor is truly specific PI3P binding shown.

      In response to your concerns, we would like to emphasize that our simulations provide robust evidence supporting the two more important aspects of the sequential binding model: 1) Membrane Approach: In all simulations, VP3 consistently approaches the membrane via its positively charged C-terminal (Ct) region. 2) PI3P Recruitment: Once the protein is positioned flat on the membrane surface, PI3P is unequivocally recruited to the positively charged P2 region. The enrichment of PI3P in the proximity to the protein is clearly observed and has been quantified via radial distribution functions, as detailed in the manuscript and supplementary material.

      While we understand that opinions may vary on the sufficiency of the data to fully validate the model, we believe the results offer meaningful insights into the proposed binding mechanism. That said, we acknowledge that the specificity of VP3 binding may not be restricted solely to PI3P but could extend to phosphoinositides in general. To address this, we performed the new set of co-flotation experiments which are discussed in detail in our response to point 5.

      Reviewer #2 (Recommendations For The Authors):

      (1) Line 1: Consider changing the title to better reflect the mostly biochemical and computational data presented in the paper: "Mechanism of Birnavirus VP3 Interactions with PI3P-Containing Membranes". There are no data to show hijacking by a virus presented.

      We appreciate this recommendation, which was also expressed by reviewer #3. Additionally, we thank for the suggested title. We have replaced the title of the manuscript by a more specific one. Thus, our current is

      "On the Role of VP3-PI3P Interaction in Birnavirus Endosomal Membrane Targeting".

      (2) Lines 53-54 and throughout: Consider rephrasing "demonstrate" to "validate" to give credit to Gimenez et al., 2018, 2022 for discovery.

      Thanks for the suggestion. We have followed it accordingly. Please see line 52 from our revised version of the manuscript.

      (3) Line 56-59 and throughout: Consider tempering and rephrasing these conclusions that are based mostly on computational data. For example, change "unveil" to "suggest" or another term.

      We have now modified the wording throughout the manuscript.

      (4) The abstract could also emphasize that this study sought to map the resides within VP3 that are important for P13P interaction.

      Thanks for the suggestion. We have followed it accordingly. Please, see lines 53-55 from our revised version of the manuscript.

      (5) Lines 63-69: This Significance paragraph seems tangential. The findings in this paper aren't at all related to the evolutionary link between birnaviruses and positive-strand RNA viruses. The significance of the work for me lies in the deep biochemical/biophysical insights into how a viral protein interacts with membranes to nucleate its replication factory.

      We have re-written the significance paragraph highlighting the mechanistic aspect of our findings. Please, see lines 62-67 in our revised version of the manuscript.

      (6) Line 74: Please define "IDBV" abbreviation.

      We apologize for the missing information. We have defined the IBDV abbreviation in our revised version of the manuscript (please, see line 73).

      (7) Line 88: Please define "pVP2" abbreviation.

      We apologize for the missing information. We have defined the pVP2 abbreviation in our revised version of the manuscript (please, see line 87).

      (8) Lines 101-105: Please change references (8, 9, 10) to be consistent with the rest of the manuscript (names, year).

      We apologize for this mistake. These citations are identifiable and consistent in the revised version of the manuscript (lines 100-105).

      (9) Line 125: For a broad audience, consider explaining that recombinant His-2xFYVE domain is known to exhibit PI3P-binding specificity and was used as a positive control.

      Thanks for the recommendation. We have incorporated a brief explanation supporting the use of His-2xFYVE as a positive control in our revised version of the manuscript. Please, see lines 127-129.

      (10) Lines 167-171: The quantitative data in Figure S3 shows that there was a non-significant co-localization coefficient of the R<sub>200</sub>D mutant. For transparency, this should be stated in the Results section when referenced.

      We agree with this recommendation. We have clearly mentioned it in the revised version of the manuscript. Please, see lines 177-179. Also, we have referred this fact when introducing the assays performed using the purified GST-2xFYVE, shown in Figure 3. Please, see lines 182-184.

      (11) Lines 156 and 173: These Results section titles have nearly identical wording. Consider rephrasing to make it distinct.

      We agree with the reviewer’s observation. In fact, we sought to do it on purpose as for them to be a “wordplay”, but we understand that could result in a awkwarded redundancy. So, in the revised version of the manuscript, both titles are:

      Role of VP3 P2 in the association of VP3 with the EE membrane (line 163).

      VP3 P2 mediates VP3-PI3P association to EE membranes (line 182).

      (12) Line 194: Is it alternatively possible that the R<sub>200</sub>D mutant lost its capacity to dimerize, and that in turn impacted PI3P interaction?

      Thanks for the relevant question. VP3 was crystallized and its structure reported in (Casañas et al., 2008) (DOI: 10.1016/j.str.2007.10.023). In that report, the authors showed that the two VP3 subunits associate in a symmetrical manner by using the crystallographic two-fold axes. Each subunit contributes with its 30% of the total surface to form the dimer, with 81 interprotomeric close contacts, including polar bonds and van der Waals contacts. The authors identified the group of residues involved in these interactions, among which the R<sub>200</sub> is not included. Addittionally, the authors determined that the interface of the VP3 dimer in crystals is biologically meaningful (not due to the crystal packing).

      To confirm that the lack of binding was not due to misfolding of the mutant, we compared the circular dichroism spectra of mutant and wild type proteins, without detecting significant differences (shown in Figure 4B). These observations do not exclude the possibility mentioned by the reviewer, but constitute solid evidences, we believe, to validate our observations.

      (13) Lines 231-243: Consider changing verbs to past tense (i.e., change "is" to "was") for the purposes of consistency and tempering.

      Thanks for the recommendation, we have proceeded as suggested. Please, see lines 249-262 in our revised version of the manuscript.

      (14) Lines 306-308: Is there any information about whether it is free VP3 (v. VP3 complexed in RNP) that binds to membrane? I am just trying to wrap my head around how these factories form during infection.

      Thanks for pointing this out. We first observed that in infected cell, all the components of the RNPs [VP3, VP1 (the viral polymerase) and the dsRNA] were associated to the endosomes. Since by this moment it had been already elucidated that VP3 "wrapped" de dsRNA within the RNPs (Luque et al., 2009) (DOI: 10.1016/j.jmb.2008.11.029), we sought that VP3 was most probably leading this association. We answered yes after studying its distribution, also endosome-associated, when ectopically expressed. These results were published in (Delgui et al., 2013) (DOI: 10.1128/jvi.03152-12).

      Thus, in our subsequent studies, we have worked with both, the infection-derived or the ectopically expressed VP3, to advance in elucidating the mechanism by which VP3 hijacks the endosomal membranes and its relevancy for viral replication, reported in this current manuscript.

      (15) Lines 320-334: This last paragraph discussing evolutionary links between birnaviruses and positive-strand RNA viruses seems tangential and distracting. Consider reducing or removing.

      Thanks for highlighting this aspect of our work. Maybe difficult to follow, but in the context of other evidences reported for the Birnaviridae family of viruses, we strongly believe that there is an evolutionary aspect in having observed that these dsRNA viruses replicate associated to membranous organelles, a hallmark of +RNA viruses. However, we agree with the reviewer that this might not be the main point of our manuscript, so we reduced this paragraph accordingly. Please, see lines 358-367 in our revised version of the manuscript.

      (16) Lines 322-324: Change "RdRd" to "RdRp" if keeping paragraph.

      Thanks. We have corrected this mistake in lines 360 and 361.

      (17) Figures 1A, 1B, and throughout: Again, please check and explain protein sizes and amounts. This would improve the clarity of the manuscript.

      All our flotation assays were performed using 1 mM concentration of purified protein in a final volume of 100 mL (mentioned in M&M section). The complete fusion protein His-2xFYVE (shown in Figs. 1A and 4A left panel) is 954 base pairs-long and contains 317 residues (~35 kDa). The complete fusion protein His-VP3 FL (shown in Figs. 1B and 1G left panel) is 861 base pairs-long and contains 286 residues (~32 kDa). The complete fusion protein His-VP3 DCt (shown in Fig. 1G, right panel) is 753 bp-long and contains 250 residues (~28 kDa). The complete fusion protein His-VP3 FL R<sub>200</sub>D (shown in Fig. 4A right panel) is 861 bp-long and contains 286 residues (~32 kDa). This latter information was incorporated in our revised version of the manuscript. Please, see lines 381-382, 396-397 and 399-400 from the M&M section, and lines in the corresponding figure legends.

      (18) Figures 1B and 1G show different results for PI3P(+) membranes. I see protein associated with the top fraction in 1B, but I don't see any such result in 1G.

      As already mentioned, liposome-based methods, such as the co-flotation assay, are well-established and widely regarded as the preferred approach for studying protein-phosphoinositide interactions. However, this approach is rather qualitative, as density gradient separation reveals whether the protein is located in the top fractions (bound to liposomes) or the bottom fractions (unbound). Our quantifications aim to demonstrate differences in the bound fraction between liposome populations with and without PI3P. Given the setting of the co-flotation assays, each protein-liposome system [2xFYVE-PI3P(-), 2xFYVE-PI3P(+), VP3-PI3P(-), or VP3-PI3P(+)] is assessed separately, and even if the conditions are homogeneous, it’s not surprising to observe differences in the protein level between each one. Indeed, the revised version of the manuscript include a membrane for Figure 1G, were His-VP3 FL associated with the top fraction is more clear. Please, see the new version of Figure 1G.

      (19) Figure 1C: Please include cryo-EM images of the liposome PI3P(-) variables to assess the visual differences of the liposomal membranes under these conditions.

      Thanks for the recommendation. it has been verified that there is no binding of gold particles to liposomes PI3P(-) when they are incubated solely with the gold-particle reagent, or when they are pre-incubated with the gold-particle reagent with either His-2xFYVE or His-VP3 FL. We have incorporated a new panel in Figure 1C showing a representative image of these results. Please, see lines 143-144 in the revised version of our manuscript and our revised version of Figure 1C.

      (20) Figures 2D, 2E, and 3A: The puncta are not obvious in these images. Consider adding Zoomed panels.

      We apologize for this aspect of Figures 2 and 3, also highlighted by reviewer #1. We believe that this was due to the low quality resulting from the PDF conversion of the original files. For Figure 3A, we have homogenized its aspect with those from 3B. Regarding Figure 2, we have incorporated zoomed panels, as suggested. Please, see the revised versions of both Figures.

      (21) Figure 4A: There is almost no protein in the control PI3P(+) blot. Why? Also, the quantification shows no significant membrane association for this control. This result is different from Figure 1A and very confusing (and concerning).

      We apologize for the confusion. We replaced membranes for Figure 4A (left panel) with more similar band intensities to that shown in Figure 1A. Please, visit our new version of Figure 4. The quantification shows no significant difference in the association to liposomes PI3P(+) compared to liposomes PI3P(+); it’s true and this is due to, once more, the intrinsically lack of homogeneity of co-flotation assays. However, this one shown in Figure 4A is a redundant control (has been shown in Figure 1A) and we believe that the new membrane is qualitative eloquent.

      Reviewer #3 (Recommendations For The Authors):

      (1) Overall, the title is general and does not summarize the study. I recommend making the title more specific. The current title is better suited for a review as opposed to a research article. This study provides further biophysical details on the interaction. This should be reflected in the title.

      We appreciate this recommendation, which was also expressed by reviewer #2. We have chosen a new title for the manuscript: “On the Role of VP3-PI3P Interaction in Birnavirus Endosomal Membrane Targeting”.

      (2) References 8,9,10 are important but they were not correctly cited in the work, this should be corrected.

      We apologize for this mistake. These citations are identifiable in our revised version of the manuscript. See lines 100-105.

      (3) Flotation experiments and cryo-EM convincingly show that VP3 binds to membranes in a PIP3-dependent manner. However, it would be advisable to include a control for cryo-EM using liposomes that do not contain PIP3 but are incubated with HIS-VP3-FL. This would allow us to rule out any unspecific binding that might not be detected on WB.

      Thanks for the advice, also given by reviewer #2. We confirmed that no gold particles were bound on liposomes PI3P(-) even when incubated with the Ni-NTA reagent alone or pre-incubated with His-2xFYVE of His-VP3 FL. We have incorporated a new panel to Figure 1C showing a representative image of these results. Please, see lines 143-144 in the revised version of the manuscript and see the revised version of Figure 1C.

      (4) It is not clear what is the difference between WB in B and WB in G. Figure 1G seems to show the same experiment as shown in B, is this a repetition? In both cases, plots next to WBs show quantification with bars, do they represent STD or SEM? Legend A mentions significance p>0.01 (**) but the plot shows ***. This should be corrected.

      The Western blot membrane in Figure 1B shows the result of co-flotation assay using His-VP3 FL protein, while the Western blot membrane in Figure 1G (left panel) shows a co-flotation assay using His-VP3 FL protein as a positive control. In another words, in 1B the His-VP3 FL protein is the question while in 1G (left panel) it’s the co-flotation positive control for His-VP3 DCt. The bar plots next to Western blots show quantification, the mean and the STD. Thanks for highlighting this inconsistency. We have now corrected it on the revised version of the manuscript.

      (5) It would be useful to indicate positively charged residues and P2 on the AF2 predicted structure in Fig 1.

      These are indicated in panels A and B of Figure 2.

      (6) Figure 1 legend: Change cryo-fixated liposomes to cryo-fixation or better to "liposomes were vitrified". There is a missing "o" in the cry-fixation in the methods section.

      Thanks for the recommendation. We have modified Figure 1. legend to "liposomes were vitrified" (line 758), and fixed the word cryo-fixation in the methods section (line 512).

      (7) Figure 2B. It is not clear how the punctated phenotype was unbiasedly characterized (Figure 2D). I see no difference in the representative images. Magnified images should be shown. This should be measured as colocalization (Pearson's and Mander's coefficient) with an early endosomal marker Rab5. Perhaps this figure could be consolidated with Figure 3.

      Unfortunately, the lack of clarity in Figure 2D was due to the PDF conversion of the original files. Please, observe the high-quality original image above in response to reviewer #1, where we have additionally included zoomed panels, as also suggested by the other reviewers. For quantification of the co-localization of VP3 and either EGFP-Rab5 orEGFP-2xFYVE, the Manders M2 coefficient was calculated out of approximately 30 cells per construct and experiment and were shown in Figure S3 and Figure 3A, respectively, in our previous version of the manuscript.

      (8) PIP3 antagonist drugs should be used to further substantiate the results. If PIP3 specifically recruits VP3, this interaction should be abolished in the presence of PIP3 drug and VP3 should show a diffused signal.

      We certainly agree with this point. These experiments were performed and the results were reported in (Gimenez et al., 2020). Briefly, in that work, we blocked the synthesis of PI3P in QM7 cells in a stable cell line overexpressing VP3, QM7-VP3, with either the pan-PI3Kinase (PI3K) inhibitor LY294002, or the specific class III PI3K Vps34 inhibitor Vps34-IN1. In Figure 4, we showed that 98% of the cells treated with these inhibitors had the biosensor GFP-2FYVE dissociated from EEs, evidencing the depletion of PI3P in EEs (Figure 4A). In QM7-VP3 cells, we showed that the depletion of PI3P by either inhibitor caused the dissociation of VP3 from EEs and the disaggregation of VP3 puncta toward a cytosolic distribution (Figure 4B). Moreover, since this observation was crucial for our hipothesis, these results were further confirmed with an alternative strategy to deplete PI3P in EEs. We employed a system to inducibly hydrolyze endosomal PI3P through rapamycin-induced recruitment of the PI3P-myotubularin 1 (MTM1) to endosomes in cells expressing MTM1 fused to the FK506 binding protein (FKBP) and the rapamycin-binding domain fused to Rab5, using the fluorescent proteins mCherry-FKBP-MTM1 and iRFP-FRB-Rab5, as described in (Hammond et al., 2014). These results, shown in Figures 5, 6 and 7 in the same manuscript, further reinforced the notion that PI3P mediates and is necessary for the association of VP3 protein with EEs.

      (9) The authors should show the localization of VP3 in IBDV-infected cells and treat cells with PI3P antagonists. The fact that R<sub>200</sub> is not rescued does not necessarily mean that this is because of the failed interaction with PI3P. As the authors wrote in the discussion: VP3 bears multiple essential roles during the viral life cycle (line 305).

      Indeed, after having confirmed that the VP3 lost its localization associated to the endosomes after the treatment of the cells with PI3P antagonists, we demonstrated that depletion of PI3P significantly reduced the production of IBDV progeny. For this aim, we used two approaches, the inhibitor Vps34-IN1 and an siRNA against VPs34. In both cases, we observed a significantly reduced production of IBDV progeny (Figures 9 and 10). Specifically related to the reviewer’s question, the localization of VP3 in IBDV-infected cells and treated with PI3P antagonists was shown and quantified in Figure 9a.

      (10) Could you provide adsorption-free energy profiles and MD simulations also for the R<sub>200</sub> mutant?

      Following the reviewer’s suggestion, we have added a new figure to the supplementary information (Figure S15). Instead of presenting a full free-energy profile for each protein, we focused on the adsorption free energy (i.e., the minimum of the adsorption free-energy profile) for VP3 ΔNt and its mutants, VP3 ΔNt R<sub>200</sub>D and VP3 ΔNt P2 Mut, as a function of salt concentration. The aim was to compare the adsorption free energy of the three proteins and evaluate the effect of electrostatic forces on it, which become increasingly screened at higher salt concentrations. As shown in the referenced figure, reducing the number of positively charged residues from VP3 ΔNt to VP3 ΔNt P2 Mut systematically weakens the protein’s binding to the membrane. This effect is particularly pronounced at lower salt concentrations, underscoring the importance of electrostatic interactions in the adsorption of the negatively charged VP3 onto the anionic membrane.

      (11) Liposome deformations in the presence of VP3 are interesting (Figure 6G), were these also observed in Figure 1C?

      Good question. The liposome deformations in the presence of VP3 shown in Figure 6G were a robust observation since, as mentioned, it was detectable in 36% of the liposomes PI3P(+), while they were completely absent in PI3P(-) liposomes. However, and unfortunately, the same deformations were not detectable in experiments performed using gold particles shown in Figure 1C. In this regard, we think that it might be possible that the procedure of gold particles incubation itself, or even the presence of the gold particles in the images, would somehow “mask” the deformations effect.

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      Qi X, Gao Y, Gao H, Deng X, Bu Z, Wang Xiaoyan, Fu C, Wang Xiaomei. 2007. An improved method for infectious bursal disease virus rescue using RNA polymerase II system. J Virol Methods 142:81–88. doi:10.1016/j.jviromet.2007.01.021

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Hua et al show how targeting amino acid metabolism can overcome Trastuzumab resistance in HER2+ breast cancer.

      Strengths:

      The authors used metabolomics, transcriptomics and epigenomics approaches in vitro and in preclinical models to demonstrate how trastuzumab-resistant cells utilize cysteine metabolism.

      Thank you for your valuable comments. We would like to extend our appreciation for your efforts. Your constructive suggestion would help improve our research.

      Weaknesses:

      However, there are some key aspects that needs to be addressed.

      Major:

      (1) Patient Samples for Transcriptomic Analysis: It is unclear from the text whether tumor tissues or blood samples were used for the transcriptomic analysis. This distinction is crucial, as these two sample types would yield vastly different inferences. The authors should clarify the source of these samples.

      Thank you for your valuable comments. In the transcriptomic analysis, we included the data of HER2 positive breast cancer patients who received trastuzumab in I-SPY2 trial (GSE181574). Tumor tissues were used in this dataset.

      (2) The study only tested one trastuzumab-resistant and one trastuzumab-sensitive cell line. It is unclear whether these findings are applicable to other HER2-positive tumor cell lines, such as HCC1954. The authors should validate their results in additional cell lines to strengthen their conclusions.

      Thank you for your valuable comments. We agree with your opinion, and the exploration of multiple cell lines would make our research findings more comprehensive. This is a limitation of our study, and we would continue to improve our design and methods in future experiments.

      (3) Relevance to Metastatic Disease: Trastuzumab resistance often arises in patients during disease recurrence, which is frequently associated with metastasis. However, the mouse experiments described in this paper were conducted only in the primary tumors. This article would have more impact if the authors could demonstrate that the combination of Erastin or cysteine starvation with trastuzumab can also improve outcomes in metastasis models.

      Thank you for your valuable comments. We agree with your suggestions. The exploration of metastatic disease would make our research more meaningful and help better address clinical key issues. In our future studies, we will continue to investigate the association between the invasive and metastatic capabilities of trastuzumab resistant HER2 positive breast cancer and cysteine metabolism.

      Minor:

      (1) The figures lack information about the specific statistical tests used. Including this information is essential to show the robustness of the results.

      Thank you for your valuable comments. We would include the statistical information in our figure legends.

      (2) Figure 3K Interpretation: The significance asterisks in Figure 3K do not specify the comparison being made. Are they relative to the DMSO control? This should be clarified.

      Thank you for your valuable comments. We would clarify the comparison information in our figure legends.

      Reviewer #2 (Public review):

      In this manuscript, Hua et al. proposed SLC7A11, a protein facilitating cellular cystine uptake, as a potential target for the treatment of trastuzumab-resistant HER2-positive breast cancer. If this claim holds true, the finding would be of significance and might be translated to clinical practice. Nevertheless, this reviewer finds that the conclusion was poorly supported by the data.

      Notably, most of the data (Figures 2-6) were based on two cell lines - JIMT1 as a representative of trastuzumab-resistant cell line, and SKBR3 as a representative of trastuzumab sensitive cell line. As such, these findings could be cell-line specific while irrelevant to trastuzumab sensitivity at all. Furthermore, the authors claimed ferroptosis simply based on lipid peroxidation (Figure 3). Cell viability was not determined, and the rescuing effects of ferroptosis inhibitors were missing. The xenograft experiments were also suspicious (Figure 4). The description of how cysteine starvation was performed on xenograft tumors was lacking, and the compound (i.e., erastin) used by the authors is not suitable for in vivo experiments due to low solubility and low metabolic stability. Finally, it is confusing why the authors focused on epigenetic regulations (Figures 5 & 6), without measuring major transcription factors (e.g., NRF2, ATF4) which are known to regulate SLC7A11.

      To sum up, this reviewer finds that the most valuable data in this manuscript is perhaps Figure 1, which provides unbiased information concerning the metabolic patterns in trastuzumab-sensitive and primary resistant HER2-positive breast cancer patients.

      Thank you for your valuable comments. We agree with your suggestions. Your feedback would help enhance the quality of our research.

      (1) Our research was mainly conducted in JIMT1 (trastuzumab resistant) and SKBR3 (trastuzumab sensitive), and this is a limitation of our study. The experimental validation using different cell lines will make our research findings more persuasive. In our future research, we will continuously optimize experimental design and methods to make our findings more comprehensive.

      (2) The detection of ferroptosis in our research was mainly performed by evaluating the lipid peroxidation. Experiments measuring cell viability and rescuing effects would help provide more evidence.

      (3) In xenograft experiments, the cysteine starvation was performed by feeding cysteine-free diet. The drug dissolution and other conditions were optimized by referring to previous relevant literature. We would clarify more details in our article.

      (4) Epigenetic modifications have been recognized as crucial factors in drug resistance formation. An increasing number of studies have emphasized the importance of epigenetic changes in regulating the abnormal expression of oncogenes and tumor suppressor genes related to drug resistance. Currently, the role of epigenetic changes in the development of trastuzumab resistance in HER2 positive breast cancer is still in exploration. We tried to investigate the dysregulation of histone modifications and DNA methylation in trastuzumab resistant HER2 positive breast cancer. Our findings indicated that targeting H3K4me3 and DNA methylation could decrease SLC7A11 expression and induce ferroptosis. This would provide more evidence in exploring trastuzumab resistance mechanisms. We will provide a more detailed discussion in the article.

      We would like to extend our appreciation for your constructive suggestions and continue to improve our research in future experiments.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      In this manuscript, the authors report that GPR55 activation in presynaptic terminals of Purkinje cells decrease GABA release at the PC-DCN synapse. The authors use an impressive array of techniques (including highly challenging presynaptic recordings) to show that GPR55 activation reduces the readily releasable pool of vesicle without affecting presynaptic AP waveform and presynaptic Ca2+ influx. This is an interesting study, which is seemingly well-executed and proposes a novel mechanism for the control of neurotransmitter release. However, the authors' main conclusions are heavily, if not solely, based on pharmacological agents that most often than not demonstrate affinity at multiple targets. Below are points that the authors should consider in a revised version.

      We thank the reviewer for the encouraging comments, and will fully address the reviewer’s concerns as detailed below.

      Major points:

      (1) There is no clear evidence that GPR55 is specifically expressed in presynaptic terminals at the PC-DCN synapse. The authors cited Ryberg 2007 and Wu 2013 in the introduction, mentioning that GPR55 is potentially expressed in PCs. Ryberg (2007) offers no such evidence, and the expression in PC suggested by Wu (2013) does not necessarily correlate with presynaptic expression. The authors should perform additional experiments to demonstrate the presynaptic expression of GPR55 at PC-DCN synapse.

      We agree with the reviewer’s concern that the present manuscript lacks the evidence for localization of GPR55 at PC axon terminals. Honestly, our previous attempt to immune-label GPR55 did not work well. Now, we realize that different antibodies are commercially available, and are going to test them. Hopefully, in the revised manuscript, we will demonstrate immunocytochemical images showing GPR55 at terminals of PCs.

      (2) The authors' conclusions rest heavily on pharmacological experiments, with compounds that are sometimes not selective for single targets. Genetic deletion of GPR55 would be a more appropriate control. The authors should also expand their experiments with occlusion experiments, showing if the effects of LPI are absent after AM251 or O-1602 treatment. In addition, the authors may want to consider AM281 as a CB1R antagonist without reported effects at GPR55.

      We appreciate the reviewer for pointing out the essential issue regarding the specificity of activation of GPR55 in our study. Regarding the direct manipulation of GPR55, such as genetic deletion, we will try acute knock-down of its expression, considering the possibility of compensation which sometimes occur when the complete knock-out is performed. In addition, according to the reviewer’s suggestion, we will examine whether the effects of LPI and AM251 occlude each other, and also perform control experiments showing the lack of CB1R involvement.

      (3) It is not clear how long the different drugs were applied, and at what time the recordings were performed during or following drug application. It appears that GPR55 agonists can have transient effects (Sylantyev, 2013; Rosenberg, 2023), possibly due to receptor internalization. The timeline of drug application should be reported, where IPSC amplitude is shown as a function of time and drug application windows are illustrated.

      As suggested, the timing and duration of drug application will be indicated together with the time course of changes of IPSC amplitudes. This change will make things much clearer. Thank you for the suggestion.

      (4) A previous investigation on the role of GPR55 in the control of neurotransmitter release is not cited nor discussed Sylantyev et al., (2013, PNAS, Cannabinoid- and lysophosphatidylinositol-sensitive receptor GPR55 boosts neurotransmitter release at central synapses). Similarities and differences should be discussed.

      We are really sorry for missing this important study in discussion and citation. In the revised version, of course, we will discuss their findings and our data.

      Minor point:

      (1) What is the source of LPI? What isoform was used? The multiple isoforms of LPI have different affinities for GPR55.

      We are sorry for insufficient explanation about the LPI used in our study. We used LPI derived from soy (Merck, catalog #L7635) that was estimated to contain 58% C16:0 and 42% C18:0 or C18:2 LPI. This information will be added to the Materials and Methods in the revised manuscript.

      Reviewer #2 (Public review):

      Summary:

      This paper investigates the mode of action of GPR55, a relatively understudied type of cannabinoid receptor, in presynaptic terminals of Purkinje cells. The authors use demanding techniques of patch clamp recording of the terminals, sometimes coupled with another recording of the postsynaptic cell. They find a lower release probability of synaptic vesicles after activation of GPR55 receptors, while presynaptic voltage-dependent calcium currents are unaffected. They propose that the size of a specific pool of synaptic vesicles supplying release sites is decreased upon activation of GPR55 receptors.

      Strengths:

      The paper uses cutting-edge techniques to shed light on a little-studied, potentially important type of cannabinoid receptor. The results are clearly presented, and the conclusions are for the most part sound.

      We are really happy to hear the encouraging comments from the reviewer.

      Weaknesses:

      The nature of the vesicular pool that is modified following activation of GPR55 is not definitively characterized.

      During revision, we will perform further analysis and additional experiments to obtain deeper insights into the vesicle pools affected by GPR55 as much as possible.

      Reviewer #3 (Public review):

      Summary:

      Inoshita and Kawaguchi investigated the effects of GPR55 activation on synaptic transmission in vitro. To address this question, they performed direct patch-clamp recordings from axon terminals of cerebellar Purkinje cells and fluorescent imaging of vesicular exocytosis utilizing synapto-pHluorin. They found that exogenous activation of GPR55 suppresses GABA release at Purkinje cell to deep cerebellar nuclei (PC-DCN) synapses by reducing the readily releasable pool (RRP) of vesicles. This mechanism may also operate at other synapses.

      Strengths:

      The main strength of this study lies in combining patch-clamp recordings from axon terminals with imaging of presynaptic vesicular exocytosis to reveal a novel mechanism by which activation of GPR55 suppresses inhibitory synaptic strength. The results strongly suggest that GPR55 activation reduces the RRP size without altering presynaptic calcium influx.

      We thank the reviewer for the positive evaluation on our conclusions.

      Weaknesses:

      The study relies on the exogenous application of GPR55 agonists. It remains unclear whether endogenous ligands released due to physiological or pathological activities would have similar effects. There is no information regarding the time course of the agonist-induced suppression. There is also little evidence that GPR55 is expressed in Purkinje cells. This study would benefit from using GPR55 knockout (KO) mice. The downstream mechanism by which GPR55 mediates the suppression of GABA release remains unknown.

      We agree with the reviewer in all respects suggested as weaknesses. Most issues will be made much clearer by the additional experiments and analysis described above to respond to respective issues raised by other reviewers. The situation of endogenous ligands for GPR55 causing the synaptic depression and its downstream mechanism are very important issues, and we are going to discuss these points in the revised manuscript, and like to work on these in the future study.

    1. Author response:

      Reviewer #1:

      In their paper entitled "Combined transcriptomic, connectivity, and activity profiling of the medial amygdala using highly amplified multiplexed in situ hybridization (hamFISH)" Edwards et al. present a new method designated as hamFISH (highly amplified multiplexed in situ hybridization) that enables sequential detection of {less than or equal to}32 genes using multiplexed branched DNA amplification. As proof-of-principle, the authors apply the new technique - in conjunction with connectivity, and activity profiling - to the medial amygdala (MeA) of the mouse, which is a critical nucleus for innate social and defensive behaviors.

      As mentioned by Edwards et al., hamFISH could prove beneficial as an affordable alternative to other in situ transcriptomic methods, including commercial platforms, that are resource-intensive and require complex analysis pipelines. Thus, the authors envision that the method they present could democratize in situ cell-type identification in individual laboratories.

      The data presented by Edwards et al. is convincing. The authors use the appropriate and validated methodology in line with the current state-of-the-art. The paper makes a strong case for the benefits of hamFISH when combining transcriptomics studies with connectivity tracing and immediate early gene-based activity profiling. Notably, the authors also discuss the caveats and limitations of their study/approach in an open and transparent manner.

      In its current state, the manuscript touches upon a number of most intriguing, yet rather preliminary findings. For example, the roles of inhibitory neuron cluster i3 or of the selective and apparently MeA neuron-specific projections (Figure 3 - Figure Supplement 2D) remain elusive. As it is the authors' prime intent to provide "a proof-of-principle example of overlaying transcriptomic types, projection, and activity in a behaviorally relevant manner and demonstrates the usefulness of hamFISH in multiplexed in situ gene expression profiling", such studies might be beyond the scope of the present manuscript. The absence of such more in-depth hypothesis-based analysis, however, prevents an even more enthusiastic overall assessment.

      We thank the reviewer for their positive assessment and agree that further studies are needed to explore and understand the MeA circuit further.

      Reviewer #2:

      The authors describe the development and implementation of hamFISH, a sensitive multiplexed ISH method. They leverage a pre-existing scRNA-seq dataset for the MeA to design 32 probes that combinatorically represent MeA neuronal populations - ~80% of MeA neurons express three of these markers. Using these markers to assess the spatial organization of the MeA, the authors identify a novel population of Ndnf+ projection neurons and characterize their connectivity with anterograde and retrograde labeling. They additionally combine hamFISH with CTB labeling of three principal MeA projection sites to show that 75% of MeA neurons have only a single projection target. Finally, they engage adult male mice in encounters with other adult males (aggression), females (mating), and pups (infanticide), followed by hamFISH and c-fos labeling to relate cell identity to behavior. Their overall conclusion is that hamFISH-defined cell types are broadly active to multiple sensory stimuli. However, the data presented are not sufficient to conclude that no selectivity exists within the MeA. A weakness of the study is that the selected hamFISH genes contain only Lhx6 as a lineage-marking transcription factor. Instead, the authors predominately use neuropeptides as markers. Genes such as Tac1, Cartpt, Adcyap1, Calb1, and Gal are expressed throughout the MeA, and many other brain regions; they are not restricted to a single transcriptomic cell type and they do not denote any developmental origins. By design, the panel has low cell type specificity as all MeA neurons express at least three of the genes. Therefore, the authors' conclusions may not hold with a more stringent classification of cell type or cell identity.

      We agree with the reviewer that a deeper level of cell type classification may reveal the selectivity of cell types that may have been missed. The design of our hamFISH bridge-readout probes allows modification to be compatible with a barcoded readout system such as MERFISH, which would substantially increase the number of genes that can be included in the gene panel. This would, however, increase the complexity of the analysis pipeline and reduce throughput, but would be a potential avenue to explore to define MeA cell types at a deeper level. An advantage of hamFISH is the ease of including and reading out alternative gene panels. For example, one panel could examine developmental-lineage-specific genes. Overall, our panel captures the highest hierarchical level (similar to the subclass level of the Allen taxonomy) of MeA transcriptomic types, based on published data available at the time of our gene panel design. Genes including Tac1, Cartpt, Adcyap1, Calb1, and Gal are expressed in specific patterns within the MeA and are useful for classification. In the original manuscript, we also included our rationale for dropping Foxp2, a lineage-specific marker gene in the MeA.

      Reviewer #3:

      In this manuscript, Edwards et al. describe hamFISH, a customizable and cost-efficient method for performing targeted spatial transcriptomics. hamFISH utilizes highly amplified multiplexed branched DNA amplification, and the authors extensively describe hamFISH development and its advantages over prior variants of this approach.

      The authors then used hamFISH to investigate an important circuit in the mouse brain for social behavior, the medial amygdala (MeA). To develop a hamFISH probe set capable of distinguishing MeA neurons, the authors mined published single-cell RNA-sequencing datasets of the MeA, ultimately creating a panel of 32 hamFISH probes that mostly cover the identified MeA cell types. They evaluated over 600,000 MeA cells and classified neurons into 16 inhibitory and 10 excitatory types, many of which are spatially clustered. The authors combined hamFISH with viral and other circuit tracer injections to determine whether the identified MeA cell populations sent and/or received unique inputs from connected brain regions, finding evidence that several cell types had unique patterns of input and output. Finally, the authors performed hamFISH on the brains of male mice that were placed in behavioral conditions that elicit aggressive, infanticidal, or mating behaviors, finding that some cell populations are selectively activated (as assessed by c-fos mRNA expression) in specific social contexts.

      Strengths:

      (1) The authors developed an optimized tissue preparation protocol for hamFISH and implemented oligopools instead of individually synthesized oligonucleotides to reduce costs. The branched DNA amplification scheme improved smFISH signal compared to previous methods, and multiple variants provide additional improvements in signal intensity and specificity. Compared to other spatial transcriptomics methods, the pipeline for imaging and analysis is streamlined and is compatible with other techniques like fluorescence-based circuit tracing. This approach is cost-effective and has several advantages that make it a valuable addition to the list of spatial transcriptomics toolkits.

      (2) Using 31 probes, hamFISH was able to detect 16 inhibitory and 10 excitatory neuron types in the MeA subregions, including the vast majority of cell types identified by other transcriptomics approaches. The authors quantified the distributions of these cell types along the anterior-posterior, dorsal-ventral, and medial-lateral axes, finding spatial segregation among some, but not all, MeA excitatory and inhibitory cell types. The authors additionally identified a class of inhibitory neurons expressing Ndnf (and a subset of these that express Chrna7) that project multiple social chemosensory circuits.

      (3) The authors combined hamFISH with MeA input and output mapping, finding cell-type biases in the projections to the MPOA, BNST, and VMHvl, and inputs from multiple regions.

      (4) The authors identified excitatory and inhibitory cell types, and patterns of activity across cell types, that were selectively activated during various social behaviors, including aggression, mating, and infanticide, providing new insights and avenues for future research into MeA circuit function.

      Weaknesses:

      (1) Gene selection for hamFISH is likely to still be a limiting factor, even with the expanded (32-probe) capacity. This may have contributed to the lack of ability to identify sexually dimorphic cell types (Figure S2B). This is an expected tradeoff for a method that has major advantages in terms of cost and adaptability.

      We recognise that the 32-plex gene detection might not be sufficient to address key questions in the transcriptomic organization of innate social behavior circuits, and that the study fell short of addressing more quantitative gene expression differences between sexes.  Detecting sexually dimorphic gene expression likely requires a more targeted approach as the dimorphism is expression differences rather than binary expression of marker genes, and the gene panel needs to be specifically configured for this purpose.

      (2) Adaptation of hamFISH, for example, to adapt it to other brain regions or tissues, may require extensive optimization.

      We have successfully performed hamFISH on at least two other mouse brain regions without needing to optimize further, suggesting that compatibility with other mouse brain regions is not an issue. We recognise, however, that optimization of hamFISH may be required for its application in other types of tissue or species. Human brain tissue, for example, typically suffers from high autofluorescence and different tissue preparation methods may need to be employed. We note that the amplification by hamFISH signal boost with v2 amplifiers may be useful to this end.

      (3) Pairing this method with behavioral experiments is likely to require further optimization, as c-fos mRNA expression is an indirect and incomplete survey of neuronal activity (e.g. not all cell types upregulate c-fos when electrically active). As such, there is a risk of false negative results that limit its utility for understanding circuit function.

      We acknowledge that c-fos is not the only readout of neuronal activity and that a panel of immediate early genes would allow a more comprehensive readout of activity-dependent gene expression. We fully agree that immediate early gene induction is an indirect readout of neural activity, and alternative methods such as in vivo physiology would provide a complementary insight into the selectivity of MeA neuron responses.

      (4) The limited compatibility of hamFISH with thicker tissue samples and lack of optical sectioning introduce additional technical limitations. For example, it would be difficult to densely sample larger neural circuits using serial 20 micron sections. Also, because the imaging modality is not clear from the methods, it is difficult to know whether the analysis methods introduce the risk of misattributing gene expression to overlapping cells.

      We agree that the use of hamFISH as described here is restricted to thin (<20 um) sections. We have shown, however, that our encoding probe and bridge-readout probe design are compatible with HCR-based mRNA detection, which is compatible with thicker sections. Regarding the misattribution of gene expression to overlapping cells in the z-axis, we used epifluorescence microscopy with 14x 500 nm z-steps to collect our raw data and generate maximum intensity projections for further analysis. Because of the thin sections (10 um) used for the imaging, the overlap between cells in z is expected to be minimal. Regarding throughput, we agree that hamFISH is likely not suitable for brain-wide questions that require large volume coverage, but its major advantage is that it allows routine use of low-level multiplexing for targeted brain areas.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews: 

      Reviewer #1 (Public review): 

      Summary: 

      This is a comprehensive study that clearly and deeply investigates the function of GATA6 in human early cardiac development. 

      Strengths: 

      This study combines hESC engineering, differentiation, detailed gene expression, genome occupancy, and pathway modulation to elucidate the role of GATA6 in early cardiac differentiation. The work is carefully executed and the results support the conclusions. The use of publicly available data is well integrated throughout the manuscript. The RIME experiments are excellent. 

      Weaknesses: 

      Much has been known about GATA6 in mesendoderm development, and this is acknowledged by the authors. 

      We appreciate the comments and have tried to highlight both the early role of GATA6 in cardiac progenitor biology as well as the haploinsufficiency for relevance to human congenital heart disease, which we believe adds value to other recent published work, among others Sharma et al. eLife 2020.

      Reviewer #2 (Public review): 

      Summary: 

      This manuscript by Bisson et al describes the role of GATA6 to regulate cardiac progenitor cell (CPC) specification and cardiomyocyte (CM) generation using human embryonic stem cells (hESCs). The authors found that GATA6 loss-of-function hESC exhibits early defects in mesendoderm and lateral mesoderm patterning stages. Using RNA-seq and CUT&RUN assays the genes of the Wnt and BMP programs were found to be affected by the loss of GATA6 expression. Modulating Wnt and BMP during early cardiac differentiation can partially rescue CPC and CM defects in GATA6 hetero- and homozygous mutant hESCs. 

      Strengths: 

      The studies performed were rigorous and the rationale for the experimental design was logical. The results obtained were clear and supported the conclusions that the authors made regarding the role of GATA6 on Wnt and BMP pathway gene expression. 

      Weaknesses: 

      Given the wealth of studies that have been performed in this research area previously, the amount of new information provided in this study is relatively modest. Nevertheless, the results and quite clear and should make a strong contribution to the field. 

      Likewise for reviewer 2, we appreciate the comments and have tried to highlight both the early role of GATA6 in cardiac progenitor biology as well as the haploinsufficiency for relevance to human congenital heart disease.

      Reviewer #3 (Public review): 

      In this study, Bison et al. analyzed the role of the GATA6 transcription factor in patterning the early mesoderm and generating cardiomyocytes, using human embryonic stem cell differentiation assays and patient-derived hiPSCs with heart defects associated with mutations in the GATA6 gene. They identified a novel role for GATA6 in regulating genes involved in the WNT and BMP pathways -findings not previously noted in earlier analyses of GATA6 mutant hiPSCs during early cardiac mesoderm specification (Sharma et al., 2020). Modulation of the WNT and BMP pathways may partially rescue early cardiac mesoderm defects in GATA6 mutant hESCs. These results provide significant insights into how GATA6 loss-of-function and heterozygous mutations contribute to heart defects. 

      I have the following comments: 

      (1) Throughout the manuscript, Bison et al. alternate between different protocols to generate cardiomyocytes, which creates some confusion (e.g., Figure 1 vs. Supplemental Figure 2A). The authors should provide a clear justification for using alternative protocols. 

      We agree and clarified this issue in the revision (p. 6). The reviewer is correct that there are two widely used protocols for directed differentiation of PSCs to cardiac fate. One is a cytokine-based protocol (Fig. 1A) and the other uses small molecules to manipulate the WNT pathway (CHIR protocol, Supplemental Fig. 2B). In our study, we used the CHIR protocol only for experiments in Supplemental Figure 2B-E. Since our data implicated BMP and WNT as mediators of the GATA6-dependent program, we did this mainly to confirm that the phenotype we observed with the cytokine-based protocol was not biased by the differentiation protocol. However, we found the CHIR protocol to be overall relatively inefficient for cardiac differentiation using the parental H1 hESCs and the various isogenic lines. The in vitro cardiac differentiation protocols for hPSCs are known to be variable depending on lines and sometimes require extensive optimization for various media components and concentrations, cell seeding densities, and batch variations for crucial reagents. The cytokine-based protocol we optimized worked most efficiently with our hPSC lines to generate cardiomyocytes, therefore we committed to using it for the bulk of experiments in this study.  

      (2) The authors should characterise the mesodermal identity and cardiomyocyte subtypes generated with the activin/BMP-induction protocol thoroughly and clarify whether defects in the expression of BMP and WNTrelated gene affect the formation of specific cardiomyocyte subtypes in a chamber-specific manner. This analysis is important, as Sharma et al. suggested a role for GATA6 in orchestrating outflow tract formation, and Bison et al. similarly identified decreased expression of NRP1, a gene involved in outflow tract septation, in their GATA6 mutant cells. 

      We agree it is important that the mesodermal identities are quite thoroughly characterized.

      For example, Fig. 2 (K+P+, Brachyury, EOMES), Fig. 3G&H (lateral mesoderm, cardiac mesoderm RNAseq & GSEA comparing datasets from Koh et al.). The capacity of the cytokine-based protocol to generate both FHF and SHF derived sub-types has been rigorously evaluated by Keller and colleagues, which we now cite (Yang et al. 2022). Since the null cells do not generate CMs, chamber specific subtypes cannot be evaluated; whether the GATA6 heterozygous mutants are biased is an interesting question. Indeed, the top GO term identified by CUT&RUN analysis for GATA6 at day 2 of

      differentiation is outflow tract morphogenesis, which is consistent with the interpretation by Sharma et al., but implicates this program at a much earlier developmental stage, long before cardiomyocyte differentiation. We think this is one of the most important findings of our study and appreciate the chance to highlight this in the revision (p. 9, 17). When we evaluated chamber-specificity for differentiated cardiomyocytes, we did not find significant differences, as indicated for the reviewer in the panel below (day 20 of differentiation). Since our study focuses on early stages of progenitor specification rather than cardiomyocyte differentiation, we agree that a more rigorous analysis would be of value, and indicated this as a limitation of our current study (p. 18).

      Author response image 1.

      (3) The authors developed an iPSC line derived from a congenital heart disease (CHD) patient with an atrial septal defect and observed that these cells generate cTnnT+ cells less efficiently. However, it remains unclear whether atrial cardiomyocytes (or those localised specifically at the septum) are being generated using the activin/BMP-induction protocol and the patient-derived iPSC line.

      As indicated above, our study is focused on cardiac progenitor specification, and we found similar differences with the patient-derived iPSC-CMs compared to using hESC heterozygous targeted mutants. While we did not note any major differences in expression of cardiomyocyte markers, whether the mutants show any biases toward sub-types of cardiomyocytes is an interesting question to be pursued in subsequent work.

      (4) The authors should also justify the necessity of using the patient-derived line to further analyse GATA6 function. 

      This is a good point, and as suggested we provided the justification (p. 5-6). This is the first patient-derived iPSC line published with a heterozygous GATA6 mutation along with an isogenic mutation-corrected control generated for cardiac directed differentiation. Patients with congenital heart disease (CHD) associated with GATA6 mutations are typically heterozygous (also true for many other CHD variants; presumably homozygous null embryos would not survive). It is important to query if phenotypes found using targeted mutations in hESCs (or iPSCs) model the human disease, since the patient cells (or the hESCs) likely have additional genetic variants that might interact with the GATA6 mutation. The fact that both types of heterozygous cells (patient-derived iPSCs and targeted hESCs) generate similar defects in CM differentiation provides evidence supporting the use of these human cellular models to study the genetic and cellular basis for congenital heart disease. This is particularly important, since other models, such as heterozygous mice, do not show such phenotypes.

      (5) Figure 3 suggests an enrichment of paraxial mesoderm genes in the context of GATA6 loss-of-function, which is intriguing given the well-established role of GATA6 in specifying cardiac versus pharyngeal mesoderm lineages in model organisms. Could the authors expand their analysis beyond GO term enrichment to explore which alternative fates GATA6 mutant cells may acquire? Additionally, how does the potential enrichment of paraxial mesoderm, rather than pharyngeal mesoderm, relate to the initial mesodermal induction from their differentiation protocol? Could the authors also rule out the possibility of increased neuronal cell fates? 

      We need to interpret our in vitro differentiation data cautiously in relation to what has been shown in vivo, since we are unlikely to be reproducing all the complex signaling taking place in the embryo. Yet we do see modest increases in gene expression levels including signatures of paraxial mesoderm and ECM/mesenchymal at days 2 or 3 of differentiation in the GATA6 mutant cells. Therefore, we now include a heatmap showing enriched paraxial mesoderm gene expression in the mutant cells, new Fig. 3I (see page 10).

      A caveat of this result is that the cells are being differentiated toward cardiac fate, so a bias for alternative fates might be suppressed. We modified the protocol to favor paraxial fate by adding CHIR at day 2 (rather than XAV) and performing qPCR assays at day 3. We found this successfully induced paraxial mesoderm gene expression, but equally comparing wildtype, heterozygous, or null cells, so do not feel it warrants highlighting further. 

      Recommendations for the authors:  

      Reviewing Editor (Recommendations for the authors): 

      Incorporation of marker analysis for various stages of iPSC to CM differentiation (mesoderm, cardiac progenitor, CM subtypes) would increase the significance and support for the findings presented. Further data on the link (direct or indirect) between GATA6 and Wnt/BMP signalling would also add to the significance of this study. A number of textual changes/clarifications are also suggested to improve the manuscript. 

      We appreciate the feedback and provide responses for issues raised for markers, direct or indirect interactions, and textual changes/clarifications in the following sections. As indicated above, we did not find obvious alterations in cardiac subtypes, but since our study is focused on early progenitor specification, this is an interesting question that we think should be more rigorously evaluated in subsequent work.  

      Reviewer #1 (Recommendations for the authors)

      Minor details: 

      (1) On p6 "Principal component analysis (PCA) showed that the cells derived from each genotype were well separated from each other (Supplemental Figure 2C)". All genotypes should be in one PCA plot to better evaluate the three genotypes. 

      We prepared the new plot as suggested, presented as new Supplemental Fig. 2C. 

      (2) p10: "Chia et al.22 and found a significantly decreased enrichment in GATA6-/- cells relative to WT at day 2" decreased enrichment of what? Direct target genes? 

      Thank you for catching this. Yes, the text was changed to indicate a “decreased enrichment in GATA6-/- cells relative to WT at day 2 for putative direct GATA6 target genes.” 

      Reviewer #2 (Recommendations for the authors): 

      Overall, this is an interesting study that addresses the early developmental roles of GATA6 on cardiac differentiation. While the identification of Wnt and BMP pathway genes to be involved in GATA6 regulation is not entirely unexpected, the authors do bring forth some useful knowledge that helps to further elucidate the mechanism of pre-cardiac mesoderm regulation. Some suggestions for improvement are included below - 

      Major points: 

      (1) Since the loss of Gata6 in this study is global (either as heterozygous or homozygous, it is likely that the very early requirement of Gata6 (e.g. mesodermal stage of differentiation) is responsible for the cardiac transcriptional phenotype observed and not due to specific role of Gata6 in the cardiac lineage which would need to be addressed using conditional knock out of Gata6 in hPSC model. The authors should be more explicit when discussing the results as disruption of mesodermal differentiation leading to loss of downstream cardiac lineage cells. For example, I would change the title "GATA6 loss-of-function impairs CM differentiation" to "GATA6 loss-of-function impairs mesodermal (or mesodermal lineage) differentiation" and show the changes in cardiac progenitor cells genes (Isl1, Tbx1, Hand1, and BAF50c/Smarcd3) in addition to cardiomyocyte genes but no change in mesodermal (e.g. Brachyury, T, Eomes, Mesp1/2, etc) genes. 

      We agree with the reviewer’s interpretation. The title for the section was changed as suggested. In Fig. 1, we show changes in cardiac progenitor cell genes (Isl1, Hand1, and BAF50c/Smarcd3) while not seeing changes in mesodermal genes in Fig. 2 (e.g. Brachyury, Eomes, Mesp1/2). We note that the defect may be specific to cardiac (or anterior lateral) mesoderm, as the ability to express paraxial mesoderm markers was not impaired.  

      (2) The use of NKX2.5, TBX5, TBX20, and GATA4 as markers for CPC is not ideal. These markers are also expressed in differentiated cardiomycytes. ISL1 or TBX1 for second heart field progenitors and HAND1 or BAF60c/Smarcd3 for first heart field progenitors would be ideal.  

      As suggested, we included additional day 6 qPCR panel (new Fig. 1E) to evaluate the heart field progenitor markers. 

      (3) Much of the findings described in this study have been known in the field including the requirement of Wnt and BMP to induce mesodermal and subsequently cardiomyocyte differentiation. The key new information here is that Gata6 knockout disrupts Wnt and BMP signaling. It would help to further validate experimentally some of the Wnt and BMP genes as either direct or indirect targets of Gata6 using reporter assays. 

      While reporter assays are feasible and do provide relevant outputs, we feel that the use of any one or even several response elements in a reporter assay adds relatively little value compared to comprehensive analysis of bona fide network components. To address the reviewers concern we have included profiling heat maps for WNT and BMP pathway components to more rigorously and specifically evaluate the disruption in the signaling networks caused by loss of GATA6. Proving direct targets of endogenous genes is challenging, but we mapped many binding peaks for GATA6 to putative enhancers of WNT/BMP pathway genes (based on histone marks). We provide a list of these genes (new Fig. 4F) and distinguish these from WNT/BMP pathway genes that were not bound by GATA6 yet are down-regulated in the GATA6 mutant cells and are likely to be indirect targets (p. 12). 

      Minor points: 

      (1) Figures 1 and 2 - in the figure legend the labels w2, w4, m2, m5, m11, and m14 should be explained as the name of the clones of targeted hESC.  

      The legends were edited to provide this information.  

      (2) Supplemental Figure 3A - the resolution of the FACS plot is suboptimal. 

      We apologize and have corrected the plot resolution in the revised manuscript.  

      (3) Supplemental Table 1 - it's intriguing that amongst all the SWI/SNF factors, the one that is known to be cardiac-specific (SMARCD3) did not come up in the GATA6-RIME-enriched proteins. Is this a reflection of the early stage in which GATA6 plays a role in development (e.g. mesendoderm development but not precardiac mesoderm development when SMARCD3 is expressed)? 

      We agree and have noted this feature in the revised manuscript (p. 17). We note that SMARCD3 is expressed in the RNA-seq data as early as day 2. Although speculative, it may be that GATA6 primarily interacts with SWI/SNF complexes prior to the role for SMARCD3 in cardiac specification.

      Reviewer #3 (Recommendations for the authors): 

      (1) Figures 3G and 3H, as well as others, have resolution issues. The gene names are unreadable, and higherresolution images should be provided. 

      We apologize for the resolution issues and these have been fixed in the revised version. 

      (2) In their early manipulation of the WNT and BMP pathways (Figure 6A), it is unclear whether the activin/BMP protocol shown in Figure 1A was used. If this is the case, the authors should compare their results to a wild-type + DOX EV condition for consistency. 

      We clarified in the revision (Fig. 6A) that all the experiments in Fig. 6 use the cytokine protocol. In the revised figure, we included the wild-type + DOX EV condition as suggested. 

      (3) In Figures 6C and 6D, the authors should include an analysis of a wild-type isogenic line under their new CHIR/LB condition for comparison. 

      As suggested, we included the WT isogenic line in the comparison. For Fig. 6C these are shown on a separate graph because the Y-axis values are very different. Note that the CHIR/LB treatments that improve mutant cell differentiation impact the WT cells in the opposite manner.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:  

      Reviewer #1 (Public Review):  

      Summary:  

      The study by Pudlowski et al. investigates how the intricate structure of centrioles is formed by studying the role of a complex formed by delta- and epsilon-tubulin and the TEDC1 and TEDC2 proteins. For this, they employ knockout cell lines, EM, and ultrastructure expansion microscopy as well as pull-downs. Previous work has indicated a role of delta- and epsilon-tubulin in triplet microtubule formation. Without triplet microtubules centriolar cylinders can still form, but are unstable, resulting in futile rounds of de novo centriole assembly during S phase and disassembly during mitosis. Here the authors show that all four proteins function as a complex and knockout of any of the four proteins results in the same phenotype. They further find that mutant centrioles lack inner scaffold proteins and contain an extended proximal end including markers such as SAS6 and CEP135, suggesting that triplet microtubule formation is linked to limiting proximal end extension and formation of the central region that contains the inner scaffold. Finally, they show that mutant centrioles seem to undergo elongation during early mitosis before disassembly, although it is not clear if this may also be due to prolonged mitotic duration in mutants.  

      Strengths:  

      Overall this is a well-performed study, well presented, with conclusions mostly supported by the data. The use of knockout cell lines and rescue experiments is convincing.  

      Weaknesses:  

      In some cases, additional controls and quantification would be needed, in particular regarding cell cycle and centriole elongation stages, to make the data and conclusions more robust. 

      We thank the reviewer for these comments and have improved our analyses of these as detailed below.

      Reviewer #2 (Public Review):  

      Summary:  

      In this article, the authors study the function of TEDC1 and TEDC2, two proteins previously reported to interact with TUBD1 and TUBE1. Previous work by the same group had shown that TUBD1 and TUBE1 are required for centriole assembly and that human cells lacking these proteins form abnormal centrioles that only have singlet microtubules that disintegrate in mitosis. In this new work, the authors demonstrate that TEDC1 and TEDC2 depletion results in the same phenotype with abnormal centrioles that also disintegrate into mitosis. In addition, they were able to localize these proteins to the proximal end of the centriole, a result not previously achieved with TUBD1 and TUBE1, providing a better understanding of where and when the complex is involved in centriole growth.  

      Strengths:  

      The results are very convincing, particularly the phenotype, which is the same as previously observed for TUBD1 and TUBE1. The U-ExM localization is also convincing:

      despite a signal that's not very homogeneous, it's clear that the complex is in the proximal region of the centriole and procentriole. The phenotype observed in U-ExM on the elongation of the cartwheel is also spectacular and opens the question of the regulation of the size of this structure. The authors also report convincing results on direct interactions between TUBD1, TUBE1, TEDC1, and TEDC2, and an intriguing structural prediction suggesting that TEDC1 and TEDC2 form a heterodimer that interacts with the TUBD1- TUBE1 heterodimer.  

      Weaknesses:  

      The phenotypes observed in U-ExM on cartwheel elongation merit further quantification, enabling the field to appreciate better what is happening at the level of this structure.  

      We thank the reviewer for these comments and have improved our analyses of cartwheel elongation as detailed below.

      Reviewer #3 (Public Review):  

      Summary:  

      Human cells deficient in delta-tubulin or epsilon-tubulin form unstable centrioles, which lack triplet microtubules and undergo a futile formation and disintegration cycle. In this study, the authors show that human cells lacking the associated proteins TEDC1 or TEDC2 have these identical phenotypes. They use genetics to knockout TEDC1 or TEDC2 in p53negative RPE-1 cells and expansion microscopy to structurally characterize mutant centrioles. Biochemical methods and AlphaFold-multimer prediction software are used to investigate interactions between tubulins and TEDC1 and TEDC2.  

      The study shows that mutant centrioles are built only of A tubules, which elongate and extend their proximal region, fail to incorporate structural components, and finally disintegrate in mitosis. In addition, they demonstrate that delta-tubulin or epsilon-tubulin and TEDC1 and TEDC2 form one complex and that TEDC1 TEDC2 can interact independently of tubulins. Finally, they show that the localization of four proteins is mutually dependent.  

      Strengths:  

      The results presented here are mostly convincing, the study is exciting and important, and the manuscript is well-written. The study shows that delta-tubulin, epsilon-tubulin, TEDC1, and TEDC2 function together to build a stable and functional centriole, significantly contributing to the field and our understanding of the centriole assembly process.  

      Weaknesses:  

      The ultrastructural characterization of TEDC1 and TEDC2 obtained by U-ExM is inconclusive. Improving the quality of the signals is paramount for this manuscript.  

      We thank the reviewer for these comments and have improved our imaging of TEDC1 and TEDC2 localization, as detailed below.

      Recommendations for the authors:

      Reviewing Editor (Recommendations For The Authors):  

      The reviewers agreed that the conclusions are largely supported by solid evidence, but felt that improving the following aspects would make some of the data more convincing:  

      (1) The UExM localizations of TEDC1/2 are not very convincing and the reviewers suggest to complement these with alternative super-resolution approaches (e.g. SIM) and/or different labeling techniques such as pre-expansion labeling using STAR red/orange secondaries (also robust for SIM and STED), use of the Halo tag, different tag antibodies, etc 

      We thank the reviewers for these recommendations and have adapted two of these strategies to improve our imaging of TEDC1 and TEDC2 localization. First, we used an alternative super-resolution approach, a Yokogawa CSU-W1 SoRA confocal scanner (resolution = 120 nm) and imaged cells grown on coverslips (not expanded). We found that TEDC1 and TEDC2 localize to procentrioles and the proximal end of parental centrioles (Fig 2 – Supplementary Figure 1a, b). Second, we used a recently described expansion gel chemistry (Kong et al., Methods Mol Biol 2024) combined with Abberior Star red and orange secondary antibodies. This technique resulted in robust signal at centrosomes and in the cytoplasm and indicated that TEDC1 and TEDC2 localize near the centriole walls of procentrioles and the proximal region of parental centrioles, near CEP44 (Fig 2 – Supplementary Figure 1c, d). These results complement and support our initial observations (Fig 2C, D) and we have edited the text to reflect this (lines 157-163). We also note that these Flag tag and V5 tag primary antibodies are specific and have little background signal in all applications (Fig 2 – Supplementary Fig 1E-J), while other commercially available antibodies against these tags did exhibit non-specific signal. 

      (2) The cell cycle classifications of centrioles would strongly benefit, apart from a better description, from adding quantifications of average centriole length at a given stage based on tubulin staining (not acTub). 

      We thank the reviewers for these recommendations. We have added an improved description of our cell cycle analyses (lines 234-237). We have also added new analyses for centriole length as measured by staining with alpha-tubulin (Fig 4 – Supp 3 and Fig 4 – Supp 4). We find that in all mutants, acetylated tubulin elongates along with alpha-tubulin in a similar way as control centrioles.

      Reviewer #1 (Recommendations For The Authors):  

      Specific points:  

      (1) The introduction is a bit oddly structured. About halfway through it summarizes what is going to be presented in the study, giving the impression that it is about to conclude, but then continues with additional, detailed introduction paragraphs. Overall, the authors may also want to consider making it more concise.

      We thank the reviewer for these suggestions and have shortened and restructured the introduction for clarity and conciseness.

      (2) The text should explain to the non-expert reader why endogenous proteins are not detected and why exogenously expressed, tagged versions are used. Related to this, the authors state overexpression, but what is this assessment based on? Does expression at the endogenous level also rescue? At least by western blot, these questions should be addressed. 

      In the text, we have added clarification about why endogenous proteins were not detected for immunofluorescence (lines 149-151). To quantify the overexpression, we have added Western blots of TEDC1 and TEDC2 to Fig 1 – Supplementary Figure 1E,F. We note that endogenous levels of both proteins are very low, and the rescue constructs are overexpressed 20 to 70 fold above endogenous levels.  

      (3) The figures should clearly indicate when tagged proteins are used and detected.

      Currently, this info is only found in the legends but should be in the figure panels as well. 

      We have made these changes to the figure panels in Fig 2, Fig 2 – Supp 1, and Fig 3.

      (4)  I could not find a description and reference to Figure 2 Supplement 2 and 3. 

      We have replaced these supplements with new supplementary figures for TEDC1 and TEDC2 localization (Fig 2 – Supp 1).

      (5) The multiple bands including unspecific (?) bands should be labeled to guide the reader in the western blots. 

      We have labeled nonspecific bands in our Western blots with asterisks (Fig 1 – Supp 1, Fig 3)

      (6) The alphafold prediction suggests that TUBD1 can bind to the TED complex in the absence of TUBE1 can this be shown? This would be a nice validation of the predicted architecture of the complex. I also missed a bit of a discussion of the predicted architecture. How could it be linked to triplet microtubule formation? Is the latest alphafold version 3 adding anything to this analysis? 

      In our pulldown experiments, we found that TUBD1 cannot bind to TEDC1 or TEDC2 in the absence of TUBE1 (Fig 3C, D, IB: TUBD1). We performed this experiment with three biological replicates and found the same result. It is possible that TUBD1 and TUBE1 form an intact heterodimer, similar to alpha-tubulin and beta-tubulin, and this will be an exciting area of future research.

      We have added new analysis from AlphaFold3 (Fig 3 – Supp 1B). AlphaFold3 predicts a similar structure as AlphaFold Multimer.

      We have also added additional discussion about the AlphaFold prediction to the text (lines 220-222, 365-367). Thanks to the reviewer for pointing out this oversight.

      (7) I suggest briefly explaining in the text how cells and centrioles at different cell cycle stages were identified. I found some info in the legend of Figure 1, but no info for other figures or in the text. Related to this, how are procentrioles defined in de novo formation? There is no parental centriole to serve as a reference. 

      We have added a brief explanation of the synchronization and identification in lines 234237. We have also clarified the text regarding de novo centrioles, and now term these “de novo centrioles in the first cell cycle after their formation” (lines 271-272).

      (8) Related to point 7: using acetylated tubulin as a universal length and width marker seems unreliable since it is a PTM. The authors should use general tubulin staining to estimate centriole dimensions, or at least establish that acetylated tubulin correlates well with the overall tubulin signal in all mutants. 

      We have added two supplementary data figures (Fig 4 – supp 3 and Fig 4 – supp 4) in which we co-stain control and mutant centrioles with alpha-tubulin. We found that acetylated tubulin marked mutant centrioles well and as alpha-tubulin length increased, acetylated tubulin length also increased. 

      (9) Presence and absence of various centriolar proteins. These analyses lack a clear reference for the precise centriole elongation stage. This is particularly problematic for proteins that are recruited at specific later stages (such as inner scaffold proteins). The staining should be correlated with centriole length measurements, ideally using general tubulin staining.  

      As described for point 8, we have added two supplementary data figures in which we costain control and mutant centrioles with alpha-tubulin and found that acetylated tubulin also increases as overall tubulin length increases in all mutants. We note that inner scaffold proteins are absent in all our mutant centrioles at all stages of the cell and centriole cycle, as also previously reported for POC5 in Wang et al., 2017.

      Reviewer #2 (Recommendations For The Authors):  

      Here's a list of points I think could be improved:  

      -  As the authors previously published, the centriole appears to have a smaller internal diameter than mature centrioles. Could the authors measure to see if the phenotype is identical? Is the centriole blocked in the bloom phase (Laporte et al. 2024)? 

      We have added an additional supplementary figure (Fig 4 – supp 5) to show that mutant centrioles have smaller diameters than mature centrioles, as we previously reported for the delta-tubulin and epsilon-tubulin mutant centrioles by EM. We thank the reviewers for the additional question of the bloom phase. Given the comparatively smaller number of centrioles we analyzed in this paper compared to Laporte et al (50 to 80 centrioles per condition here, versus 800 centrioles in Laporte et al), it is difficult to definitively conclude whether there is a block in bloom phase. This would be an interesting area for future research.  

      -  The images of the centrioles in EM are beautiful. Would it be possible to apply a symmetrisation on it to better see the centriolar structures? For example, is the A-C linker present? 

      We thank the reviewer for this excellent suggestion. Using centrioleJ, we find that the A-C linker is absent from mutant centrioles. The symmetrized images have been added to Fig 1 – Supplementary Fig 2, and additional discussion has been added to the text (line 143-144, line 368-374).  

      -  How many EM images were taken? Did the centrioles have 100% A-microtubule only or sometimes with B-MT? 

      For TEM, we focused on centrioles that were positioned to give perfect cross-section images of the centriolar microtubules, and thus did not take images of off-angle or rotated centrioles. Given the difficulty of this experiment (centrioles are small structures within the cell, centrosomes are single-copy organelles, and off-angle centrioles were not imaged), we were lucky to image 3 centrioles that were in perfect cross-section – 2 for Tedc1<sup>-/-</sup> and 1 for Tedc2<sup>-/-</sup>. Our images indicate that these centrioles only have A-tubules (Fig 1 – Supp Fig

      2).

      -  In Figure 2 - it would be preferable to write TEDC2-flag or TEDC1-flag and not TEDC2/1. 

      We have made this change

      -  It seems that Figures 2C and D aren't cited, and some of the data in the supplemental data are not described in the main text. 

      We have replaced these supplements with new supplementary figures for TEDC1 and TEDC2 localization (Fig 2 – Supp 1).

      -  The signal in U-ExM with the anti-Flag antibody is heterogeneous. Did the authors test several anti-FLAG antibodies in U-ExM? 

      We tested several anti-Flag and anti-V5 antibodies for our analyses, and chose these because they have little background signal in all applications (Fig 2 – Supplementary Fig 1E-J). Other commercially available antibodies against these tags did exhibit non-specific signal.

      -  The AlphaFold prediction is difficult to interpret, the authors should provide more views and the PDB file. 

      We have added 2 additional views of the AlphaFold prediction in Fig 3 – Supp 1A.

      -  In general, but particularly for Figure 4: the length doesn't seem to be divided by the expansion factor, it is therefore difficult to compare with known EM dimensions. Can the authors correct the scale bars? 

      We have corrected the scale bars for all figures to account for the expansion factor.

      -  Concerning Gamma-tubulin that is "recruited to the lumen of centrioles by the inner scaffold, had localization defects in mutant centrioles. However, we were unable to reliably detect gamma-tubulin within the lumen of control or de novo-formed centrioles in S or G2-phase (Figure 4 - Supplement 1E), and thus were unable to test this hypothesis". In Laporte et al 2024, Gamma-tubulin arrives later than the inner scaffold and only on mature centrioles, so this result appears to be in line with previous observation. However, the authors should be able to detect a proximal signal under the microtubules of the procentriole, is this the case? 

      We agree that this is an exciting question. However, in our expansion microscopy staining, we frequently observe that gamma-tubulin surrounds centrioles, corresponding to its role in the pericentriolar material (PCM). In our hands, we find it difficult to distinguish between centriolar gamma-tubulin at the base of the A-tubule from gamma-tubulin within the PCM.  

      -  In the signal elongation of SAS-6, STIL, CEP135, CPAP, and CEP44, would it be possible to quantify the length of these signals (with dimensions divided by the expansion factor for comparison with known TEM distances)? 

      We have quantified the lengths of SAS-6 and CEP135 in new Fig 4 – Supp 3 and Fig 4 – Supp 4.  

      -  The authors observe that centrin is present, but only as a SFI1 dot-like localization (which is another protein that would be interesting to look at), and not an inner scaffold localization. Can the authors elaborate? These results suggest that the distal part is correctly formed with only a microtubule singlet. 

      We agree with the reviewer’s interpretation that the centriole distal tip is likely correctly formed with only singlet microtubules, as both distal centrin and CP110 are present. We have added this point to the discussion (line 415).

      -The authors observe that CPAP is elongated, but CPAP has two locations, proximal and distal. Is it distal or proximal elongation? Is the proximal signal of CPAP longer than that of CEP44 in the mutants? The authors discuss that the elongation could come from overexpression of CPAP, but here it seems that the centriole is not overlong, just the structures around the cartwheel. 

      We thank the reviewer for this point. It is difficult for us to conclude whether the proximal or distal region is extended in the mutants, as our mutant centrioles lacks a visible separation between these two regions. It would be interesting to probe this question in the future by testing whether subdomains of CPAP may be differentially regulated in our mutants.

      Reviewer #3 (Recommendations For The Authors):  

      It isn't apparent to me what was counted in Figure 1C. Were all centrioles (mother centrioles and procentrioles) counted? Where is the 40% in control cells coming from? Can this set of data be presented differently? 

      We apologize for the confusion. In this figure, all centrioles were counted. We have updated the figure legend for clarity. We performed this analysis in a similar way as in Wang et al., 2017 to better compare phenotypes.  

      Figure 2C. and the text lines 182-187: The ultrastructural characterization of TEDC1 and TEDC2 suffers from the low quality of the TEDC1 and TEDC2 signals obtained postexpansion. In comparison with robust low-resolution immunosignal, it appears that most of the signal cannot be recovered after expansion. Another sub-resolution imaging method to re-analyze TEDC1 and TEDC22 localization would be essential. The same concern applies to Figures 2 - Supplement 2 and 3. Also, Figure 2 - Supplement 2 and Supplement 3 do not seem to be cited. 

      We thank the reviewer for these recommendations. As also mentioned above, we used an alternative super-resolution approach, a Yokogawa CSU-W1 SoRA confocal scanner (resolution = 120 nm), and found that TEDC1 and TEDC2 localize to procentrioles and the proximal end of parental centrioles (Fig 2 – Supplementary Figure 1a, b). Second, we used a recently described expansion gel chemistry (Kong et al., Methods Mol Biol 2024) combined with Abberior Star red and orange secondary antibodies. This technique resulted in robust signal at centrosomes and in the cytoplasm and indicated that TEDC1 and TEDC2 localize near the centriole walls of procentrioles and the proximal region of parental centrioles, near CEP44 (Fig 2 – Supplementary Figure 1c, d). These stainings complement and support our initial observations (Fig 2C, D) and we have edited the text to reflect this (lines 157-163). We have also removed the supplementary figures that were uncited in the text.

      TUBD1 and TUBE1 form a dimer and TEDC2 and TEDC1 can interact. Any speculation as to why TEDC2 does not pull down both TUBE1 and TUBD1? 

      We apologize for the confusion. TEDC2 does pull down both TUBE1 and TUBD1 (Fig 3D, pull-down, second column, Tedc2-V5-APEX2 rescuing the Tedc2<sup>-/-</sup> cells pulls down TUBD1, TUBE1, and TEDC1).  

      Figure 4A and B. The authors use acetylated tubulin to determine the length of procentrioles in the S and G2 phases. However, procentrioles are not acetylated on their distal ends in these cell phase phases (as the authors also mention further in the text). Why has alpha tubulin not been used since it works well in U-ExM? The average size of the control, G2 procentrioles, seems too small in Figure 4A and not consistent with other imaging data (for instance, in Figure 4 - Supplement 1 C, Cp110, and CPAP staining). There is no statistical analysis in F4A.  

      We have added two supplementary data figures (Fig 4 – supp 3 and Fig 4 – supp 4) in which we co-stain control and mutant centrioles with alpha-tubulin. We found that acetylated tubulin correlates well with overall tubulin signal in all mutants. We have added statistical analysis to the figure legend of Fig 4A.

      Lines 260 - 262: "These results indicate that centrioles with singlet microtubules can elongate to the same length as controls, and therefore that triplet microtubules are not essential for regulating centriole length." It is hard to agree with this statement. Mutant procentrioles show aberrantly elongated proximal signals of several tested proteins. In addition, in lines 326 - 328, the authors state that "Together, these results indicate that centrioles lacking compound microtubules are unable to properly regulate the length of the proximal end."  

      We thank the reviewer and have clarified the statement to state that these results indicate that centrioles with singlet microtubules can elongate to the same overall length as control centrioles in G2 phase.  

      Line 353: The authors suggest that elongated procentriole structure in mitosis may represent intermediates in centriole disassembly. Another interpretation, more in line with the EM data from Wang et al., 2017, would be that these mutant procentrioles first additionally elongate before they disassemble in late mitosis. The aberrant intermediate structure concept would need further exploration. For instance, anti-alpha/beta-tubulin antibodies could be used to investigate centriole microtubules.  

      We apologize for the confusion and have edited this section for clarity (lines 341-343): “We conclude that in our mutant cells, centrioles elongate in early mitosis to form an aberrant intermediate structure, followed by fragmentation in late mitosis.”

      References need to be included in lines 122, 277, 279. 

      We have added these references

      Line 281: Add references PMID: 30559430 and PMID: 32526902.  

      We have added these references (lines 265-266).

      Line 289: "Moreover, our results suggest that centriole glutamylation is a multistep process, in which long glutamate side chains are added later during centriole maturation." This does not seem like an original observation. For instance, see PMID: 32526902.  

      We have added this reference (lines 273-274).

    1. Author response:

      Reviewer 1:

      (1) Provide Rsmd and DALI scores to show how similar the AlphaFold-predicted structures of BrrG are to other anti-termination factors. This should be done for Fig1B and also for Suppl. Fig 1 to support the claim that BrrG, GafA, GafZ, Q21 share structural features.

      In the revised manuscript we will provide Rsmd and DALI scores.

      (2) Throughout the manuscript, flow cytometry data of gfp expression was used and shown as single replicate. Korotaev et al wrote in the legends that error bars are shown (that is not true for e.g. Figs. 3, 4, and 5). It is difficult for reviewers/readers to gauge how reliable are their experiments.

      As stated in the manuscript all flow cytometry data were performed in triplicate. In the revised manuscript we will include the two replicates not presented in the main figures as supplementary information.

      (3) I am unsure how ChIP-seq in Fig. 2A was performed (with anti-FLAG or anti-HA antibodies? I cannot tell from the Materials & Methods). More importantly, I did not see the control for this ChIP-seq experiment. If a FLAG-tagged BrrG was used for ChIP-seq, then a WT non-tagged version should be used as a negative control (not sequencing INPUT DNA), this is especially important for anti-terminator that can co-travel with RNA polymerase. Please also report the number of replicates for ChIP-seq experiments.

      Fig. 2A presents a coverage plot from the ChIP-Seq of ∆brrG +pTet:brrG-3xFLAG (N’). A replicate of this N-terminally tagged construct will be added as supplementary data in the revised version. As anticipated by the referee, we had used ∆brrG +pTet:brrG (untagged) as control.

      (4) Korotaev et al mentioned that BrrG binds to DNA (as well as to RNA polymerase). With the availability of existing ChIP-seq data, the authors should be able to locate the DNA-binding element of BrrG, this additional information will be useful to the community.

      We will mine the ChIP-Seq data to define the BrrG binding site as closely as possible and include the analysis in the revised version of the manuscript.

      (5) Mutational experiments to break the potential hairpin structure are required to strengthen the claim that this putative hairpin is the potential transcriptional terminator.

      We did not claim that the identified hairpin is a terminator but rather suggested it as a candidate terminator. We agree with the referee that the proposed experiment would be necessary to definitively prove its terminator function. However, our primary aim was to demonstrate that BrrG acts as a processive terminator, which we have shown by replacing the putative terminator with a well-characterized synthetic terminator that BrrG successfully overcame. Therefore, we prefer not to conduct the proposed experiment and will instead further tone down our conclusions regarding the putative terminator function of the identified hairpin structure.

      Reviewer 2:

      (1) The authors wrote "GTAs are not self-transmitting because the DNA packaging capacity of a GTA particle is too small to package the entire gene cluster encoding it" (page 3). I thought that at least the Bartonella capsid gene cluster should be self-transmissible within the 14 kb packaged DNA (https://doi.org/10.1371/journal.pgen.1003393, https://doi.org/10.1371/journal.pgen.1000546). This was also concluded by Lang et al (https://doi.org/10.1146/annurev-virology-101416-041624). In this case the presented results would have important implications. As the gene cluster and the anti-terminator required for its expression are separated on the chromosome, it would not be possible to transfer an active GTA gene cluster, although the DNA coding for the genes required for making the packaging agent itself, theoretically fits into a BaGTA particle. Could the authors comment on that? I think it would be helpful to add the sizes of the different gene clusters and the distance between them in Fig. 2A. The ROR amplified region spans 500kb, is the capsid gene cluster within this region?

      We thank the reviewer for bringing up this interesting point. The bgt cluster (capsid cluster) is approximately 9.2 kb in size and could feasibly be packaged in its entirety into a GTA particle. In contrast, the ror gene cluster, which encodes the antiterminator BrrG, is approximately 20 kb in size—exceeding the packaging limit of GTA particles—and is separated from the bgt cluster by approximately 35 kb. Consequently, if the bgt cluster is transferred via a GTA particle into a recipient host that does not encode the ror gene cluster, the bgt cluster would not be expressed.

      (2) Another side-note regarding the introduction: On page three the authors write: "GTAs encode bacteriophage-like particles and in contrast to phages transfer random pieces of host bacterial DNA". While packaging is not specific, certain biases in the packaging frequency are observed in both studied GTA families. For Bartonella this is ROR. In the two GTA-producing strains D. shibae and C. crescentus origin and terminus of replication are not packaged and certain regions are overrepresented (https://doi.org/10.1093/gbe/evy005, https://doi.org/10.1371/journal.pbio.3001790). Furthermore, D. shibae plasmids are not packaged but chromids are. I think the term "random" does not properly describe these observations. I would suggest using "not specific" instead.

      We thank the reviewer for this suggestion and will adjust the working accordingly.

      (3) Page 5: Remove "To address this". It is not needed as you already state "To test this hypothesis" in the previous sentence.

      We will adjust the working accordingly.

      (4) I think the manuscript would greatly benefit from a summary figure to visualize the Q-like antiterminator-dependent regulatory circuit for GTA control and its four components described on pages 15 and 16.

      We thank the reviewer for this valuable suggestion and will include a summary figure illustrating the deduced regulatory mechanism in the revised manuscript.

      (5) Page 17: It might be worth noting that GafA is highly conserved along GTAs in Rhodobacterales (https://doi.org/10.3389/fmicb.2021.662907) and so is probably regulatory integration into the ctrA network (https://doi.org/10.3389/fmicb.2019.00803). It's an old mechanism. It would be also interesting to know if it is a common feature of the two archetypical GTAs that the regulator is not part of the cluster itself.

      We agree with the points raised by the reviewer and will address them in the revised manuscript. Specifically, we will highlight the high conservation of GafA in GTAs across Rhodobacterales and its regulatory integration within the ctrA network. Additionally, we will analyze whether the GafA-like antitermination regulator is typically located outside the regulated gene cluster, as we have demonstrated for BrrG of BaGTA in the Bartonellae.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this work, Huang et al used SMRT sequencing to identify methylated nucleotides (6mA, 4mC, and 5mC) in Pseudomonas syringae genome. They show that the most abundant modification is 6mA and they identify the enzymes required for this modification as when they mutate HsdMSR they observe a decrease of 6mA. Interestingly, the mutant also displays phenotypes of change in pathogenicity, biofilm formation, and translation activity due to a change in gene expression likely linked to the loss of 6mA. Overall, the paper represents an interesting set of new data that can bring forward the field of DNA modification in bacteria.

      Thank you for your valuable feedback on our paper exploring the impact of 6mA modification in P. syringae.

      Major Concerns:

      Most of the authors' data concern Psph pathovar. I am not sure that the authors' conclusions are supported by the two other pathovars they used in the initial 2 figures. If the authors want to broaden their conclusions to Pseudomonas syringe and not restrict it to Psph, the authors should have stronger methylation data using replicates. Additionally, they should discuss why Pss is so different than Pst and Psph. Could they do a blot to confirm it is really the case and not a sequencing artifact? Is the change of methylation during bacterial growth conserved between the pathovar? The authors should obtain mutants in the other pathovar to see if they have the same phenotype. The authors have a nice set of data concerning Psph but the broadening of the results to other pathovar requires further investigation.

      We appreciate the reviewer’s insightful comments. While the majority of our data focuses on the Psph, we recognize the importance of validating these findings in Pss and Pst. To this end, we have performed additional experiments using dot blot and mutant construction to enhance our conclusions in other pathovars.

      We agree that we should discuss why Pss is different from Psph and Pst. We performed a dot blot assay using genome DNA in Pss and Pst, presented in Figure S5A. Meanwhile, we compared the 6mA modification level of Pss and Pst in different growth phases. As shown in Figure S5A, the change of methylation during bacterial growth is conserved in Pst. The change was not obvious in Pss, which might be due to the lack of a type I R-M system.

      “In accordance with previous studies showing that growth conditions affect the bacterial methylation status, we applied dot blot experiments using the same amount of DNA (1 μg) from these three P. syringae strains to detect the 6mA levels during both logarithmic and stationary phases. The results revealed that 6mA levels in the stationary phase were much higher compared to the logarithmic phase in Psph and Pst, but no significant change in Pss. Additionally, we found that during the stationary phase, 6mA methylation levels in Psph and Pst were higher than those in Pss. These findings were consistent with the MTases predication on these three strains, since Pss does not harbor any type I R-M systems, which are important for 6mA medication in bacteria.”

      Please see Figure S5A and Lines 220-228 in the revised manuscript.

      We also tried to construct an HsdM mutant in Pst to explore whether the influence of 6mA methylation was conserved in P. syringae, but it failed after multiple attempts. We did not construct a Pss mutant because no type I R-M system was predicted, and few methylation sites were identified via SMRT-seq in this strain. Therefore, we overexpressed HsdM in Pst instead. We have performed additional experiments in WT and the HsdM overexpression strains, including dot blot and growth curve assays.

      Please see Figures S5B-C and Lines228-232 in the revised manuscript.

      The authors should include proper statistical analysis of their data. A lot of terms are descriptive but not supported by a deeper analysis to sustain the conclusions. For example, in Figure 4E, we do not know if the overlap is significant or not. Are DEGs more overlapping to 6mA sites than non-DEGs? Here is a non-exhaustive list of terms that need to be supported by statistics: different level (L145), greater conservation (L162), significant conservation (L165), considerable similarity (L175), credible motifs (L189), Less strong (L277) and several "lower" and "higher" throughout the text.

      Thank you for the insightful feedback. We have made the following revisions in the manuscript to ensure that the terms are more precise and do not require statistical significance testing.

      (1) Statistical analysis: We have added statistical tests for the overlap between DEGs and 6mA sites in Figure 4E. We performed the Fisher test, and we found the overlap was not significant (p> 0.05). DEGs and non-DEGs were both non-significant overlapped 6mA sites. Please see Figure 4E and Lines 261-262.

      “Less strong” was used to indicate the influence of HsdM on biofilm in Figure 5D. All Figures with “*” labels were analyzed using students' two-tailed t-tests with a significant change (p < 0.05).

      (2) Revised wording: For terms used to describe comparisons, we have revised the wording to be clearer and ensure that the terminology used did not imply the need for statistical significance testing when not required. For example:

      “Different level” has been removed. Please see Lines 143-144.

      “Greater conservation” has been revised to “more conserved functional terms”. Please see Lines 161-162.

      “Significant conservation” has been revised to “notable conservation”. Please see Line 165.

      “Credible motifs” has been revised to “identified motifs”. Please see Line 186.

      The authors performed SMRT sequencing of the delta hsdMSR showing a reduction of 6mA. Could they include a description of their results similar to Figures 1-2. How reduced is the 6mA level? Is it everywhere in the genome? Does it affect other methylation marks? This analysis would strengthen their conclusions.

      Yes, we agree. We have provided additional analysis and descriptions to strengthen the conclusions regarding these valuable comments. We determined three methylation sites in the HsdMSR mutant strain and compared the overlapped genes within these modification patterns. Specifically, we focused on the 6mA sites in Psph WT, HsdMSR mutant, and HsdM motif CAGCN<sub>(6)</sub>CTC. As expected, we found almost all of the reduction 6mA sites in the ΔhsdMSR were from motif CAGCN<sub>(6)</sub>CTC. We also noticed that 5mC and 4mC sites in the mutant were relatively similar to that in WT, and the slight difference might be caused by sequencing errors. Overall, we propose that HsdMSR only catalyze the 6mA located on the motif CAGCN<sub>(6)</sub>CTC, but does not affect other 6mA sites and other modification types.

      Please see Figures S4D-E and Lines 212-218 in the revised manuscript.

      In Figure 6E to conclude that methylation is required on both strands, the authors are missing the control CAGCN6CGC construct otherwise the effect could be linked to the A on the complementary strand.

      Thank you for your valuable suggestions. We have provided the control result on the complementary strand. Please see Figure 6C. The new result evidences the conclusion that 6mA methylation regulates gene transcription based on methylation on both strands.

      Please see Figure 6C and Lines 329-330 in the revised manuscript.

      Reviewer #2 (Public Review):

      In the present manuscript, Huang et.al. revealed the significant roles of the DNA methylome in regulating virulence and metabolism within Pseudomonas syringae, with a particular focus on the HsdMSR system in this model strain. The authors used SMRT-seq to profile the DNA methylation patterns (6mA, 5mC, and 4mC) in three P. syringae strains (Psph, Pss, and Psa) and displayed the conservation among them. They further identified the type I restriction-modification system (HsdMSR) in P. syringae, including its specific motif sequence. The HsdMAR participated in the process of metabolism and virulence (T3SS & Biofilm formation), as demonstrated through RNA-seq analyses. Additionally, the authors revealed the mechanisms of the transcriptional regulation by 6mA. Strictly from the point of view of the interest of the question and the work carried out, this is a worthy and timely study that uses third-generation sequencing technology to characterize the DNA methylation in P. syringae. The experimental approaches were solid, and the results obtained were interesting and provided new information on how epigenetics influences the transcription in P. syringae. The conclusions of this paper are mostly well supported by data, but some aspects of data analysis and discussion need to be clarified and extended.

      Thank you for your positive feedback and recognition of the importance of our study. We appreciate the suggestions for further clarification and extension of some aspects of data analysis and discussion. We added further analysis of the SMRT-seq result of the ΔhsdMSR and overexpressed HsdM in Pst to provide more information on conservation. We added these contents to the discussion in the revised manuscript. Please see Figure 6C and  Figure S5.

      Reviewer #3 (Public Review):

      Summary:

      The article by Huang et.al. presents an in-depth study on the role of DNA methylation in regulating virulence and metabolism in Pseudomonas syringae, a model phytopathogenic bacterium. This comprehensive research utilized single-molecule real-time (SMRT) sequencing to profile the DNA methylation landscape across three model pathovars of P. syringae, identifying significant epigenetic mechanisms through the Type-I restriction-modification system (HsdMSR), which includes a conserved sequence motif associated with N6-methyladenine (6mA). The study provides novel insights into the epigenetic mechanisms of P. syringae, expanding the understanding of bacterial pathogenicity and adaptation. The use of SMRT sequencing for methylome profiling, coupled with transcriptomic analysis and in vivo validation, establishes a robust evidence base for the findings

      Strengths:

      The results are presented clearly, with well-organized figures and tables that effectively illustrate the study's findings.

      Weaknesses:

      It would be helpful to add more details, especially in the methods, which make it easy to evaluate and enhance the manuscript's reproducibility.

      Thank you for the positive evaluation of our study, as well as the constructive feedback provided. We have added more details in methods for RNA-seq analysis and Ribo-seq analysis. Please see Lines 484-515.

      “Briefly, bacteria were cultured to an OD<sub>600</sub> of 0.4, at which point chloramphenicol was added to a final concentration of 100 µg/mL for 2 minutes. Cells were then pelleted and washed with pre-chilled lysis buffer [25 mM Tris-HCl, pH 8.0; 25 mM NH4Cl; 10 mM MgOAc; 0.8% Triton X-100; 100 U/mL RNase-free DNase I; 0.3 U/mL Superase-In; 1.55 mM chloramphenicol; and 17 mM GMPPNP]. The pellet was resuspended in lysis buffer, followed by three freeze-thaw cycles using liquid nitrogen. Sodium deoxycholate was then added to a final concentration of 0.3% before centrifugation. The resulting supernatant was adjusted to 25 A260 units and mixed with 2 mL of 500 mM CaCl<sub>2</sub> and 12 µL MNase, making up a total volume of 200 µL. After the digestion, the reaction was quenched with 2.5 mL of 500 mM EGTA. Monosomes were isolated using Sephacryl S400 MicroSpin columns, and RNA was purified using the miRNeasy Mini Kit (Qiagen). rRNA was removed using the NEBNext rRNA Depletion Kit, and the final library was constructed with the NEBNext Small RNA Library Prep Kit. For each sample, ribosome footprint reads were mapped to the Psph 1448A reference genome, and the translational efficiency was calculated by dividing the normalized Ribo-seq counts by the normalized RNA counts. Two biological replicates were performed for all experiments.”

      Recommendations For The Authors:

      Reviewer #1 (Recommendations For The Authors):

      I would recommend the authors limit their manuscript to Psph pathovar and include statistical analysis supporting their conclusions.

      Thank you for your suggestion.

      Minor

      • L104: "significantly" please add a p-value and explain the analysis.

      Sorry for the confusion. We have added the p-value and explained the analysis in the method section. The p-value used for SMRT-seq was the modification quality value (QV) score, which were used to call the modified bases A (QV=50) and C (QV=100). Please see Lines 452-454.

      • Figures 1B, D, F, and Figure 2A: make the Venn diagram to scale

      Yes, we have revised.

      • L110-111: missing p-value to say that the authors observe a bigger overlap in Pst than Psph as they observed more modified sites in Pst

      Sorry for the confusion. We said it had a bigger overlap in Pst because the number 17.7 was bigger than the number of 15 in Psph. To avoid misunderstanding, we revised the wording to “more genes equipped with all three modification types were detected in Pst than Psph”. Please see Lines 110-111.

      • L112: missing description of their Pss analysis (IDP, sites...)

      We have added the information for Pss in the revised manuscript.

      “Additionally, the methylome atlas of Pss revealed a lower incidence of methylation than those of Psph and Pst, particularly in terms of 6mA modifications, which were only seen in 457 significant 6mA occurrences under the same threshold (IPD > 1.5) and a total of 2,853 and 1,438 methylation sites were detected as 5mC and 4mC, respectively”. Please see Lines 114-116.

      • L118: "modification" to "modified "

      We have revised. Please see Line 119.

      • L120: "modification sites" to "modified nucleotides"

      We have revised. Please see Line 121.

      • L142: correct the title "Methylated genes revealed highly functional conservation among three P. syringae strains" maybe to "Methylated genes are functionally conserved among ..."

      We have revised. Please see Line 142.

      • Figure 2C: not easy to read and interpret

      Sorry for the confusion. Figure 2C revealed the significantly enriched functional pathways in GO and KEGG databases among three P. syringae strains. The specific names of each pathway were listed on the left, and each column with dots indicated the number of genes within one kind of methylation in one of three P. syringae strains. The larger the size, the bigger the number.

      We have revised the legend of Figure 2C. Please see Lines 575-579.

      “The dot plot revealed the significantly enriched functional pathways in GO and KEGG databases among three P. syringae strains. The specific names of each pathway were listed on the left, and each column with dots indicated the number of genes within one kind of methylation in one of three P. syringae strains. The size of the dots indicates the number of related genes.”

      • Figure 6B-C: what is the difference between B 24h and C?

      Figure 6B revealed the expression difference between WT and mutant during 24 hours. Figure 6C only showed a time point in 24 hours. To avoid repetition, we have removed Figure 6C.

      • Figure 6C-D: if the same maybe remove Figure 2C

      We have removed Figure 6D.

      Reviewer #2 (Recommendations For The Authors):

      The manuscript could be improved by addressing the following concerns:

      (1) In line 146: How to understand the percentage conserved in "more than two of the strains"?

      Sorry for the confusion, we planned to indicate the pattern that conserved in two strains and three strains. We have revised it to: “Notable, about 25% to 45% of methylated genes were conserved in two and three strains”. Please see Line 145.

      (2) In line 178: Five conserved sequence motifs should be replaced by "Six conserved sequence motifs".

      We have revised. Please see Line 176.

      (3) In Figure 2B, specify the C1, C2 and C3. "m6A" should be replaced by "6mA".

      Yes, we have revised.

      (4) In Figure S2, "m6A" should be replaced by "6mA".

      Yes, we have revised.

      (5) In line 212, please add references for the previous studies showing that growth conditions affect bacterial methylation status.

      Thank you for your suggestion. We have added the relevant references (Gonzalez and Collier, 2013), (Krebes et al., 2014), (Sanchez-Romero and Casadesus, 2020).

      (6) In line 217, "illustrate" should be "illustrated".

      Yes, we have revised. Please see Line 210.

      (7) There are some genes colored in grey, revealing bigger differences between the two strains than those related to ribosomal protein, T3SS, and alginate synthesis in Fig. 4A. Do they have important functional roles as well?

      Thank you for your suggestion. A total of 116 genes with bigger differences (|Log<sub>2</sub>FC| > 2) except for genes related to ribosomal protein, T3SS, and alginate synthesis. Among these genes, 31 were annotated as hypothetical proteins and 4 as transcription factors with unknown functions, and the remaining genes mostly encoded metabolism-related enzymes. These enzymes might have effects on growth defects in ΔhsdMSR. We added this information in the revised manuscript. Please see Line 249-254.

      (8) The authors should discuss what will be the potential signals or factors that can regulate the activity of HsdMSR. In other words, what can decide the extent of methylation through activating or suppressing the expression of HsdMSR?

      Thank you for your valuable suggestion. We have added this part in the discussion part. Please see Lines 404-415.

      “Apart from the established roles of 6mA and HsdMSR in P. syringae, certain signals or factors may influence HsdMSR expression. For instance, we confirmed that the growth phase affects methylation levels in P. syringae. Previous studies have shown that increased temperatures can reduce methylation levels, as observed in PAO1(Doberenz et al., 2017). These findings suggest that HsdMSR expression may be responsive to both intrinsic cellular states and extrinsic environmental conditions. To further explore potential upstream TFs regulating the expression of HsdMSR, we searched for TF-binding sites in the HsdMSR promoter using our own databases (Fan et al., 2020; Shao et al., 2021; Sun et al., 2024). As a result, we found three candidate TFs (PSPPH_0061, PSPPH_3268, and PSPPH_3504) that might directly bind and regulate HsdMSR expression. Future studies on these TFs and their interactions with the HsdMSR promoter would help clarify the regulatory network governing HsdMSR activity.”

      Reviewer #3 (Recommendations For The Authors):

      (1) Some figures contain dense information, which may be overwhelming for readers. Streamlining the legend for Figure 1 and resizing the Venn diagrams within it could enhance clarity and visual appeal.

      Thank you for your suggestion. We have scaled all the Venn plots in the revised version.

      (2) Incorporating a discussion about the role of the restriction-modification (RM) system in bacterial defense against phage infection into the discussion section could enrich the manuscript's context and relevance.

      Thank you for your valuable suggestion. We have added this part in the Discussion part. Please see Lines 416-427.

      “RM systems are known for their intrinsic role as innate immune systems in anti-phage infection, and present in around 90% of bacterial genomes(Oliveira et al., 2014). RM systems protect bacteria self by recognizing and degrading foreign phage DNA via methylation-specific site and restriction endonucleases (REases) (Loenen et al., 2014). In addition, other phage-resistance systems are similar to RM systems but carry extra genes. One is called the phage growth limitation (Pgl) system, which modifies and cleaves phage DNA. However, the Pgl only modifies the phage DNA in the first infection cycle, and cleaves phage DNA in the subsequent infections, which gives a warn to the neighboring cells(Hampton et al., 2020; Hoskisson et al., 2015). To counteract RM and RM-like systems, phages have evolved strategies, including unusual modifications such as hydroxymethylation, glycosylation, and glucosylation. They can also encode their own MTases to protect their DNA or employ strategies to evade restriction systems and other anti-RM defenses.(Iida et al., 1987; Murphy et al., 2013; Vasu and Nagaraja, 2013).

      (3) In line 152: What is the importance of the mentioned example of Cro/CI family TF?

      Thank you for your comments. The Cro/CI are important TFs present in phages. The interaction between Cro and CI affects bacteria immunity status in Enterohemorrhagic Escherichia coli (EHEC) strains(Jin et al., 2022). RM systems are known as a kind of phage-defense system, and hence we mentioned it here. We have added this description in the revised manuscript. Please see Lines 152-154.

      Reference:

      (1) Doberenz, S., Eckweiler, D., Reichert, O., Jensen, V., Bunk, B., Sproer, C., Kordes, A., Frangipani, E., Luong, K., Korlach, J., et al. (2017). Identification of a Pseudomonas aeruginosa PAO1 DNA Methyltransferase, Its Targets, and Physiological Roles. mBio 8. 10.1128/mBio.02312-16.

      (2) Fan, L., Wang, T., Hua, C., Sun, W., Li, X., Grunwald, L., Liu, J., Wu, N., Shao, X., Yin, Y., et al. (2020). A compendium of DNA-binding specificities of transcription factors in Pseudomonas syringae. Nat Commun 11, 4947. 10.1038/s41467-020-18744-7.

      (3) Gonzalez, D., and Collier, J. (2013). DNA methylation by CcrM activates the transcription of two genes required for the division of Caulobacter crescentus. Mol Microbiol 88, 203-218. 10.1111/mmi.12180.

      (4) Hampton, H.G., Watson, B.N., and Fineran, P.C. (2020). The arms race between bacteria and their phage foes. Nature 577, 327-336.

      (5) Hoskisson, P.A., Sumby, P., and Smith, M.C. (2015). The phage growth limitation system in Streptomyces coelicolor A (3) 2 is a toxin/antitoxin system, comprising enzymes with DNA methyltransferase, protein kinase and ATPase activity. Virology 477, 100-109.

      (6) Iida, S., Streiff, M.B., Bickle, T.A., and Arber, W. (1987). Two DNA antirestriction systems of bacteriophage P1, darA, and darB: characterization of darA− phages. Virology 157, 156-166.

      (7) Jin, M., Chen, J., Zhao, X., Hu, G., Wang, H., Liu, Z., and Chen, W.-H. (2022). An engineered λ phage enables enhanced and strain-specific killing of enterohemorrhagic Escherichia coli. Microbiology Spectrum 10, e01271-01222.

      (8) Krebes, J., Morgan, R.D., Bunk, B., Sproer, C., Luong, K., Parusel, R., Anton, B.P., Konig, C., Josenhans, C., Overmann, J., et al. (2014). The complex methylome of the human gastric pathogen Helicobacter pylori. Nucleic Acids Res 42, 2415-2432. 10.1093/nar/gkt1201.

      (9) Loenen, W.A., Dryden, D.T., Raleigh, E.A., Wilson, G.G., and Murray, N.E. (2014). Highlights of the DNA cutters: a short history of the restriction enzymes. Nucleic Acids Res 42, 3-19.

      (10) Murphy, J., Mahony, J., Ainsworth, S., Nauta, A., and van Sinderen, D. (2013). Bacteriophage orphan DNA methyltransferases: insights from their bacterial origin, function, and occurrence. Appl Environ Microb 79, 7547-7555.

      (11) Oliveira, P.H., Touchon, M., and Rocha, E.P. (2014). The interplay of restriction-modification systems with mobile genetic elements and their prokaryotic hosts. Nucleic Acids Res 42, 10618-10631.

      (12) Sanchez-Romero, M.A., and Casadesus, J. (2020). The bacterial epigenome. Nature reviews. Microbiology 18, 7-20. 10.1038/s41579-019-0286-2.

      (13) Shao, X., Tan, M., Xie, Y., Yao, C., Wang, T., Huang, H., Zhang, Y., Ding, Y., Liu, J., Han, L., et al. (2021). Integrated regulatory network in Pseudomonas syringae reveals dynamics of virulence. Cell Rep 34, 108920. 10.1016/j.celrep.2021.108920.

      (14) Sun, Y., Li, J., Huang, J., Li, S., Li, Y., Lu, B., and Deng, X. (2024). Architecture of genome-wide transcriptional regulatory network reveals dynamic functions and evolutionary trajectories in Pseudomonas syringae. bioRxiv, 2024.2001. 2018.576191.

      (15) Vasu, K., and Nagaraja, V. (2013). Diverse functions of restriction-modification systems in addition to cellular defense. Microbiol Mol Biol Rev 77, 53-72. 10.1128/MMBR.00044-12.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      The authors present a modelling study to test the hypothesis that horizontal gene transfer (HGT) can modulate the outcome of interspecies competition in microbiomes, and in particular promote bistability in systems across scales. The premise is a model developed by the same authors in a previous paper where bistability happens because of a balance between growth rates and competition for a mutual resource pool (common carrying capacity). They show that introducing a transferrable element that gives a "growth rate bonus" expands the region of parameter space where bistability happens. The authors then investigate how often (in terms of parameter space) this bistability occurs across different scales of complexity, and finally under selection for the mobile element (framed as ABR selection).

      Strengths:

      The authors tackle an important, yet complex, question: how do different evolutionary processes impact the ecology of microbial ecosystems? They do a nice job at increasing the scales of heterogeneity and asking how these impact their main observable: bistability.

      We appreciate the reviewer for agreeing with the potential value of our analysis. We are also grateful for the constructive comments and suggestions on further analyzing the influence of the model structure and the associated assumptions. We have fully addressed the raised issues in the updated manuscript and below.

      Weaknesses:

      The author's starting point is their interaction LV model and the manuscript then explores how this model behaves under different scenarios. Because the structure of the model and the underlying assumptions essentially dictate these outcomes, I would expect to see much more focus on how these two aspects relate to the specific scenarios that are discussed. For example:

      A key assumption is that the mobile element conveys a multiplicative growth rate benefit (1+lambda). However, the competition between the species is modelled as a factor gamma that modulates the competition for overall resource and thus appears in the saturation term (1+ S1/Nm + gamma2*S2/Nm). This means that gamma changes the perceived abundance of the other species (if gamma > 1, then from the point of view of S1 it looks like there are more S2 than there really are). Most importantly, the relationship between these parameters dictates whether or not there will be bistability (as the authors state).

      This decoupling between the transferred benefit and the competition can have different consequences. One of them is that - from the point of view of the mobile element - the mobile element competes at different strengths within the same population compared to between. To what degree introducing such a mobile element modifies the baseline bistability expectation thus strongly depends on how it modifies gamma and lambda.

      Thus, this structural aspect needs to be much more carefully presented to help the reader follow how much of the results are just trivial given the model assumptions and which have more of an emergent flavour. From my point of view, this has an important impact on helping the reader understand how the model that the authors present can contribute to the understanding of the question "how microbes competing for a limited number of resources stably coexist". I do appreciate that this changes the focus of the manuscript from a presentation of simulation results to more of a discussion of mathematical modelling.

      We thank the reviewer for the insightful suggestions. We agree with the reviewer that the model structure and the underlying assumptions need to be carefully discussed, in order to understand the generality of the theoretical predictions. In particular, the reviewer emphasized that how HGT affects bistability might depend on how mobile genetic elements modified growth rates and competition. In the main text, we have shown that when mobile genes only influence species growth rates, HGT is expected to promote multistability (Fig. 1 and 2). However, when mobile genes modify species interactions, the effect of HGT on multistability is dependent on how mobile genes change competition strength (Fig. 3a to f). When mobile genes increase competition, HGT promotes multistability (Fig. 3c and e). In contrast, when mobile genes relax competition, HGT is expected to reduce multistability (Fig. 3d and f).

      In light of the reviewer’s comments, we have further generalized the model structure, by accounting for the scenario where mobile genes simultaneously modify growth rates and competition. The effect of mobile genes on growth rates is represented by the magnitude of 𝜆’s, and the influence on competition is described by another parameter 𝛿. By varying these two parameters, we can evaluate how the model structure and the underlying assumptions affect the baseline expectation. We performed additional simulations with broad ranges of 𝜆 and 𝛿 values. In particular, we analyzed whether HGT would promote the likelihood of bistability in two-species communities compared with the scenario without gene transfer (Fig. 3g-i). Our results suggested that: (1) With or without HGT, reducing 𝜆 (increasing neutrality) promotes bistability; (2) With HGT, increasing 𝛿 promotes bistability; (2) Compared with the population without HGT, gene transfer promotes bistability when 𝛿 is zero or positive, while reduces bistability when 𝛿 is largely negative. These results agree with the reviewer’s comment that the baseline bistability expectation depends on how HGT modifies gamma and lambda. In the updated manuscript, we have thoroughly discussed how the model structure and the underlying assumptions can influence the predictions (line 238-253). 

      We further expanded our analysis, by calculating how other parameters, including competition strength, growth rate ranges, and death/dilution rate, would affect the multistability of communities undergoing horizontal gene transfer (Fig. S2, S3, S9, S10, S11, S12, S13, S15). Together with the results presented in the first draft, these analysis enables a more comprehensive understanding of how different mechanisms, including but not limited to HGT, collectively shaped community multistability. In the updated manuscript, the reviewer can see the change of focus from exploring the effects of HGT to a more thorough discussion of the mathematical model. The revised texts highlighted in blue and the supplemented figures reflect such a change.

      Reviewer #2 (Public review):

      Summary:

      In this work, the authors use a theoretical model to study the potential impact of Horizontal Gene Transfer on the number of alternative stable states of microbial communities. For this, they use a modified version of the competitive Lotka Volterra model-which accounts for the effects of pairwise, competitive interactions on species growth-that incorporates terms for the effects of both an added death (dilution) rate acting on all species and the rates of horizontal transfer of mobile genetic elements-which can in turn affect species growth rates. The authors analyze the impact of horizontal gene transfer in different scenarios: bistability between pairs of species, multistability in communities, and a modular structure in the interaction matrix to simulate multiple niches. They also incorporate additional elements to the model, such as spatial structure to simulate metacommunities and modification of pairwise interactions by mobile genetic elements. In almost all these cases, the authors report an increase in either the number of alternative stable states or the parameter region (e.g. growth rate values) in which they occur.

      In my opinion, understanding the role of horizontal gene transfer in community multistability is a

      very important subject. This manuscript is a useful approach to the subject, but I'm afraid that a thorough analysis of the role of different parameters under different scenarios is missing in order to support the general claims of the authors. The authors have extended their analysis to increase their biological relevance, but I believe that the analysis still lacks comprehensiveness.

      Understanding the origin of alternative stable states in microbial communities and how often they may occur is an important challenge in microbial ecology and evolution. Shifts between these alternative stable states can drive transitions between e.g. a healthy microbiome and dysbiosis. A better understanding of how horizontal gene transfer can drive multistability could help predict alternative stable states in microbial communities, as well as inspire novel treatments to steer communities towards the most desired (e.g. healthy) stable states.

      Strengths:

      (1) Generality of the model: the work is based on a phenomenological model that has been extensively used to predict the dynamics of ecological communities in many different scenarios.

      (2) The question of how horizontal gene transfer can drive alternative stable states in microbial communities is important and there are very few studies addressing it.

      We thank the reviewer for the positive comments on the potential novelty and conceptual importance of our work. We are also grateful for the constructive suggestions on the generality and comprehensiveness of our analysis. In particular, we agree with the reviewer that a thorough analysis of the role of different parameter could further improve the rigor of this work. We have fully addressed the raised issues in the updated manuscript and below.

      Weaknesses:

      (1) There is a need for a more comprehensive analysis of the relative importance of the different model parameters in driving multistability. For example, there is no analysis of the effects of the added death rate in multistability. This parameter has been shown to determine whether a given pair of interacting species exhibits bistability or not (see e.g. Abreu et al 2019 Nature Communications 10:2120). Similarly, each scenario is analyzed for a unique value of species interspecies interaction strength-with the exception of the case for mobile genetic elements affecting interaction strength, which considers three specific values. Considering heterogeneous interaction strengths (e.g. sampling from a random distribution) could also lead to more realistic scenarios - the authors generally considered that all species pairs interact with the same strength. Analyzing a larger range of growth rates effects of mobile genetic elements would also help generalize the results. In order to achieve a more generic assessment of the impact of horizontal gene transfer in driving multistability, its role should be systematically compared to the effects of the rest of the parameters of the model.

      We appreciate the suggestions. For each of the parameters that the reviewer mentioned, we have performed additional simulations to evaluate its importance in driving multistability. 

      For the added death rate, we have calculated the bistability feasibility of two-species populations under different values of 𝐷. Our results suggested that (1) varying death rate indeed changed the bistability probability of the system; (2) when the death rate was zero, mobile genetic elements that only modify growth rates would have no effects on system’s bistability. These results highlighted the importance of added death rate in driving multistability (Fig. S2, line 136-142). 

      For the interspecies interaction strength, we first extended our analysis on two-species populations. By calculating the bistability probability under different values of 𝛾, we showed that when interspecies interaction strength was smaller than 1, the influence of HGT on population bistability became weak (Fig. S3, line 143-147). We also considered heterogenous interaction strengths in multispecies communities, by randomly sampling 𝛾<sub>ij</sub> values from uniform distributions. While our results suggested the heterogeneous distribution of 𝛾<sub>ij</sub> didn’t fundamentally change the main conclusion, the mean value and variance of 𝛾<sub>ij</sub> affected the influence of HGT on multistability. The effects of HGT on community multistability becomes stronger when the mean value of 𝛾<sub>ij</sub> gets larger than 1 and the variance of 𝛾<sub>ij</sub> is small (Fig. S12, line 190-196).

      We also analyzed different ranges of growth rates effects of mobile genetic elements. In particular, we sampled 𝜆<sub>ij</sub> values from uniform distributions with given widths. Greater width led to larger range of growth rate effects. We used five-species populations as an example and tested different ranges. Our results suggested that multistability was more feasible when the growth rate effects of MGEs were small. The qualitative relationship between HGT and community was not dependent on the range of growth rate effects (Fig. S13, line 197-205).

      (2) The authors previously developed this theoretical model to study the impact of horizontal gene transfer on species coexistence. In this sense, it seems that the authors are exploring a different (stronger interspecies competition) range of parameter values of the same model, which could potentially limit novelty and generality.

      We appreciate the comment. In a previous work (PMID: 38280843), we developed a theoretical model that incorporated horizontal gene transfer process into the classic LV framework. This model provides opportunities to investigate the role of HGT in different open questions of microbial ecology. In the previous work, we considered one fundamental question: how competing microbes coexist stably. In this work, however, we focused on a different problem: how alternative stable states emerge in complex communities. While the basic theoretical tool that we applied in the two works were similar, the scientific questions, application contexts and the implications of our analysis were largely different. The novelty of this work arose from the fact that it revealed the conceptual linkage between alternative stable states and a ubiquitous biological process, horizontal gene transfer. This linkage is largely unknown in previous studies. Exploring such a linkage naturally required us to consider stronger interspecies competitions, which in general would diminish coexistence but give rise to multistability. We believe that the analysis performed in this work provide novel and valuable insights for the field of microbial ecology. 

      With all the supplemented simulations that we carried out in light of the all the reviewer’s comments, we believe the updated manuscript also provide a unified framework to understand how different biological processes collectively shaped the multistability landscape of complex microbiota undergoing horizontal gene transfer. The comprehensive analyses performed and the diverse scenarios considered in this study also contribute to the novelty and generality of this work.  

      (3) The authors analyze several scenarios that, in my opinion, naturally follow from the results and parameter value choices in the first sections, making their analysis not very informative. For example, after showing that horizontal gene transfer can increase multistability both between pairs of species and in a community context, the way they model different niches does not bring significantly new results. Given that the authors showed previously in the manuscript that horizontal gene transfer can impact multistability in a community in which all species interact with each other, one might expect that it will also impact multistability in a larger community made of (sub)communities that are independent of (not interacting with) each-which is the proposed way for modelling niches. A similar argument can be made regarding the analysis of (spatially structured) metacommunities. It is known that, for smaller enough dispersal rates, space can promote regional diversity by enabling each local community to remain in a different stable state. Therefore, in conditions in which the impact of horizontal gene transfer drives multistability, it will also drive regional diversity in a metacommunity.

      Thanks. Based on the reviewer’s comments, we have move Fig. 3 and 4 to Supplementary Information. In the updated manuscript, we have focused more on analyzing the roles of different parameters in shaping community multistability.

      (4) In some cases, the authors consider that mobile genetic elements can lead to ~50% growth rate differences. In the presence of an added death rate, this can be a relatively strong advantage that makes the fastest grower easily take over their competitors. It would be important to discuss biologically relevant examples in which such growth advantages driven by mobile genetic elements could be expected, and how common such scenarios might be.

      We appreciate the suggestion. Mobile genetic elements can drive large growth rate differences when they encode adaptative traits like antibiotic resistance (line 197-198). 

      We also analyzed different ranges of growth rates effects of mobile genetic elements, by sampling 𝜆<sub>ij</sub> values from uniform distributions with given widths. Our results suggested that multistability was more feasible when the fitness effects of MGEs were small (Fig. S13b). The qualitative relationship between HGT and community was not dependent on the range of growth rate effects (Fig. S13a and b). We discussed these results in line 197-205 of the updated main text.

      Reviewer #3 (Public review):

      Hong et al. used a model they previously developed to study the impact of horizontal gene transfer (HGT) on microbial multispecies communities. They investigated the effect of HGT on the existence of alternative stable states in a community. The model most closely resembles HGT through the conjugation of incompatible plasmids, where the transferred genes confer independent growth-related fitness effects. For this type of HGT, the authors find that increasing the rate of HGT leads to an increasing number of stable states. This effect of HGT persists when the model is extended to include multiple competitive niches (under a shared carrying capacity) or spatially distinct patches (that interact in a grid-like fashion). Instead, if the mobile gene is assumed to reduce between-species competition, increasing HGT leads to a smaller region of multistability and fewer stable states. Similarly, if the mobile gene is deleterious an increase in HGT reduces the parameter region that supports multistability.

      This is an interesting and important topic, and I welcome the authors' efforts to explore these topics with mathematical modeling. The manuscript is well written and the analyses seem appropriate and well-carried out. However, I believe the model is not as general as the authors imply and more discussion of the assumptions would be helpful (both to readers + to promote future theoretical work on this topic). Also, given the model, it is not clear that the conclusions hold quite so generally as the authors claim and for biologically relevant parameters. To address this, I would recommend adding sensitivity analyses to the manuscript.

      We thank the reviewer for the agreeing that our work addressed an important topic and was wellconducted. We are also grateful for the suggestion on sensitivity analysis, which is very helpful to improve the rigor and generality of our conclusion. All the raised issues have been fully addressed in the updated manuscript and below.

      Specific points

      (1) The model makes strong assumptions about the biology of HGT, that are not adequately spelled out in the main text or methods, and will not generally prove true in all biological systems. These include:

      a) The process of HGT can be described by mass action kinetics. This is a common assumption for plasmid conjugation, but for phage transduction and natural transformation, people use other models (e.g. with free phage that adsorp to all populations and transfer in bursts).

      b) A subpopulation will not acquire more than one mobile gene, subpopulations can not transfer multiple genes at a time, and populations do not lose their own mobilizable genes. [this may introduce bias, see below].

      c) The species internal inhibition is independent of the acquired MGE (i.e. for p1 the self-inhibition is by s1).

      These points are in addition to the assumptions explored in the supplementary materials, regarding epistasis, the independence of interspecies competition from the mobile genes, etc. I would appreciate it if the authors could be more explicit in the main text about the range of applicability of their model, and in the methods about the assumptions that are made.

      We are grateful for the reviewer’s suggestions. In main text and methods of the updated manuscript, we have made clear the assumptions underlying our analysis. For point (a), we have clarified that our model primarily focused on plasmid transfer dynamics (line 74, 101, 517). Therefore, the process of HGT can be described by mass action kinetics, which is commonly assumed for plasmid transfer (line 537-538). For point (b), our model allows a cell to acquire more than one mobile genes. Please see our response to point (3) for details. We have also made it clear that we assumed the populations would not lose their own mobile gene completely (line 526-527). For (c), we have also clarified it in the updated manuscript (line 111-112, 527-528). 

      We have also performed a series of additional simulations to show the range of applicability of our model. In particular, we discuss the role of other mechanisms, including interspecies interaction strength, the growth rate effects of MGEs, MGE epistasis and microbial death rates in shaping the multistability of microbial communities undergoing HGT. These results were provided in Fig. S2, S3, S9, S10, S11, S12, S13 and S15.

      (2) I am not surprised that a mechanism that creates diversity will lead to more alternative stable states. Specifically, the null model for the absence of HGT is to set gamma to zero, resulting in pij=0 for all subpopulations (line 454). This means that a model with N^2 classes is effectively reduced to N classes. It seems intuitive that an LV-model with many more species would also allow for more alternative stable states. For a fair comparison, one would really want to initialize these subpopulations in the model (with the same growth rates - e.g. mu1(1+lambda2)) but without gene mobility.

      We appreciate the insightful comments. The reviewer was right that in our model HGT created additional subpopulations in the community. However, with or without HGT, we calculated the species diversity and multistability based on the abundances of the 𝑁 species (s<sub>i</sub> in our model), instead of all the p<sub>ij</sub> subpopulations. Therefore, although there exist more ‘classes’ in the model with HGT, the number of ‘classes’ considered when we calculated community diversity and multistability was equal. In light of the reviewer’s suggestion, we have also performed additional simulations, where we initialized the subpopulations in the model with nonzero abundances. Our results suggested that initializing the p<sub>ij</sub> subpopulations with non-zero abundances didn’t change the main conclusion (Fig. S11, line 188-189).

      (3) I am worried that the absence of double gene acquisitions from the model may unintentionally promote bistability. This assumption is equivalent to an implicit assumption of incompatibility between the genes transferred from different species. A highly abundant species with high HGT rates could fill up the "MGE niche" in a species before any other species have reached appreciable size. This would lead to greater importance of initial conditions and could thus lead to increased multistability.

      This concern also feels reminiscent of the "coexistence for free" literature (first described here http://dx.doi.org/10.1016/j.epidem.2008.07.001 ) which was recently discussed in the context of plasmid conjugation models in the supplementary material (section 3) of https://doi.org/10.1098/rstb.2020.0478 .

      We appreciate the comments. Our model didn’t assume the incompatibility between MGEs transferred from different species. Instead, it allows a cell to acquire more than one MGEs. In our model, p<sub>ij</sub> described the subpopulation in the 𝑖-th species that acquired the MGE from the 𝑗th species. Here, p<sub>ij</sub> can have overlaps with p<sub>ik</sub> (𝑗 ≠ 𝑘). In other words, a cell can belong to p<sub>ij</sub> and p<sub>ik</sub> at the same time. The p<sub>ij</sub> subpopulation is allowed to carry the MGEs from the other species. In the model, we used to describe the influence of the other MGEs on the growth of p<sub>ij</sub>.

      We also thank the reviewer for bringing two papers into our attention. We have cited and discussed these papers in the updated manuscript (line 355-362).

      (4) The parameter values tested seem to focus on very large effects, which are unlikely to occur commonly in nature. If I understand the parameters in Figure 1b correctly for instance, lambda2 leads to a 60% increase in growth rate. Such huge effects of mobile genes (here also assumed independent from genetic background) seem unlikely except for rare cases. To make this figure easier to interpret and relate to real-world systems, it could be worthwhile to plot the axes in terms of the assumed cost/benefit of the mobile genes of each species.

      Thanks for the comments. In the main text, we presented one simulation results that assumed relatively large effects of MGE on species fitness, as the reviewer pointed out. In the updated manuscript, we have supplemented numerical simulations that considered different ranges of fitness effects, including the fitness effect as small as 10% (Fig. S13a). We have also plotted the relationship between community multistability and the assumed fitness effects of MGEs, as the reviewer suggested (Fig. S13b). Our results suggested that multistability was more feasible when the fitness effects of MGEs were small, and changing the range of MGE fitness effects didn’t fundamentally change our main conclusion. These results were discussed in line 197-205 of the updated main text.

      Something similar holds for the HGT rate (eta): given that the population of E. coli or Klebsiella in the gut is probably closer to 10^9 than 10^12 (they make up only a fraction of all cells in the gut), the assumed rates for eta are definitely at the high end of measured plasmid transfer rates (e.g. F plasmid transfers at a rate of 10^-9 mL/CFU h-1, but it is derepressed and considered among the fastest - https://doi.org/10.1016/j.plasmid.2020.102489 ). To adequately assess the impact of the HGT rate on microbial community stability it would need to be scanned on a log (rather than a linear) scale. Considering the meta-analysis by Sheppard et al. it would make sense to scan it from 10^-7 to 1 for a community with a carrying capacity around 10^9.

      We thank the reviewer for the constructive suggestion. We have carried out additional simulations by scanning the 𝜂 value from 10<sup>-7</sup> to 1. The results suggested that increasing HGT rates started to promote multistability when 𝜂 value exceeded 10<sup>-2</sup> per hour (Fig. S9, line 337-346). This corresponds to a conjugation efficiency of 10<sup>-11</sup> cell<sup>-1</sup> ∙ mL<sup>-1</sup>∙ mL when the maximum carrying capacity equals 10<sup>9</sup> cellsmL<sup>-1</sup>, or a conjugation efficiency of 10<sup>-14</sup> cell<sup>-1</sup> ∙ hr<sup>-1</sup>∙ mL when the maximum carrying capacity equals 10<sup>12</sup> cellsmL<sup>-1</sup>.

      (5) It is not clear how sensitive the results (e.g. Figure 2a on the effect of HGT) are to the assumption of the fitness effect distribution of the mobile genes. This is related to the previous point that these fitness effects seem quite large. I think some sensitivity analysis of the results to the other parameters of the simulation (also the assumed interspecies competition varies from figure to figure) would be helpful to put the results into perspective and relate them to real biological systems.

      We appreciate the comments. In light of the reviewer’s suggestion, we have changed the range of the fitness effects and analyzed the sensitivity of our predictions to this range. As shown in Fig. S13, changing the range of MGE fitness effects didn’t alter the qualitative interplay between HGT and community multistability. We have also examined the sensitivity of the results to the strength of interspecies competition strength (Fig. S3, S10, S12). These results suggested that while the strength of interspecies interactions played an important role in shaping community multistability, the relationship between HGT rate and multistability was not fundamentally changed by varying interaction strength. In addition, we examined the role of death rates (Fig. S2). In the updated manuscript, we discussed the sensitivity of our prediction to these parameters in line 136-147, 190205, 335-354.

      Recommendations for the authors:

      Reviewer #2 (Recommendations for the authors):

      Please find below a few suggestions that, in my opinion, could help improve the manuscript.

      TITLE

      It might not be clear what I 'gene exchange communities' are. Perhaps it could be rewritten for more specificity (e.g. '...communities undergoing horizontal gene transfer').

      We have updated the title as the reviewer suggested.

      ABSTRACT

      The abstract could also be edited to improve clarity and specificity. Terms like 'complicating factors' are vague, and enumerating specific factors would be better. The results are largely based on simulations, no analytical results are plotted, so I find that the sentence starting with 'Combining theoretical derivation and numerical simulations' can be a bit misleading.

      We appreciate the suggestions. We have enumerated the specific factors and scenarios in the updated abstract (line 18-26). We have also replaced 'Combining theoretical derivation and numerical simulations' with ‘Combining mathematical modeling and numerical simulations’.

      INTRODUCTION

      -  Line 42, please revise this paragraph. The logical flow is not so clear, it seems a bit like a list of facts, but the main message might not be clear enough. Also, it would be good to define 'hidden' states or just rewrite this sentence.

      We appreciate the suggestion. In the updated manuscript, we have rewritten this paragraph to improve the logical flow and clarity (line 46-52).

      -  Line 54, there is little detail about both theoretical models and HGT in this paragraph, and mixing the two makes the paragraph less focused. I suggest to divide into two paragraphs and expand its content. For example, you could explain a bit some relevant implications of MGE.

      We appreciate the suggestion. In the updated manuscript, we have divided this paragraph into two paragraphs, focusing on theoretical models and HGT, respectively (line 55-71). In particular, we have added explanations on the implications of MGEs (line 66-69), as the reviewer suggested.

      -  Line 72, as mentioned in the abstract, it would be better to explicitly mention which confounding factors are going to be discussed.

      Thanks for the suggestion. We have rewritten this part as “We further extended our analysis to scenarios where HGT changed interspecies interactions, where microbial communities were subjected to strong environmental selections and where microbes lived in metacommunities consisting of multiple local habitats. We also analyzed the role of different mechanisms, including interspecies interaction strength, the growth rate effects of MGEs, MGE epistasis and microbial death rates in shaping the multistability of microbial communities. These results created a comprehensive framework to understand how different dynamic processes, including but not limited to HGT rates, collectively shaped community multistability and diversity” (line 75-82).

      RESULTS

      -  The basic concepts (line 77) should be explained with more detail, keeping the non-familiar reader in mind. The reader might not be familiar with the concept of bistability in terms of species abundance. Also, note that mutual inhibition does not necessarily lead to positive feedback, as an interaction strength between 0 and 1 might still be considered inhibition. In any case, in Figure 1 it is not obvious how the positive feedback is represented, the caption should explain it. Note that neither the main text nor the caption explains the metaphor of the landscape and the marble that you are using in Figure 1a.

      We have rewritten this paragraph to provide more details on the basic concepts (line 86-99). We have removed the statement about ‘mutual inhibition’ to avoid being misleading. We have also updated the caption of Fig. 1a to explain the metaphor of the landscape and the marble (line 389396). 

      -  In the classical LV model, bistability does not depend on growth rates, but only on interaction strength. Therefore, I think that much of the results are significantly influenced by the added death rate. I believe that if the death rate is set to zero, mobile genetic elements that only modify growth rates will have no effect on the system's bistability. Because of this, I think that a thorough analysis of the role of the added death (dilution) rate and the distribution of growth rates is especially needed.

      We are grateful for the reviewer’s insightful comments. In the updated manuscript, we have thoroughly analyzed the role of the added death (dilution) rate on the bistability of communities composed of two species (Fig. S2). Indeed, as the reviewer pointed out, if the death rate equals zero, mobile genetic elements that only modify growth rates will have no effect on the system's bistability. We have discussed the role of death rate in line 136-142 of the updated manuscript.

      We have also expanded our analysis on the distribution of growth rates. In particular, we considered different ranges of growth rates effects of mobile genetic elements, by sampling 𝜆<sub>ij</sub> values from uniform distributions with given widths (Fig. S13). Greater width led to larger range of growth rate effects. We used five-species populations as an example and tested different ranges.

      Our results suggested that multistability was more feasible when the growth rate effects of MGEs were small (Fig. S13b). The qualitative relationship between HGT and community was not dependent on the range of growth rate effects (Fig. S13a). These results are discussed in line 197205 of the updated manuscript.

      -  The analysis uses gamma values that, in the absence of an added death rate, render a species pair bistable. Therefore, multistability would be quite expected for a 5 species community. Note that, multistability is possible in communities of more than 2 species even if all gamma values are smaller than 1. Analyzing a wide range of interaction strength distributions would really inform on the relative role of HGT in multistability across different community scenarios.

      We are grateful for the reviewer’s suggestion. In light of the reviewer’s comments, in the updated manuscript, we have performed additional analysis by focusing on a broader range of interaction strengths (Fig. S3, S10, S12), especially the gamma values below 1 (Fig. S10). Our results agreed with the reviewer’s notion that multistability was possible in communities of more than 2 species even if all gamma values were smaller than 1 (Fig. S10). 

      -  I would recommend the authors extend the analysis of the model used for Figures 1 and 2. Figures 3 and 4 could be moved to the supplement (see my point in the public review), unless the authors extend the analysis to explain some non-intuitive outcomes for niches and metacommunities.

      Thanks. In the updated manuscript we have performed additional simulations to extend the analysis in Figure 1 and 2. These results were presented in Fig. S2, S3, S9, S10, S11, S12, and S13. We have also moved Figure 3 and 4 to SI as the reviewer suggested.

      -  The authors seem to refer to fitness and growth rates as the same thing. This could lead to confusion - the strongest competitor in a species pair could also be interpreted as the fittest species despite being the slowest grower. I think there's no need to use fitness if they refer to growth rates. In any case, they should define fitness if they want to use this concept in the text.

      We are grateful for the insightful suggestion. To avoid confusion, we have used ‘growth rate’ throughout the updated manuscript.

      -  Across the text, the language needs some revision for clarity, specificity, and scientific style. In lines 105 - 109 there are some examples, like the use of 'in a lot of systems', and ' interspecies competitions' (I believe they mean interspecies interaction strengths).

      We appreciate the reviewer for pointing them out. We have thoroughly checked the text and made the revisions whenever applicable to improve the clarity and specificity.

      -  Many plots present the HGT rate on the horizontal axis. Could the authors explain why is it that the rate of HGT is relatively important for the number of alternative stable states? I understand how from zero to a small positive number there is a qualitative change. Beyond that, it shouldn't affect bistability too much, I think. If I am right, then other parameters could be more informative to plot in the horizontal axis. If I am wrong, I think that providing an explanation for this would be valuable.

      Thanks. To address the reviewer’s comment, we have systematically analyzed the effects of HGT on community multistability, by scanning the HGT rate from 10<sup>-7</sup> to 10<sup>0</sup>hr<sup>-1</sup> . In communities of two or multiple species, our simulation results showed that multistability gradually increased with HGT rate when HGT rate exceeded 10<sup>2</sup>hr<sup>-1</sup>. These results, presented in Fig. S9 and discussed in line 337-346, provided a more quantitative relationship between multistability and HGT rate.

      While in this work we showed the potential role of HGT in modulating community multistability, our results didn’t exclude the role of the other parameters. Motivated by the comments raised by the reviewers, in the updated manuscript, we have performed additional simulations to analyze the influence of other parameters in shaping community multistability. These parameters include death or dilution rate (Fig. S2), interaction strength (Fig. S3, S9, S10, S11, S12, S14, S15), 𝜆 range (Fig. S13, S15) and 𝛿 value (Fig. 3g, h, i). In many of the supplemented results (Fig. S2b, S3b, S13b, Fig. 3g, 3h and 3i), we have also plotted the data by using these parameters as the x axis. We believe the updated work now provided a more comprehensive framework to understand how different mechanisms, including but not limited to HGT, might shape the multistability of complex microbiota. These points were discussed in line 136-147, 190-205, 238-253, 334-354 of the updated main text. 

      -  My overall thoughts on the case of antibiotic exposure are similar to those of previous sections. Very few of the different parameters of the model are analyzed and discussed. In this case, the authors increased the interaction strength to ~0.4 times higher compared to previous sections. Was this necessary, and why?

      Thanks for the comments. In the previous draft, the interaction strength 𝛾=1.5 was tested as an example. Motivated by the reviewer’s comments, in the updated manuscript, we have examined different interaction strengths, including the strength ( 𝛾 = 1.1 ) commonly tested in other scenarios. The prediction equally held for different 𝛾 values (Fig. S15). We have also analyzed different 𝜆 ranges (Fig. S15). These results, together with the analyses presented in the earlier version of the manuscript, suggested the potential role of HGT in promoting multistability for communities under strong selection. The supplemented results were presented in Fig. S15 and discussed in line 293-295 of the updated manuscript.

      -  Line 195, if a gene encodes for the production of a public good, why would its HGT reduce interaction strength? I can think of the opposite scenario: the gene is a public good, and without HGT there is only one species that can produce it. Let's imagine that the public good is an enzyme that deactivates an antibiotic that is present in the environment, and then the species that produces has a positive interaction with another species in a pairwise coculture. If HGT happens, the second species becomes a producer and does not need the other one to survive in the presence of antibiotics anymore. The interaction can then become more competitive, as e.g. competition for resources could become the dominant interaction.

      We are grateful for pointing it out. In the updated manuscript, we have removed this statement.

      DISCUSSION

      -  L 267 "by comparison with empirical estimates of plasmid conjugation rates from a previous study [42], the HGT rates in our analysis are biologically relevant in a variety of natural environments". The authors are using a normalized model and the relevance of other parameter values is not discussed. If the authors want to claim that they are using biologically relevant HGT, they should also discuss whether the rest of the parameter values are biologically relevant. I recommend relaxing this statement about HGT rates.

      We appreciate the suggestion. We agree with the reviewer that other parameters including the death/dilution rate, interactions strength and 𝜆 ranges are also important in shaping community multistability. We have performed additional analysis to show the effects of these parameters. In light of the reviewer’s suggestion, we have relaxed this statement and thoroughly discussed the context-dependent effect of HGT as well as the roles of different parameters (line 334-354).

      -  Last sentence: "Therefore, inhibiting the MGE spread using small molecules might offer new opportunities to reshape the stability landscape and narrow down the attraction domains of the disease states". It is not clear what procedure/technique the authors are suggesting. If they want to keep this statement, the authors should give more details on how small molecules can be/are used to inhibit MGE.

      We appreciated the comments. Previous studies have shown some small molecules like unsaturated fatty acids can inhibit the conjugative transfer of plasmids. By binding the type IV secretion traffic ATPase TrwD, these compounds limit the pilus biogenesis and DNA translocation. We have provided more details regarding this statement in the updated manuscripts (line 376-379).

      METHODS

      -  Line 439, mu_i should be presented as the maximum 'per capita' growth rate.

      We have updated the definition of 𝜇i following the suggestion (line 529).

      -  Line 444, this explanation is hard to follow, please expand it to provide more details. You could provide an example, like explaining that all individuals from S1 have the MGE1 and therefore they have mu_1 = mu_01 ... After HGT, their fitness changes if they get the plasmid from S2, so a term lambda2 appears.

      Thanks. In the updated manuscript, we have expanded the explanation by providing an example as the reviewer suggested (line 534-537).

      -  The normalization assumes a common carrying capacity Nm (Eqs 1-4) and then it's normalized (Eqs. 5-8). It would be better to start from a more general scenario in which each species has a different carrying capacity and then proceed with the normalization.

      We appreciate the suggestion. In the updated manuscript, we have started our derivation from the scenario where each species has a different carrying capacity before proceeding with the normalization (section 1 of Methods, line 516-554). The same equations can be obtained after normalization.

      -  I think that the meaning of kappa (the plasmid loss rate) is not explained in the text.

      Thanks for pointing it out. We have explained the meaning of kappa in the updated text (line 108, 154, 539-541, 586-587, 607).

      SUPPLEMENT

      -  Figure S4, what are the different colors in panel b?

      In panel b of Fig. S4, the different colors represent the simulation results repeated with randomized growth rates. We have made it clear in the updated SI.

      Reviewer #3 (Recommendations for the authors):

      (1) Please extend your description of the model, so it is easier to understand for readers who have not read the first paper. Especially the choice to describe the model as species and subpopulations, as opposed to writing it as MGE-carrying and MGE-free populations of each species makes it quite complicated to understand which parameters influence each other.

      Thanks for the suggestion. We have extended the model description in the updated manuscript, which provides a more detailed introduction on model configurations and parameter definitions (line 86-99, 101-113, 151-159). We have also updated the Methods to extend the model description.

      (2) Please define gamma_ji in equation 13 and eta_jki in equation 14 (how to map the indices onto the assumed directionality of the interaction).

      We have defined these two parameters in the updated manuscript (line 584-586, 630-632).

      (3)  Line 511: please add at the beginning of this paragraph that you are assuming a grid-like arrangement of patches which will be captured by dispersal term H.

      We have updated this paragraph to make this assumption clear (line 636-637).

      (4)  Line 540: "used in our model" (missing a word).

      We have corrected it in the updated manuscript.

      (5)  Currently the analyses looking at the types of growth effects HGT brings (Figures 5-7) feel very "tacked on". These are not just "confounding factors", but rather scenarios that are much more biologically realistic than the assumption of independent effects. I would introduce them earlier in the text, as I think many readers may not trust your results until they know this was considered (+ how it changes the conclusions).

      We are grateful for the suggestion. We agree with the reviewer that these biologically realistic scenarios should be introduced earlier in the text. In the updated manuscript, we have moved these analyses forward, as sections 3, 4 and 5. We have also avoided the term “confounding factors”. Instead, in the updated manuscript, we have separated these analyses into different sections, and clearly described each scenario in the section title (line 217-218, 254, 275).

      (6)  In some places the manuscript refers to HGT, in others to MGE presence (e.g. caption of Figure 6). These are not generally the same thing, as HGT could also occur due to extracellular vesicles or natural transformation etc. Please standardize the nomenclature and make it clearer which type of processes the model describes.

      We appreciate the comment. The model in this work primarily focused on the process of plasmid transfer. We have made it clear throughout the main text. 

      (7)  In many figures the y-axis starts at a value other than 0. This is a bit misleading. In addition, I would recommend changing the title "Area of bistability region" to "Area of bistability" or perhaps even "Area of multistability" (since more than two species are considered).

      Thanks for the suggestion. We have updated all the relevant figures to make sure that their y-axes start at 0. We have also changed the title “Area of bistability region” to “Area of multistability”, whenever it is applicable.

      (8)  Figure 7: what are the assumed fitness effects of the mobile genes in the simulation? Which distribution were they drawn from? Please add this info to the figure caption here and elsewhere.

      In Figure 7, we explored an extreme scenario of the fitness effects of the mobile genes, where the population was subjected to strong environmental selection and only cells carrying the mobile gene could grow. Therefore, the carriage of the mobile gene changed the species growth rate from 0 to a positive value µ<sub>i</sub>. When calculating the number of stable states in the communities, we randomly drew the µ<sub>i</sub> values from a uniform distribution between 0.3 and 0.7 hr<sup>-1</sup>. We had added this information in the figure caption (line 505-508) and method (line 615-617) of the updated manuscript.

    1. Author response:

      The following is the authors’ response to the original reviews.

      We thank the editors and reviewers for the comments and suggestions on our manuscript.  The main point that we wished to convey in this paper was the concept and the kinetic model that enabled the estimation of nuclear export rate from an image of single mRNAs localised in single cells.  By studying the influenza viral transcripts with this model, we report the variation in the mRNA nuclear export rate of the eight viral segments.  Of note, the hemagglutinin and neuraminidase mRNAs were the slowest among the eight segments in exiting the nucleus.  We agree that the potential mechanism and the biological impact of this observation require further validation, as the reviewers pointed out.  We revised our manuscript to describe these points separately (Lines 21-25, Abstract; Lines 86-91, Introduction; Lines 316-320, Results; Lines 372-381, Discussion).  We also highlight below, the revisions that we made to address the specific points raised by the reviewers.  

      Influenza viral transcription

      The authors used specific settings for their virology experiments and several assumptions regarding their mathematical modelling, so it's extremely important that the reader has the viral life cycle clearly understood before immersing themselves in the results. Thus, a detailed explanation of the viral life cycle, including the kinetics of each step, would be extremely helpful if included in the introduction section.  Reviewer #1

      We have included the molecular composition of influenza vRNP and the mechanism of viral transcription in the revised manuscript (Lines 46-53).  

      Line 45: "Eight viral RNA segments are transcribed by the same set of molecular machinery" (Ref. 7). What's known about the arrival of the viral RNA segments in the nucleus? Is it synchronized? The authors will understand that my concern is related to the fact that a differential arrival would indeed impact the transcription and export processes.  Reviewer #1

      The arrival of eight vRNPs in the nucleus is not synchronised, with each of the eight vRNPs arriving independently (Chou et al. PLOS Pathogens 2013) (Lakadamyali et al, PNAS 2003).  This does not compromise our model, as our model estimates the export rate of each mRNA species individually (also please see our response in Model assumption below).  This is included in the second paragraph of the Discussion section (Lines 390-400).  

      Model assumption

      Even though I do not have the expertise to assess the authors' mathematical model, I do not doubt its robustness. Even so, I find some virological concerns related to the set-up of their experiments. According to what I understand, the authors performed non-synchronized 2 h-long infections with the WSN strain of influenza A virus. They did this to avoid cRNA production (and cross-reaction of the probes), which they claim to occur "much later than mRNA synthesis". Then they omit the degradation of the mRNAs for their model without giving an explanation for having done so. So, taking all these into account, it seems to me that too many assumptions are made without a strong argument. I understand that they are made in order to simplify their model, but I strongly consider that the model would gain strength if some of these events were experimentally considered. Thus, would it be possible to perform synchronized infections? Would it be possible to empirically demonstrate that cRNA production does not occur within the first 2 hours of infection and/or separate transcription and replication? Would it be possible to incorporate a degradation inhibitor of the mRNAs into their infections? If all these could be achieved, then the results coming out of the mathematical model would be enormously reinforced.  Reviewer #1

      * The study lacks experimental data that would help support the conclusions. For instance, perturbations are many times used to prove a point related to gene expression. An example for Fig. 2 for such an experiment could be to treat the cells with transcription inhibitors (e.g. DRB, 5,6-dichloro1-beta-D-ribofuranosylbenzimidazole). Preventing transcription leaves only mature RNAs in the nucleus, and then using this system one can compare the export rate of different RNAs.  Reviewer #2

      We agreed that the primary concern in our model was the assumption that the mRNA degradation could be omitted.  Synchronised infection is not necessary; in fact, non-synchronised infection is preferred, as we explain later in our response.  Additionally, the dominance of mRNA production over the cRNA production has been documented elsewhere.  To address mRNA degradation and validate our model estimation, we performed a time-course measurement using baloxavir.  Baloxavir efficiently blocks the viral transcription by inhibiting the nuclease activity in PA.  DRB, suggested by the reviewer, allows influenza viral transcription and causes viral transcripts to accumulate in the nucleus for unknown mechanisms (Amorim et al. Traffic 2007 and our observation using smFISH, not shown).  The additional experiment, now presented in Fig. 5 in the revised manuscript, indicated that the mRNA degradation is minimal, and the export rate estimated in our model and the time-course experiment agreed well for the HA segment.  The experiment raised the possibility that the time-course measurement underestimates the export rate of transcripts that exit the nucleus rapidly, such as NP.  A real-time imaging of single transcripts would be necessary to directly measure the true nuclear export rate; however, this is beyond the scope of our paper.  The new result is now presented in Fig. 5, Supplementary figures 3 and 4, and in the main text (Lines 322-360).  An alteration was also made in Line 286 to guide to Fig. 5.  The Materials and Methods section was updated (Lines 478-482).  

      We note that our model does not require synchronised infection.  Even under synchronised infection, such as incubating cells with the virus at 4°C to facilitate attachment and subsequently shifting to 37°C to allow viral entry, the inherent heterogeneity in vRNP migration to the nucleus still remains.  This randomness does not compromise our model; rather, our model exploits this random arrival of each vRNP in each cell in the system.  This variation, in turn, generates cells carrying varying amounts of transcripts, enabling the estimation of nuclear export rate.  Importantly, more variation ensures the broader distribution of transcript levels, enabling more precise parameter fitting in our model.  It is also important to note that our model does not require the correlation between segments.  Our model estimates the export rate of each mRNA species individually.  These important points were explained in the Discussion section (Lines 390-400).  

      * There is no concrete value given for the export rates and what they might mean biologically (e.g. time present/stuck in the nucleus) - Fig. 4D. This leaves the reader in the dark.  Reviewer #2

      The export rate lambda (previously denoted as k) in our model (Fig. 4) and the decay constant k in the time-course measurement (Fig. 5) represent the proportion of mRNAs exported from the nucleus in an infinitesimal time, defining the nuclear export rate.  This has been clarified in the revised manuscript (Lines 314-316), with some alterations to make the parameter use more comprehensive.  

      -  The Greek letter k previously used in Fig. 4 and the associated equations was consistently replaced with lambda to avoid the confusion with the parameter k that is subsequently used for the exponent decay in Fig. 5 in the revised manuscript.  

      -  The Greek letter epsilon (previously used to represent export) was replaced with mu, slightly more common for representing the rate of transport.  

      -  The term “velocity” was consistently replaced with “rate” in the context of the nuclear export (Lines 163, 215, 320, 441).  

      -  The phrase “molar concentrations of mRNAs” was corrected for “molecules of mRNAs” (Line 282).

      Also, we have now described our model in two sections: “Conceiving the model” and “Implementing a kinetic model to estimate the nuclear export rate” in the Result.  The first section outlines the conceptual framework of the model, and the second focuses on its implementation and the parameter extraction (Lines 227 and 277).  

      Applicability of the model

      Lines 27-29. "Our framework presented in this study can be widely used for investigating the nuclear retention of nascent transcripts produced in a transcription burst." In my opinion, this is the strongest point of the manuscript: developing a mathematical model to analyze nuclear export retention as a mechanism of protein expression control, which could lay the foundation for further biological processes. The authors revisit this idea in the Discussion section. However, which would be those processes for which the model could be helpful? I consider that a more conspicuous discussion on this topic would broaden the readers scope, a crucial point under the eLife scope.  Reviewer #1

      * Could this framework be used to quantify the nuclear export rate of cellular RNAs? According to the explanation in the Discussion, it would seem that this approach is limited to quantifying the export rate of influenza RNAs.  Reviewer #2

      Our model is not limited to the influenza virus infection.  Our model is applicable for systems where transcription is initiated concurrently, such as when stimuli trigger the activation of a certain set of genes for transcription.  Therefore, this makes it particularly valuable for quantifying the nuclear retention of mRNAs in a transcription burst.  This point is reiterated in Line 383-390.  

      Potential mechanisms for differential nuclear export rate of viral segments

      * There is no mechanistic insight in the study. The idea driven by this study is that gene expression is regulated by the RNA export rate. But how is that explained? Is there any molecular pathway or explanation for this model? If the transcripts are ready for export, why do the mRNAs stay inside the nucleus? One option to consider are the export factors. Viral RNAs are exported by different pathways as mentioned (line 362), or by TREX2 (Bhat P et al Nat Comm 2023). The data shows that there is no difference observed in the export rate of different pathways. How about knocking down an important export factor to show how this affects the export rates. Or the opposite, overexpress a certain factor, would this change the nucleus/cytoplasm distribution of the retained RNAs.  Reviewer #2

      As we discussed in the paper, we are beginning to consider that each viral segment has an intrinsic sequence that determines its nuclear export rate, because previous studies on the export factors does not fully explain the variation in the nuclear export rate observed in our study.  As the reviewer suggested, a recent study (Bhat et al. Nature Communications 2023) exactly pointed out the internal sequence in the HA segment, aligning with our working hypothesis.  This point is discussed and their work (Bhat et al. 2023) has been cited in the Discussion section in the revised manuscript (Lines 446-449).  

      Biological impact of the nuclear retention

      The authors mention several times throughout the manuscript that the virus might use the nuclear retention of mRNA for HA and NA to postpone the expression of these antigenic molecules. At this point, I need to admit that a great question mark appeared in my mind, maybe related to the fact that some knowledge is lacking in my analysis. Lines 328-330: "On the other hand, pushing back the expression of viral antigens HA and NA would be beneficial for the virus to delay the host immune response against the infected cells in which the virus is being replicated." As I tend to understand, the host immune response recognizes HA and NA within the viral particle, if so and independently of the time that HA and Na arrive at the virus assembly step, the progeny' viral particles that are complete and extruded from the cells would be those awakening the host immunity response. If this is right, how would the delayed export of those proteins from the nucleus (and their late expression) be beneficial for delaying the immune response? I would appreciate an explanation for this point, and if I am wrong, then there could exist a relationship between nuclear export rate and the pathogenicity of different strains of influenza A virus. If so, could the authors challenge their model with additional viral strains showing a differential immune response pattern? A deeper analysis in this direction would greatly strengthen the message in their manuscript.  Reviewer #1

      * Is the timing of viral protein appearance in accordance with the time the mRNA is exported to the cytoplasm. It is logical that the first mRNA to go to the cytoplasm would be the first to become a protein. Can the authors show that nuclear retention of mRNA would push back the expression of the viral antigens HA and NA.  Reviewer #2

      Three types of immune reactions are being studied extensively.  The first is the humoral immune response, where antibodies target the viral antigens HA and NA on the viral envelope, coating and inactivating the viral particles.  The second is the cytotoxic T cell response.  There is growing evidence that cytotoxic T cells react against NP, eliciting cross-reaction to broader range of influenza viral strains.  This reaction is not specific to HA and NA, and antigens are processed in the cytoplasm and presented on the MHC.  The third is antibody-dependent cellular cytotoxicity (ADCC), where antibodies recognise the viral proteins on the cellular surface (HA and NA) of infected cells, facilitating their elimination by the NK cells.  Although protein translation may begin as soon as the first mRNA exits the nucleus, the virus may delay the peak of the antigen production and therefore, postpone the NK-mediated ADCC.  This specific point, along with references to ADCC in influenza virus infection, has been clarified in the Discussion section (Lines 377-381).  

      Data analysis and presentation

      Lines 99-101. "Viral mRNAs were detected as single diffraction-limited spots in the three-dimensional image stacks, allowing for absolute mRNA quantification (Fig. 1B)". What do the authors mean to say by "absolute mRNA quantification"? Do they refer to the total spots or the total mRNAs? Is it assumed that one spot corresponds to a single mRNA transcript? This is not clear at all for this reviewer, which could be the situation for a potential reader. Since it's the beginning of the story, this should be clearly stated in the manuscript.  Reviewer #1

      Each spot of fluorescent signal corresponds to a single molecule of viral mRNA.  We quantified the absolute number of transcripts in each cell.  This is clarified in the revised manuscript (Lines 104-106).  

      * Line 151: does the baseline change according to the RNA in question? The authors say that the "baseline is defined by the median of the Z distribution of peripheral mRNAs" - it seems that the number 0.731 refers only to one type of RNA (which is not mentioned at all not in the text and not in the legend). Reviewer #2

      The baseline was set using the NP mRNAs in the cytoplasm because the NP mRNA showed the widest distribution across the cytoplasm (Line 157).  

      * Also, what is all the signal that is seen outside the marked cells in Fig. 2B? There seems to be significant background in the field, does this mean much false-positive in the multiplex FISH? If so, then how do the authors know that the staining inside the cells isn't to some degree non-specific? It would be necessary to back this up with some other type of quantitative assay like qRT-PCR.  Reviewer #2

      The cells were removed from the analysis if the cytoplasmic boundary touched any edge of the field-of-view, while the signals were recovered across the entire field-of-view.  This is clarified in the figure legend (Lines 194-195).  

      Others

      * The meaning and explanation for Figure 1H -are unclear. Rephrase and make the legend more reader friendly.  Reviewer #2

      We made alterations to the legend (Lines 132-134) and the relevant lines in the main text (Lines 148-151).  

      * Fig. 2E: Is this the total transcript count or only in the nucleus? Would it be possible to find some correlation between the segments if a pair-wise analysis is performed according to nuclear-cytoplasm distribution?  Reviewer #2

      The total counts are presented.  This is clarified in the legend (Lines 199-200).  

      * Abstract -"A mathematical modelling indicated that the relationship between the nuclear ratio and the total count of mRNAs in single cells is dictated by a proxy for the nuclear export rate." - this sentence is very unclear.  Reviewer #2

      The sentence was removed in the revised manuscript (Line 21).  This removal did not affect the overall meaning in the abstract.  We also made an alteration to Line 279 that contained a similar phrase.  

      * The use of the word "acutely" (lines 16 and 35) is strange.  Reviewer #2

      They have been removed (now Lines 15, 33).  

      * Line 157 - "This result indicates that the velocity of viral mRNA export from the nucleus varies according to the viral segments." - not velocity, maybe timing.  Reviewer #2

      We consistently replaced “velocity” with “rate” (Lines 163, 215, 320, 441).

      * Reference for line 41.  Reviewer #2

      A reference (Waker et al. Trends Microbiol. 2019) has been cited (Line 39).  

      * Reference for lines 105-106.  Reviewer #2

      The gene length of each segment was indicated in the sentence (Line 137).  

      * Line 264- why here is 0.02 M.O.I used compared to line 97 where 2 is used?  Reviewer #2

      We used M.O.I. of 0.02 to allow for spot quantification over longer periods of observation (Lines 269-270).  

      * NS1 is expressed at late infection times and might alter the nuclear export of viral mRNAs (line 352). Need to show that indeed it is not expressed in the experiments done here.  Reviewer #2

      It is not possible to definitely prove that NS1 is not expressed due to the sensitivity limitations.  However, we minimised the its impact by investigating at the early time point (Lines 415416).  

      * Line 459- 30% formamide? Is this correct or should it be 10%?  Reviewer #2

      This is correct.  The probes used were longer than the others for smFISH.  Therefore, we washed away the probes with the stringent condition.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      This paper presents a model of the whole somatosensory non-barrel cortex of the rat, with 4.2 million morphologically and electrically detailed neurons, with many aspects of the model constrained by a variety of data. The paper focuses on simulation experiments, testing a range of observations. These experiments are aimed at understanding how the multiscale organization of the cortical network shapes neural activity.

      Strengths:

      (1) The model is very large and detailed. With 4.2 million neurons and 13.2 billion synapses, as well as the level of biophysical realism employed, it is a highly comprehensive computational representation of the cortical network.

      (2) Large scope of work - the authors cover a variety of properties of the network structure and activity in this paper, from dendritic and synaptic physiology to multi-area neural activity.

      (3) Direct comparisons with experiments, shown throughout the paper, are laudable.

      (4) The authors make a number of observations, like describing how high-dimensional connectivity motifs shape patterns of neural activity, which can be useful for thinking about the relations between the structure and the function of the cortical network.

      (5) Sharing the simulation tools and a "large subvolume of the model" is appreciated.

      We thank the reviewer for these comments and are pleased they appreciated these aspects of the work.

      Weaknesses:

      (1) A substantial part of this paper - the first few figures - focuses on single-cell and single-synapse properties, with high similarity to what was shown in Markram et al., 2015. Details may differ, but overall it is quite similar.

      We thank the reviewer for this useful comment and agree that it is important to better highlight the incremental improvements to the model’s low-level physiology. The validity of any model can continuously be improved at all spatial scales and the validity of emergent network activity increases with improved validity at lower levels. For this reason, we felt it was valuable to improve the low-level physiology of the model.

      Regarding neuron physiology, we have added the following in Section 2.1 on page 5:

      “2.1 Improved modeling and validation of neuron physiology

      Similarly to Markram et al. (2015), electrical properties of single neurons were modelled by optimizing ion channel densities in specific compartment-types (soma, axon initial segment (AIS), basal dendrite, and apical dendrite) (Figure 2B) using an evolutionary algorithm (IBEA; Van Geit et al., 2016) so that each neuron recreates electrical features of its corresponding electrical type (e-type) under multiple standardized protocols. Compared to Markram et al. (2015), electrical models were optimized and validated using 1) additional in vitro data, features and protocols, 2) ion channel and electrophysiological data corrected for the liquid junction potential, and 3) stochastic channels (StochKv3) now including inactivation profiles. The methodology and resulting electrical models are described in Reva et al. (2023) (see Methods), and generated quantitatively more accurate electrical activity, including improved attenuation of excitatory postsynaptic potentials (EPSPs) and back-propagating action potentials.”

      And page 8:

      “The new neuron models saw a 5-fold improvement in generalizability compared to Markram et al. (2015) (Reva et al., 2023).”

      We have also made the descriptions of the improvements to synaptic physiology more explicit in Section 2.2 on page 9:

      “2.2 Improved modeling and validation of synaptic physiology

      The biological realism of synaptic physiology was improved relative to Markram et al. (2015) using additional data sources and by extending the stochastic version of the Tsodyks-Markram model (Tsodyks and Markram, 1997; Markram et al., 1998; Fuhrmann et al., 2002; Loebel et al., 2009) to feature multi-vesicular release, which in turn improved the accuracy of the coefficient of variations (CV; std/mean) of postsynaptic potentials (PSPs) as described in Barros-Zulaica et al. (2019) and Ecker et al. (2020). The model assumes a pool of available vesicles that is utilized by a presynaptic action potential, with a release probability dependent on the extracellular calcium concentration ([Ca2+]o; Ohana and Sakmann, 1998; Rozov et al., 2001; Borst, 2010). Additionally, single vesicles spontaneously release as an additional source of variability with a low frequency (with improved calibration relative to Markram et al. (2015)). The utilization of vesicles leads to a postsynaptic conductance with bi-exponential kinetics. Short-term plasticity (STP) dynamics in response to sustained presynaptic activation are either facilitating (E1/I1), depressing (E2/I2), or pseudo-linear (I3). E synaptic currents consist of both AMPA and NMDA components, whilst I currents consist of a single GABAA component, except for neurogliaform cells, whose synapses also feature a slow GABAB component. The NMDA component of E synaptic currents depends on the state of the Mg2+ block (Jahr and Stevens, 1990), with the improved fitting of parameters to cortical recordings from Vargas-Caballero and Robinson (2003) by Chindemi et al. (2022).”

      (2) Although the paper is about the model of the whole non-barrel somatosensory cortex, out of all figures, only one deals with simulations of the whole non-barrel somatosensory cortex. Most figures focus on simulations that involve one or a few "microcolumns". Again, it is rather similar to what was done by Markram et al., 2015 and constitutes relatively incremental progress.

      We thank the reviewer for this comment and have added the following text to the Discussion on page 33 to explain our rationale:

      “In keeping with the philosophy of compartmentalization of parameters and continuous model refinement (see Introduction), it was essential to improve validity at the columnar scale (relative to Markram et al. (2015)) as part of demonstrating validity of the full nbS1. Indeed, improved parametrization and validation at smaller scales was essential to parameterizing background input which generated robust nbS1 activity within realistic [Ca<sup>2+</sup>]<sub>o</sub> and firing rate ranges. We view this as a major achievement, as it was unknown whether the model would achieve a stable and meaningful regime at the start of our investigation. Whilst we would have liked to go further, our primary goal was to publish a well characterized model as an open resource that others could use to undertake further in-depth studies. In this regard, we are pleased that the parametrization of the nbS1 model has already been used to study EEG signals (Tharayil et al., 2024), as well as propagation of activity between two subregions (Bolaños-Puchet and Reimann, 2024).”

      We also make it clearer in the Introduction on page 4 that the improved validation of the emergent columnar regime was essential to stable activity at the larger scale:

      “These initial validations demonstrated that the model was in a more accurate regime compared to Markram et al. (2015) – an essential step before testing more complex or larger-scale validations. For example, under the same parameterization we then observed selective propagation of stimulus-evoked activity to downstream areas, and…”

      (3) With a model like this, one has an opportunity to investigate computations and interactions across an extensive cortical network in an in vivo-like context. However, the simulations presented are not addressing realistic specific situations corresponding to animals performing a task or perceiving a relevant somatosensory stimulus. This makes the insights into the roles of cell types or connectivity architecture less interesting, as they are presented for relatively abstract situations. It is hard to see their relationship to important questions that the community would be excited about - theoretical concepts like predictive coding, biophysical mechanisms like dendritic nonlinearities, or circuit properties like feedforward, lateral, and feedback processing across interacting cortical areas. In other words, what do we learn from this work conceptually, especially, about the whole non-barrel somatosensory cortex?

      We thank the reviewer for this comment and agree that it would be very interesting to explore such topics. In the Introduction on page 4, we have updated the list of papers which have so far used the model for more in depth studies:

      “…propagation of activity between cortical areas (Bolaños-Puchet and Reimann, 2024) the role of non-random connectivity motifs on network activity (Pokorny et al., 2024) and reliability (Egas Santander et al., 2024), the composition of high-level electrical signals such as the EEG (Tharayil et al., 2024), and how spike sorting biases population codes (Laquitaine et al., 2024).”

      In the Discussion on page 33 we also add our additional thoughts on this topic:

      “Whilst we would have liked to go further, our primary goal was to publish a well characterized model as an open resource that others could use to undertake further in-depth studies. In this regard, we are pleased that the parametrization of the nbS1 model has already been used to study EEG signals (Tharayil et al., 2024), as well as propagation of activity between two subregions (Bolaños-Puchet and Reimann, 2024). Investigation, improvement and validation must be continued at all spatial scales in follow up papers with detailed description, figures and analysis, which cannot be covered in this manuscript. Each new study increases the scope and validity of future investigations. In this way, this model and paper act as a stepping stone towards more complex questions of interest to the community such as perception, task performance, predictive coding and dendritic processing. This was similar for Markram et al. (2015) where the initial paper was followed by more detailed studies. Unlike the Markram et al. (2015) model, the new model can also be exploited by the community and has already been used in a number of follow up papers studying (Ecker et al., 2024a,b; Bolaños-Puchet and Reimann, 2024; Pokorny et al., 2024; Egas Santander et al., 2024; Tharayil et al., 2024; Laquitaine et al., 2024). We believe that the number of use cases for such a general model is vast, and is made larger by the increased size of the model.”

      (4) Most comparisons with in vivo-like activity are done using experimental data for whisker deflection (plus some from the visual stimulation in V1). But this model is for the non-barrel somatosensory cortex, so exactly the part of the cortex that has less to do with whiskers (or vision). Is it not possible to find any in vivo neural activity data from the non-barrel cortex?

      We agree with the reviewer that this is a weakness. We have expanded our discussion of the need to mix data sources to also consider our view for network level activity:

      “This paper and its companion paper serve to present a methodology for modeling micro- and mesoscale anatomy and physiology, which can be applied for other cortical regions and species. With the rapid increase in openly available data, efforts are already in progress to build models of mouse brain regions with reduced reliance on data mixing thanks to much larger quantities of available atlas-based data. This also includes data for the validation of emergent network level activity. Here we chose to compare network-level activity to data mostly from the barrel cortex, as well as a single study from primary visual cortex. Whilst a lot of the data used to build the model was from the barrel cortex, the barrel cortex also represents a very well characterized model of cortical processing for simple and controlled sensory stimuli. The initial comparison of population-wise responses in response to accurate thalamic input for single whisker deflections was essential to demonstrating that the model was closer to in vivo, and we were unaware of similar data for nonbarrel somatosensory regions. Moreover, our optogenetic & lesion study demonstrated the capacity to compare and extend studies of canonical cortical processing in the whisker system.”

      (5) The authors almost do not show raw spike rasters or firing rates. I am sure most readers would want to decide for themselves whether the model makes sense, and for that, the first thing to do is to look at raster plots and distributions of firing rates. Instead, the authors show comparisons with in vivo data using highly processed, normalized metrics.

      We thank the reviewer for this comment and agree that better visualizations of the network activity under different conditions is essential for helping the reader assess the work. In addition to raster plots in Video 1, Video 3, Fig 6, Fig 5C, Fig S9a, S16a, we have additionally:

      a) Changed the histograms of spontaneous activity in Fig 4G on page 13 to raster plots for the seven column subvolume for two contrasting meta-parameter regimes.

      b) Added 4 new videos (Video 6a,b and 8a,b) showing all spontaneous and evoked meta-parameter combinations in hex0 and hex39 of the nbS1:

      We have added improved plots showing the distributions of firing rates in the seven column subvolume on page 74:

      With more detailed consideration in the Results on page 15:

      “Long-tailed population firing rate distributions with means ∼ 1Hz

      To study the firing rate distributions of different subpopulations and m-types, we ran 50s simulations for the meta-parameter combinations: [Ca<sup>2+</sup>]<sub>o</sub>: 1.05mM, R<sub>OU</sub>: 0.4,P<sub>FR</sub>: 0.3, 0.7 (Figure S4). Different subpopulations showed different sparsity levels (proportion of neurons spiking at least once) ranging from 6.6 to 42.5%. Wohrer et al. (2013) considered in detail the biases and challenges in obtaining ground truth firing rate distributions in vivo, and discuss the wide heterogeneity of reports in different modalities using different recording techniques. They conclude that most evidence points towards longtailed distributions with peaks just below 1Hz. We confirmed that spontaneous firing rate distributions were long-tailed (approximately lognormally distributed) with means on the order of 1Hz for most subpopulations. Importantly the layer-wise means were just below 1Hz in all layers for the P<sub>FR</sub> = 0.3 meta-parameter combination. Moreover, our recent work applying spike sorting to extracellular activity using this meta-parameter combination found spike sorted firing rate distributions to be lognormally distributed and very similar to in vivo distributions obtained using the same probe geometry and spike sorter (Laquitaine et al., 2024).

      (6) While the authors claim that their model with one set of parameters reproduces many experimentally established metrics, that is not entirely what one finds. Instead, they provide different levels of overall stimulation to their model (adjusting the target "P_FR" parameter, with values from 0 to 1, and other parameters), and that influences results. If I get this right (the figures could really be improved with better organization and labeling), simulations withP<sub>FR</sub> closer to 1 provide more realistic firing rate levels for a few different cases, however, P<sub>FR</sub> of 0.3 and possibly above tends to cause highly synchronized activity - what the authors call bursting, but which also could be called epileptic-like activity in the network.

      We thank the reviewer for this comment. We can now see that the motivation for P<sub>FR</sub> parameter was introduced very briefly in the results and that the results of the calibration and analysis of the spontaneous activity regime are not interpreted in relation to this parameter.

      To address this, we have given more detail where it is first introduced in the Results on page 12:

      “to account for uncertainty in the firing rate bias during spontaneous activity from extracellular spike sorted recordings…”

      We then reconsider that it represents an unknown bias when interpreting the calibration and spontaneous activity results on page 15:

      “We reemphasize that the [Ca<sup>2+</sup>]<sub>o</sub>, R<sub>OU</sub> and P<sub>FR</sub> meta-parameters account for uncertainty of in vivo extracellular calcium concentration, the nature of inputs from other brain regions and the bias of extracellularly recorded firing rates. Whilst estimates for [Ca<sup>2+</sup>]<sub>o</sub> are between 1.0 - 1.1mM (Jones and Keep, 1988; Massimini and Amzica, 2001; Amzica et al., 2002; Gonzalez et al., 2022) and estimates for PFR are in the range of 0.1 - 0.3 (Olshausen and Field, 2006), combinations of these parameters supporting in vivo-like stimulus responses in later sections will offer a prediction for the true values of these parameters. Both these later results and our recent analysis of spike sorting bias using this model (Laquitaine et al., 2024) predict a spike sorting bias corresponding to P<sub>FR</sub> ∼ 0.3, confirming the prediction of Olshausen and Field (2006).”

      And in relation to the stimulus evoked responses on page 17:

      “Specifically, simulations with PFR from 0.1 to 0.5 robustly support realistic stimulus responses, with the middle of this range (0.3) corresponding with estimates of in vivo recording bias; both the previous estimates of Olshausen and Field (2006) and from a spike sorting study using this model (Laquitaine et al., 2024).”

      Following these considerations, the remainder of the experiments using the seven column subvolume only use a single meta-parameter on page 19.

      For the full nbS1 we further discuss the importance of a P_FR value between 0.1 and 0.3 in the Results on page 26:

      “Stable spontaneous activity only emerges in nbS1 at predicted in vivo firing rates

      After calibrating the model of extrinsic synaptic input for the seven column subvolume, we tested to what degree the calibration generalizes to the entire nbS1. Notably, this included the addition of mid-range connectivity (Reimann et al., 2024). The total number of local and mid-range synapses in the model was 9138 billion and 4075 billion, i.e., on average full model simulations increased the number of intrinsic synapses onto a neuron by 45%. Particularly, we ran simulations for P<sub>FR</sub></i ∈ [0.1, 0.15, ..., 0.3] using the OU parameters calibrated for the seven column subvolume for [Ca<sup>2+</sup>]<sub>o</sub> = 1.05mM and R<sub>OU</sub> = 0.4. Each of these full nbS1 simulations produced stable non-bursting activity (Figure 8A), except for the simulation for P<sub>FR</sub></i = 0.3, which produced network-wide bursting activity (Video 6). Activity levels in the simulations of spontaneous activity were heterogeneous (Figure 8B, Video 7). In some areas, firing rates were equal to the target P<sub>FR</sub>, whilst in others they increased above the target (Figure 8C). In the more active regions, mean firing rates (averaged over layers) were on the order of 30-35% of the in vivo references for the maximum non-bursting P<sub>FR</sub> simulation (target P<sub>FR</sub> : 0.25). This range of firing rates again fits with the estimate of firing rate bias from our paper studying spike sorting bias (Laquitaine et al., 2024) and the meta-parameter range supporting realistic stimulus responses in the seven column subvolume. This also predicts that the nbS1 cannot sustain higher firing rates without entering a bursting regime.

      Finally, we also added to our discussion of biases in extracellular firing rates in the Discussion on page 32:

      “This is also inline with our recent work using the model, which estimated a spike sorting bias corresponding to PFR = 0.3 using virtual extracellular electrodes (Laquitaine et al., 2024).”

      We also thank the reviewer for pointing out that we did not define the term “bursting” in the main text. We have added the following definition and discussion in the Results on page 15:

      “Note that the most correlated meta-parameter combination [Ca<sup>2+</sup>]<sub>o</sub>: 1.1mM, R<sub>OU</sub>: 0.2, P<sub>FR</sub>: 1.0 produced network-wide “bursting” activity, which we define as highly synchronous all or nothing events (Video 1). Such activity, which may be characteristic of epileptic activity, can be studied with the model but is not the focus of this study.”

      (7) The authors mention that the model is available online, but the "Resource availability" section does not describe that in substantial detail. As they mention in the Abstract, it is only a subvolume that is available. That might be fine, but more detail in appropriate parts of the paper would be useful.

      Firstly, we are pleased to say that the full nbS1 model is now available to download, in addition to the seven hexagon subvolume. In the manuscript, we have:

      a) Added to the Introduction at the bottom of page 4:

      “To provide a framework for further studies and integration of experimental data, the full model is made available with simulation tools, as well as a smaller subvolume with the optional new connectome capturing inhibitory targeting rules from electron microscopy”.

      b) Updated the open source panel of Figure 1:

      Secondly, we thank the reviewer for noticing that the description of the available model is not well described in the “Resource availability” statement and have addressed this by:

      a) Adding the following to the “Resource availability” statement on page 36:

      “Both the full nbS1 model and smaller seven hexagon subvolume are available on Harvard Dataverse and Zenodo respectively in SONATA format (Dai et al., 2020) with simulation code. DOIs are listed under the heading ``Final simulatable models'' in the Key resources table. An additional link is provided to the SM-Connectome with instructions on how to use it with the seven hexagon subvolume model.”

      b) Creating a new subheading in the “Key resources table” titled: “Final simulatable models” to make it clearer which links refer to the final models.

      Reviewer #2 (Public review):

      Summary:

      This paper is a companion to Reimann et al. (2022), presenting a large-scale, data-driven, biophysically detailed model of the non-barrel primary somatosensory cortex (nbS1). To achieve this unprecedented scale of a bottom-up model, approximately 140 times larger than the previous model (Markram et al., 2015), they developed new methods to account for inputs from missing brain areas, among other improvements. Isbister et al. focus on detailing these methodological advancements and describing the model's ability to reproduce in vivo-like spontaneous, stimulus-evoked, and optogenetically modified activity.

      Strengths:

      The model generated a series of predictions that are currently impossible in vivo, as summarized in Table S1. Additionally, the tools used in this study are made available online, fostering community-based exploration. Together with the companion paper, this study makes significant contributions by detailing the model's constraints, validations, and potential caveats, which are likely to serve as a basis for advancing further research in this area.

      We thank the reviewer for these comments, and are pleased they appreciate these aspects of the work.

      Weaknesses:

      That said, I have several suggestions to improve clarity and strengthen the validation of the model's in vivo relevance.

      Major:

      (1) For the stimulus-response simulations, the authors should also reference, analyze, and compare data from O'Connor et al. (2010; https://pubmed.ncbi.nlm.nih.gov/20869600/) and Yu et al .(2016; https://pubmed.ncbi.nlm.nih.gov/27749825/) in addition to Yu et al. 2019, which is the only data source the authors consider for an awake response. The authors mentioned bias in spike rate measurements, but O'Connor et al. used cell-attached recordings, which do not suffer from activity-based selection bias (in addition, they also performed Ca2+ imaging of L2/3). This was done in the exact same task as Yu et al., 2019, and they recorded from over 100 neurons across layers. Combining this data with Yu et al., 2019 would provide a comprehensive view of activity across layers and inhibitory cell types. Additionally, Yu et al. (2016) recorded VPM neurons in the same task, alongside whole-cell recordings in L4, showing that L4 PV neurons filter movement-related signals encoded in thalamocortical inputs during active touch. This dataset is more suitable for extracting VPM activity, as it was collected under the same behavior and from the same species (Unlike Diamond et al., 1992, which used anesthetized rats). Furthermore, this filtering is an interesting computation performed by the network the authors modeled. The validation would be significantly strengthened and more biologically interesting if the authors could also reproduce the filtering properties, membrane potential dynamics, and variability in the encoding of touch across neurons, not just the latency (which is likely largely determined by the distance and number of synapses).

      We thank the reviewer for pointing out these very useful studies. We have taken on board this suggestion for a future model of the mouse barrel cortex.

      (2) The authors mention that in the model, the response of the main activated downstream area was confined to L6. Is this consistent with in vivo observations? Additionally, is there any in vivo characterization of the distance dependence of spiking correlation to validate Figure 8I?

      We are not aware of data confirming the propagation of activity to downstream areas being confined to layer 6 but have considered the connectivity further between these two regions on page 27, as well as studying this further in follow up work:

      “Stable propagation of evoked activity through mid-range connectivity only emerges in nbS1 at predicted in vivo firing rates

      We repeated the previous single whisker deflection evoked activity experiment in the full model, providing a synchronous thalamic input into the forelimb sub-region (S1FL; Figure 8E; Video 8 & 9). Responses in S1FL were remarkably similar to the ones in the seven column subvolume, including the delays and decays of activity (Figure 8F). However, in addition to a localized primary response in S1FL within 350μm of the stimulus, we found several secondary responses at distal locations (Figure 8E; Video 9), which was suggestive of selective propagation of the stimulus-evoked signal to downstream areas efferently connected by mid-range connectivity. The response of the main activated downstream area (visible in Figure 8E) was confined to L6 (Figure 8G). In a follow up study using the model to explore the propagation of activity between cortical regions (Bolaños-Puchet and Reimann, 2024), it is described how the model contains both a feedforward projection pattern, which projects to principally to synapses in L1 & L23, and a feedback type pattern, which principally projects to synapses in L1 & L6. On visualizing the innervation profile from the stimulated hexagon to the downstream hexagon we can see that we have stimulated a feedback pathway (Figure S16)”

      With referenced Figure S16 on page 85:

      We did find in vivo evidence of similar layer-wise and distance dependence of correlations in the somatosensory cortex discussed on page 27 of the Results:

      “The distance dependence of correlations followed a similar profile to that observed in a dataset characterizing spontaneous activity in the somatosensory cortex (Reyes-Puerta et al., 2015a) (compare red line in Figure 8I with Figure S16). In the in vivo dataset spiking correlation was also low but highest in lower layers, with short “up-states” in spiking activity constrained to L5 & 6 (see Figure 1E,F in (Reyes-Puerta et al., 2015a)). In the model, they are constrained to L6.”

      With Figure S16a on page 85 showing the distance dependence of correlations in the anaesthetized barrel cortex during spontaneous activity (digitization from the reference paper):

      (3) Across the figures, activity is averaged across neurons within layers and E or I cell types, with a limited description of single-cell type and single-cell responses. Were there any predictions regarding the responses of particular cell types that significantly differ from others in the same layer? Such predictions could be valuable for future investigations and could showcase the advantages of a data-driven, biophysically detailed model.

      We thank the review for this comment. In addition to new analyses at higher granularity addressed in other comments, we have added the following comparison of stimulus-evoked membrane potential dynamics in different subpopulations for the original connectome and SM-connectome in Figure 7 on page 24.

      This gave interesting results discussed in a new subsection on page 26:

      “EM targeting trends hyperpolarize Sst+ and HT3aR+ late response, and disinhibit L5/6 E

      Studying somatic membrane potentials for different subpopulations in response to whisker deflections shows that PV+, L23E and L4E subpopulations are largely unaffected in the SM-connectome (Figure 7E). Interestingly, Sst+ and 5HT3aR+ subpopulations show a strong hyperpolarization in the late response that isn’t present in the original connectome. Interestingly, this corresponds with a stronger late response in L5/6 E populations, which could be caused by disinhibition due to the Sst+ and 5HT3aR+ hyperpolarization. This could be explored further in follow up studies using our connectome manipulator tool (Pokorny et al., 2024).”

      (4) 2.4: Are there caveats to assuming the OU process as a model for missing inputs? Inputs to the cortex are usually correlated and low-dimensional (i.e., communication subspace between cortical regions), but the OU process assumes independent conductance injection. Can (weakly) correlated inputs give rise to different activity regimes in the model? Can you add a discussion on this?

      We agree with the reviewer that there are caveats to assuming an OU process for the model of missing inputs and have added the following to the Discussion on page 31:

      “The calibration framework could optimize per population parameters for other compensation methods, whilst still offering an interpretable spectrum of firing rate regimes at different levels of P<sub>FR</sub>. For example, more realistic compensation schemes could be explored which introduce a) correlations between the inputs received by different neurons and b) compensation distributed across dendrites, as well as at the soma. We predict that such changes would make spontaneous activity more correlated at the lower spontaneous firing rates which supported in vivo like responses (P<sub>FR</sub> : 0.1 − 0.5), which would in turn make stimulus-responses more noise correlated.”

      (5) 2.6: The network structure is well characterized in the companion paper, where the authors report that correlations in higher dimensions were driven by a small number of neurons with high participation ratios. It would be interesting to identify which cell types exhibit high node participation in high-dimensional simplices and examine the spiking activity of cells within these motifs. This could generate testable predictions and inform theoretical cell-type-specific point neuron models for excitatory/inhibitory balanced networks and cortical processing.

      We thank the reviewer for this suggestion. We have added two supplementary figures to address this suggestion, which are discussed in the Results on Page 16:

      “Additionally, we studied the structural effect on the firing rate (here measured as the inverse of the inter-spike interval, ISI, which can be thought of as a proxy of non-zero firing rate). We found that for the connected circuit, the firing rate increases with simplex dimension; in contrast with the disconnected circuit, where this relationship remains flat (see Figure S6 red vs. blue curves and Methods).

      This also demonstrates high variability between neurons, in line with biology, both structurally (Towlson et al., 2013; Nigam et al., 2016) and functionally (Wohrer et al., 2013; Buzs´aki and Mizuseki, 2014). We next identified the cell types that are overexpressed in the group of neurons that have the 5% highest values of node participation across dimensions (Figure S7). This could inform theoretical point neuron models with cell-type specificity, for example. We found that while in dimension one (i.e., node degree) this consists mostly of inhibitory cells, in higher dimensions the cell types concentrate in layers 4, 5 and 6, especially for TPC neurons. This is in line with our structural layer-wise findings in Figure 8B in Reimann et al. (2024).”

      Which reference new Figures S6 and S7:

      With the methodology for S6 described on page 49 of the Methods:

      “For any numeric property of neurons, e.g., firing rate, we evaluate the effect of dimension on it by taking weighted averages across dimensions. That is for each dimension k, we take the weighted average of the property across neurons where the weights are given by node participation on dimension k. More precisely, let N be the number of neurons and −→V ∈ RN, be a vector of a property on all the neurons e.g., the vector of firing rates. Then in each dimension k we compute

      Where is the vector of node participation on dimension k for all neurons and ・ is the dot product.

      To measure the over and underexpression of the different m-types among those with the highest 5% of values of node participation, we used the hypergeometric distribution to determine the expected distribution of m-types in a random sample of the same size. More precisely, for each dimension k and m-type m, let N<sub>total</sub> be the total number of neurons in the circuit, Nm be the number of neurons of m-type m in the circuit, Ctop be the number of neurons with the highest 5% values of node participation in dimension k, Cm the number of neurons of mtype m among these, and let P = hypergeom(N<sub>total</sub<,N<sub>m</sub>,C<sub>top</sub>) be the hypergeometric distribution.

      By definition, P(x) describes the probability of sampling x neurons of m-type m in a random sample of size C<sub>top</sub>. Therefore, using the cumulative distribution F(x) = P(Counts ≤ x), we can compute the p-values as follows:

      Small values indicate under and over representation respectively….”

      Minor:

      (1) Since the previous model was published in 2015, the neuroscience field has seen significant advancements in single-cell and single-nucleus sequencing, leading to the clustering of transcriptomic cell types in the entire mouse brain. For instance, the Allen Institute has identified ~10 distinct glutamatergic cell types in layer 5, which exceeds the number incorporated into the current model. Could you discuss 1) the relationship between the modeled me-types and these transcriptomic cell types, and 2) how future models will evolve to integrate this new information? If there are gaps in knowledge in order to incorporate some transcriptome cell types into your model, it would be helpful to highlight them so that efforts can be directed toward addressing these areas.

      We thank the reviewer for this suggestion, particularly the idea to describe what types of data would be valuable towards improving the model in future. We have added the following to the Discussion on page 33:

      “In our previous work (Roussel et al., 2023) we linked mouse inhibitory me-models to transcriptomic types (t-types) in a whole mouse cortex transcriptomic dataset (Gouwens et al., 2019). This can provide a direct correspondence in future large-scale mouse models. As we model only a single electrical type for pyramidal cells there is no one-to-one correspondence between our me-models and the 10 different pyramidal cell types identified there. We are not currently aware of any method which can recreate the electrical features of different types of pyramidal cells using only generic ion channel models. To achieve the firing pattern behavior of more specific electrical types, usually ion channel kinetics are tweaked, and this would violate the compartmentalization of parameters. In future we hope to build morpho-electric-transcriptomic type (met-type) models by selecting gene-specific ion channel models (Ranjan et al., 2019, 2024) based on the met-type’s gene expression. Data specific to different neuron sections (i.e. soma, AIS, apical/basel dendrites) of different met-types, such as gene expression, distribution of ion channels, and voltage recordings under standard single cell protocols would be particularly useful.”

      (2) For the optogenetic manipulation, it would be interesting if the model could reproduce the paradoxical effects (for example, Mahrach et al. reported paradoxical effects caused by PV manipulation in S1; https://pubmed.ncbi.nlm.nih.gov/31951197/). This seems a more relevant and non-trivial network phenomenon than the V1 manipulation the authors attempted to replicate.

      We thank the reviewer for this valuable idea. Indeed, our model is able to reproduce paradoxical effects under certain conditions. We added the following new supplementary Figure S12 demonstrating this finding (black arrows).

      Which we discuss in the Results on page 22:

      “However, at high contrasts, we observed a paradoxical effect of the optogenetic stimulation on L6 PV+ neurons, reducing their activity with increasing stimulation strength (Figure S12B; cf. Mahrach et al. (2020)). This effect did not occur under grey screen conditions (i.e., at contrast 0.0) with a constant background firing rate of 0.2 Hz or 5 Hz respectively (not shown). The individual…”

      and added to the Discussion on page 32:

      “Also, we predicted a paradoxical effect of optogenetic stimulation on L6 PV+ interneurons, namely a decrease in firing with increased stimulus strength. This is reminiscent of the paradoxical responses found by Mahrach et al. (2020) in the mouse anterior lateral motor cortex (in L5, but not in L2/3) and barrel cortex (no layer distinction) respectively. While Mahrach et al. (2020) conducted their recordings in awake mice not engaged in any behavior, we observed this effect only when drifting grating patterns with high contrast were presented. Nevertheless, consistent with their findings, we found the effect only in deep but not in superficial layers, and only for PV+ interneurons but not for PCs. Our model could therefore be used to improve the understanding of this paradoxical effect in follow up studies. These examples demonstrate that the approach of modeling entire brain regions can be used to further probe the topics of the original articles and cortical processing.”

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      My specific comments are in the Public Review. The summarizing point is that this is a sprawling paper, and it is easy for readers to get confused. Focusing on specific connections between known functional properties and findings in this model, especially for the full-scale model, will be helpful.

      We thank the reviewer for this comment and for their related recommendation (4) below, and have added subheadings through-out the results.

      Reviewer #2 (Recommendations for the authors):

      (1) P4. What are the 10 free parameters?

      We thank the reviewer for pointing out that it would be useful to summarize the 10 parameters at this stage of the text, and have adjusted the sentence to:

      “As a result, the emerging in-vivo like activity is the consequence of only 10 free parameters representing the strength of extrinsic input from other brain regions into 9 layer-specific excitatory and inhibitory populations, and a parameter controlling the noise structure of this extrinsic input.”

      (2) Table 1 and S1 are extremely useful. Could you provide a table summarizing the major assumptions or gaps in the model, their potential influence on the results, and possible ways to collect data that could support or challenge these assumptions? Currently, this information is scattered throughout the manuscript.

      We thank the reviewer for this very useful suggestion and have added a Table S8 on page 68:

      (3) Figure 4F is important, but the legend is unclear. What is the unit on the x-axis? The values seem too large to represent per-neuron measurements.

      Thank you to the reviewer for raising this. Indeed the values are estimated mean numbers of missing number synapses per neuron by population. Such numbers are difficult to estimate but we have further discussed our rationale, justification and consideration of whether these numbers are accurate in the Results, as follows:

      “Heterogeneity in synaptic density within and across neuron classes and sections makes estimating the number of missing synapses challenging (DeFelipe and Fariñas, 1992). Changing the assumed synaptic density value of 1.1 synapses/μm would only change the slope of the relationship, however. Estimates of mean number of existing and missing synapses per population were within reasonable ranges; even the larger estimate for L5 E (due to higher dendritic length; Figure S3) was within biological estimates of 13,000 ± 3,500 total afferent synapses (DeFelipe and Fariñas, 1992).”

      This text references the new supplementary Figure S3:

      Moreover, these numbers represent the number of synapses, rather than the number of connections. The number of connections is usually used for quantifications such as indegree, and are usually much lower.

      We have also updated the caption and axis labels of the original figure:

      (4) Including additional subsections or improving the indexing in the Results section could be beneficial. In its current format, it's difficult to distinguish where the model description ends and where the validation begins. Some readers may want to focus more on the validation than other parts, so clearer segmentation would improve readability.

      We have addressed this comment with the opening comment in the authors “Recommendations for authors”.

      (5) P4. 2nd paragraph. Original vs rewired connectome. The term "rewired connectome" may give the impression that it refers to an artificial manipulation rather than a modification based on the latest data. It might be helpful to use a different term (e.g., SM-connectome as described later in the paper?).

      We have adjusted the text in the introduction:

      “Additionally, we generated a new connectome which captured recently characterized spatially-specific targeting rules for different inhibitory neuron types (Schneider-Mizell et al., 2023) in the MICrONS electron microscopy dataset (MICrONS-Consortium et al., 2021), such as increased perisomatic targeting by PV+ neurons, and increased targeting of inhibitory populations by VIP+ neurons. Comparing activity to the original connectome gave predictions about the role of these additional targeting rules.”

      (6) Figures 7 B, C, D: what is v1/v2? Original vs SM-Connectome?

      We thank the reviewer for noticing this and have corrected the figure to use “Orig” and “SM” consistent with the rest of the figure.

      (7) Page 23, 2.10: what is phi?

      We thank the reviewer for noticing this inconsistency with the earlier text, and have updated the text to read: “Particularly, we ran simulations for PF R ∈ [0.1, 0.15, ..., 0.3] using the OU para-maters calibrated for the seven column subvolume for [Ca<sup>2+</sup>] = 1.05 mM and R<sub>OU</sub> = 0.4.”

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Giménez-Orenga et al. investigate the origin and pathophysiology of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and fibromyalgia (FM). Using RNA microarrays, the authors compare the expression profiles and evaluate the biomarker potential of human endogenous retroviruses (HERV) in these two conditions. Altogether, the authors show that HERV expression is distinct between ME/CFS and FM patients, and HERV dysregulation is associated with higher symptom intensity in ME/CFS. HERV expression in ME/CFS patients is associated with impaired immune function and higher estimated levels of plasma cells and resting CD4 memory T cells. This work provides interesting insights into the pathophysiology of ME/CFS and FM, creating opportunities for several follow-up studies.

      Strengths:

      (1) Overall, the data is convincing and supports the authors' claims. The manuscript is clear and easy to understand, and the methods are generally well-detailed. It was quite enjoyable to read.

      (2) The authors combined several unbiased approaches to analyse HERV expression in ME/CFS and FM. The tools, thresholds, and statistical models used all seem appropriate to answer their biological questions.

      (3) The authors propose an interesting alternative to diagnosing these two conditions. Transcriptomic analysis of blood samples using an RNA microarray could allow a minimally invasive and reproducible way of diagnosing ME/CFS and FM.

      Weaknesses:

      (1) The cohort analysed in this study was phenotyped by a single clinician. As ME/CFS and FM are diagnosed based on unspecific symptoms and are frequently misdiagnosed, this raises the question of whether the results can be generalised to external cohorts.

      Thank you for your comment. Surely the study of larger cohorts will determine the external validity of these results in a clinical scenario. However, this pilot study, first of its kind, was designed to maximize homogeneity across participants which seemed primarily ensured by inclusion of females only diagnosed by a single experienced observer.

      (2) The analyses performed to unravel the causes and effects of HERV expression in ME/CFS and FM are solely based on sequencing data. Experimental approaches could be used to validate some of the transcriptomic observations.

      Certainly, experimental approaches may add robustness to our findings. We in fact consider taking this avenue to deepen in the observations presented here. However, the limited knowledge of HERV-mediated physiological functions may hinder the task of revealing causes and effects of HERV expression in ME/CFS and FM in the short term.

      Reviewer #2 (Public review):

      Summary:

      Giménez-Orenga carried out this study to assess whether human endogenous retroviruses (HERVs) could be used to improve the diagnosis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and Fibromyalgia (FM). To this end, they used the HERV-V3 array developed previously, to characterize the genome-wide changes in the expression of HERVs in patients suffering from ME/CFS, FM, or both, compared to controls. In turn, they present a useful repertoire of HERVs that might characterize ME/CFS and FM. For the most part, the paper is written in a manner that allows a natural understanding of the workflow and analyses carried out, making it compelling. The figures and additional tables present solid support for the findings. However, some statements made by the authors seem incomplete and would benefit from a more thorough literature review. Overall, this work will be of interest to the medical community seeking in better understanding of the co-occurrence of these pathologies, hinting at a novel angle by integrating HERVs, which are often overlooked, into their assessment.

      Strengths:

      (1) The work is well-presented, allowing the reader to understand the overall workflow and how the specific aims contribute to filling the knowledge gap in the field.

      (2) The analyses carried out to understand the potential impact on gene expression mediated by HERVs are in line with previous works, making it solid and robust in the context of this study.

      Weaknesses:

      (1) The authors claim to obtain genome-wide HERV expression profiles. However, the array used was developed using hg19, while the genomic analysis of this work are carried out using a liftover to hg38. It would improve the statement and findings to include a comparison of the differences in HERVs available in hg38, and how this could impact the "genome-wide" findings.

      This is an important point. However, the low number of probes that were excluded from our analysis by lack of correspondence with hg38, less than 100 among the 1,290,800 probesets, was interpreted as insignificant for "genome-wide" claims. An aspect that will be detailed in the revised version of this manuscript.

      (2) The authors in some points are not thorough with the cited literature. Two examples are:

      a) Lines 396-397 the authors say "the MLT1, usually found enriched near DE genes (Bogdan et al., 2020)". I checked the work by Bogdan, and they studied bacterial infection. A single work in a specific topic is not sufficient to support the statement that MLT1 is "usually" in close vicinity to differentially expressed genes. More works are needed to support this.

      b) After the previous statement, the authors go on to mention "contributing to the coding of conserved lncRNAs (Ramsay et al., 2017)". First, lnc = long non-coding, so this doesn't make sense. Second, in the work by Ramsay they mention "that contributed a significant amount of sequence to primate lncRNAs whose expression was conserved", which is different from what the authors in this study are trying to convey. Again, additional work and a rephrasing might help to support this idea.

      Certainly, these two sentences need rephrasing to better adjust statements to current evidence and will be replaced in the revised version of this manuscript.

      (3) When presenting the clusters, the authors overlook the fact that cluster 4 is clearly control-specific, and fail to discuss what this means. Could this subset of HERV be used as bona fide markers of healthy individuals in the context of these diseases? Are they associated with DE genes? What could be the impact of such associations?

      Using control DE HERV as bona fide markers of healthy individuals seems like an interesting possibility worth exploring. Control DE HERVs (cluster 4) are indeed associated with DE genes involved in apoptosis, T cell activation and cell-cell adhesion (modules 1 and 6) (Figure 3A). The impact of which deserves further study.

      Appraisals on aims:

      The authors set specific questions and presented the results to successfully answer them. The evidence is solid, with some weaknesses discussed above that will methodologically strengthen the work.

      Likely impact of work on the field:

      This work will be of interest to the medical community looking for novel ways to improve clinical diagnosis. Although future works with a greater population size, and more robust techniques such as RNA-Seq, are needed, this is the first step in presenting a novel way to distinguish these pathologies.

      It would be of great benefit to the community to provide a table/spreadsheet indicating the specific genomic locations of the HERVs specific to each condition. This will allow proper provenance for future researchers interested in expanding on this knowledge, as these genomic coordinates will be independent of the technique used (as was the array used here).

      We agree with the reviewer that sharing genomic locations of DE HERVs in these pathologies would contribute to further development of our findings. Unfortunately, we do not hold the rights to share probe coordinates from this custom HERV-V3 microarray which we used under MTA agreement with its developer.

      Reviewer #3 (Public review):

      The authors find that HERV expression patterns can be used as new criteria for differential diagnosis of FM and ME/CFS and patient subtyping. The data are based on transcriptome analysis by microarray for HERVs using patient blood samples, followed by differential expression of ERVs and bioinformatic analyses. This is a standard and solid data processing pipeline, and the results are well presented and support the authors' claim.

    1. Author response:

      Thank you to the reviewers and editors for their positive and constructive comments. Based on this feedback, we can see that we need to clarify that the primary goal of this paper is a test of potential changes in public health policy rather than a test of technical improvements to forecasting models. We briefly summarize the primary goal below to address these public reviews and list our proposed revisions to the manuscript based on reviewer feedback.

      All real-time forecasting models contend with 2 major constraints:

      (1) How far into the future they have to predict

      (2) How rapidly the data used for predictions become available in real time

      In the case of evolutionary influenza forecasts, the current values of these constraints are 1) 12 months into the future and 2) an average lag of ~3 months for hemagglutinin (HA) sequences to become available after sample collection. Regardless of the predictors we use in these models (genetic or phenotypic), our units of prediction always depend on HA: the HA protein is the primary target of our immunity, HA is the only gene whose composition is determined by the vaccine selection process, and influenza diversity is historically defined by clades in HA phylogenies.

      Our primary goal of this study was to understand the relative effect sizes of these two common constraints on forecasting while holding all other variables as constant as possible. With this understanding, we hoped to better inform public health priorities and set realistic expectations for current and future forecasting efforts regardless of the technical specifications of each forecasting model. In other words, the goal of this study was not to optimize prediction methods but to estimate the effects of potential policy changes on forecast accuracy.

      We found that reducing how far into the future we need to predict consistently reduced our forecasting error in simulated populations (where we knew the true fitness of each virus) and in natural populations (where we either estimated fitness from genetic predictors or we knew the true fitness of each virus based on its future success). Figure 6 and its first supplemental figure show these effect sizes for natural and simulated populations, respectively, when the future fitness of each virus is known at the time of prediction. By definition, we cannot hope to improve our estimates of viral fitness for these forecasts by using other genetic or phenotypic information.

      Figure 6 shows that reducing how far into the future we need to predict from 12 to 6 months improves our forecasting accuracy 3 times as much as reducing the lag between sample collection and HA sequence submission to public databases. The impact of this finding is the confirmation that a faster vaccine development process would improve our forecast accuracy substantially more than faster turnaround between sample collection and sequence submission. If our public health goal is to make better predictions of future influenza populations, then this result indicates that our main priority is to speed up the vaccine development process.

      If our public health goal is to better understand the composition of currently circulating influenza populations (the units of our forecasts), then Figure 3 shows that reducing the lag between sample collection and HA sequence submission from ~3 months on average to 1 month on average reduces our uncertainty in current clade frequency estimates by half. This impact is also independent of the predictors we use in our forecasting models and is not lessened by the lack of other genetic or phenotypic information in our analyses.

      We realize that neither a 6-month vaccine development process nor a 1-month average sequence submission lag exist yet, but we believe that these are realistic and achievable goals for scientific and public health communities. We also realize that these public health goals are not mutually exclusive. By measuring the effects of these realistic changes to current policy through our forecasting experiments, we hope to inspire and motivate researchers and decision-makers who are empowered to make both of these goals a reality.

      Finally, we want to emphasize that the use of phenotypic data in forecasts introduces additional delays caused by the lag between when genetic sequences become available and when serological experiments can be performed. Most WHO influenza collaborating centers use a "sequence-first" approach where they characterize the genetic sequence and use available sequences to prioritize phenotypic experiments with serology. This additional lag in availability of phenotypic data means that a forecasting model based on genetic and phenotypic data will necessarily have a greater lag in data availability than a model based on genetic data only. This lag is important for practical forecasts, too, but because the lag reflects specific characteristics of each collaborating center and not a global policy change, we believe this topic falls outside of the scope of this study.

      Based on these public reviews and the private recommendations from reviewers, we plan to make the following revisions to this manuscript.

      ● Clarify the introduction, discussion, and abstract to emphasize the primary goal for this study to test effects of realistic changes to public health policy and note that this study does not cover improvements to forecasting models. As part of these changes, we will include a rationale for our choice of a genetic-information-only approach rather than a model that integrates phenotypic data. We will also refine Figure 1 to more clearly communicate the two factors we tested in this study.

      ● Provide a clearer explanation for the subsampling approach we use, include supplemental materials to communicate the geographic and temporal biases that exist in available HA sequence data, and discuss potential effects of different subsampling strategies.

      ● Evaluate the robustness of our results to different randomly subsampled data. We will perform additional technical replicates of our analysis workflow for natural populations, and summarize the effects of realistic interventions across replicates in a supplemental figure and the main text of the results.

      ● Investigate time-dependent effects of forecast horizons and submission lags on model accuracy to identify any potential biases in accuracy during specific historical epochs or any seasonal trends in accuracy associated with predicting future populations for the Northern or Southern Hemispheres.

      ● In the discussion, clarify how reducing submission lags would practically improve the WHO's ability to select vaccine candidate viruses and minimize jargon that currently makes the discussion less accessible to the average reader.

      ● Investigate how changes in forecast horizons and submission lags change the distance between predicted and observed future populations at antigenic positions (i.e., "epitope" positions) to understand whether we see the same effects with that subset of positions as we see across all HA positions.

    1. Author Response:

      We greatly appreciate the feedback provided by reviewers on this manuscript. One of our key objectives was to provide a comprehensive, detailed resource for researchers using single-cell transcriptomics to study arthritis, especially immune cells like macrophages. We strived to perform thorough, wide-ranging analyses that are both accessible and useful to other scientists in the field, and that we hope will serve as the basis for many future avenues of study. As such, we acknowledge that this work is a “first step”, providing a strong descriptive foundation with some mechanistic insight that we and others will continue pursuing. Preliminary studies in our laboratory seeking to dissect signaling mechanisms associated with the M-CSF pathway have illuminated how complex and context-dependent this signaling is, which is an important consideration for future in vivo investigations. Further, it is indeed true that attempting to harmonize transcriptomic data across studies, models, laboratories, and dissection/processing methods is fraught with difficulty and prone to misinterpretation – and we made an effort to highlight this in our manuscript, particularly with respect to where synovial immune cells were recovered from, and how. We encourage healthy discussion within the field for developing shared, unified protocols for harvests and processing upstream of transcriptomic experiments.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review)

      Summary:

      The authors wanted to use AlphaFold-multimer (AFm) predictions to reduce the challenge of physics-based protein-protein docking.

      Strengths:

      They found that two features of AFm predictions are very useful. 1) pLLDT is predictive of flexible residues, which they could target for conformational sampling during docking; 2) the interface-pLLDT score is predictive of the quality of AFm predictions, which allows the authors to decide whether to do local or global docking.

      Weaknesses:

      (1) As admitted by the authors, the AFm predictions for the main dataset are undoubtedly biased because these structures were used for AFm training. Could the authors find a way to assess the extent of this bias?

      Indeed, the AFm training included most of the structures in the DB5 benchmark for its training as many structures (either unbound or bound) were deposited before the training cut-off period. One of the challenges of estimating this bias is the availability of new structures - both bound and unbound deposited after the training cut-off. Estimating the extent of training bias is therefore conditional on these factors and difficult. A few studies have attempted to address this bias (Yin et al, 2022, https://doi.org/10.1002/pro.4379).

      In our study, we assess this bias by comparing the AFm structures to the bound and unbound forms and calculating their Ca RMSDs and TM-scores (new addition). We now elaborate in the Results:Dataset curation section and we have added a figure comparing the TM-scores in the supplement.

      We added a clarifying text and a note about the TM-score calculation in the manuscript as follows:

      “Since most of the benchmark targets in DB5.5 were included in AlphaFold training, there would be training bias associated with their predictions (i.e. our measured success rates are an upper bound).”

      “We also calculated the TM-scores of the AFm predicted complex structures with respect to the bound and the unbound crystal structures (Supplementary Figure S2). As TM-scores reflect a global comparison between structures and are less sensitive to local structural deviations, no strong conclusions could be derived. This is in agreement with our intuition that since both unbound and bound states of proteins will share a similar fold, and AlphaFold can predict structures with high TM-scores in most cases, gauging the conformational deviations with TM-scores would be inconclusive.”

      (2) For the CASP15 targets where this bias is absent, the presentation was very brief. In particular, it would be interesting to see how AFm helped with the docking. The authors may even want to do a direct comparison with docking results without the help of AFm.

      Unfortunately since this was a CASP-CAPRI round, the structure of the unbound Antigen or the nanobodies was unavailable. Thus we cannot perform a comparison without using AF2 at all since we need a structure prediction tool to produce the unbound nanobody and the nanobody-antigen complex template structure to dock. This has been clarified in the main text for better understanding for the readers.

      “Since the nanobody-antigen complexes were CASP targets, we did not have unbound structures, rather only the sequences of individual chains. Therefore, for each target, we employed the AlphaRED strategy as described in Fig 7.”

      Reviewer #1 (Recommendations For The Authors):

      For suggestions for major improvements, see comments under weaknesses. One additional suggestion: the authors found that pLLDT is predictive of flexible residues. Can they try to find AFm features that are predictive of the interface site? Such information may guide their docking to a local site.

      This is a great idea that we and others have been thinking about considerably. Prior work by Burke et al. (Towards a structurally resolved human protein interaction network) examines AlphaFold’s ability to predict PPIs. For high-confidence predicted models of interacting protein complexes, the authors showed that pDockQ correlated reasonably well with correct protein interactions.

      That being said, binding site identification, particularly in a partner-agnostic fashion, i.e. determining binding patches on a given protein, is an area of on-going research . We hope a future study examines AlphaFold3 or ESM3 specifically for this task.

      “Further, we tested multiple thresholds to estimate the optimum cut-off for distinguishing near-native structures (defined as an interface-RMSD < 4 Å) from the predictions. Figure 3.B summarizes the performance with a confusion matrix for the chosen interface-pLDDT cutoff of 85. 79 % of the targets are classified accurately with a precision of 75%, thereby validating the utility of interface-pLDDT as a discriminating metric to rank the docking quality of the AFm complex structure predictions. With AlphaFold3 and ESM3 being released, investigating features that could predict flexible residues or interface site would be valuable, as this information may guide local docking.”

      Minor:

      Page 3, lines 73-77, state how many targets were curated from DB5.5.

      We have now clarified this in the manuscript. All 254 targets curated from DB5.5 at the time of this benchmark study.

      “For each protein target, we extracted the amino acid sequences from the bound structure and predicted a corresponding three-dimensional complex structure with the ColabFold implementation of the AlphaFold multimer v2.3.0 (released in March 2023) for the 254 benchmark targets from DB5.5.”

      In Figure 1, the color used for medium is too difficult to distinguish from the grey color used for rigid.

      We thank you for this suggestion. We have updated the color to olive. Further, based on Reviewer 2’s suggestions, we have moved this plot to the Supplementary.

      Reviewer #2 (Public Review):

      Summary:

      In short, this paper uses a previously published method, ReplicaDock, to improve predictions from AlphaFold-multimer. The method generated about 25% more acceptable predictions than AFm, but more important is improving an Antibody-antigen set, where more than 50% of the models become improved.

      When looking at the results in more detail, it is clear that for the models where the AFm models are good, the improvement is modest (or not at all). See, for instance, the blue dots in Figure 6. However, in the cases where AFm fails, the improvement is substantial (red dots in Figure 6), but no models reach a very high accuracy (Fnat ~0.5 compared to 0.8 for the good AFm models). So the paper could be summarized by claiming, "We apply ReplicaDock when AFm fails", instead of trying to sell the paper as an utterly novel pipeline. I must also say that I am surprised by the excellent performance of ReplicaDock - it seems to be a significant step ahead of other (not AlphaFold) docking methods, and from reading the original paper, that was unclear. Having a better benchmark of it alone (without AFm) would be very interesting.

      We thank the reviewer for highlighting the performance of ReplicaDock. ReplicaDock alone is benchmarked in the original paper (10.1371/journal.pcbi.1010124), with full details on the 2022 version of DB5.5 in the supplement. Indeed ReplicaDock2 achieves the highest reported success rates on flexible docking targets reported in the literature (until this AlphaRED paper!).

      Regarding this statement about “the paper could be summarized…” it might be helpful to give more context. ReplicaDock is a replica exchange Monte Carlo sampling approach for protein docking that incorporates flexibility in an induced-fit fashion. However, the choice of which backbone residues to move is solely dependent on contacts made during each docking trajectory. In the last section of the ReplicaDock paper, we introduced “Directed Induced-fit” where we biased the backbone sampling only towards those residues where we knew the backbone is flexible (this information is obtained because for the benchmark set, we had both unbound and bound structures and hence could cherry-pick the specific residues which are mobile). We agree with the reviewers that AlphaRED is essentially a derivative of ReplicaDock, however, the two major claims that we make in this paper are:

      (1) AlphaFold pLDDT is an effective predictor of backbone flexibility for practical use in docking.

      (2) We can automate the Directed InducedFit approach within ReplicaDock by utilizing this pLDDT information per residue for conformational sampling in protein docking; and in doing so, create a pipeline that would allow us to go from sequence-to-structure-to-complex, specifically capturing conformational changes.

      To conclude these claims, we pose the following questions in the Introduction:

      “(1) Do the residue-specific estimates from AF/AFm relate to potential metrics demonstrating conformational flexibility?

      (2) Can AF/AFm metrics deduce information about docking accuracy?

      (3) Can we create a docking pipeline for in-silico complex structure prediction incorporating AFm to convert sequence-to-structure-to-docked complexes?”

      This work requires a pipeline, the center of which lies in ReplicaDock as a docking method, but has functionalities that were absent in prior work. The goal is also to develop a one-stop shop without manual intervention (a prerequisite for biasing backbone sampling in ReplicaDock) that could be utilized by structural biologists efficiently.

      We clarify this points in the abstract and main text as follows:

      Abstract: “In this work, we combine AlphaFold as a structural template generator with a physics-based replica exchange docking algorithm \add{to better sample conformational changes.”

      Introduction:

      “The overarching goal is to create a one-stop, fully-automated pipeline for simple, reproducible, and accurate modeling of protein complexes. We investigate the aforementioned questions and create a protocol to resolve AFm failures and capture binding-induced conformational changes. We first assess the utility of AFm confidence metrics to detect conformational flexibility and binding site confidence.”

      These results also highlight several questions I try to describe in the weakness section below. In short, they boil down to the fact that the authors must show how good/bad ReplicaDock is at all targets (not only the ones where AFm fails. In addition, I have several more technical comments.

      Strengths:

      Impressive increase in performance on AB-AG set (although a small set and no proteins).

      We thank the reviewer for their comments.

      Weaknesses:

      The presentation is a bit hard to follow. The authors mix several measures (Fnat, iRMS, RMSDbound, etc). In addition, it is not always clear what is shown. For instance, in Figure 1, is the RMSD calculated for a single chain or the entire protein? I would suggest that the author replace all these measures with two: TM-score when evaluating the quality of a single chain and DockQ when evaluating the results for docking. This would provide a clearer picture of the performance. This applies to most figures and tables.

      We apologize for the lack of clarity owing to different metrics. Irms and fnat are standard performance metrics in the docking field, but we agree that DockQ would be simpler when the detail of the other metrics are not required. We have updated the figures Figure 5 and Figure 8 to also show DockQ comparisons.

      Regarding Figure 1, as highlighted in Line 90 of the main-text, “Figure 1 shows the Ca-RMSD of all protein partners of the AFm predicted complex structures with respect to the bound and the unbound.” As suggested by the reviewer in their further comments, we have moved this FIgure to the Supplementary. We have also included TM-score comparison in the Supplementary ( SupFig S2) and included clarifying statements in the main text:

      “We also tested TM-scores to measure the structural deviations of the AFm predicted complex structures with respect to the bound and unbound structures (Supplementary Figure S2). However, this metric is not sensitive enough to detect the subtle, local conformational changes upon binding.”

      For instance, Figure 9 could be shown as a distribution of DockQ scores.

      We have now updated Figure 5 to include DockQ scores in Panel D. Since DockQ is a function of iRMSD, fnat and L-RMSD, it shows cumulative improvement in performance. Some of the nuanced details, such as, the protocol improves i-RMSD considerably but fnat improvement is lacking, and can highlight whether backbone sampling is the challenge or is it sidechain refinement.Therefore, we need to retain the iRMSD and fnat metrics in panel A-C . But We have incorporated this in the main text as follows:

      “Finally, to evaluate docking success rates, we calculate DockQ for top predictions from AFm and AlphaRED respectively (Figure 5D). AlphaRED demonstrates a success rate (DockQ>0.23) for 63% of the benchmark targets. Particularly for Ab-Ag complexes, AFm predicted acceptable or better quality docked structures in only 20% of the 67 targets. In contrast, the AlphaRED pipeline succeeds in 43% of the targets, a significant improvement.”

      Further, we have reevaluated success rates in Figure 8 (previously Figure 9) and have updated the manuscript to report these updated success rates.

      “By utilizing the AlphaRED strategy, we show that failure cases in AFm predicted models are improved for all targets (lower Irms for 97 of 254 failed targets) with CAPRI acceptable-quality or better models generated for 62% of targets overall (Fig 8)”.

      The improvements on the models where AFm is good are minimal (if at all), and it is unclear how global docking would perform on these targets, nor exactly why the plDDT<0.85 cutoff was chosen.

      We agree with the reviewers that the improvement on the models with good AFm predictions is minimal. We acknowledge this in the text now as follows:

      “Most of the improvements in the success rates are for cases where AFm predictions are worse. For targets with good AFm predictions, AlphaRED refinement results in minimal improvements in docking accuracy.”

      The choice of pLDDT cutoff = 85 is elaborated in the “Interface-pLDDT correlates with DockQ and discriminates poorly docked structures” section, paragraph 3. Briefly, we tested multiple metrics and the interface pLDDT had the highest AUC, indicating that it is the best metric for this task. For interface-pLDDT we tested multiple thresholds, and the cutoff of 85 resulted in the highest percentage of true-positive and true-negative rates. This is illustrated with the confusion matrix in Figure 3.B with the precision scores. We now clarify this in the text as follows:

      “With interface-pLDDT as a discriminating metric, we tested multiple thresholds to estimate the optimum cut-off for distinguishing near-native structures (defined as an interface-RMSD < 4 Å) from the predictions. Figure 3B summarizes the performance with a confusion matrix for the chosen interface-pLDDT cutoff of 85. 79% of the targets are classified accurately with a precision of 75%, thereby validating the utility of interface-pLDDT as a discriminating metric to rank the docking quality of the AFm complex structure predictions.”

      To better understand the performance of ReplicaDock, the authors should therefore (i) run global and local docking on all targets and report the results, (ii) report the results if AlphaFold (not multimer) models of the chains were used as input to ReplicaDock (I would assume it is similar). These models can be downloaded from AlphaFoldDB.

      The performance of ReplicaDock on DB5.5 is tabulated in our prior work (https://doi.org/10.1371/journal.pcbi.1010124) and we direct the reviewers there for the detailed performance and results. In our opinion, the benchmark suggested by the reviewer would be redundant and not worth the computational expense.

      The scope of this paper is to highlight a structure prediction + physics-based modeling pipeline for docking to adapt to the accuracy of up-and-coming structure prediction tools.

      Using AlphaFold monomer chains as input and benchmarking on that, albeit interesting scientifically, will not be useful for either the pipeline or biologists who would want a complex structure prediction. We thank the authors for their comments but want to reemphasize that the end goal of this work is to increase the accuracy of complex structure predictions and PPIs obtained from computational tools.

      Further, it would be interesting to see if ReplicaDock could be combined with AFsample (or any other model to generate structural diversity) to improve performance further.

      We would like to highlight that ReplicaDock is a stand-alone tool for protein docking and here we demonstrate the ability of adapting it with metrics derived from AlphaFold or other structure prediction tools (say ESMFold) such as pLDDT for conformational sampling and improving docking accuracy. We definitely agree that adapting it to use with tools such as AFSample will be interesting but it is out of scope of this work.

      The estimates of computing costs for the AFsample are incorrect (check what is presented in their paper). What are the computational costs for RepliaDock global docking?

      The authors of the AFSample paper report that “AFsample requires more computational time than AF2, as it generates 240 models, and including the extra recycles, the overall timing is 1000 more costly than the baseline.” We have reported these exact numbers in our manuscript.

      The computational costs of ReplicaDock are 8-72 CPU hours on a single node with 24 processors as reported in our prior work.

      For AlphaRED, the costs are slightly higher owing to the structure prediction module in the beginning and are up to 100 CPU hrs for our largest (max Nres) target.

      It is unclear strictly what sequences were used as input to the modelling. The authors should use full-length UniProt sequences if they were not done.

      We report this in the methods section of the manuscript as well as in Figure 5. Full length complex sequences were used for the models that we extracted from DB5.5.

      “As illustrated in Fig. 5, given a sequence of a protein complex, we use the ColabFold implementation of AF2-multimer to obtain a predictive template.”

      We clarify this in the methods section as:

      “For each target in the DB5.5 dataset, we first extracted the corresponding FASTA sequence for the bound complex and then obtained AlphaFold predicted models with the ColabFold v1.5.2 implementation of AlphaFold and AlphaFold-multimer (v.2.3.0).”

      The antibody-antigen dataset is small. It could easily be expanded to thousands of proteins. It would be interesting to know the performance of ReplicaDock on a more extensive set of Antibodies and nanobodies.

      This work demonstrates the performance on the docking benchmark, i.e. given unbound structure can you predict the bound complexes. With this regard, our analysis has been focussed on targets where both the unbound and bound structures are available so that we could evaluate the ability of AlphaRED on modeling protein flexibility and docking accuracy. For antibody-antigen complexes, there are only 67 structures with both unbound and bound complexes available and they constituted our dataset. Benchmarking AlphaRED on all antibody-antigen targets can give biased results as most Ab-Ag complexes are in AlphaFold training set. Further, our work is more aimed towards predicting conformational flexibility in docking and not rigid-body docked complexes, so benchmarking on existing bound Ab-Ag structures is out of scope for this work.

      Using pLDDT on the interface region to identify good/bas models is likely suboptimal. It was acceptable (as a part of the score) for AlphaFold-2.0 (monomer), but AFm behaves differently. Here, AFm provides a direct score to evaluate the quality of the interaction (ipTM or Ranking Confidence). The authors should use these to separate good/bad models (for global/local docking), or at least show that these scores are less good than the one they used.

      We thank the reviewers for this suggestion.

      Reviewer #2 (Recommendations For The Authors):

      Some Figures could be skipped/improved

      Fig 1: Use TM-score instead a much better measure (and the figure is not necessary).

      Figure 1 compares the bias of AlphaFold towards unbound or bound forms of the proteins. We believe that this figure highlights the slight inherent bias of AlphaFold towards bound structures over unbound.

      As the reviewers have suggested we have included a plot comparing the TM-scores for the structures. Further, we have moved this figure to the Supplementary.

      Fig 2. Skip B (why compare RMSD with pLDDT?). Add a figure to see how this correlates over all targets not just two.

      RMSD and LDDT both represent metrics to evaluate conformational variability between two structures, such as the bound and unbound forms of the same protein structure. On one hand where RMSD measures overall deviation of residues, LDDT allows the estimation of relative domain orientations and concerted proteins. We have elaborated this in Methods as well as in the Results section titled “AlphaFold pLDDT provides a predictive confidence measure for backbone flexibility”.

      The data for the benchmark targets is now included in the Supplementary (Supplementary Figures S3-S4).

      Fig 3. Color the different chains of a protein differently. Thereby the Receptor/Ligand/Bound labels can be omitted.

      We thank the reviewers for this suggestion. However, the color scheme is chosen to highlight (1) the relative orientation of protein partners relative to each other. We have ensured that the alignment is over one partner (Receptor) so that you could see the relative orientation of the other partner (Ligand) in the modeled protein over the bound structure (in one color). (2) The coloring of the receptor and ligand chain is by pLDDT (from red to blue) to highlight that for decoys with incorrectly predicted interfaces, the pLDDT scores of the interface residues are indeed lower and can be a discriminating metric. We elaborate this in the caption of Figure 3 as well as in the section “Interface-pLDDT correlates with DockQ and discriminates poorly docked structures”. Coloring the chains of a protein differently will obfuscate the point that we are aiming to make and will be inconclusive for the readers as they would need to rely only on quantitative metrics (Irms and DockQ) reported but won’t be able to visualize the interface pLDDT of the incorrectly bound structures. We hope that this justifies the choice of our color scheme.

      Fig 4. Include RankConf, ipTM, pDockQ, and other measures in the plos (they are likely better). Include DockQ for the top targets. It is difficult to estimate for multi chain complexes.

      We thank the reviewer for this suggestion. We have now included the DockQ performances for all targets in Figure 5 (previously Figure 6) as well as re-evaluated our final success rates based on the DockQ calculations in Figure 8 (previously Figure 9).

      Fig 5. use a better measure to split (see above).

      We have elaborated on the choice of the split for the comments above and the interface pLDDT threshold of 85 is a decision made post observation on the docking benchmark. We do want to highlight that the cut-off is arbitrary and in our online server (ROSIE) as well as in custom scripts, this cut-off can be tuned by the user as required. We would suggest a cut-off of 85 based on our observations but the users are welcome to tune this as per their needs.

      Fig 6. Replace lrms/fnat with DockQ.

      We have now included DockQ scores in our manuscript.

      Fig 7. Color the different chains of a protein differently.

      We have colored the protein chains differently. AlphaFold models are in Orange, Bound complexes are in Gray, and predicted proteins from AlphaRED are in Blue-Green indicating the two partners. All models are aligned over the receptor so relative orientations of the ligand protein can be observed.

      Fig 8 Color the different chains of a protein differently.

      The chains are colored differently. We would like the reviewer to elaborate more on what they would like to observe as we believe our color scheme makes intuitive sense for readers.

      Fig 9. Use DockQ instead of CAPRI criteria.

      The figure has been updated based on DockQ. To elaborate, the CAPRI criteria is set based on DockQ scores as elaborated in the figure caption.

    1. Author response:

      eLife Assessment <br /> This manuscript reports important findings that the methyltransferase METTL3 is involved in the repair of abasic sites and uracil in DNA, mediating resistance to floxuridine-driven cytotoxicity. The presented evidence for the involvement of m6A in DNA is incomplete and requires further validation with orthogonal approaches to conclusively show the presence of 6mA in the DNA and exclude that the source is RNA or bacterial contamination. 

      We thank the editors for recognizing the importance of our work and the relevance of METTL3 in DNA repair. However, we wholly disagree with the second sentence in the eLife assessment, and we want to clarify why our evidence for the involvement of 6mA in DNA is complete.  

      The identification of 6mA in DNA, upon DNA damage, is based first on immunofluorescence observations using an anti-m6A antibody. In this setting, removal of RNA with RNase treatment fails to reduce the 6mA signal, excluding the possibility that the source of signal is RNA. In contrast, removal of DNA with DNase treatment removes all 6mA signal, strongly suggesting that the species carrying the N6-methyladenosine modification is DNA (Figure 3D, E). Importantly, in Figure 3F, we provide orthogonal, quantitative mass spectrometry data that independently confirm this finding. Mass spectrometry-liquid chromatography of DNA analytes, conclusively shows the presence of 6mA in DNA upon treatment with DNA damaging agents and excludes that the source is RNA, based on exact mass. Reviewer #2 recognized the strengths of this approach to generate solid evidence for 6mA in DNA.

      Cells only show the 6mA signal when treated with DNA damaging agents, and the 6mA is absent from untreated cells (Figure 3D, E, F). This provides strong evidence that the 6mA signal is not a result of bacterial contamination in our cell lines. Moreover, our cell lines are routinely tested for mycoplasma contamination. It could be possible that stock solutions of DNA damaging agents may be contaminated, but this would need to be true for all individual drugs and stocks tested. The data showing 6mA signal is not significantly different from untreated cells when a DNA damaging agent is combined with a METTL3 inhibitor (Figure 3G, H) provides strong evidence against bacterial contamination in our stocks.  

      In summary, we provide conclusive evidence, based on orthogonal methods, that the METTL3-dependent N6-methyladenosine modification is deposited in DNA, not RNA, in response to DNA damage. 

      Public Reviews: <br /> Reviewer #1 (Public review): <br /> Summary: 

      The authors sought to identify unknown factors involved in the repair of uracil in DNA through a CRISPER knockout screen. 

      Typo above: “CRISPER” should be “CRISPR”.

      Strengths: 

      The screen identified both known and unknown proteins involved in DNA repair resulting from uracil or modified uracil base incorporation into DNA. The conclusion is that the protein activity of METTL3, which converts A nucleotides to 5mA nucleotides, plays a role in the DNA damage/repair response. The importance of METTL3 in DNA repair, and its colocalization with a known DNA repair enzyme, UNG2, is well characterized. 

      Typo above: “5mA” should be “6mA”.

      Weaknesses: <br /> This reviewer identified no major weaknesses in this study. The manuscript could be improved by tightening the text throughout, and more accurate and consistent word choice around the origin of U and 6mA in DNA. The dUTP nucleotide is misincorporated into DNA, and 6mA is formed by methylation of the A base present in DNA. Using words like 6mA "deposition in DNA" seems to imply it results from incorporation of a methylated dATP nucleotide during DNA synthesis.

      The increased presence of 6mA during DNA damage could result from methylation at the A base itself (within DNA) or from incorporation of pre-modified 6mA during DNA synthesis. Our data do not directly discriminate between these two mechanisms, and we will clarify this point in the discussion.

      Reviewer #2 (Public review): <br /> Summary: <br /> In this work, the authors performed a CRISPR knockout screen in the presence of floxuridine, a chemotherapeutic agent that incorporates uracil and fluoro-uracil into DNA, and identified unexpected factors, such as the RNA m6A methyltransferase METTL3, as required to overcome floxuridine-driven cytotoxicity in mammalian cells. Interestingly, the observed N6-methyladenosine was embedded in DNA, which has been reported as DNA 6mA in mammalian genomes and is currently confirmed with mass spectrometry in this model. Therefore, this work consolidated the functional role of mammalian genomic DNA 6mA, and supported with solid evidence to uncover the METTL3-6mA-UNG2 axis in response to DNA base damage. <br /> Strengths: <br /> In this work, the authors took an unbiased, genome-wide CRISPR approach to identify novel factors involved in uracil repair with potential clinical interest. 

      The authors designed elegant experiments to confirm the METTL3 works through genomic DNA, adding the methylation into DNA (6mA) but not the RNA (m6A), in this base damage repair context. The authors employ different enzymes, such as RNase A, RNase H, DNase, and liquid chromatography coupled to tandem mass spectrometry to validate that METTL3 deposits 6mA in DNA in response to agents that increase genomic uracil. <br /> They also have the Mettl3-KO and the METTL3 inhibition results to support their conclusion. <br /> Weaknesses:<br /> Although this study demonstrates that METTL3-dependent 6mA deposition in DNA is functionally relevant to DNA damage repair in mammalian cells, there are still several concerns and issues that need to be improved to strengthen this research.

      First, in the whole paper, the authors never claim or mention the mammalian cell lines contamination testing result, which is the fundamental assay that has to be done for the mammalian cell lines DNA 6mA study.

      Our cell lines are routinely tested for bacterial contamination, specifically mycoplasma, and we plan to state this information in a revised version of the manuscript.

      Importantly, we do not observe 6mA in untreated cells, strongly suggesting that the 6mA signal observed is dependent on the presence of DNA damage and not caused by contamination in the cell lines (Figure 3D, E, F). While it could be possible that stock solutions of DNA damaging agents may be contaminated, this would need to be the case for all individual drugs and stocks tested that induce 6mA, which seems very unlikely. Finally, the data showing 6mA signal is not significantly different from untreated cells when a DNA damaging agent is combined with a METTL3 inhibitor (Figure 3 G, H) provides strong evidence against bacterial contamination in our drug stocks.

      Second, in the whole work, the authors have not supplied any genomic sequencing data to support their conclusions. Although the sequencing of DNA 6mA in mammalian models is challenging, recent breakthroughs in sequencing techniques, such as DR-Seq or NT/NAME-seq, have lowered the bar and improved a lot in the 6mA sequencing assay. Therefore, the authors should consider employing the sequencing methods to further confirm the functional role of 6mA in base repair.

      While we agree that it could be important to understand the precise genomic location of 6mA in relation to DNA damage, this is outside the scope of the current study. Moreover, this exercise may prove unproductive. If 6mA is enriched in DNA at damage sites or as DNA is replicated, the genomic mapping of 6mA is likely to be stochastic. If stochastic, it would be impossible to obtain the read depth necessary to map 6mA accurately.

      Third, the authors used the METTL3 inhibitor and Mettl3-KO to validate the METTL3-6mA-UNG2 functional roles. However, the catalytic mutant and rescue of Mettl3 may be the further experiments to confirm the conclusion. 

      We believe this to be an excellent suggestion from Reviewer #2 but we are unable to perform the proposed experiment at this time. We encourage future studies to explore the rescue experiment.

      Reviewer #3 (Public review):

      Summary:

      The authors are showing evidence that they claim establishes the controversial epigenetic mark, DNA 6mA, as promoting genome stability.

      Strengths:

      The identification of a poorly understood protein, METTL3, and its subsequent characterization in DDR is of high quality and interesting.

      Weaknesses:

      (1) The very presence of 6mA (DNA) in mammalian DNA is still highly controversial and numerous studies have been conclusively shown to have reported the presence of 6mA due to technical artifacts and bacterial contamination. Thus, to my knowledge there is no clear evidence for 6mA as an epigenetic mark in mammals, and consequently, no evidence of writers and readers of 6mA. None of this is mentioned in the introduction. Much of the introduction can be reduced, but a paragraph clearly stating the controversy and lack of evidence for 6mA in mammals needs to be added, otherwise, the reader is given an entirely distorted view of the field.

      These concerns must also be clearly in the limitations section and even in the results section which fails to nuance the authors' findings.

      We agree with the reviewer that the presence and potential function of 6mA in mammalian DNA has been debated. Importantly, the debate regarding the presence and quantity of 6mA in DNA has been previously restricted to undamaged, baseline conditions. In complete agreement with this notion, we do not detect appreciable levels of 6mA in untreated cells. We will revise the introduction to introduce the debate about 6mA in DNA. We, however, want to highlight that our study provides for the first time, convincing evidence (based on orthogonal methods) that 6mA is present in DNA in response to a stimulus, DNA damage.

      (2) What is the motivation for using HT-29 cells? Moreover, the materials and methods do not state how the authors controlled for bacterial contamination, which has been the most common cause of erroneous 6mA signals to date. Did the authors routinely check for mycoplasma?

      HT-29 is a cell line of colorectal origin and chemotherapeutic agents that introduce uracil and uracil derivatives in DNA, as those used in this study, are relevant for the treatment of colorectal cancer. As indicated above, we do not observe 6mA in untreated cells, strongly suggesting that the 6mA signal observed is dependent on DNA damage and not caused by a potential bacterial contamination (Figure 3D, E, F). Additionally, our cell lines are routinely tested for bacterial contamination, specifically mycoplasma.

      (3) The single-cell imaging of 6mA in various cells is nice but must be confirmed by orthogonal approaches. PacBio would provide an alternative and quantitative approach to assessing 6mA levels. Similarly, it is unclear why the authors have not performed dot-blots of 6mA for genomic DNA from the given cell lines.

      We are confused by this point since an orthogonal approach to detect 6mA, mass spectrometry-liquid chromatography, was employed. This method does not use an antibody and confirms the increase of 6mA in DNA when cells were treated with DNA damaging agents. This data is presented in Figure 3F.

      It is sensible to hypothesize that the localization of 6mA is consistent with DNA replication (like uracil deposition). In this event, the genomic mapping of 6mA is likely to be stochastic. This would make quantification with PacBio sequencing difficult because it would be very challenging to achieve the appropriate read depth to call a modified base.

      Dot blots rely on an antibody and thus are not truly orthogonal to our immunofluorescence-based measurements. We preferred the mass spectrometry-liquid chromatography approach we took as a true orthogonal approach.

      (4) The results of Figure 3 need further investigation and validation. If the results are correct the authors are suggesting that the majority of 6mA in their cell lines is present in the DNA, and not the RNA, which is completely contrary to every other study of 6mA in mammalian cells that I am aware of. This could suggest that the antibody is not, in fact, binding to 6mA, but to unmodified adenine, which would explain why the signal disappears after DNAse treatment. Indeed, binding of 6mA to unmethylated DNA is a commonly known problem with most 6mA antibodies and is well described elsewhere.

      Based on this and the following comment, we are convinced that Reviewer #3 has overlooked two critical elements of our study:

      First, the immunofluorescence work presented in Figure 3, showing 6mA signal in response to DNA damage, uses cells that were pre-extracted to remove excess cytoplasmic RNA. This method is often used in immunofluorescence experiments of this kind. The pre-extraction method removes most of the cytoplasmic content, and the majority of the cytoplasmic m6A RNA signal. Supplementary Figure 3D shows cells that have not been pre-extracted prior to staining. These images show the cytoplasmic m6A signal is abundant if we do not perform the pre-extraction step.

      If the antibody used to label 6mA significantly reacted with unmodified adenine, we would expect a large signal in untreated or untreated and denatured conditions. In contrast, an increase in 6mA is not observed in either case.

      Second, the orthogonal approach we employed, mass spectrometry coupled with liquid chromatography, measures 6mA DNA analytes specifically by exact mass. This approach does not depend on an antibody and yields results consistent with those from the immunofluorescence experiments.

      (5) Given the lack of orthologous validation of the observed DNA 6mA and the lack of evidence supporting the presence of 6mA in mammalian DNA and consequently any functional role for 6mA in mammalian biology, the manuscript's conclusions need to be toned down significantly, and the inherent difficultly in assessing 6mA accurately in mammals acknowledged throughout.

      Typo above: “difficultly” should be “difficulty”.

      As discussed in response to prior comments, Figure 3 does provide two independent and orthologous methods that demonstrate 6mA presence in DNA specifically, and not RNA, in response to DNA damage. Complementary and orthogonal datasets are presented using either immunofluorescence microscopy or mass spectrometry-liquid chromatography of extracted DNA. The latter method does not rely on an antibody and can discriminate 6mA DNA versus RNA based on exact mass. We will revise the text to clarify that Figure 3F is a completely orthogonal approach.

    1. Author response:

      The following is the authors’ response to the original reviews.

      We thank the reviewers for their time and thoughtful comments. We believe that the further analyses suggested have made the results clearer and more robust. Below, we briefly highlight the key points addressed in the revision and the new evidence supporting them. Then, we address each reviewer’s critiques point-by-point.

      - Changes in variability with respect to time/experience

      Both reviewers #1 and #3 asked whether the variability in grid properties observed was dependent on time or experience. This is an important point, given that such a dependence on time could lead to interesting hypotheses about the underlying dynamics of the grid code. However, in the new analyses we performed, we do not observe changes in grid variability within a session (Fig S5 of the revised manuscript), suggesting that the grid variability seen is constant within the timescale of the data set.

      - The assumption of constant grid parameters in the literature

      Reviewer #2 pointed out that it had been appreciated by experimentalists that grid properties are variable within a module. We agree that we may have overstated the universality of this assumption in the original manuscript, and we have toned down the language in the revision. However, we note that many previous theoretical studies assumed these properties to be constant, within a given module. We provide some examples below, and have added evidence of this assertion, with citations to the theoretical literature, to the revised manuscript .

      - Additional sources of variability

      Reviewer #3 pointed out additional sources that might explain the variability observed in the paper (beyond time and experience). These sources include: field width, border location, and the impact of conjunctive cells. We have run additional analyses and have found no significant impact on the observed variability from any of these factors. We believe that these are important controls, and have added them to the manuscript (Fig S4-S7 of the revised manuscript)

      - Analysis of computational models

      Reviewer #3 noted that our results could be strengthened by performing similar analyses on the output of computational models of grid cells. This is a good idea. We have now measured the variability of grid properties in a recent normative recurrent neural network (RNN) model that develops grid cells when trained to perform path integration (Sorscher et al., 2019). This model has been shown to develop signatures of a 2D toroidal attractor (Sorscher et al., 2023) and achieves a high accuracy on a simple path integration task. Interestingly, the units with the greatest grid scores also exhibit a range of grid spacings and grid orientations (Fig S8 of the revised manuscript). Furthermore, by decreasing the amount of sparsity (through decreasing the weight decay regularization), we found an increase in the variability of the grid properties. This analysis demonstrates a heretofore unknown similarity between the RNN models trained to perform path integration and recorded grid cells from MEC. It additionally provides a framework for computational analysis of the emergence of grid property variability.

      Reviewer #1:

      (1) Is the variability in grid spacing and orientation that the authors found intrinsically organized or is it shaped by experience? Previous research has shown that grid representations can be modified through experience (e.g., Boccara et al., Science 2019). To understand the dynamics of the network, it would be important to investigate whether robust variability exists from the beginning of the task period (recording period) or whether variability emerges in an experience-dependent manner within a session.

      This is an interesting question that was not addressed in the paper. To test this, we performed additional analysis to resolve whether the variability changes across a session.

      Using a sliding window, we have measured changes in variability with respect to recording time (Fig S5A). To this end, we compute grid orientation and spacing over a time-window whose length is half the total length of the recording. From the population distribution of orientation and spacing values, we compute the standard deviation as a measure of variability. We repeat the same procedure, sliding the window forward until the variability for the second half of the recording is computed.

      We applied this approach to recording ID R12 (the same as in Figs 2-4) given that this recording session was significantly longer than the rest (nearly two hours). Results are shown in Fig S5B-C. For both orientation and spacing, no changes of variability with respect to time can be observed. Similar results were found for other modules (see caption of Fig S5 for statistics).

      We also note that the rats were already familiarized with the environment for 10-20 sessions prior to the recordings, so there may not be further learning during the period of the grid cell recordings. No changes in variability can be seen in Rat R across days (e.g., in Fig 5B R12 and R22 have similar distributions of variability). However, we note that it may be possible that there are changes in grid properties at time-scales greater than the recordings.

      (2) It is important to consider the optimal variability size. The larger the variability, the better it is for decoding. On the other hand, as the authors state in the

      Discussion, it is assumed that variability does not exist in the continuous attractor model. Although this study describes that it does not address how such variability fits the attractor theory, it would be better if more detailed ideas and suggestions were provided as to what direction the study could take to clarify the optimal size of variability.

      We appreciate this suggestion and agree that more discussion is warranted on how our results can be reconciled with previously observed attractor dynamics. To explore this, we studied the recurrent neural network (RNN) model from Sorscher et al. (2019), which develops grid responses when trained on path integration. This network has previously been found to develop signatures of toroidal topology (Sorscher et al., 2023), yet we find its grid responses also contain heterogeneity in grid properties (Fig S8). By decreasing the strength of the weight decay regularization (which leads to denser connectivity in the recurrent layer), we find an increase in the grid property variability. Interestingly, decreasing the weight decay regularization has been previously found to lead to weaker grid responses and worse ability of the RNN to perform path integration on environments larger than it was trained on. This approach not only provides preliminary evidence to our claim that too much variability can lead to weaker continuous attractor structure, but also provides a modeling framework with which future work can explore this question in more detail. We have added discussion of this issue to the manuscript text (Discussion).

      Reviewer #2:

      (1) Even though theoreticians might have gotten the mistaken impression that grid cells are highly regular, this might be due to an overemphasis on regularity in a subset of papers. Most experimentalists working with grid cells know that many if not most grid cells show high variability of firing fields within a single neuron, though this analysis focuses on between neurons. In response to this comment, the reviewers should tone down and modify their statements about what are the current assumptions of the field (and if possible provide a short supplemental section with direct quotes from various papers that have made these assumptions).

      We agree that some experimentalists are aware of variability in the recorded grid response patterns and that this work may not come as a complete surprise to them. We have toned down our language in the Introduction, changing “our results challenge a long-held assumption” to “our results challenge a frequently made assumption in the theoretical literature”. Additionally, we have added a caveat that “experimentalists have been aware” of the observed variability in grid properties.

      We would like to emphasize that the lack of work carefully examining the robustness of this variability has prevented a firm understanding of whether this is an inherent property of grid cells or due to measurement noise. The impact of this can be seen in theoretical neuroscience work where a considerable number of articles (including recent publications) start with the assumption that all grid cells within a module have identical properties, with the exception of phase shift and noise. We have now cited a number of these papers in the Introduction, to provide specific references. To further illustrate the pervasiveness of this assumption being explicitly made in theoretical neuroscience, below we provide quotes from a few important papers:

      “Cells with a common spatial period also share a common grid orientation; their responses differ only by spatial translations, or different preferred firing phases, with respect to their common response period” (Sreenivasan and Fiete, 2011)”

      “Grid cells are organized into discrete modules; within each module, the spatial scale and orientation of the grid lattice are the same, but the lattice for different cells is shifted in space.” (Stemmler et al., 2015)”

      “Recently, it was shown that grid cells are organized in discrete modules within which cells share the same orientation and periodicity but vary randomly in phase” (Wei et al., 2015)”

      “...cells within one module have receptive fields that are translated versions of one another, and different modules have firing lattices of different scales and orientations” (Dorrell et al., 2023)”

      In these works, this assumption is used to derive properties relating to the computational properties of grid cells (e.g., error correction, optimal scaling between grid spacings in different modules).

      In addition, since grid cells are assumed to be identical in the computational neuroscience community, there has been little work on quantifying how much variability a given model produces. This makes it challenging to understand how consistent different models are with our observations. This is illustrated in our analysis of a recent recurrent neural network (RNN) model of grid cells (Fig S8), which does exhibit variability.

      (2) The authors state that "no characterization of the degree and robustness of variability in grid properties within individual modules has been performed." It is always dangerous to speak in absolute terms about what has been done in scientific studies. It is true that few studies have had the number of grid cells necessary to make comparisons within and between modules, but many studies have clearly shown the distribution of spacing in neuronal data (e.g. Hafting et al., 2005; Barry et al., 2007; Stensola et al., 2012; Hardcastle et al., 2015) so the variability has been visible in the data presentations. Also, most researchers in the field are well aware that highly consistent grid cells are much rarer than messy grid cells that have unevenly spaced firing fields. This doesn't hurt the importance of the paper, but they need to tone down their statements about the lack of previous awareness of variability (specific locations are noted in the specific comments).

      We have toned down our language in the Introduction. However, we note that our point that no detailed analysis had been done on measuring the robustness of this variability stands. Thus, for the general community, it has not been clear whether this previously observed variability is noise or a real feature of the grid code.

      (3) The methods section needs to have a separate subheading entitled: How grid cells were assigned to modules" that clearly describes how the grid cells were assigned to a module (i.e. was this done by Gardner et al., or done as part of this paper's post-processing?

      We thank the reviewer for pointing out this missing information. We have added a new subsection in the Materials and Methods section, entitled “Grid module classification” to clarify how the grid cells are assigned to modules. In short, this was done by Gardner et al. (2022) using an unsupervised clustering approach that was viewed as enabling a less biased identification of modules. We did not perform any additional processing steps on module identity.

      Reviewer #3:

      (1) One possible explanation of the dispersion in lambda (not in theta) could be variability in the typical width of the field. For a fixed spacing, wider fields might push the six fields around the center of the autocorrelogram toward the outside, depending on the details of how exactly the position of these fields is calculated. We recommend authors show that lambda does not correlate with field width, or at least that the variability explained by field width is smaller than the overall lambda variability.

      We agree that this option had not been carefully ruled out by our previous analyses. To tackle this question, we compute the field width of a given cell using the value at the minima of its spatial autocorrelogram (Fig S4A-B). For all cells in recording ID R12, there is a non-significant negative linear correlation between grid field width and between-cell variability (Fig S4C) . The variability explained by the width of the field is 4% of the variability, as indicated by the R<sup>2</sup> value of the linear fit. Similar results were found for all other modules (see caption of Fig S4C for statistics). Therefore, we do not think that grid field width explains spacing variability.

      (2) An alternative explanation could be related to what happens at the borders. The authors tackle this issue in Figure S2 but introduce a different way of measuring lambda based on three fields, which in our view is not optimal. We recommend showing that the dispersions in lambda and theta remain invariant as one removes the border-most part of the maps but estimating lambda through the autocorrelogram of the remaining part of the map. Of course, there is a limit to how much can be removed before measures of lambda and theta become very noisy.

      We have performed additional analysis to explore the role of borders in grid property variability. To do so, we have followed the suggestion by the reviewer and have re-analyzed grid properties from the autocorrelogram when the border-most part of the maps are removed (Fig S6A-B). For all modules, we do not see any changes in variability (computed as the standard deviation of the population distribution) for either orientation or spacing. As predicted by the reviewer, after removing about 25% of the border-most part of the environment we start seeing changes in variability, as measures of theta and lambda become noisy and computed over a smaller spatial range. This result holds for all other modules (Fig S6C-D).

      (3) A third possibility is slightly more tricky. Some works (for example Kropff et al, 2015) have shown that fields anticipate the rat position, so every time the rat traverses them they appear slightly displaced opposite to the direction of movement. The amount of displacement depends on the velocity. Maps that we construct out of a whole session should be deformed in a perfectly symmetric way if rats traverse fields in all directions and speeds. However, if the cell is conjunctive, we would expect a deformation mainly along the cell's preferred head direction. Since conjunctive cells have all possible preferred directions, and many grid cells are not conjunctive at all, this phenomenon could create variability in theta and lambda that is not a legitimate one but rather associated with the way we pool data to construct maps. To rule away this possibility, we recommend the authors study the variability in theta and lambda of conjunctive vs non-conjunctive grid cells. If the authors suspect that this phenomenon could explain part of their results, they should also take into account the findings of Gerlei and colleagues (2020) from the Nolan lab, that add complexity to this issue.

      We appreciate the reviewer pointing out the possible role conjunctive cells may play. To investigate how conjunctive cells may affect the observed grid property variability, we have performed additional analyses taking into account if the grid cells included in the study are conjunctive. Comparing within- and between-cell variability of conjunctive vs. non-conjunctive cells in recording R12, we do not see any qualitative differences for either orientation or spacing (Fig S7A-B). When excluding conjunctive cells from the between-variability comparison, we do not see any significant difference compared to when these cells are included (Fig S7C-D). As such, it does not appear that conjunctive cells are the source of variability in the population.

      We further note that the number of putative conjunctive cells varied across modules and recordings. For instance, in recording Q1 and Q2, Gardner et al. (2022) reported 3 (out of 97) and 1 (out of 66) conjunctive cells, respectively. Given that we see variability robustly across recordings (Fig 5), we do not believe that conjunctive cells can explain the presence of variability we observe.

      (4) The results in Figure 6 are correct, but we are not convinced by the argument. The fact that grid cells fire in the same way in different parts of the environment and in different environments is what gives them their appeal as a platform for path integration since displacement can be calculated independently of the location of the animal. Losing this universal platform is, in our view, too much of a price to pay when the only gain is the possibility of decoding position from a single module (or non-adjacent modules) which, as the authors discuss, is probably never the case. Besides, similar disambiguation of positions within the environment would come for free by adding to the decoding algorithm spatial cells (non-hexagonal but spatially stable), which are ubiquitous across the entorhinal cortex. Thus, it seems to us that - at least along this line of argumentation - with variability the network is losing a lot but not gaining much.

      We agree that losing the continuous attractor network (CAN) structure and the ability to path integrate would be a very large loss. However, we do not believe that the variability we observe necessarily destroys either the CAN or path integration. We argue this for two reasons. First, the data we analyzed [from Gardner et al. (2022)] is exactly the data set that was found to have toroidal topology and therefore viewed to be consistent with a major prediction of CANs. Thus, the amount of variability in grid properties does not rule out the underlying presence of a continuous attractor. Second, path integration may still be possible with grid cells that have variable properties. To illustrate this, we analyzed data from Sorscher et al. (2019) recurrent neural network model (RNN) that was trained explicitly on path integration, and found that the grid representations that emerged had variability in spacing and orientation (see point #6 below).

      (5) In Figure 4 one axis has markedly lower variability. Is this always the same axis? Can the authors comment more on this finding?

      We agree that in Fig 4 the first axis has lower variability. We believe that this is specific to the module R12 and does not reflect any differences in axis or bias in the methods used to compute the axis metrics. To test this, we have performed the same analyses for other modules, finding that other recordings do not exhibit the same bias. Results for the modules with the most cells are shown below (Author response image 1).

      Author response image 1.

      Grid propertied along Axis 1 are not less variable for many recorded grid modules. Same as Fig.4C-D, but for four other recorded modules. Note that the variability along each axis is similar.

      (6) The paper would gain in depth if maps coming out of different computational models could be analyzed in the same way.

      We agree with the reviewer that examining computational models using the same approach would strengthen our results and we appreciate the suggestion. To address this, we have analyzed the results from a previous normative model for grid cells [Sorscher et al., (2019)] that trained a recurrent neural network (RNN) model to perform path integration and found that units developed grid cell like responses. These models have been found to exhibit signatures of toroidal attractor dynamics [Sorscher et al. (2023)] and exhibit a diversity of responses beyond pure grid cells, making them a good starting point for understanding whether models of MEC may contain uncharacterized variability in grid properties.

      We find that RNN units in these normative models exhibit similar amounts of variability in grid spacing and orientation as observed in the real grid cell recordings (Fig S8A-D). This provides additional evidence that this variability may be expected from a normative framework, and that the variability does not destroy the ability to path integrate (which the RNN is explicitly trained to perform).

      The RNN model offers possibilities to assess what might cause this variability. While we leave a detailed investigation of this to future work, we varied the weight decay regularization hyper-parameter. This value controls how sparse the weights in the hidden recurrent layer are. Large weight decay regularization strength encourages sparser connectivity, while small weight decay regularization strength allows for denser connectivity. We find that increasing this penalty (and enforcing sparser connectivity) decreases the variability of grid properties (Fig S8E-F). This suggests that the observed variability in the Gardner et al. (2022) data set could be due to the fact that grid cells are synaptically connected to other, non-grid cells in MEC.

      (7) Similarly, it would be very interesting to expand the study with some other data to understand if between-cell delta_theta and delta_lambda are invariant across environments. In a related matter, is there a correlation between delta_theta (delta_lambda) for the first vs for the second half of the session? We expect there should be a significant correlation, it would be nice to show it.

      We agree this would be interesting to examine. For this analysis, it is essential to have a large number of grid cells, and we are not aware of other published data sets with comparable cell numbers using different environments.

      Using a sliding window analysis, we have characterized changes in variability with respect to the recording time (Figure S5A). To do so, we compute grid orientation and spacing over a time-window whose length is half of the total length of the recording. From the population distribution of orientation and spacing values, we compute the standard deviation as a measure of between-cell variability. We repeat the same procedure, sliding the window forward until the variability for the second half of the recording is computed.

      We applied this approach to recording ID R12 (the same as in Figs 2-4) given that this recording session was significantly longer than the rest (almost two hours). Results are shown in Fig S5 B-C. For both orientation and spacing, no systematic changes of variability with respect to time were observed. Similar results were found for other modules (see caption of Fig S5 for statistics).

      We also note that the rats were already familiarized with the environment for 10-20 sessions prior to the recordings, so there may not be further learning during the period of the grid cell recordings. No changes in variability can be seen in Rat R across days (e.g., in Fig 5B R12 and R22 have similar distributions of variability). However, we note that it may be possible that there are changes in grid properties at time-scales greater than the recordings.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review): 

      Hotinger et al. explore the population dynamics of Salmonella enterica serovar Typhimurium in mice using genetically tagged bacteria. In addition to physiological observations, pathology assessments, and CFU measurements, the study emphasizes quantifying host bottleneck sizes that limit Salmonella colonization and dissemination. The authors also investigate the genetic distances between bacterial populations at various infection sites within the host.

      Initially, the study confirms that pretreatment with the antibiotic streptomycin before inoculation via orogastric gavage increases the bacterial burden in the gastrointestinal (GI) tract, leading to more severe symptoms and heightened fecal shedding of bacteria. This pretreatment also significantly reduces between-animal variation in bacterial burden and fecal shedding. The authors then calculate founding population sizes across different organs, discovering a severe bottleneck in the intestine, with founding populations reduced by approximately 10^6-fold compared to the inoculum size. Streptomycin pretreatment increases the founding population size and bacterial replication in the GI tract. Moreover, by calculating genetic distances between populations, the authors demonstrate that, in untreated mice, Salmonella populations within the GI tract are genetically dissimilar, suggesting limited exchange between colonization sites. In contrast, streptomycin pretreatment reduces genetic distances, indicating increased exchange.

      In extraintestinal organs, the bacterial burden is generally not substantially increased by streptomycin pretreatment, with significant differences observed only in the mesenteric lymph nodes and bile. However, the founding population sizes in these organs are increased. By comparing genetic distances between organs, the authors provide evidence that subpopulations colonizing extraintestinal organs diverge early after infection from those in the GI tract. This hypothesis is further tested by measuring bacterial burden and founding population sizes in the liver and GI tract at 5 and 120 hours post-infection. Additionally, they compare orogastric gavage infection with the less injurious method of infection via drinking, finding similar results for CFUs, founding populations, and genetic distances. These results argue against injuries during gavage as a route of direct infection. 

      To bypass bottlenecks associated with the GI tract, the authors compare intravenous (IV) and intraperitoneal (IP) routes of infection. They find approximately a 10-fold increase in bacterial burden and founding population size in immune-rich organs with IV/IP routes compared to orogastric gavage in streptomycin-pretreated animals. This difference is interpreted as a result of "extra steps required to reach systemic organs."

      While IP and IV routes yield similar results in immune-rich organs, IP infections lead to higher bacterial burdens in nearby sites, such as the pancreas, adipose tissue, and intraperitoneal wash, as well as somewhat increased founding population sizes. The authors correlate these findings with the presence of white lesions in adipose tissue. Genetic distance comparisons reveal that, apart from the spleen and liver, IP infections lead to genetically distinct populations in infected organs, whereas IV infections generally result in higher genetic similarity. 

      Finally, the authors investigate GI tract reseeding, identifying two distinct routes. They observe that the GI tracts of IP/IV-infected mice are colonized either by a clonal or a diversely tagged bacterial population. In clonally reseeded animals, the genetic distance within the GI tract is very low (often zero) compared to the bile population, which is predominantly clonal or pauciclonal. These animals also display pathological signs, such as cloudy/hardened bile and increased bacterial burden, leading the authors to conclude that the GI tract was reseeded by bacteria from the gallbladder bile. In contrast, animals reseeded by more complex bacterial populations show that bile contributes only a minor fraction of the tags. Given the large founding population size in these animals' GI tracts, which is larger than in orogastrically infected animals, the authors suggest a highly permissive second reseeding route, largely independent of bile. They speculate that this route may involve a reversal of known mechanisms that the pathogen uses to escape from the intestine. 

      The manuscript presents a substantial body of work that offers a meticulously detailed understanding of the population dynamics of S. Typhimurium in mice. It quantifies the processes shaping the within-host dynamics of this pathogen and provides new insights into its spread, including previously unrecognized dissemination routes. The methodology is appropriate and carefully executed, and the manuscript is well-written, clearly presented, and concise. The authors' conclusions are well-supported by experimental results and thoroughly discussed. This work underscores the power of using highly diverse barcoded pathogens to uncover the within-host population dynamics of infections and will likely inspire further investigations into the molecular mechanisms underlying the bottlenecks and dissemination routes described here.

      Major point:

      Substantial conclusions in the manuscript rely on genetic distance measurements using the Cavalli-Sforza chord distance. However, it is unclear whether these genetic distance measurements are independent of the founding population size. I would anticipate that in populations with larger founding population sizes, where the relative tag frequencies are closer to those in the inoculum, the genetic distances would appear smaller compared to populations with smaller founding sizes independent of their actual relatedness. This potential dependency could have implications for the interpretation of findings, such as those in Figures 2B and 2D, where antibiotic-pretreated animals consistently exhibit higher founding population sizes and smaller genetic distances compared to untreated animals.

      Thank you for raising this important point regarding reliance on cord distances for gauging genetic distance in barcoded populations. The reviewer is correct that samples with more founders will be more similar to the inoculum and thus inherently more similar to other samples that also have more founders. However, creation of libraries containing very large numbers of unique barcodes can often circumvent this issue. In this case, the effect size of chance-based similarity is not large enough to change the interpretation of the data in Figures 2B and 2D. In our case, the library has ~6x10<sup>4</sup> barcodes, and the founding populations in Figure 2B are ~10<sup>3</sup>. Randomly resampling to create two populations of 10<sup>3</sup> cells from an initial population with 6x10<sup>4</sup> barcodes is expected to yield largely distinct populations with very little similarity. Thus, the similarity between streptomycin-treated populations in Figure 2D is likely the result of biology rather than chance.  

      Reviewer #2 (Public review):

      In this paper, Hotinger et. al. propose an improved barcoded library system, called STAMPR, to study Salmonella population dynamics during infection. Using this system, the authors demonstrate significant diversity in the colonization of different Salmonella clones (defined by the presence of different barcodes) not only across different organs (liver, spleen, adipose tissues, pancreas, and gall bladder) but also within different compartments of the same gastrointestinal tissue. Additionally, this system revealed that microbiota competition is the major bottleneck in Salmonella intestinal colonization, which can be mitigated by streptomycin treatment. However, this has been demonstrated previously in numerous publications. They also show that there was minimal sharing between populations found in the intestine and those in the other organs. Upon IV and IP infection to bypass the intestinal bottleneck, they were able to demonstrate, using this library, that Salmonella can renter the intestine through two possible routes. One route is essentially the reverse path used to escape the gut, leading to a diverse intestinal population; while the other, through the bile, typically results in a clonal population. Although the authors showed that the STAMPR pipeline improved the ability to identify founder populations and their diversity within the same animal during infections, some of the conclusions appear speculative and not fully supported.

      (1) It's particularly interesting how the authors, using this system, demonstrate the dominant role of the microbiota bottleneck in Salmonella colonization and how it is widened by antibiotic treatment (Figure 1). Additionally, the ability to track Salmonella reseeding of the gut from other organs starting with IV and IP injections of the pathogen provides a new tool to study population dynamics (Figure 5). However, I don't think it is possible to argue that the proximal and distal small intestine, Peyer's patches (PPs), cecum, colon, and feces have different founder populations for reasons other than stochastic variations. All the barcoded Salmonella clones have the same fitness and the fact that some are found or expanded in one region of the gastrointestinal tract rather than another likely results from random chance - such as being forced in a specific region of the gut for physical or spatial reasons-and subsequent expansion, rather than any inherent biological cause. For example, some bacteria may randomly adhere to the mucus, some may swim toward the epithelial layer, while others remain in the lumen; all will proliferate in those respective sites. In this way, different founder populations arise based on random localization during movement through the gastrointestinal tract, which is an observation, but it doesn't significantly contribute to understanding pathogen colonization dynamics or pathogenesis. Therefore, I would suggest placing less emphasis on describing these differences or better discussing this aspect, especially in the context of the gastrointestinal tract.

      Thank you for helping us identify this area for further clarification. We agree with the reviewer’s interpretation that seeding of proximal and distal small intestine, Peyer's patches (PPs), cecum, colon, and feces with different founder populations is likely caused by stochastic variations, consistent with separate stochastic bottlenecks to establishing these separate niches. To clarify this point we have modified the text in the results section, “Streptomycin treatment decreases compartmentalization of S. Typhimurium populations within the intestine”.

      Change to text:

      “Except for the cecum and colon, in untreated animals the S. Typhimurium populations in different regions of the intestine were dissimilar (Avg. GD ranged from 0.369 to 0.729, 2D left); i.e., there is little sharing between populations in the intestine. These data suggest that there are separate bottlenecks in different regions of the intestine that cause stochastic differences in the identity of the founders. Interestingly, when these founders replicate, they do not mix, remaining compartmentalized with little sharing between populations throughout the intestinal tract (i.e., barcodes found in one region are not in other regions, Figure S3). This was surprising as the luminal contents, an environment presumably conducive to bacterial movement, were not removed from these samples.”

      In this section we are interested in the underlying biology that occurs after the initial bottleneck to preserve this compartmentalization during outgrowth of the intestinal population. In other words, what prevents these separate populations from merging (e.g., what prevents the bacteria replicating in the proximal small intestine from traveling through the intestine and establishing a niche in the distal small intestine)? While we do not explore the mechanisms of compartmentalization, we observe that it is disrupted by streptomycin pretreatment, suggesting a microbiota-dependent biological cause. 

      (2) I do think that STAMPR is useful for studying the dynamics of pathogen spread to organs where Salmonella likely resides intracellularly (Figure 3). The observation that the liver is colonized by an early intestinal population, which continues to proliferate at a steady rate throughout the infection, is very interesting and may be due to the unique nature of the organ compared to the mucosal environment. What is the biological relevance during infection? Do the authors observe the same pattern (Figures 3C and G) when normalizing the population data for the spleen and mesenteric lymph nodes (mLN)? If not, what do the authors think is driving this different distribution?

      Thank you for raising this interesting point. These data indicate that the liver is seeded from the intestine early during infection. The timing and source of dissemination have relevance for understanding how host and pathogen variables control the spread of bacteria to systemic sites. For example, our conclusion (early dissemination) indicates that the immune state of a host at the time of exposure to a pathogen, and for a short period thereafter, are what primarily influence the process of dissemination, not the later response to an active infection. 

      We observe that the liver and mucosal environments within the intestine have similar colonization behaviors. Both niches are seeded early during infection, followed by steady pathogen proliferation and compartmentalization that apparently inhibits further seeding. This results in the identity of barcodes in the liver population remaining distinct from the intestinal populations, and the intestinal populations remaining distinct from each other.

      We observe a similar pattern to the liver in the spleen and MLN (the barcodes in the spleen and MLN are dissimilar to the population in the intestine). To clarify this point, we have modified the text (below) and added this analysis as a supplemental figure (S4).

      Change to text:

      Genetic distance comparison of liver samples to other sites revealed that, regardless of streptomycin treatment, there was very little sharing of barcodes between the intestine and extraintestinal sites (Avg. GD >0.75, Figure 3C). Furthermore, the MLN and spleen populations also lacked similarity with the intestine (Figure S4). These analyses strongly support the idea that S. Typhimurium disseminates to extraintestinal organs relatively early following inoculation, before it establishes a replicative niche in the intestine.

      (3) Figure 6: Could the bile pathology be due to increased general bacterial translocation rather than Salmonella colonization specifically? Did the authors check for the presence of other bacteria (potentially also proliferating) in the bile? Do the authors know whether Salmonella's metabolic activity in the bile could be responsible for gallbladder pathology?

      The reviewer raises interesting points for future work. We did not check whether other bacterial species are translocating during S. Typhimurium infection. The relevance of Salmonella’s metabolic activity is also very interesting, and we hope these questions will be answered by future studies.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Minor points:

      (1) P. 9/10 "... the marked delay in shedding after IP and IV relative to orogastric inoculation suggest that the S. Typhimurium population encounters substantial bottleneck(s) on the route(s) from extraintestinal sites back to the intestine.": Can you conclude that from the data? It could also be possible that there is a biological mechanism (other than chance events) that delays the re-entry to the intestine.

      We propose that the delay in shedding indicates additional obstacles that bacteria face when re-entering the intestine, and that there are likely biological mechanisms that cause this delay. However, these unknown mechanisms effectively act as additional bottlenecks by causing a stochastic loss of population diversity. 

      (2) P. 11 "...both organs would likely contain all 10 barcodes. In contrast, a library with 10,000 barcodes can be used to distinguish between a bottleneck resulting in Ns = 1,000 and Ns = 10,000, since these bottlenecks result in a different number of barcodes in output samples. Furthermore, high diversity libraries reduce the likelihood that two tissue samples share the same barcode(s) due to random chance, enabling more accurate quantification of bacterial dissemination.": I agree with the general analysis, but I find it misleading to talk about the presence of barcodes when the analyses in this manuscript are based on the much more powerful comparison of relative abundance of individual tags instead of their presence or absence.

      The reviewer raises an excellent point, and the distinction between relative abundance versus presence/absence is discussed extensively in the original STAMPR manuscript. Although relative abundance is powerful, the primary metric used in this study (Ns) is calculated principally from the number of barcodes, corrected (via simulations) for the probability of observing the same barcode across distinct founders. Although this correction procedure does rely on barcode abundance, the primary driver of founding population quantification is the number of barcodes.

      (3) P.14 "the library in LB supplemented with SM was not significantly different than the parent strain" and Figure 2C: How was significance tested? How many times were the growth curves recorded? On my print-out, the red color has different shades for different growth curves.

      Significance was tested with a Mann-Whitney and growth curves were performed 5 times. Growth curves are displayed with 50% opacity, and as a result multiple curves directly on top of each other appear darker. The legend to S2 has been modified accordingly.

      (4) P.16: close bracket in the equation for FRD calculation.

      Done

      (5) Figure 2C "Average CFU per founder": I found the wording confusing at first as I thought you divided the average bacterial burden per organ by Ns, instead of averaging the CFU/Ns calculated for each mouse.

      The wording has been clarified. 

      (6) Figure 3B: It would be helpful to include expected genetic distances in the schematic as it is difficult to infer the genetic distance when only two of three, respectively, different "barcode colors" are used. While I find the explanation in the main text intuitive, a graphical representation would have helped me.

      Thank you for the suggestion. Unfortunately, using colors to represent barcodes is imperfect and limits the diversity that can be depicted. We have modified Figure 3B to further clarify. 

      (7) Figure 3C: Why do you compare the genetic distance to the liver, when you discuss the genetic distance of the intestinal population? Is it not possible that the intestinal populations are similar to the extraintestinal organs except the liver?

      For clarity, we chose to highlight exclusively the liver. However, we observed a similar pattern to the liver in other extraintestinal organs. To clarify the generalizability of this point we have added a supplemental figure with comparisons to MLN and Spleen (Supplemental figure S4) as well as further text.

      (8) Figure 3C & S5A: I found "+SM" and "+SM, Drinking" confusing and would have preferred "+SM, Gavage" and "+SM, Drinking" for clarity.

      Done, thank you for the suggestion.

      (9) Figure 3G&H: I find it worthy of discussion that the bacterial burden increases over time, while the founding population decreases. Does that not indicate that replication only occurs at specific sites leading to the amplification of only a few barcodes and thereby a larger change of the relative barcode abundance compared to the inoculum?

      From 5h to 120h the size of the founding population decreases in multiple intestinal sites. This likely indicates that the impact of the initial bottleneck is still ongoing at 5h, although further temporal analysis would be required to define the exact timing of the bottleneck. Notably, the passage time through the mouse intestine is ~5h. Many of the founders observed at 5h could be a population that will never establish a replicative niche, and failing to colonize be shed in the feces, bottlenecking the population between 5h and 120h. To clarify this point we have added the following text:

      Section “S. Typhimurium disseminates out of the intestine before establishing an intestinal replicative niche”.

      “In contrast to the liver, there were more founders present in samples from the intestine (particularly in the colon) at 5 hours versus 120 hours (Figure 3H). These data likely indicate that many of the founders observed in the intestine at 5 hours are shed in the feces prior to establishing a replicative niche, and demonstrates that the forces restricting the S. Typhimurium population in the intestine act over a period of > 5 hours.”  

      (10) Figure S2A: I do not understand this figure. Why are there more than 70.000 tags listed? I was under the impression the barcode library in S. Typhimurium had 55.000 tags while only the plasmid pSM1 had more than 70.000 (but the plasmid should not be relevant here). Why are there distinct lines at approximately 10^-5 and a bit lower? I would have expected continuously distributed barcode frequencies.

      During barcode analysis, each library is mapped to the total barcode list in the barcode donor pSM1, which contains ~70,000 barcodes. This enables consistent analysis across different bacterial libraries. The designation “barcode number” refers to the barcode number in pSM1, meaning many of the barcodes in the Salmonella library are at zero reads. This graph type was chosen to show there was no bias toward a particular barcode, however there is significant overlap of the points, making individual barcode frequencies difficult to see. We have changed the x-axis to state “pSM1 Barcode Number” and clarified in the figure legend.

      Since the y-axes on these graphs is on a log10 scale, the lines represent barcodes with 1 read, 2 reads, 3 reads, etc. As the number of reads per barcode increases linearly, the space between them decreases on logarithmic axes.

      (11) There are a few typos in the figure legends of the supplementary material. For example Figure S2: S. Typhimurium not italicized, ~7x105 no superscript. Fig. S4&5 ", Open circles" is "O" is capitalized.

      Typos have been corrected.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      This is an interesting manuscript where the authors systematically measure rG4 levels in brain samples at different ages of patients affected by AD. To the best of my knowledge this is the first time that BG4 staining is used in this context and the authors provide compelling evidence to show an association with BG4 staining and age or AD progression, which interestingly indicates that such RNA structure might play a role in regulating protein homeostasis as previously speculated. The methods used and the results reported seems robust and reproducible. There were two main things that needed addressing:

      (1) Usually in BG4 staining experiments to ensure that the signal detected is genuinely due to rG4 an RNase treatment experiment is performed. This does not have to be extended to all the samples presented but having a couple of controls where the authors observe loss of staining upon RNase treatment will be key to ensure with confidence that rG4s are detected under the experimental conditions. This is particularly relevant for this brain tissue samples where BG4 staining has never been performed before.

      (2) The authors have an association between rG4-formation and age/disease progression. They also observe distribution dependency of this, which is great. However, this is still an association which does not allow the model to be supported. This is not something that can be fixed with an easy experiment and it is what it is, but my point is that the narrative of the manuscript should be more fair and reflect the fact that, although interesting, what the authors are observing is a simple correlation. They should still go ahead and propose a model for it, but they should be more balanced in the conclusion and do not imply that this evidence is sufficient to demonstrate the proposed model. It is absolutely fine to refer to the literature and comment on the fact that similar observations have been reported and this is in line with those, but still this is not an ultimate demonstration.

      Comments on current version:

      The authors have now addressed my concerns.

      We thank the reviewer for their support!

      Reviewer #2 (Public review):

      RNA guanine-rich G-quadruplexes (rG4s) are non-canonical higher order nucleic acid structures that can form under physiological conditions. Interestingly, cellular stress is positively correlated with rG4 induction.

      In this study, the authors examined human hippocampal postmortem tissue for the formation ofrG4s in aging and Alzheimer Disease (AD). rG4 immunostaining strongly increased in the hippocampus with both age and with AD severity. 21 cases were used in this study (age range 30-92).

      This immunostaining co-localized with hyper-phosphorylated tau immunostaining in neurons. The BG4 staining levels were also impacted by APOE status. rG4 structure was previously found to drive tau aggregation. Based on these observations, the authors propose a model of neurodegeneration in which chronic rG4 formation drives proteostasis collapse.

      This model is interesting, and would explain different observations (e.g., RNA is present in AD aggregates and rG4s can enhance protein oligomerization and tau aggregation).

      Main issue from the previous round of review:

      There is indeed a positive correlation between Braak stage severity and BG4 staining, but this correlation is relatively weak and borderline significant ((R = 0.52, p value = 0.028). This is probably the main limitation of this study, which should be clearly acknowledged (together with a reminder that "correlation is not causality"). Related to this, here is no clear justification to exclude the four individuals in Fig 1d (without them R increases to 0.78). Please remove this statement. On the other hand, the difference based on APOE status is more striking.

      Comments on current version:

      The authors have made laudable efforts to address the criticisms I made in my evaluation of the original manuscript.

      We thank the reviewer for their support!

      Recommendations for the authors:

      Reviewing Editor:

      I would suggest two minor edits:

      - The findings are correlative and descriptive, but the title implies functionality (A New Role for RNA G-quadruplexes in Aging and Alzheimer′s Disease). I would suggest toning down this title).

      - While I understand the limitations in performing additional biochemical experiments to validate the immunofluorescence study, I think this is worth mentioning as a limitation in the text.

      We have made these two changes as requested, altering the title to remove the word Role that may imply more meaning than intended, and adding a line to the discussion on the need for future additional biochemical experiments.

      Reviewer #1 (Recommendations for the authors):

      Thanks for addressing the concerns raised.

      We thank the reviewer for their support!

      Reviewer #2 (Recommendations for the authors):

      Minor point:

      Related to the "correlation is not causality" remark I made in my evaluation of the original manuscript: the authors' answer is reasonable. Still, I would suggest to modify the abstract: "we propose a model of neurodegeneration in which chronic rG4 formation drives proteostasis collapse" => "we propose a model of neurodegeneration in which chronic rG4 formation is linked to proteostasis collapse"

      All other remarks I made have been answered properly.

      We thank the reviewer for their support! We have made the change exactly as requested by the reviewer.

    1. Author response:

      Reviewer #1 (Public review):

      Summary:

      The manuscript investigates lipid scrambling mechanisms across TMEM16 family members using coarse-grained molecular dynamics (MD) simulations. While the study presents a statistically rigorous analysis of lipid scrambling events across multiple structures and conformations, several critical issues undermine its novelty, impact, and alignment with experimental observations.

      Critical issues:

      (1) Lack of Novelty:

      The phenomenon of lipid scrambling via an open hydrophilic groove is already well-established in the literature, including through atomistic MD simulations. The authors themselves acknowledge this fact in their introduction and discussion. By employing coarse-grained simulations, the study essentially reiterates previously known findings with limited additional mechanistic insight. The repeated observation of scrambling occurring predominantly via the groove does not offer significant advancement beyond prior work.

      We agree with the reviewer’s statement regarding the lack of novelty when it comes to our observations of scrambling in the groove of open Ca<sup>2+</sup>-bound TMEM16 structures. However, we feel that the inclusion of closed structures in this study, which attempts to address the yet unanswered question of how scrambling by TMEM16s occurs in the absence of Ca<sup>2+</sup>, offers new observations for the field. In our study we specifically address to what extent the induced membrane deformation, which has been theorized to aid lipids cross the bilayer especially in the absence of Ca<sup>2+</sup>, contributes to the rate of scrambling (see references 36, 59, and 66). There are also several TMEM16F structures solved under activating conditions (bound to Ca<sup>2+</sup> and in the presence of PIP2) which feature structural rearrangements to TM6 that may be indicative of an open state (PDB 6P48) and had not been tested in simulations. We show that these structures do not scramble and thereby present evidence against an out-of-the-groove scrambling mechanism for these states. Although we find a handful of examples of lipids being scrambled by Ca<sup>2+</sup>-free structures of TMEM16 scramblases, none of our simulations suggest that these events are related to the degree of deformation.

      (2) Redundancy Across Systems:

      The manuscript explores multiple TMEM16 family members in activating and non-activating conformations, but the conclusions remain largely confirmatory. The extensive dataset generated through coarse-grained MD simulations primarily reinforces established mechanistic models rather than uncovering fundamentally new insights. The effort, while statistically robust, feels excessive given the incremental nature of the findings.

      Again, we agree with the reviewer’s statement that our results largely confirm those published by other groups and our own. We think there is however value in comparing the scrambling competence of these TMEM16 structures in a consistent manner in a single study to reduce inconsistencies that may be introduced by different simulation methods, parameters, environmental variables such as lipid composition as used in other published works of single family members. The consistency across our simulations and high number of observed scrambling events have allowed us to confirm that the mechanism of scrambling is shared by multiple family members and relies most obviously on groove dilation.

      (3) Discrepancy with Experimental Observations:

      The use of coarse-grained simulations introduces inherent limitations in accurately representing lipid scrambling dynamics at the atomistic level. Experimental studies have highlighted nuances in lipid permeation that are not fully captured by coarse-grained models. This discrepancy raises questions about the biological relevance of the reported scrambling events, especially those occurring outside the canonical groove.

      We thank the reviewer for bringing up the possible inaccuracies introduced by coarse graining our simulations. This is also a concern for us, and we address this issue extensively in our discussion. As the reviewer pointed out above, our CG simulations have largely confirmed existing evidence in the field which we think speaks well to the transferability of observations from atomistic simulations to the coarse-grained level of detail. We have made both qualitative and quantitative comparisons between atomistic and coarse-grained simulations of nhTMEM16 and TMEM16F (Figure 1, Figure 4-figure supplement 1, Figure 4-figure supplement 5) showing the two methods give similar answers for where lipids interact with the protein, including outside of the canonical groove. We do not dispute the possible discrepancy between our simulations and experiment, but our goal is to share new nuanced ideas for the predicted TMEM16 scrambling mechanism that we hope will be tested by future experimental studies.

      (4) Alternative Scrambling Sites:

      The manuscript reports scrambling events at the dimer-dimer interface as a novel mechanism. While this observation is intriguing, it is not explored in sufficient detail to establish its functional significance. Furthermore, the low frequency of these events (relative to groove-mediated scrambling) suggests they may be artifacts of the simulation model rather than biologically meaningful pathways.

      We agree with the reviewer that our observed number of scrambling events in the dimer interface is too low to present it as strong evidence for it being the alternative mechanism for Ca<sup>2+</sup>-independent scrambling. This will require additional experiments and computational studies which we plan to do in future research. However, we are less certain that these are artifacts of the coarse-grained simulation system as we observed a similar event in an atomistic simulation of TMEM16F.

      Conclusion:

      Overall, while the study is technically sound and presents a large dataset of lipid scrambling events across multiple TMEM16 structures, it falls short in terms of novelty and mechanistic advancement. The findings are largely confirmatory and do not bridge the gap between coarse-grained simulations and experimental observations. Future efforts should focus on resolving these limitations, possibly through atomistic simulations or experimental validation of the alternative scrambling pathways.

      Reviewer #2 (Public review):

      Summary:

      Stephens et al. present a comprehensive study of TMEM16-members via coarse-grained MD simulations (CGMD). They particularly focus on the scramblase ability of these proteins and aim to characterize the "energetics of scrambling". Through their simulations, the authors interestingly relate protein conformational states to the membrane's thickness and link those to the scrambling ability of TMEM members, measured as the trespassing tendency of lipids across leaflets. They validate their simulation with a direct qualitative comparison with Cryo-EM maps.

      Strengths:

      The study demonstrates an efficient use of CGMD simulations to explore lipid scrambling across various TMEM16 family members. By leveraging this approach, the authors are able to bypass some of the sampling limitations inherent in all-atom simulations, providing a more comprehensive and high-throughput analysis of lipid scrambling. Their comparison of different protein conformations, including open and closed groove states, presents a detailed exploration of how structural features influence scrambling activity, adding significant value to the field. A key contribution of this study is the finding that groove dilation plays a central role in lipid scrambling. The authors observe that for scrambling-competent TMEM16 structures, there is substantial membrane thinning and groove widening. The open Ca<sup>2+</sup>-bound nhTMEM16 structure (PDB ID 4WIS) was identified as the fastest scrambler in their simulations, with scrambling rates as high as 24.4 {plus minus} 5.2 events per μs. This structure also shows significant membrane thinning (up to 18 Å), which supports the hypothesis that groove dilation lowers the energetic barrier for lipid translocation, facilitating scrambling.

      The study also establishes a correlation between structural features and scrambling competence, though analyses often lack statistical robustness and quantitative comparisons. The simulations differentiate between open and closed conformations of TMEM16 structures, with open-groove structures exhibiting increased scrambling activity, while closed-groove structures do not. This finding aligns with previous research suggesting that the structural dynamics of the groove are critical for scrambling. Furthermore, the authors explore how the physical dimensions of the groove qualitatively correlate with observed scrambling rates. For example, TMEM16K induces increased membrane thinning in its open form, suggesting that membrane properties, along with structural features, play a role in modulating scrambling activity.

      Another significant finding is the concept of "out-of-the-groove" scrambling, where lipid translocation occurs outside the protein's groove. This observation introduces the possibility of alternate scrambling mechanisms that do not follow the traditional "credit-card model" of groove-mediated lipid scrambling. In their simulations, the authors note that these out-of-the-groove events predominantly occur at the dimer interface between TM3 and TM10, especially in mammalian TMEM16 structures. While these events were not observed in fungal TMEM16s, they may provide insight into Ca<sup>2+</sup>-independent scrambling mechanisms, as they do not require groove opening.

      Weaknesses:

      A significant challenge of the study is the discrepancy between the scrambling rates observed in CGMD simulations and those reported experimentally. Despite the authors' claim that the rates are in line experimentally, the observed differences can mean large energetic discrepancies in describing scrambling (larger than 1kT barrier in reality). For instance, the authors report scrambling rates of 10.7 events per μs for TMEM16F and 24.4 events per μs for nhTMEM16, which are several orders of magnitude faster than experimental rates. While the authors suggest that this discrepancy could be due to the Martini 3 force field's faster diffusion dynamics, this explanation does not fully account for the large difference in rates. A more thorough discussion on how the choice of force field and simulation parameters influence the results, and how these discrepancies can be reconciled with experimental data, would strengthen the conclusions. Likewise, rate calculations in the study are based on 10 μs simulations, while experimental scrambling rates occur over seconds. This timescale discrepancy limits the study's accuracy, as the simulations may not capture rare or slow scrambling events that are observed experimentally and therefore might underestimate the kinetics of scrambling. It's however important to recognize that it's hard (borderline unachievable) to pinpoint reasonable kinetics for systems like this using the currently available computational power and force field accuracy. The faster diffusion in simulations may lead to overestimated scrambling rates, making the simulation results less comparable to real-world observations. Thus, I would therefore read the findings qualitatively rather than quantitatively. An interesting observation is the asymmetry observed in the scrambling rates of the two monomers. Since MARTINI is known to be limited in correctly sampling protein dynamics, the authors - in order to preserve the fold - have applied a strong (500 kJ mol-1 nm-2) elastic network. However, I am wondering how the ENM applies across the dimer and if any asymmetry can be noticed in the application of restraints for each monomer and at the dimer interface. How can this have potentially biased the asymmetry in the scrambling rates observed between the monomers? Is this artificially obtained from restraining the initial structure, or is the asymmetry somehow gatekeeping the scrambling mechanism to occur majorly across a single monomer? Answering this question would have far-reaching implications to better describe the mechanism of scrambling.

      The main aim of our computational survey was to directly compare all relevant published TMEM16 structures in both open and closed states using the Martini 3 CGMD force field. Our standardized simulation and analysis protocol allowed us to quantitatively compare scrambling rates across the TMEM16 family, something that has never been done before. We do acknowledge that direct comparison between simulated versus experimental scrambling rates is complicated and is best to be interpreted qualitatively. In line with other reports (e.g., Li et al, PNAS 2024), lipid scrambling in CGMD is 2-3 orders of magnitude faster than typical experimental findings. In the CG simulation field, these increased dynamics due to the smoother energy landscape are a well known phenomenon. In our view, this is a valuable trade-off for being able to capture statistically robust scrambling dynamics and gain mechanistic understanding in the first place, since these are currently challenging to obtain otherwise. For example, with all-atom MD it would have been near-impossible to conclude that groove openness and high scrambling rates are closely related, simply because one would only measure a handful of scrambling events in (at most) a handful of structures.

      Considering the elastic network: the reviewer is correct in that the elastic network restrains the overall structure to the experimental conformation. This is necessary because the Martini 3 force field does not accurately model changes in secondary (and tertiary) structure. In fact, by retaining the structural information from the experimental structures, we argue that the elastic network helped us arrive at the conclusion that groove openness is the major contributing factor in determining a protein’s scrambling rate. This is best exemplified by the asymmetric X-ray structure of TMEM16K (5OC9), in which the groove of one subunit is more dilated than the other. In our simulation, this information was stored in the elastic network, yielding a 4x higher rate in the open groove than in the closed groove, within the same trajectory.

      Notably, the manuscript does not explore the impact of membrane composition on scrambling rates. While the authors use a specific lipid composition (DOPC) in their simulations, they acknowledge that membrane composition can influence scrambling activity. However, the study does not explore how different lipids or membrane environments or varying membrane curvature and tension, could alter scrambling behaviour. I appreciate that this might have been beyond the scope of this particular paper and the authors plan to further chase these questions, as this work sets a strong protocol for this study. Contextualizing scrambling in the context of membrane composition is particularly relevant since the authors note that TMEM16K's scrambling rate increases tenfold in thinner membranes, suggesting that lipid-specific or membrane-thickness-dependent effects could play a role.

      Considering different membrane compositions: for this study, we chose to keep the membranes as simple as possible. We opted for pure DOPC membranes, because it has (1) negligible intrinsic curvature, (2) forms fluid membranes, and (3) was used previously by others (Li et al, PNAS 2024). As mentioned by the reviewer, we believe our current study defines a good standardized protocol and solid baseline for future efforts looking into the additional effects of membrane composition, tension, and curvature that could all affect TMEM16-mediated lipid scrambling.

      Reviewer #3 (Public review):

      Strengths:

      The strength of this study emerges from a comparative analysis of multiple structural starting points and understanding global/local motions of the protein with respect to lipid movement. Although the protein is well-studied, both experimentally and computationally, the understanding of conformational events in different family members, especially membrane thickness less compared to fungal scramblases offers good insights.

      We appreciate the reviewer recognizing the value of the comparative study. In addition to valuable insights from previous experimental and computational work, we hope to put forward a unifying framework that highlights various TMEM16 structural features and membrane properties that underlie scrambling function.

      Weaknesses:

      The weakness of the work is to fully reconcile with experimental evidence of Ca²⁺-independent scrambling rates observed in prior studies, but this part is also challenging using coarse-grain molecular simulations. Previous reports have identified lipid crossing, packing defects, and other associated events, so it is difficult to place this paper in that context. However, the absence of validation leaves certain claims, like alternative scrambling pathways, speculative.

      It is generally difficult to quantitatively compare bulk measurements of scrambling phenomena with simulation results. The advantage of simulations is to directly observe the transient scrambling events at a spatial and temporal resolution that is currently unattainable for experiments. The current experimental evidence for the precise mechanism of Ca<sup>2+</sup>-independent scrambling is still under debate. We therefore hope to leverage the strength of MD and statistical rigor of coarse-grained simulations to generate testable hypotheses for further structural, biochemical, and computational studies.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews: 

      Reviewer #1 (Public review):

      This experiment sought to determine what effect congenital/early-onset hearing loss (and associated delay in language onset) has on the degree of inter-individual variability in functional connectivity to the auditory cortex. Looking at differences in variability rather than group differences in mean connectivity itself represents an interesting addition to the existing literature. The sample of deaf individuals was large, and quite homogeneous in terms of age of hearing loss onset, which are considerable strengths of the work. The experiment appears well conducted and the results are certainly of interest. R: Thank you for your positive and thoughtful feedback.

      Reviewer #3 (Public review):

      Summary:

      This study focuses on changes in brain organization associated with congenital deafness. The authors investigate differences in functional connectivity (FC) and differences in the variability of FC. By comparing congenitally deaf individuals to individuals with normal hearing, and by further separating congenitally deaf individuals into groups of early and late signers, the authors can distinguish between changes in FC due to auditory deprivation and changes in FC due to late language acquisition. They find larger FC variability in deaf than normal-hearing individuals in temporal, frontal, parietal, and midline brain structures, and that FC variability is largely driven by auditory deprivation. They suggest that the regions that show a greater FC difference between groups also show greater FC variability.

      Strengths:

      The manuscript is well-written, and the methods are clearly described and appropriate. Including the three different groups enables the critical contrasts distinguishing between different causes of FC variability changes. The results are interesting and novel.

      Weaknesses:

      Analyses were conducted for task-based data rather than resting-state data. The authors report behavioral differences between groups and include behavioral performance as a nuisance regressor in their analysis. This is a good approach to account for behavioral task differences, given the data. Nevertheless, additional work using resting-state functional connectivity could remove the potential confound fully.

      The authors have addressed my concerns well.

      Thank you for your thoughtful feedback. We appreciate your acknowledgment of the strengths of our study and the approaches taken to address potential confounds. As noted, we discuss the limitation of not including resting-state data in the manuscript, and we agree that this represents an important avenue for future research. We hope to address this question in future studies.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      The paper proposes that the placement of criteria for determining whether a stimulus is 'seen' or 'unseen' can significantly impact the validity of neural measures of consciousness. The authors found that conservative criteria, which require stronger evidence to classify a stimulus as 'seen,' tend to inflate effect sizes in neural measures, making conscious processing appear more pronounced than it is. Conversely, liberal criteria, which require less evidence, reduce these effect sizes, potentially underestimating conscious processing. This variability in effect sizes due to criterion placement can lead to misleading conclusions about the nature of conscious and unconscious processing.

      Furthermore, the study highlights that the Perceptual Awareness Scale (PAS), a commonly used tool in consciousness research, does not effectively mitigate these criterion-related confounds. This means that even with PAS, the validity of neural measures can still be compromised by how criteria are set. The authors emphasize the need for careful consideration and standardization of criterion placement in experimental designs to ensure that neural measures accurately reflect the underlying cognitive processes. By addressing this issue, the paper aims to improve the reliability and validity of findings in the field of consciousness research.

      Strengths:

      (1) This research provides a fresh perspective on how criterion placement can significantly impact the validity of neural measures in consciousness research.

      (2) The study employs robust simulations and EEG experiments to demonstrate the effects of criterion placement, ensuring that the findings are well-supported by empirical evidence.

      (3) By highlighting the limitations of the PAS and the impact of criterion placement, the study offers practical recommendations for improving experimental designs in consciousness research.

      Weaknesses:

      The primary focused criterion of PAS is a commonly used tool, but there are other measures of consciousness that were not evaluated, which might also be subject to similar or different criterion limitations. A simulation could applied to these metrics to show how generalizable the conclusion of the study is.

      We would like to thank reviewer 1 for their positive words and for taking the time to evaluate our manuscript. We agree that it would be important to gauge generalization to other metrics of consciousness. Note however, that the most commonly used alternative methods are postdecision wagering and confidence, both of which are known to behave quite similarly to the PAS (Sandberg, Timmermans , Overgaard & Cleeremans, 2010). Indeed, we have confirmed in other work that confidence is also sensitive to criterion shifts (see https://osf.io/preprints/psyarxiv/xa4fj). Although it has been claimed that confidence-derived aggregate metrics like meta-d’ or metacognitive efficiency may overcome criterion shifts, it would require empirical data rather than simulation to settle whether this is true or not (also see the discussion in https://osf.io/preprints/psyarxiv/xa4fj). Furthermore, out of these metrics, the PAS seems to be the preferred one amongst consciouness researchers (see figure 4 in Francken, Beerendonk, Molenaar, Fahrenfort, Kiverstein, Seth, Gaal S van, 2022; as well as https://osf.io/preprints/psyarxiv/bkxzh). Thus, given the fact that other metrics are either expected to behave in similar ways and/or because it would require more empirical work to determine along which dimension(s) criterion shifts would operate in alternative metrics, we see no clear path to implement the suggested simulations. We anticipate that aiming to do this would require a considerable amount of additional work, figuring out many things which we believe would better suit a future project. We would of course be open to doing this if the reviewer would have more specific suggestions for how to go about the proposed simulations.

      Reviewer #2 (Public review):

      Summary:

      The study investigates the potential influence of the response criterion on neural decoding accuracy in consciousness and unconsciousness, utilizing either simulated data or reanalyzing experimental data with post-hoc sorting data.

      Strengths:

      When comparing the neural decoding performance of Target versus NonTarget with or without post-hoc sorting based on subject reports, it is evident that response criterion can influence the results. This was observed in simulated data as well as in two experiments that manipulated the subject response criterion to be either more liberal or more conservative. One experiment involved a two-level response (seen vs unseen), while the other included a more detailed four-level response (ranging from 0 for no experience to 3 for a clear experience). The findings consistently indicated that adopting a more conservative response criterion could enhance neural decoding performance, whether in conscious or unconscious states, depending on the sensitivity or overall response threshold.

      Weaknesses:

      (1) The response criterion plays a crucial role in influencing neural decoding because a subject's report may not always align with the actual stimulus presented. This discrepancy can occur in cases of false alarms, where a subject reports seeing a target that was not actually there, or in cases where a target is present but not reported. Some may argue that only using data from consistent trials (those with correct responses) would not be affected by the response criterion. However, the authors' analysis suggests that a conservative response criterion not only reduces false alarms but also impacts hit rates. It is important for the authors to further investigate how the response criterion affects neural decoding even when considering only correct trials.

      We would like to thank reviewer 2 for taking the time to evaluate our manuscript. We appreciate the suggestion to investigate neural decoding on only correct trials. What we in fact did is consider target trials that are 'correct' (hits = seen target present trials) and 'incorrect' (misses = unseen target present trials) separately, see figure 4A and figure 4B. This shows that the response criterion also affects the neural measure of consciousness when only considering correct target present trials. Note however, that one cannot decode 'unseen' (target present) trials if one only aims to decode 'correct' trials, because those are all incorrect by definition. We did not analyze false alarms (these would be the 'seen' trials on the noise distribution of Figure 1A), as there were not enough trials of those, especially in the conservative condition (see Figure 2C and 2D), making comparisons between conservative and liberal impossible. However, the predictions for false alarms are pretty straightforward, and follow directly from the framework in Figure 1.

      (2) The author has utilized decoding target vs. nontarget as the neural measures of unconscious and/or conscious processing. However, it is important to note that this is just one of the many neural measures used in the field. There are an increasing number of studies that focus on decoding the conscious content, such as target location or target category. If the author were to include results on decoding target orientation and how it may be influenced by response criterion, the field would greatly benefit from this paper.

      We thank the reviewer for the suggestion to decode orientation of the target. In our experiments, the target itself does not have an orientation, but the texture of which it is composed does. We used four orientations, which were balanced out within and across conditions such that presence-absence decoding is never driven by orientation, but rather by texture based figure-ground segregation (for similar logic, see for example Fahrenfort et al, 2007; 2008 etc). There are a couple of things to consider when wanting to apply a decoding analysis on the orientation of these textures:

      (1) Our behavioral task was only on the presence or absence of the target, not on the orientation of the textures. This makes it impossible to draw any conclusions about the visibility of the orientation of the textures. Put differently: based on behavior there is no way of identifying seen or unseen orientations, correctly or incorrectly identified orientations etc. For examply, it is easy to envision that an observer detects a target without knowing the orientation that defines it, or vice versa a situation in which an observer does not detect the target while still being aware of the orientation of a texture in the image (either of the figure, or of the background). The fact that we have no behavioral response to the orientation of the textures severely limits the usefulness of a hypothetical decoding effect on these orientations, as such results would be uninterpretable with respect to the relevant dimension in this experiment, which is visibility.

      (2) This problem is further excarbated by the fact that the orientation of the background is always orthogonal to the orientation of the target. Therefore, one would not only be decoding the orientation of the texture that constitutes the target itself, but also the texture that constitutes the background. Given that we also have no behavioral metric of how/whether the orientation of the background is perceived, it is similarly unclear how one would interpret any observed effect.

      (3) Finally, it is important to note that – even when categorization/content is sometimes used as an auxiliary measure in consciousness research (often as a way to assay objective performance) - consciousness is most commonly conceptualized on the presence-absence dimension. A clear illustration of this is the concept of blindsight. Blindsight is the ability of observers to discriminate stimuli (i.e. identify content) without being able to detect them. Blindsight is often considered the bedrock of the cognitive neuroscience of consciousness as it acts as proof that one can dissociate between unconscious processing (the categorization of a stimulus, i.e. the content) and conscious processing of that stimulus (i.e. the ability to detect it).

      Given the above, we do not see how the suggested analysis could contribute to the conclusions that the manuscript already establishes. We hope that – given the above - the reviewer agrees with this assessment.

      Reviewer #3 (Public review):

      Summary:

      Fahrenfort et al. investigate how liberal or conservative criterion placement in a detection task affects the construct validity of neural measures of unconscious cognition and conscious processing. Participants identified instances of "seen" or "unseen" in a detection task, a method known as post hoc sorting. Simulation data convincingly demonstrate that, counterintuitively, a conservative criterion inflates effect sizes of neural measures compared to a liberal criterion. While the impact of criterion shifts on effect size is suggested by signal detection theory, this study is the first to address this explicitly within the consciousness literature. Decoding analysis of data from two EEG experiments further shows that different criteria lead to differential effects on classifier performance in post hoc sorting. The findings underscore the pervasive influence of experimental design and participants report on neural measures of consciousness, revealing that criterion placement poses a critical challenge for researchers.

      Strengths and Weaknesses:

      One of the strengths of this study is the inclusion of the Perceptual Awareness Scale (PAS), which allows participants to provide more nuanced responses regarding their perceptual experiences. This approach ensures that responses at the lowest awareness level (selection 0) are made only when trials are genuinely unseen. This methodological choice is important as it helps prevent the overestimation of unconscious processing, enhancing the validity of the findings.

      A potential area for improvement in this study is the use of single time-points from peak decoding accuracy to generate current source density topography maps. While we recognize that the decoding analysis employed here differs from traditional ERP approaches, the robustness of the findings could be enhanced by exploring current source density over relevant time windows. Event-related peaks, both in terms of timing and amplitude, can sometimes be influenced by noise or variability in trial-averaged EEG data, and a time-window analysis might provide a more comprehensive and stable representation of the underlying neural dynamics.

      We thank reviewer 3 for their positive words and for taking the time to evaluate our manuscript. If we understand the reviewer correctly, he/she suggests that the signal-to-noise ratio could be improved by averaging over time windows rather than taking the values at singular peaks in time. Before addressing this suggestion, we would like to point out that we plotted the relevant effects across time in Supplementary Figure S1A and S1B. These show that the observed effects were not somehow limited in time, i.e. only occuring around the peaks, but that they consistenly occured throughout the time course of the trial. In line with this observation one might argue that the results could be improved further by averaging across windows of interest rather than taking the peak moments alone, as the reviewer suggests. Although this might be true, there are many analysis choices that one can make, each of which could have a positive (or negative) effect on the signal to noise ratio. For example, when taking a window of interest, one is faced with a new choice to make, this time regarding the number of consecutive samples to average across (i.e. the size of the window), etc. More generally there is a long list of choices that may affect the precise outcome of analyses, either positively or negatively. Having analyzed the data in one way, the problem with adding new analysis approaches is that there is no objective criterion for deciding which analysis would be ‘best’, other than looking at the outcome of the statistical analyses themselves. Doing this would constitute an explorative double-dipping-like approach to analyzing the results, which – aside from potentially increasing the signal to noise ratio – is likely to also result in an increase of the type I error rate. In the past, when the first author of this manuscript has attempted to minimize the number of statistical tests, he has lowered the number of EEG time points by simply taking the peaks (for example see https://doi.org/10.1073/pnas.1617268114), and that is the approach that was taken here as well. Given the above, we prefer not to further ‘try out’ additional analytical approaches on this dataset, simply to improve the results. We hope the reviewer sympathizes with our position that it is methodologically most sound to stick to the analyses we have already performed and reported, without further exploration.

      It is helpful that the authors show the standard error of the mean for the classifier performance over time. A similar indication of a measure of variance in other figures could improve clarity and transparency.

      That said, the paper appears solid regarding technical issues overall. The authors also do a commendable job in the discussion by addressing alternative paradigms, such as wagering paradigms, as a possible remedy to the criterion problem (Peters & Lau, 2015; Dienes & Seth, 2010). Their consideration of these alternatives provides a balanced view and strengthens the overall discussion.

      We thank the reviewer for this suggestion. Note that we already have a measure of variance in the other figures too, namely showing the connected data points of individual participants. Indvidual data points as a visualization of variance is preferred by many journals (e.g., see https://www.nature.com/documents/cr-gta.pdf), and also shows the spread of relevant differences when paired points are connected. For example, in Figure 2, 3 and 4, the relevant difference is between the liberal and conservative condition. When wanting to show the spread of the differences between these conditions, one option would be to first subtract the two measures in a pairwise fashion (e.g., liberal-conservative), and then plot the spread of those differences using some metric (e.g. standard error/CI of the mean difference). However, this has the disadvantage of no longer separately showing the raw scores on the conditions that are being compared. Showing conditions separately provides clarity to the reader about what is being compared to what. The most common approach to visualizing the variance of the relevant difference in such cases, is to plot the connected individual data points of all participants in the same plot. The uniformity of the slope of these lines in such a visualization provides direct insight into the spread of the relevant difference. Plotting the standard error of the mean on the raw scores of the conditions in these plots would not help, because this would not visualize the spread of the relevant difference (liberal-conservative). We therefore opted in the manuscript to show the mean scores on the conditions that we compare, while also showing the connected raw data points of individual participants in the same plot. One might argue that we should then use that same visualization in figure 3A, but note that this figure is merely intended to identify the peaks, i.e. it does not compare liberal to conservative. Furthermore, plotting the decoding time lines of individual participants would greatly diminish the clarity of this figure. Given our explanation, we hope the reviewer agrees with the approach that we chose, although we are of course open to modifying the figures if the reviewer has a suggestion for doing so while taking into account the points we raise here in our response.

      Impact of the Work:

      This study effectively demonstrates a phenomenon that has been largely unexplored within the consciousness literature. Subjective measures may not reliably capture the construct they aim to measure due to criterion confounds. Future research on neural measures of consciousness should account for this issue, and no-report measures may be necessary until the criterion problem is resolved.

      Recommendations for the authors:

      Reviewer #2 (Recommendations for the authors):

      The authors could further elaborate on the results of the PAS to provide a clearer insight into the impact of response criteria, which is notably more complex than in other experiments. Specifically, the results demonstrate that conservative response criterion condition displays a considerably higher sensitivity compared to those with a liberal response criterion. It would be interesting to explore whether this shift in sensitivity suggests a correlation between changes in response criteria and conscious experiences, and how the interaction between sensitivity and response criteria can affect the neural measure of consciousness.

      We thank the reviewer for this suggestion. Note that the change in sensitivity that we observed is minor compared to the change we observed in response criterion (hedges g criterion in exp 2 = 2.02, compared to hedges g sensitivity/d’ in exp 2 = 0.42). However, we do investigate the effect of sensitivity (disregarding response criterion) on decoding accuracy. To this end we devised Figure 3C (for the full decoding time course see Supplementary Figure S1B). These figures show that the small behavioral sensitivity effects observed in both experiments (hedges g sensitivity in exp 1 = 0.30, exp 2 = 0.42) did not translate into significant decoding differences between conservative and liberal in either experiment. This comes as no surprise given the small corresponding behavioral effects. Note that small sensitivity differences between liberal and conservative conditions are commonplace, plausibly driven by the fact that being liberal also involves being more noisy in one’s response tendencies (i.e. sometimes randomly indicating presence). Further, the reviewer suggests that we might correlate changes in response criteria to changes in conscious experience. The only relevant metric of conscious experience for which we have data in this manuscript is the Perceptual Awareness Scale (PAS), so we assume the reviewer asks for a correlation between experimentally induced changes in response criterion with the equivalent changes in d’. To this end we computed the difference in the PAS-based d’ metric between conservative and liberal, as well as the difference in the PAS-based criterion metric between conservative and liberal, and correlated these across subjects (N=26) using a Spearman rank correlation. The result shows that these metrics do not correlate r(24)=0.04, p=0.85. Note however that small-N correlations like these are only somewhat reliable for large effect sizes. An N of 26 and a mere power of 80% requires an effect size of at least r=0.5 to be detectable, so even if a correlation were to exist we may not have had enough power to detect it. Due to these caveats we opted to not report this null-correlation in the manuscript, but we are of course willing to do so if the reviewer and/or editor disagrees with this assessment.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Tubert C. et al. investigated the role of dopamine D5 receptors (D5R) and their downstream potassium channel, Kv1, in the striatal cholinergic neuron pause response induced by thalamic excitatory input. Using slice electrophysiological analysis combined with pharmacological approaches, the authors tested which receptors and channels contribute to the cholinergic interneuron pause response in both control and dyskinetic mice (in the LDOPA off state). They found that activation of Kv1 was necessary for the pause response, while activation of D5R blocked the pause response in control mice. Furthermore, in the LDOPA off-state of dyskinetic mice, the absence of the pause response was restored by the application of clozapine. The authors claimed that (1) the D5R-Kv1 pathway contributes to the cholinergic interneuron pause response in a phasic dopamine concentration-dependent manner, and (2) clozapine inhibits D5R in the L-DOPA off state, which restores the pause response.

      Strengths:

      The electrophysiological and pharmacological approaches used in this study are powerful tools for testing channel properties and functions. The authors' group has well-established these methodologies and analysis pipelines. Indeed, the data presented were robust and reliable.

      Thank you for your comments.

      Weaknesses:

      Although the paper has strengths in its methodological approaches, there is a significant gap between the presented data and the authors' claims.

      There was no direct demonstration that the D5R-Kv1 pathway is dominant when dopamine levels are high. The term 'high' is ambiguous, and it raises the question of whether the authors believe that dopamine levels do not reach the threshold required to activate D5R under physiological conditions.

      We acknowledge that further work is necessary to clarify the role of the D5R in physiological conditions. While we haven’t found effects of the D1/D5 receptor antagonist SCH23390 on the pause response in control animals (Fig. 3), it is still possible that dopamine levels reach the threshold to stimulate D5R when burst firing of dopaminergic neurons contributes to dopamine release. We believe the pause response depends, among other factors, on the relative stimulation levels of SCIN D2 and D5 receptors, which is likely not an all-or-nothing phenomenon. To reduce ambiguity, we have eliminated the labels referring to dopamine levels in Figure 6F.

      Furthermore, the data presented in Figure 6 are confusing. If clozapine inhibits active D5R and restores the pause response, the D5R antagonist SCH23390 should have the same effect. The data suggest that clozapine-induced restoration of the pause response might be mediated by other receptors, rather than D5R alone.

      Thank you for letting us clarify this issue. Please note that the levels of endogenous dopamine 24 h after the last L-DOPA challenge in severe parkinsonian mice are expected to be very low. In the absence of an agonist, a pure D1/D5 antagonist would not exert an effect, as demonstrated with SCH23390 alone, which did not have an impact on the SCIN response to thalamic stimulation (Fig. 6). While clozapine can also act as a D1/D5 receptor antagonist, its D1/D5 effects in absence of an agonist are attributed to its inverse agonist properties (PMID: 24931197). Notably, SCH23390 prevented the effect of clozapine, allowing us to conclude that ligand-independent D1/D5 receptor-mediated mechanisms are involved in suppressing the pause response in dyskinetic mice. We now made it clearer in the third paragraph of the Discussion.

      Reviewer #2 (Public review):

      Summary:

      This manuscript by Tubert et al presents the role of the D5 receptor in modulating the striatal cholinergic interneuron (CIN) pause response through D5R-cAMP-Kv1 inhibitory signaling. Their model elucidates the on / off switch of CIN pause, likely due to the different DA affinity between D2R and D5R. This machinery may be crucial in modulating synaptic plasticity in cortical-striatal circuits during motor learning and execution. Furthermore, the study bridges their previous finding of CIN hyperexcitability (Paz et al., Movement Disorder 2022) with the loss of pause response in LID mice.

      Strengths:

      The study had solid findings, and the writing was logically structured and easy to follow. The experiments are well-designed, and they properly combined electrophysiology recording, optogenetics, and pharmacological treatment to dissect/rule out most, if not all, possible mechanisms in their model.

      Thank you for your comments.

      Weaknesses:

      The manuscript is overall satisfying with only some minor concerns that need to be addressed. Manipulation of intracellular cAMP (e.g. using pharmacological analogs or inhibitors) can add additional evidence to strengthen the conclusion.

      Thank you for the suggestion. While we acknowledge that we are not providing direct evidence of the role of cAMP, we chose not to conduct these experiments because cAMP levels influence several intrinsic and synaptic currents beyond Kv1, significantly affecting  membrane oscillations and spontaneous firing, as shown in Paz et al. 2021. However, we are modifying the fourth paragraph of the Discussion so there is no misinterpretation about our findings in the current work.

      Reviewer #3 (Public review):

      Summary:

      Tubert et al. investigate the mechanisms underlying the pause response in striatal cholinergic interneurons (SCINs). The authors demonstrate that optogenetic activation of thalamic axons in the striatum induces burst activity in SCINs, followed by a brief pause in firing. They show that the duration of this pause correlates with the number of elicited action potentials, suggesting a burst-dependent pause mechanism. The authors demonstrated this burst-dependent pause relied on Kv1 channels. The pause is blocked by an SKF81297 and partially by sulpiride and mecamylamine, implicating D1/D5 receptor involvement. The study also shows that the ZD7288 does not reduce the duration of the pause and that lesioning dopamine neurons abolishes this response, which can be restored by clozapine.

      Weaknesses:

      While this study presents an interesting mechanism for SCIN pausing after burst activity, there are several major concerns that should be addressed:

      (1) Scope of the Mechanism:

      It is important to clarify that the proposed mechanism may apply specifically to the pause in SCINs following burst activity. The manuscript does not provide clear evidence that this mechanism contributes to the pause response observed in behavioral animals. While the thalamus is crucial for SCIN pauses in behavioral contexts, the exact mechanism remains unclear. Activating thalamic input triggers burst activity in SCINs, leading to a subsequent pause, but this mechanism may not be generalizable across different scenarios. For instance, approximately half of TANs do not exhibit initial excitation but still pause during behavior, suggesting that the burst-dependent pause mechanism is unlikely to explain this phenomenon. Furthermore, in behavioral animals, the duration of the pause seems consistent, whereas the proposed mechanism suggests it depends on the prior burst, which is not aligned with in vivo observations. Additionally, many in vivo recordings show that the pause response is a reduction in firing rate, not complete silence, which the mechanism described here does not explain. Please address these in the manuscript.

      Thank you for your valuable feedback. While the absence of an initial burst in some TANs in vivo may suggest the involvement of alternative or additional mechanisms, this does not exclude a participation of Kv1 currents. We have seen that subthreshold depolarizations induced by thalamic inputs are sufficient to produce an afterhyperpolarization (AHP) mediated by Kv1 channels (see Tubert et al., 2016, PMID: 27568555). Although such subthreshold depolarizations are not captured in current recordings from behaving animals, intracellular in vivo recordings have demonstrated an intrinsically generated AHP after subthreshold depolarization of SCIN caused by stimulation of excitatory afferents (PMID: 15525771). Additionally, when pause duration is plotted against the number of spikes elicited by thalamic input (Fig. 1G), we found that one elicited spike is followed by an interspike interval 1.4 times longer than the average spontaneous interspike interval. We acknowledge the potential involvement of additional factors, including a decrease of excitatory thalamic input coinciding with the pause, followed by a second volley of thalamic inputs (Fig. 1J-K, after observations by Matsumoto et al., 2001- PMID: 11160526), as well as the timing of elicited spikes relative to ongoing spontaneous firing (Fig. 1D-E). Dopaminergic modulation (Fig. 3) and regional differences among striatal regions (PMID: 24559678) may also contribute to the complexity of these dynamics. 

      (2) Terminology:

      The use of "pause response" throughout the manuscript is misleading. The pause induced by thalamic input in brain slices is distinct from the pause observed in behavioral animals. Given the lack of a clear link between these two phenomena in the manuscript, it is essential to use more precise terminology throughout, including in the title, bullet points, and body of the manuscript.

      While we acknowledge that our study does not include in vivo evidence, we believe ex vivo preparations have been instrumental in elucidating the mechanisms underlying the responses observed in vivo. We also agree with previous ex vivo studies in using consistent terminology. However, we will clarify the ex vivo nature of our work in the abstract and bullet points for greater transparency.

      (3) Kv1 Blocker Specificity:

      It is unclear how the authors ruled out the possibility that the Kv1 blocker did not act directly on SCINs. Could there be an indirect effect contributing to the burst-dependent pause? Clarification on this point would strengthen the interpretation of the results.

      Thank you for letting us clarify this issue. In our previous work (Tubert et al., 2016) we showed that the Kv1.3 and Kv1.1 subunits are selectively expressed in SCIN throughout the striatum. Moreover, gabaergic transmission is blocked in our preparations. We are including a phrase to make it clearer in the manuscript (Results section, subheading “The pause response to thalamic stimulation requires activation of Kv1 channels”).

      (4) Role of D1 Receptors:

      While it is well-established that activating thalamic input to SCINs triggers dopamine release, contributing to SCIN pausing (as shown in Figure 3), it would be helpful to assess the extent to which D1 receptors contribute to this burst-dependent pause. This could be achieved by applying the D1 agonist SKF81297 after blocking nAChRs and D2 receptors.

      Thank you for letting us clarify this point. We show that blocking D2R or nAChR reduces the pause only for strong thalamic stimulation eliciting 4 SCIN spikes (Figure 3G), whereas the D1/D5 agonist SKF81297 is able to reduce the pause induced by weaker stimulation as well (Figure 3C). In addition, the D1/D5 receptor antagonist SCH23390 does not modify the pause response (Figure 3C). This may indicate that nAChR-mediated dopamine release induced by thalamic-induced bursts more efficiently activates D2R compared to D5R. We speculate that, in this context, lack of D5R activation may be necessary to keep normal levels of Kv1.3 currents necessary for SCIN pauses.

      (5) Clozapine's Mechanism of Action:

      The restoration of the burst-dependent pause by clozapine following dopamine neuron lesioning is interesting, but clozapine acts on multiple receptors beyond D1 and D5.

      Although it may be challenging to find a specific D5 antagonist or inverse agonist, it would be more accurate to state that clozapine restores the burst-dependent pause without conclusively attributing this effect to D5 receptors.

      Thank you for your insightful observation. We acknowledge the difficulty of targeting dopamine receptors pharmacologically due to the lack of highly selective D1/D5 inverse agonists. We used SCH23390, which is a highly selective D1/D5 receptor antagonist devoid of inverse agonist effects, to block clozapine’s ability to restore SCIN pauses (Figure 6C). This indicates that the restoration of SCIN pauses by clozapine depends on D1/D5 receptors. Furthermore, in a previous study, we demonstrated that clozapine’s effect on restoring SCIN excitability in dyskinetic mice (a phenomenon mediated by Kv1 channels in SCIN; Tubert et al., 2016) was not due to its action on serotonin receptors (Paz, Stahl et al., 2022). While our data do not rule out the potential contribution of other receptors, such as muscarinic acetylcholine receptors, we believe they strongly support the role of D1/D5 receptors. To reflect this, we added a statement discussing the potential contribution of receptors beyond D1/D5 in the last paragraph of the Discussion.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) The effect of MgTx was not consistent with the previous study (Tubert, 2016). I expected MgTx to increase the basal firing rate of cholinergic interneurons.

      Thank you for highlighting this. In our previous study we used ACSF in the recording pipette, instead of the intracellular solution -higher in potassium- used in the present study. This is likely related to the higher spontaneous firing rates of SCIN observed in the present study, which made the SCIN response stand out. In addition, our previous study analyzed the effect of MgTx on spontaneous firing frequency of SCIN isolated from major circuit regulation by adding CNQX and picrotoxin to the bath, while in this study we needed to preserve the thalamic input and only picrotoxin in the bath was used. Given these differences, the two conditions are not strictly comparable but rather give complementary information.

      (2) In the text, the authors claim that "SCINs recorded in the parkinsonian OFF-L-DOPA condition show an increase in membrane excitability that mimics changes acutely induced by SKF81297 in SCINs from control mice." However, the data for SKF81297 do not support this claim.

      We modified the text to make it clearer that the cited phrase refers to a previous publication (PMID: 35535012) in which SCIN intrinsic excitability was characterized via analysis of responses to somatic current injection in whole-cell recordings. In the present study Fig. 3D shows SKF81297 effects on interspike intervals during spontaneous activity with a trend towards increased firing, and Fig. 4E a lack of effect on “burst duration” for responses with different numbers of spikes elicited by thalamic afferent stimulation. 

      (3) I recommend testing whether other receptors, such as D2R, contribute to the clozapineinduced pause response in the L-DOPA off state.

      Thank you for your suggestion. We acknowledge that studying the role of D2R is important. However, our preliminary data suggest that a comprehensive follow up study, which is beyond the scope of this manuscript, is necessary to understand their role. 

      Reviewer #2 (Recommendations for the authors):

      (1) For Figure 1D-E, I understand that the authors are trying to state that the previous spontaneous spike contributes to a hyperpolarized window that induces a delay in the evoked spikes. However, it is almost impossible to discriminate between spontaneous and evoked spikes in this experiment. Furthermore, considering the tonic firing property, I highly suspect that even a sham control design (no optogenetic light) will give you a similar distribution as in Figure 1E (the longer IN X1, the shorter in X2).

      We agree that some spikes following stimulus onset may have occurred independently of the light stimulus, as it is also possible during behavioral tasks. We used the baseline recordings to estimate the effects of a sham stimulus as requested and included the data in Fig. 1E-F. As expected, the sham stimulation data showed a similar inverse relationship with the time elapsed from the preceding spike, but latencies were longer than with the stimulus (except for times close to the average ISI), suggesting that the optical stimulation increased the probability of evoking a spike (Fig. 1F). Remarkably, the pause following this threshold stimulation was significantly longer than the baseline ISI, as reported in the main text (Results section, last sentence of first paragraph).

      (2) The authors used optogenetics to induce thalamic inputs to induce the pause after bursts. Considering CINs also receive inputs from different brain regions, e.g. cortex, does this phenomena/pause after bursts also exist following cortical inputs?

      We did not study the SCIN response to cortical inputs, but both thalamic and cortical inputs seem to drive SCIN pause-responses as observed by others (PMID: 24553950).  

      (3) The effect of the D5R inverse agonism, and the further combined with D5R agonist and antagonist, faithfully reveal/confirm the increase of ligand-independent activity of D5R in LID reported previously. It would be ideal to also directly modulate intracellular cAMP (as in the 2022 paper) to confirm the rescue effects on the CIN pause response.

      Please, see our response in the public review.

      (4) In healthy conditions, the balance between D2R and D5R signaling (shown in Figure 6F left) switches the pause and no pause modes which potentially contributes to cortical-striatal plasticity. How about in LID off L-DOPA condition? Is it possible to rescue/modulate the pause on/off mode by D2R agonism in LID?

      We haven’t tested the effect of D2 agonists yet, but this is scheduled for follow up studies. 

      Reviewer #3 (Recommendations for the authors):

      (1) The authors use the ratio of pause duration to baseline ISI to describe the pause, which is useful for detecting significant differences. However, it would be beneficial to also report the actual duration of the burst-dependent pause to provide readers with a clearer understanding of the variation in pauses.

      In all figures we report the average baseline ISI duration for each experiment / experimental condition, allowing readers to estimate actual pause durations. We added in the main text actual average pause durations corresponding to Fig. 1H, which are representative of those observed along the study.

      (2) In Figure 3D, a more detailed comparison would be helpful, as there appears to be a significant difference between the SKF81297 group and others.

      We acknowledge that there might be a trend towards reduced ISIs, however, it was statistically non-significant (see legend of figure 3). In addition, the effect of SKF81297 seems unrelated to this trend, as its effect is also seen under the effect of ZD7288, which substantially prolongs the baseline ISI (Fig. 4E-F).

    1. Author response:

      The following is the authors’ response to the current reviews.

      Comments on revisions:

      I thank the authors for addressing my comments.

      - I believe that additional in vivo experiments, or the inclusion of controls for the specificity of the inhibitor, which the authors argue are beyond the scope of the current study, are essential to address the weaknesses and limitations stated in my current evaluation.

      We respectfully acknowledge the reviewer's concern but would like to reiterate that demonstrating the specificity of the inhibitor is beyond the scope of this study. Alpelisib (BYL-719) is a clinically approved drug widely recognized as a specific inhibitor of p110α, primarily used in the treatment of breast cancer. Its selectivity for the p110α isoform has been extensively validated in the literature.

      In our study, we used Alpelisib to assess whether pharmacological inhibition of p110α would produce effects similar to those observed in our genetic model, which is particularly relevant for the potential translational implications of our findings. Given the well-documented specificity of this inhibitor, we believe that additional controls to confirm its selectivity are unnecessary within the context of this study. Instead, our focus has been to investigate the functional role of p110α activity in macrophage-driven inflammation using the models described.

      We appreciate the reviewer’s insight and hope this clarification addresses their concern.

      - While the neutrophil depletion suggests neutrophils are not required for the phenotype, there are multiple other myeloid cells, in addition to macrophages, that could be contributing or accounting for the in vivo phenotype observed in the mutant strain (not macrophage specific).

      We appreciate the reviewer's observation regarding the potential involvement of other myeloid cells. However, it is important to highlight that the inflammatory process follows a well-characterized sequential pattern. Our data clearly demonstrate that in the paw inflammation model:

      ·       Neutrophils are effectively recruited, as evidenced by the inflammatory abscess filled with polymorphonuclear cells.

      ·       However, macrophages fail to be recruited in the RBD model.

      Given that this critical step is disrupted, it is reasonable to expect that any subsequent steps in the inflammatory cascade would also be affected. A precise dissection of the role of other myeloid populations would require additional lineage-specific models to selectively target each subset, which, as we have previously stated, would be the focus of an independent study.

      While we cannot entirely exclude the contribution of other myeloid cells, our data strongly support the conclusion that macrophages are, at the very least, a key component of the observed phenotype. We explicitly address this point in the Discussion section, where we acknowledge the potential involvement of other myeloid populations.

      - Inclusion of absolute cell numbers (in addition to the %) is essential. I do not understand why the authors are not including these data. Have they not counted the cells?

      We appreciate the reviewer’s concern regarding the inclusion of absolute cell numbers. However, as stated in the Materials and Methods section, we analyzed 50,000 cells per sample, and the percentages reported in the manuscript are directly derived from this standardized count.

      Our decision to present the data as percentages follows standard practices in flow cytometry-based analyses, as it allows for a clearer and more biologically relevant comparison of relative changes between conditions. This approach ensures consistency across samples and facilitates the interpretation of population dynamics during inflammation.

      We would also like to clarify that all data are based on actual counts, and rigorous controls were implemented throughout the study to ensure accuracy and reproducibility. We hope this explanation addresses the reviewer’s concern and provides further clarity on our approach.

      - Lastly, inclusion of representatives staining and gating strategies for all immune profiling measurements carried out by flow cytometry is important. This point has not been addressed, not even in writing.

      We appreciate the reviewer’s concern regarding the inclusion of absolute cell numbers. However, as stated in the Materials and Methods section, we analyzed 50,000 cells per sample, and the percentages reported in the manuscript are directly derived from this standardized count.

      Our decision to present the data as percentages follows standard practices in flow cytometry-based analyses, as it allows for a clearer and more biologically relevant comparison of relative changes between conditions. This approach ensures consistency across samples and facilitates the interpretation of population dynamics during inflammation.

      We would also like to clarify that all data are based on actual counts, and rigorous controls were implemented throughout the study to ensure accuracy and reproducibility. We hope this explanation addresses the reviewer’s concern and provides further clarity on our approach.


      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      This study by Alejandro Rosell et al. reveals the immunoregulatory role of the RAS-p110α pathway in macrophages, specifically in regulating monocyte extravasation and lysosomal digestion during inflammation. Disrupting this pathway, through genetic tools or pharmacological intervention in mice, impairs the inflammatory response, leading to delayed resolution and more severe acute inflammation. The authors suggest that activating p110α with small molecules could be a potential therapeutic strategy for treating chronic inflammation. These findings provide important insights into the mechanisms by which p110α regulates macrophage function and the overall inflammatory response.

      The updates made by the authors in the revised version have addressed the main points raised in the initial review, further improving the strength of their findings.

      Reviewer #2 (Public review):

      Summary:

      Cell intrinsic signaling pathways controlling the function of macrophages in inflammatory processes, including in response to infection, injury or in the resolution of inflammation are incompletely understood. In this study, Rosell et al. investigate the contribution of RAS-p110α signaling to macrophage activity. p110α is a ubiquitously expressed catalytic subunit of PI3K with previously described roles in multiple biological processes including in epithelial cell growth and survival, and carcinogenesis. While previous studies have already suggested a role for RAS-p110α signaling in macrophage function, the cell intrinsic impact of disrupting the interaction between RAS and p110α in this central myeloid cell subset is not known.

      Strengths:

      Exploiting a sound previously described genetically engineered mouse model that allows tamoxifen-inducible disruption of the RAS-p110α pathway and using different readouts of macrophage activity in vitro and in vivo, the authors provide data consistent with their conclusion that alteration in RAS-p110α signaling impairs various but selective aspects of macrophage function in a cell-intrinsic manner.

      Weaknesses:

      My main concern is that for various readouts, the difference between wild-type and mutant macrophages in vitro or between wild-type and Pik3caRBD mice in vivo is modest, even if statistically significant. To further substantiate the extent of macrophage function alteration upon disruption of RAS-p110α signaling and its impact on the initiation and resolution of inflammatory responses, the manuscript would benefit from a more extensive assessment of macrophage activity and inflammatory responses in vivo.

      Thank you for raising this point. We understand the reviewer’s concern regarding the modest yet statistically significant differences observed between wild-type and mutant macrophages in vitro, as well as between wild-type and Pik3ca<sup>RBD</sup> mice in vivo. Our current study aimed to provide a foundational exploration of the role of RAS-p110α signaling in macrophage function and inflammatory response, focusing on a set of core readouts that demonstrate the physiological relevance of this pathway. While a more extensive in vivo assessment could offer additional insights into macrophage activity and the nuanced effects of RAS-p110α disruption, it would require an array of new experiments that are beyond the current scope.

      However, we believe that the current data provide significant insights into the pathway’s role, highlighting important alterations in macrophage function and inflammatory processes due to RAS-p110α disruption. These findings lay the groundwork for future studies that can build upon our results with a more comprehensive analysis of macrophage activity in various inflammatory contexts.

      In the in vivo model, all cells have disrupted RAS-p100α signaling, not only macrophages. Given that other myeloid cells besides macrophages contribute to the orchestration of inflammatory responses, it remains unclear whether the phenotype described in vivo results from impaired RAS-p100α signaling within macrophages or from defects in other haematopoietic cells such as neutrophils, dendritic cells, etc.

      Thank you for raising this point. To address this, we have added a paragraph in the Discussion section acknowledging that RAS-p110α signaling disruption affects all hematopoietic cells (lines 461-470 in the discussion). However, we also provide several lines of evidence that support macrophages as the primary cell type involved in the observed phenotype. Specifically, we note that neutrophil depletion in chimera mice did not alter transendothelial extravasation, and that macrophages were the primary cell type showing significant functional defects in the paw edema model. These findings, combined with specific deficiencies in myeloid populations, suggest a predominant role of macrophages in the impaired inflammatory response, though we acknowledge the potential contributions of other myeloid cells.

      Inclusion of information on the absolute number of macrophages, and total immune cells (e.g. for the spleen analysis) would help determine if the reduced frequency of macrophages represents an actual difference in their total number or rather reflects a relative decrease due to an increase in the number of other/s immune cell/s.

      Thank you for this suggestion. We understand the value of presenting actual measurements; however, we opted to display normalized data to provide a clearer comparison between WT and RBD mice, as this approach highlights the relative differences in immune populations between the two groups. Normalizing data helps to focus on the specific impact of the RAS-p110α disruption by minimizing inter-sample variability that can obscure these differences.

      To further address the reviewer’s concern regarding the interpretation of macrophage frequencies, we have included a pie chart that represents the relative proportions of the various immune cell populations studied within our dataset. Author response image 1 provides a visual overview of the immune cell distribution, enabling a clearer understanding of whether the observed decrease in macrophage frequency represents an actual reduction in total macrophage numbers or a shift in their relative abundance due to changes in other immune populations.

      We hope this approach satisfactorily addresses reviewer’s concerns by providing both a normalized dataset for clearer interpretation of genotype-specific effects and an overall immune profile that contextualizes macrophage frequency within the broader immune cell landscape.

      Author response image 1.

      Recommendations for the authors:

      Reviewer #2 (Recommendations for the authors):

      As proof of concept data that activation of RAS-p110α signaling constitutes indeed a putative approach for treating chronic inflammation is not included in the manuscript, I suggest removing this implication from the abstract.

      Thank you for this suggestion. We have now removed this implication from the abstract to maintain clarity and to better reflect the scope of the data presented in the manuscript.

      Inclusion of a control in which RBD/- cells are also treated with BYL719, across experiments in which the inhibitor is used, would be important to determine, among other things, the specificity of the inhibitor.

      We appreciate the reviewer’s suggestion to include RBD/- cells treated with BYL719 as an additional control. However, we would like to clarify that this approach would raise a different biological question, as treating RBD mice with BYL719 would not only address the specificity of the inhibitor but also examine the combined effects of genetic and pharmacologic disruptions on PI3K pathway signaling. Investigating this dual disruption falls outside the scope of our current study, which is focused specifically on the effects of RAS-p110α disruption.

      It is also important to note that our RBD mouse model selectively disrupts RAS-mediated activation of p110α, while PI3K activation can still occur through other pathways, such as receptor tyrosine kinases (RTKs) and G protein-coupled receptors (GPCRs). Thus, inhibiting p110α with BYL719 would produce broader effects beyond the inhibition of RAS-PI3K signaling, impacting PI3K activation regardless of its upstream source.

      In addition, incorporating this control would require us to repeat nearly all experiments in the manuscript, as it would necessitate generating and analyzing new samples for each experimental condition. Given the scope and resources involved, we believe this approach is unfeasible at this stage of the revision process.

      We hope this explanation is satisfactory and that the current data in the manuscript provide a rigorous assessment of the RAS-p110α signaling pathway within the defined experimental scope.

      Figure 3I is missing the statistical analysis (this is mentioned in the legend though).

      Thank you for pointing this out. We apologize for the oversight. The statistical analysis for Figure 3I has now been added.

      Gating strategies and representative staining should be included more generally across the manuscript.

      Thank you for this suggestion. To address this, we have added a new supplementary figure (Figure 2-Supplement Figure 2) that illustrates the gating strategy along with a representative dataset. Additionally, a brief summary of the gating strategy has been included in the main text to further clarify the methodology.

      It is recommended that authors show actual measurements rather than only data normalized to the control (or arbitrary units).

      Thank you for this suggestion. We understand the value of presenting actual measurements; however, we opted to display normalized data to provide a clearer comparison between WT and RBD mice, as this approach highlights the relative differences in immune populations between the two groups. Normalizing data helps to focus on the specific impact of the RAS-p110α disruption by minimizing inter-sample variability that can obscure these differences.

      To further address the reviewer’s concern regarding the interpretation of macrophage frequencies, we have included a pie chart that represents the relative proportions of the various immune cell populations studied within our dataset. Author response image 1 provides a visual overview of the immune cell distribution, enabling a clearer understanding of whether the observed decrease in macrophage frequency represents an actual reduction in total macrophage numbers or a shift in their relative abundance due to changes in other immune populations.

      We hope this approach satisfactorily addresses reviewer’s concerns by providing both a normalized dataset for clearer interpretation of genotype-specific effects and an overall immune profile that contextualizes macrophage frequency within the broader immune cell landscape.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment  

      This manuscript reports important findings that the methyltransferase METTL3 is involved in the repair of abasic sites and uracil in DNA, mediating resistance to floxuridine-driven cytotoxicity. Convincing evidence shows the involvement of m6A in DNA based on single cell imaging and mass spec data. The authors present evidence that the m6A signal does not result from bacterial contamination or RNA, but the text does not make this overly clear.

      We thank the editors for recognizing the importance of our work and the relevance of METTL3 and 6mA in DNA repair. We agree the evidence presented can be regarded as convincing, in that it includes validation with orthogonal approaches and excludes the source of 6mA being RNA or bacterial contamination.

      To clarify, the identification of 6mA in DNA, upon DNA damage, is based first on immunofluorescence observations using an anti-m6A antibody. In this setting, removal of RNA with RNase treatment fails to reduce the 6mA signal, excluding the possibility that the source of signal is RNA. In contrast, removal of DNA with DNase treatment removes all 6mA signal, strongly suggesting that the species carrying the N6-methyladenosine modification is DNA (Figure 3D, E). Importantly, in Figure 3F, G, we provide orthogonal, quantitative mass spectrometry data that independently confirm this finding. Mass spectrometry-liquid chromatography of DNA analytes, conclusively shows the presence of 6mA in DNA upon treatment with DNA damaging agents and excludes that the source is RNA, based on exact mass. 

      Cells only show the 6mA signal when treated with DNA damaging agents, and the 6mA is absent from untreated cells (Figure 3D, E, H, I). This provides strong evidence that the 6mA signal is not a result of bacterial contamination in our cell lines. Furthermore, our cell lines are routinely tested for mycoplasma contamination. It could be possible that stock solutions of DNA damaging agents may be contaminated, but this would need to be true for all individual drugs and stocks tested, which is highly unlikely. Moreover, the data showing 6mA signal is not significantly different from untreated cells when a DNA damaging agent is combined with a METTL3 inhibitor (Figure 3H, I) provides strong evidence against bacterial contamination in our stocks.  

      In summary, we provide conclusive evidence, based on orthogonal methods, that the METTL3-dependent N6-methyladenosine modification is deposited in DNA, not RNA, in response to DNA damage and have now clarified these points in the results and discussion. 

      Public Reviews:  

      Reviewer #1 (Public review):  

      Summary:  

      The authors sought to identify unknown factors involved in the repair of uracil in DNA through a CRISPR knockout screen.  

      Strengths:  

      The screen identified both known and unknown proteins involved in DNA repair resulting from uracil or modified uracil base incorporation into DNA. The conclusion is that the protein activity of METTL3, which converts A nucleotides to 6mA nucleotides, plays a role in the DNA damage/repair response. The importance of METTL3 in DNA repair, and its colocalization with a known DNA repair enzyme, UNG2, is well characterized.  

      Weaknesses:  

      This reviewer identified no major weaknesses in this study. The manuscript could be improved by tightening the text throughout, and more accurate and consistent word choice around the origin of U and 6mA in DNA. The dUTP nucleotide is misincorporated into DNA, and 6mA is formed by methylation of the A base present in DNA. Using words like 6mA "deposition in DNA" seems to imply it results from incorporation of a methylated dATP nucleotide during DNA synthesis.  

      The increased presence of 6mA during DNA damage could result from methylation at the A base itself (within DNA) or from incorporation of pre-modified 6mA during DNA synthesis. Our data do not directly discriminate between these two mechanisms, and we clarified this point in the discussion.  

      Reviewer #2 (Public review):  

      Summary:  

      In this work, the authors performed a CRISPR knockout screen in the presence of floxuridine, a chemotherapeutic agent that incorporates uracil and fluoro-uracil into DNA, and identified unexpected factors, such as the RNA m6A methyltransferase METTL3, as required to overcome floxuridine-driven cytotoxicity in mammalian cells. Interestingly, the observed N6-methyladenosine was embedded in DNA, which has been reported as DNA 6mA in mammalian genomes and is currently confirmed with mass spectrometry in this model. Therefore, this work consolidated the functional role of mammalian genomic DNA 6mA, and supported with solid evidence to uncover the METTL3-6mA-UNG2 axis in response to DNA base damage.  

      Strengths:  

      In this work, the authors took an unbiased, genome-wide CRISPR approach to identify novel factors involved in uracil repair with potential clinical interest.  

      The authors designed elegant experiments to confirm the METTL3 works through genomic DNA, adding the methylation into DNA (6mA) but not the RNA (m6A), in this base damage repair context. The authors employ different enzymes, such as RNase A, RNase H, DNase, and liquid chromatography coupled to tandem mass spectrometry to validate that METTL3 deposits 6mA in DNA in response to agents that increase genomic uracil.  

      They also have the Mettl3-KO and the METTL3 inhibition results to support their conclusion.  

      Weaknesses:  

      Although this study demonstrates that METTL3-dependent 6mA deposition in DNA is functionally relevant to DNA damage repair in mammalian cells, there are still several concerns and issues that need to be improved to strengthen this research.  

      First, in the whole paper, the authors never claim or mention the mammalian cell lines contamination testing result, which is the fundamental assay that has to be done for the mammalian cell lines DNA 6mA study.  

      Our cell lines are routinely tested for bacterial contamination, specifically mycoplasma, and we state this information in the revised manuscript. 

      Importantly, we do not observe 6mA in untreated cells, strongly suggesting that the 6mA signal observed is dependent on the presence of DNA damage and not caused by contamination in the cell lines (Figure 3D, E, H, I). While it could be possible that stock solutions of DNA damaging agents may be contaminated, this would need to be the case for all individual drugs and stocks tested that induce 6mA, which is very unlikely. Finally, the data showing 6mA signal is not significantly different from untreated cells when a DNA damaging agent is combined with a METTL3 inhibitor (Figure 3 H, I) provides strong evidence against bacterial contamination in our drug stocks.  

      Second, in the whole work, the authors have not supplied any genomic sequencing data to support their conclusions. Although the sequencing of DNA 6mA in mammalian models is challenging, recent breakthroughs in sequencing techniques, such as DR-Seq or NT/NAME-seq, have lowered the bar and improved a lot in the 6mA sequencing assay. Therefore, the authors should consider employing the sequencing methods to further confirm the functional role of 6mA in base repair.  

      While we agree that it could be important to understand the precise genomic location of 6mA in relation to DNA damage, this is outside the scope of the current study. Moreover, this exercise may prove unproductive. If 6mA is enriched in DNA at damage sites or as DNA is replicated, the genomic mapping of 6mA is likely to be stochastic. If stochastic, it would be impossible to obtain the read depth necessary to map 6mA accurately. 

      Third, the authors used the METTL3 inhibitor and Mettl3-KO to validate the METTL36mA-UNG2 functional roles. However, the catalytic mutant and rescue of Mettl3 may be the further experiments to confirm the conclusion.  

      We believe this to be an excellent suggestion from Reviewer #2 but we are unable to perform the proposed experiment at this time. We encourage future studies to explore the rescue experiment.  

      Reviewer #3 (Public review):  

      Summary:  

      The authors are showing evidence that they claim establishes the controversial epigenetic mark, DNA 6mA, as promoting genome stability.  

      Strengths:  

      The identification of a poorly understood protein, METTL3, and its subsequent characterization in DDR is of high quality and interesting.  

      Weaknesses:  

      (1) The very presence of 6mA (DNA) in mammalian DNA is still highly controversial and numerous studies have been conclusively shown to have reported the presence of 6mA due to technical artifacts and bacterial contamination. Thus, to my knowledge there is no clear evidence for 6mA as an epigenetic mark in mammals, and consequently, no evidence of writers and readers of 6mA. None of this is mentioned in the introduction. Much of the introduction can be reduced, but a paragraph clearly stating the controversy and lack of evidence for 6mA in mammals needs to be added, otherwise, the reader is given an entirely distorted view of the field.  

      These concerns must also be clearly in the limitations section and even in the results section which fails to nuance the authors' findings. 

      We agree with the reviewer that the presence and potential function of 6mA in mammalian DNA has been debated. Importantly, the debate regarding the presence and quantity of 6mA in DNA has been previously restricted to undamaged, baseline conditions. In complete agreement with this notion, we do not detect appreciable levels of 6mA in untreated cells. We revised the introduction section to present the debate about 6mA in DNA. We, however, want to highlight that our study provides, for the first time, convincing evidence (based on two orthogonal methods) that 6mA is present in DNA in response to a stimulus, DNA damage. We do not claim or provide any data that suggest 6mA is a baseline epigenetic mark.  

      (2) What is the motivation for using HT-29 cells? Moreover, the materials and methods do not state how the authors controlled for bacterial contamination, which has been the most common cause of erroneous 6mA signals to date. Did the authors routinely check for mycoplasma? 

      HT-29 is a cell line of colorectal origin and chemotherapeutic agents that introduce uracil and uracil derivatives in DNA, as those used in this study, are relevant for the treatment of colorectal cancer. As indicated above, we do not observe 6mA in untreated cells, strongly suggesting that the 6mA signal observed is dependent on DNA damage and not caused by a potential bacterial contamination (Figure 3D, E, H, I). Additionally, our cell lines are routinely tested for bacterial contamination, specifically mycoplasma. 

      (3) The single cell imaging of 6mA in various cells is nice. The results are confirmed by mass spec as an orthogonal approach. Another orthogonal and quantitative approach to assessing 6mA levels would be PacBio. Similarly, it is unclear why the authors have not performed dot-blots of 6mA for genomic DNA from the given cell lines.

      We are confused by this point since an orthogonal approach to detect 6mA, mass spectrometry-liquid chromatography, was employed. This method does not use an antibody and confirms the increase of 6mA in DNA when cells were treated with DNA damaging agents. This data is presented in Figure 3F, G. 

      It is sensible to hypothesize that the localization of 6mA is consistent with DNA replication (like uracil deposition). In this event, the genomic mapping of 6mA is likely to be stochastic. This would make quantification with PacBio sequencing difficult because it would be very challenging to achieve the appropriate read depth to call a modified base. 

      Dot blots rely on an antibody and thus are not truly orthogonal to our immunofluorescence-based measurements. We preferred the mass spectrometry-liquid chromatography approach we took as a true orthogonal approach.  

      (4) The results of Figure 3 need further investigation and validation. If the results are correct the authors are suggesting that the majority of 6mA in their cell lines is present in the DNA, and not the RNA, which is completely contrary to every other study of 6mA in mammalian cells that I am aware of. This could suggest that the antibody is not, in fact, binding to 6mA, but to unmodified adenine, which would explain why the signal disappears after DNAse treatment. Indeed, binding of 6mA to unmethylated DNA is a commonly known problem with most 6mA antibodies and is well described elsewhere.  

      Based on this and the following comment, we are convinced that Reviewer #3 has overlooked two critical elements of our study:

      First, the immunofluorescence work presented in Figure 3, showing 6mA signal in response to DNA damage, uses cells that were pre-extracted to remove excess cytoplasmic RNA. This method is often used in immunofluorescence experiments of this kind. The pre-extraction method removes most of the cytoplasmic content, and the majority of the cytoplasmic m6A RNA signal. Supplementary Figure 3D shows cells that have not been pre-extracted prior to staining. These images show the cytoplasmic m6A signal is abundant if we do not perform the pre-extraction step. 

      If the antibody used to label 6mA significantly reacted with unmodified adenine, we would expect a large signal in untreated or untreated and denatured conditions. In contrast, an increase in 6mA is not observed in either case.

      Second, the orthogonal approach we employed, mass spectrometry coupled with liquid chromatography, measures 6mA DNA analytes specifically by exact mass. This approach does not depend on an antibody and yields results consistent with those from the immunofluorescence experiments. 

      (5) Given the lack of orthologous validation of the observed DNA 6mA and the lack of evidence supporting the presence of 6mA in mammalian DNA and consequently any functional role for 6mA in mammalian biology, the manuscript's conclusions need to be toned down significantly, and the inherent difficulty in assessing 6mA accurately in mammals acknowledged throughout.  

      As discussed in response to prior comments, Figure 3 does provide two independent and orthologous methods that demonstrate 6mA presence in DNA specifically, and not RNA, in response to DNA damage. Complementary and orthogonal datasets are presented using either immunofluorescence microscopy or mass spectrometry-liquid chromatography of extracted DNA. The latter method does not rely on an antibody and can discriminate 6mA DNA versus RNA based on exact mass. We revised the text to clarify that Figure 3F, G is a completely orthogonal approach. 

      Recommendations for the authors:

      Reviewer #2 (Recommendations for the authors):  

      The authors cited most of the related publications; however, the reviewer suggested that three 2015 papers in Cell (Dahua Chen's, Yang Shi's, and Chuan He's) and the 2016 Nature (Andrew Xiao's) article are worth citing here because those are the milestone works reported the genomic DNA 6mA, for the first wave, in eukaryotic and mammalian genomes.  

      Furthermore, in Tao P. Wu and Andrew Z. Xiao's 2016 Nature article, the result has already emphasized the genomic DNA 6mA is enriched in the H2A.X sites; therefore, that work indicated the link between DNA damage and repair and 6mA's functional role. The authors may add some comments or discussion on this point.  

      Last but not least, the authors may also need to discuss the reported evidence of DNA 6mA's function in mitochondria.  

      We thank the reviewer for these suggestions. We revised our introduction and include additional references and discussion points, as suggested by the reviewer. 

      Reviewer #3 (Recommendations for the authors):  

      Minor points:  

      (1) In general, the manuscript is too verbose, and the amount of text can be dramatically reduced/sharpened. The introduction in particular is too long. 

      We revised the manuscript and reduced text when appropriate.

      (2) Each results section can also be condensed to improve clarity significantly. Indeed the results section reads like a 'Result & Discussion' section, which is then followed by a Discussion. Maybe the discussion section can be shortened to a 'conclusion'.

      We revised the results section when appropriate and reworked the discussion.

      Importantly, we revised the text related to Figure 3 as it does appear that Reviewer #3 did not appreciate key results present in this figure, specifically the orthogonal, mass spectrometry approach validating the discovery of 6mA DNA species (Figure 3F, G). We added a schematic as Figure 3F to further clarify this point as well. 

      (3) The accession number for sequencing data in GEO data should be provided.  

      The accession numbers is now provided in the manuscript. GSE282260.

      (4) All figures are unnecessarily small and in some cases, supporting figures from the supplementary data should be moved into the main figure to improve clarity. 

      The figures are of high image quality and can be enlarged easily. If there are specific figures that the reviewer believes will improve clarity, we would be happy to move them.

    1. Author response:

      Thank you for sharing a detailed review of our manuscript titled, Variations and predictability of epistasis on an intragenic fitness landscape. We have now carefully gone through the reviewers’ and the editor’s comments and have the following preliminary responses.

      (1) Measurement noise in the folA fitness landscape. All three reviewers and the editors raise the important matter of incorporating measurement noise in the fitness landscape. The paper by Papkou and coworkers makes the fitness measurements of the landscape in six independent repeats. They show that the fitness data is highly correlated in each repeat, and use the weighted mean of the repeats to report their results. They do not study how measurement noise influences their findings. The results by Papkou and coworkers were our starting point, and hence, we built on the landscape properties reported in their study. As a result, we also analyse our results working with the same mean of the six independent measurements.

      The main result of the work by Papkou and coworkers is that largest subgraph in the landscape has 514 fitness peaks. 

      We revisit this result by quantifying how measurement noise changes this number. By doing this, we note the subgraph contains only 127 peaks which are statistically significant. We define a sequence as a peak when its corresponding fitness is greater than all its one-distance neighbours with a p-value < 0.05. This shows that, as pointed out in the reviews, incorporating noise in the landscape results significantly changes how we view the landscape – a facet not included in Papkou et al and the current version of our manuscript. 

      Not incorporating measurement noise means that the entire landscape has 4055 peaks. When measurement noise is included in the analysis, this number reduces to 137, out of which 136 are high fitness backgrounds (functional). 

      In the revised version of our manuscript, we will incorporate measurement noise in our analysis. Through this, we will also address the concern regarding the use of an arbitrary cut-off to study “fluid” epistasis. However, we note that arbitrary cut-offs to define DFEs have been recently used (Sane et al., PNAS, 2023).

      We also note that previous work with large scale landscapes (Wu et al, eLife, 2016) also reported a fitness landscape with a single experiment, with no repeats. 

      (2) Global nonlinearities and higher-order leading to fluid epistasis. Attempts at building models for higher-order epistasis from empirical data have largely been confined to landscapes of a limited data size. For example, Sailer & Harms, Genetics, 2017 propose models for higher-order epistasis from seven empirical data sets, each with less than a 100 data points. Another recent attempt (Park et al, Nat Comm, 2024) proposes rule for protein structure-function with 20 fitness landscapes. In this study, only one landscape which used fitness as a phenotype had ~160000 data points (of which only 42% were included for analysis). All other data sets which used fitness as a phenotype contained less than 10000 data points. While these statistical proposals of how higher-order epistasis operates exist, none of them are reliant of large scale, exhaustive network, like the one proposed by Papkou and coworkers.  

      In the edited manuscript, we will replace our arbitrary cut-off with results of statistical tests carried out based on measurement noise. 

      Global non-linearities shape evolutionary responses. We would like to emphasize that the goal of this work to study and understand how these global non-linearities result in patterns on a large fitness landscape by presenting the sum total of these fundamental factors in shaping statistical patterns. 

      While we understand that we may not have sufficiently explained the effects of global non-linearities on our results, we do not agree with the reviewer’s conclusion that our results are artifacts of these non-linearities. We will expand on the role of these nonlinearities on the patterns that we observe (like, fitness being bounded, as pointed out by reviewer 2, or differential impact of a mutation in functional vs. non-functional variants).

      We also speculate that changing our arbitrary cut-off (selection coefficient of 0.05) to measurement noise will not alter our results qualitatively. 

      The question we address in our work is, therefore, how does the nature of epistasis change with genetic background over a large, exhaustive landscape. The nature of epistasis between two mutations is analysed in all 4<sup>7</sup> backgrounds. The causative agents for the change in epistasis will be context-dependent, depending on the precise nature of the two mutations and the background. For instance, a certain background might simply introduce a Stop codon in the sequence. Notwithstanding these precise, local mechanistic explanations, we seek to answer how epistasis changes statistically in a sequence. Investigating statistical patterns which explain switch in nature of epistasis in deep, exhaustive landscapes is a long-term goal of this research.

      (3) Last, in our revised manuscript, we will address the reviewers’ other minor comments on the various aspects of the manuscript.

  2. Jan 2025
    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This important work proposes a neural network model of interactions between the prefrontal cortex and basal ganglia to implement adaptive resource allocation in working memory, where the gating strategies for storage are adjusted by reinforcement learning. Numerical simulations provide convincing evidence for the superiority of the model in improving effective capacity, optimizing resource management, and reducing error rates, as well as solid evidence for its human-like performance. The paper could be strengthened further by a more thorough comparison of model predictions with human behavior and by improved clarity in presentation. This work will be of broad interest to computational and cognitive neuroscientists, and may also interest machine-learning researchers who seek to develop brain-inspired machine-learning algorithms for memory.

      We thank the reviewers for their thorough and constructive comments, which have helped us clarify, augment and solidify our work. Regarding the suggestion to include a “more thorough comparison with with human behavior”, we believe this comment reflects one of the reviewer’s suggestion to compare with sequential order effects. We now include a new section with simulations showing that the network exhibits clear recency effects in accordance with the literature, and where such recency effects are known to be related to WM interference and not due to passive decay. Overall our work makes substantial contact with human behavioral patterns that have been documented in the human literature (and which as far as we know have not been jointly captured by any one model), such as the shape of the error distributions, including probability of recall and variable precision;  attraction to recently presented items,  sensitivity to reinforcement history, set-size dependent chunking, recency effects,  dopamine manipulation effects, as well of a range of human data linking capacity limitations to frontostriatal function. It also provides a theoretical proposal for the well established phenomenon of capacity limitations in humans, suggesting that they arise due to difficulty in WM management.

      Below we address each reviewer individually, responding to each comment and providing the relevant location in the paper that the changes and additions were made. Reviewer responses are included in blue/bold for clarity.  

      Public Reviews:

      Reviewer 1:

      Thank you for your comments. We appreciate your statements of the strengths of this paper and your suggestions to improve this paper.

      First, the method section appears somewhat challenging to follow. To enhance clarity, it might be beneficial to include a figure illustrating the overall model architecture. This visual aid could provide readers with a clearer understanding of the overall network model.

      Additionally, the structure depicted in Figure 2 could be potentially confusing. Notably, the absence of an arrow pointing from the thalamus to the PFC and the apparent presence of two separate pathways, one from sensory input to the PFC and another from sensory input to the BG and then to the thalamus, may lead to confusion. While I recognize that Figure 2 aims to explain network gating, there is room for improvement in presenting the content accurately.

      As suggested, we added a figure (new figure 2) illustrating the overall model architecture before expanding it to show the chunking circuitry. This figure also shows the projections from thalamus to PFC (we preserve the previous figure 2, now figure 3, as an example sequence of network gating decisions, in more abstract form to help facilitate a functional understanding of the sequence of events without too much clutter). We also made several other general clarifications to the methods sections to make it more transparent and easier to follow, as per your suggestions.   

      Still, for the method part, it would enhance clarity to explicitly differentiate between predesigned (fixed) components and trainable components. Specifically, does the supplementary material state that synaptic connection weights in striatal units (Go&NoGo) are trained using XCAL, while other components, such as those in the PFC and lateral inhibition, are not trained (I found some sentences in 'Limitations and Future Directions')?

      We have now explicitly specified learned and fixed components. We have further explained the role of XCAL and how striatal Go/NoGo weights are trained. We have also added clarification on how gating policies are learned via eligibility traces and synaptic tags.

      I'm not sure about the training process shown in Figure 8. It appears that the training may not have been completed, given that the blue line representing the chunk stripe is still ascending at the endpoint. The weights depicted in panel d) seem to correspond with those shown in panels b) and c), no? Then, how is the optimization process determined to be finished? Alternatively, could it be stated that these weight differences approach a certain value asymptotically? It would be better to clarify the convergence criteria of the optimization process.

      The training process has been clarified and we specify (in the last paragraph of the Base PBWM Model) how we determine when training is complete. We also can confirm that the network behavior has stabilized in learning even if the Go/NoGo weights continue to grow over time for the chunked layer (due to imperfect performance and reinforcement of the chunk gating strategy).

      Reviewer 2:

      Thank you for your comments. We appreciate your notes on the strengths of the paper and your suggestions to help improve the paper.

      The model employs a spiking neural network, which is relatively complex. Additionally, while this paper validates the effectiveness of chunking strategies used by the brain to enhance working memory efficiency through computational simulations, further comparison with related phenomena observed in cognitive neuroscience experiments on limited working memory capacity, such as the recency effect, is necessary to verify its generalizability.

      Thank you for proposing we add in more connections with human WM. Based on your specific recommendation, we have included the section “Network recapitulates human sequential effects in working memory.” where we discuss recency effects in human working memory and how our model recapitulates this effect. We have also made the connections to human data and human work more explicit throughout the manuscript (Figure 4c). As noted in response to the assessment, we believe our model does make contact with a wide variety of cognitive neuroscience data in human WM, such as the shape of the error distributions,  including probability of recall and variable precision;  attraction to recently presented items,  sensitivity to

      reinforcement history, set-size dependent chunking, recency effects, and dopamine manipulation effects, as well of a range of human data linking capacity limitations to frontostriatal function. It also provides a theoretical proposal for the well established phenomenon of capacity limitations in humans, suggesting that they arise due to difficulty in WM management.

      Recommendations For The Authors:

      Reviewer 1:

      I appreciate the authors' clear discussion of the limitations of this work in the section "Limitations and Future Directions". The development of a comprehensive model framework to overcome these constraints should require a separate paper, though, I am curious if the authors have attempted any experiments, such as using two identically designed chunking layers, that could partially support the assumptions presented in the paper.

      Expanding the number of chunking layers is a great future direction. We felt that it was most effective for this paper to begin with a minimal set up with proof of concept. We hypothesize that, given our results, a reinforcement learning algorithm would be able to learn to select the best level of abstraction (degree of chunking) in more continuous form, but would require more experience across a range of tasks to do so.

      I'm not sure whether it's appropriate that "Frontostriatal Chunking Gating..." precedes "Dopamine Balance is...", maybe it would be better to reverse the order thus avoiding the need to mention the role of dopamine before delving into the details. Additionally, including a summary at the end of the Introduction, outlining how the paper is organized, could provide readers with a clear roadmap of the forthcoming content.

      We appreciate this suggestion. After careful thought, we wanted to preserve the order because we felt it was important to make the direct connection between set size and stripe usage following the discussion on performance based on increasing stripes.  

      The authors could improve the overall polish of the paper. The equations in the Method section are somewhat confusing: Eq. (2) appears incorrect, as it lacks a weight w_i and n should presumably be in the denominator. For Eq. (3), the comma should be replaced with ']'... It would be advisable to cross-reference these equations with the original O'Reilly and Frank paper for consistency.

      Thank you for pointing out the errors in the method equations- those equations were indeed rendering incorrectly. We have fixed this problem.  

      Additionally, there are frequent instances of missing figure and reference citations (many '?'s), and it would be beneficial to maintain consistent citation formatting throughout the paper: sometimes citations are presented as "key/query coding (Traylor, Merullo, Frank, and Pavlick, 2024; see also Swan and Wyble, 2014)", while other times they are written as "function (O'Reilly & Frank, 2006)"...

      Lastly, there is an empty '3.1' section in the supplementary material that should be addressed.

      The citation issues were fixed. The supplementary information was cleaned and the missing section was removed. Thank you for mentioning these errors.  

      Reviewer 2:

      Thank you for the following recommendations and suggestions. We respond to each individual point based on the numbering system used in your review.  

      (1) This paper utilizes the experimental paradigm of visual working memory, in which different visual stimuli are sequentially loaded into the working memory system, and the accuracy of memory for these stimuli is calculated.

      The authors could further plot the memory accuracy curve as the number of items (N) increases, under both chunking and non-chunking strategies. This would allow for the examination of whether memory accuracy suddenly declines at a specific value of N (denoted as Nc), thereby determining the limited capacity of working memory within this experimental framework, which is about 4 different items or chunks. Additionally, it could be investigated whether the value of Nc is larger when the chunking strategy is applied.

      We have included an additional plot (Probability of Recall) as a supplemental figure to Figure 5 to explore the probability of recall as a function of set size for both chunking and no chunking models.  This plot shows that the chunking model increases probability of recall when set size exceeds allocated capacity (but that nevertheless both models show decreases in recall with set size, consistent with the literature).

      (2) The primacy effect or recency effect observed in the experiments and traditional working memory models, including the slot model and the limited resource model, should be examined to see if it also appears in this model.

      The literature on human working memory shows a prevalent recency effect (but not a primacy effect, which is thought to be due to episodic memory, and which is not included in our model). We have added a section showing that our model demonstrates clear recency effects.

      (3) The construction of the model and the single neuron dynamics involved need further refinement and optimization:

      Model Description: The details of the model construction in the paper need to be further elaborated to help other researchers better understand and apply the model in reproducing or extending research. Specifically:

      a) The construction details of different modules in the model (such as Input signal, BG, striatum, superficial PFC, deep PFC) and the projection relationships between different modules. Adding a diagram to illustrate the network construction would be beneficial.

      To aid in the understanding of the model construction and model components, we have included an additional figure (Figure 1: Base Model) that explains the key layers and components of the model.  We have also altered the overall model figures to show more clearly that the inputs project to both PFC and striatum, to highlight that information is temporarily represented in superficial PFC layers even before striatal gating, which is needed for storage after the input decays.

      We have expanded the methods and equations and we also provide a link to the model github for purposes of reproducibility and sharing.  

      A base model figure was added to specify key connections.  

      a) The numbers of excitatory and inhibitory neurons within different modules and the connections between neurons.

      We added clarification on the type of connections between layers (specifying which are fixed and learned). We have also added the size of layers in a new appendix section “Layer Sizes and Inner Mechanics”

      b) The dynamics of neurons in different modules need to be elaborated, including the description of the dynamic equations of variables (such as x) involved in single neuron equations.

      Single neuron dynamics are explained in equations 1-4. Equations 5-6 explain how activation travels between layers. The specific inhibitory dynamics in the chunking layer are elaborated in Figure 4. PBWM Model and Chunking Layer Details. The Appendix section “Neural model  implementational details” states the key equations, neural information and connectivity. Since there is a large corpus of background information underlying these models, we have linked the Emergent github and specifically the Computational Cognitive Neuroscience textbook which has a detailed description of all equations. For the sake of paper length and understability, we chose the most relevant equations that distinguish our model.  

      c) The selection of parameters in the model, especially those that significantly affect the model's performance.

      The appendix section hyperparameter search details some of the key parameters and why those values were chosen.  

      d) The model employs a sequential working memory paradigm, the forms of external stimuli involved in the encoding and recalling phases (including their mathematical expressions, durations, strengths, and other parameters) need to be elaborated further.

      We appreciate this comment. We have expanded the Appendix section “Continuous Stimuli” to include the details of stimuli presentation (including durations etc).  

      (4) The figures in the paper need optimization. For example, the size of the schematic diagram in Figure 2 needs to be enlarged, while the size of text such as "present stimulus 1, 2, recall stimulus 1" needs to be reduced. Additionally, the citation of figures in the main text needs to be standardized. For example, Figure 1b, Figure 1c, etc., are not cited in the main text.

      The task sequence figure (original Figure 2) has been modified and following your suggestions, text sizes have been modified.  

      (5) Section 3.1 in the appendix is missing.

      Supplemental section 3.1 is removed.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      MacDonald et al., investigated the consequence of double knockout of substance P and CGRPα on pain behaviors using a newly created mouse model. The investigators used two methods to confirm knockout of these neuropeptides: traditional immunolabeling and a neat in vitro assay where sensory neurons from either wildtype or double knock are co-cultured with substance P "sniffer cells", HEK cells stably expressing NKR1 (a substance P receptor), GCaMP6s and Gα15. It should be noted that functional assays confirming CGRPα knockout were not performed. Subsequently, the authors assayed double knockout mice (DKO) and wildtype (WT) mice in numerous behavioral assays using different pain models, including acute pain and itch stimuli, intraplanar injection of Complete Freund's Adjuvant, prostaglandin E2, capsaicin, AITC, oxaliplatin, as well as the spared nerve injury model. Surprisingly, the authors found that pain behaviors did not differ between DKO and WT mice in any of the behavioral assays or pain paradigms. Importantly, female and male mice were included in all analyses. These data are important and significant, as both substance P and CGRPα have been implicated in pain signaling, though the magnitude of the effect of a single knockout of either gene has been variable and/or small between studies.

      The conclusions of the study are largely supported by the data; however, additional experimental controls and analyses would strengthen the authors claims.

      We thank the reviewer for their insightful comments and have answered them below.

      (1) The authors note that single knockout models of either substance P or CGRPα have produced variable effects on pain behaviors that are study-dependent. Therefore, it would have strengthened the study if the authors included these single knockout strains in a side-by-side analysis (in at least some of the behavioral assays), as has been done in prior studies in the field when using double- or triple-knockout mouse models (for example, see PMID: 33771873). If in the authors hands, single knockouts of either peptide also show no significant differences in pain behaviors, then the finding that double knockouts also do not show significant differences would be less surprising.

      In our study, we found no phenotypic differences between WT and DKO mice, suggesting Substance P and CGRPα are largely dispensable for pain behavior. We agree that if we had we observed significant changes in behavior, it would have been interesting to examine the effects of knocking out each gene individually to determine which peptide is responsible for the phenotype. However, given the double deletion had no effect, we can predict that loss of each alone would have no or minor effects. In line with this, a more recent study that comprehensively phenotyped the Calca KO mouse found no deficits in a range of danger related behaviors (PMID: 34376756). Overall, as we are reporting negative data about the Double KO, we do not believe extensive studies of the single KOs is necessary to support the findings of our paper.

      (2) It is unclear why the authors only show functional validation of substance P knockout using "sniffer" cells, but not CGRPα. Inclusion of this experiment would have added an additional layer of rigor to the study.

      Imaging of CGRPα release is more challenging using the ‘sniffer’ approach because functional CGRP receptors require the expression of two genes: Calcrl (or Calcr) along with Ramp1. We now have succeeded in generating a new stable cell line expressing Calcrl and Ramp1, along with GCaMPs and human Galpha15 and include new data in the revised Figure 1F-H and Figure Supplement 1B. These cells respond robustly to CGRPalpha, but not to SP. In contrast, the existing SP cell line responds to SP but not CGRPalpha. Capsaicin evokes a strong response in these cells in co-culture with DRGs. This response is dramatically reduced in the DKO. This data therefore confirms our mice have a loss of CGRPalpha signaling as indicated by IHC.

      (3) The authors should be a bit more reserved in the claims made in the manuscript. The main claim of the study is that "CGRPα and substance P are not required for pain transmission." However, the authors also note that neuropeptides can have opposing effects that may produce a net effect of no change. In my view, the data presented show that double knockout of substance P and CGRPα do not affect somatic pain behaviors, but do not preclude a role for either of these molecules in pain signaling more generally. Indeed, the authors also note that these neuropeptides could be involved in nociceptor crosstalk with the immune or vascular systems to promote headache. The authors only assayed pain responses to glabrous skin stimulation. How the DKO mice would behave in orofacial pain assays, migraine assays, visceral pain assays, or bone/joint pain assays, for example, was not tested. I do not suggest the authors include these experiments, only that they address the limitations/weaknesses of their study more thoroughly.

      The reviewer makes an important point that we agree with. Our study assesses acute and chronic pain in peptide DKO mice lacking Substance P and CGRPα. Most of our data focuses on the hindpaw as pain in the paw is the gold-standard approach for phenotyping pain targets and numerous well-validated chronic pain models have been developed for this body site.  However, to extend the conclusions to other tissues, we did also look at visceral pain and GI distress using acetic acid and LiCl models (Figure 2J and Figure 2 supplement). We agree with the reviewer that given the utility of CGRP monoclonal antibodies, migraine experiments would be interesting for future studies using these mice, a point we highlight in the discussion. Bone/joint pain is also clearly important from a translational perspective, but outside the scope of the current study.

      (4) A more minor but important point, the authors do not describe the nature of the WT animals used. Are the littermates or a separately maintained colony of WT animals? The WT strain background should be included in the methods section.

      The WT strain are C57/BL6j from Jackson Lab. This has been added to the methods.

      Reviewer #2 (Public Review):

      Summary:

      The paper aimed to examine the effect of co-ablating Substance P and CGRPα peptides on pain using Tac1 and Calca double knockout (DKO) mice. The authors observed no significant changes in acute, inflammatory, and neuropathic pain. These results suggest that Substance P and CGRPα peptides do not play a major role in mediating pain in mice. Moreover, they reveal that the lack of behavioral phenotype cannot be explained by the redundancy between the two peptides, which are often co-expressed in the same neuron

      Strengths:

      The paper uses a straightforward approach to address a significant question in the field. The authors confirm the absence of Substance P and CGRPα peptides at the levels of DRG, spinal cord, and midbrain. Subsequently, they employ a comprehensive battery of behavioral tests to examine pain phenotypes, including acute, inflammatory, and neuropathic pain. Additionally, they evaluate neurogenic inflammation by measuring edema and extravasation, revealing no changes in DKO mice. The data are compelling, and the study's conclusions are well-supported by the results. The manuscript is succinct and well-presented.

      We thank the reviewer for their enthusiasm for the importance of our work.

      Reviewer #3 (Public Review):

      In this study, the authors were assessing the role of double global knockout of substance P and CGPRα on the transmission of acute and chronic pain. The authors first generated the double knockout (DKO) mice and validated their animal model. This is then followed by a series of acute and chronic pain assessments to evaluate if the global DKO of these neuropeptides are important in modulating acute and chronic pain behaviors. Authors found that these DKO mice Substance P and CGRPα are not required for the transmission of acute and chronic pain although both neuropeptides are strongly implicated in chronic pain. This study does provide more insight into the role of these neuropeptides on chronic pain processing, however, more work still needs to be done. (see the comments below).

      We thank the reviewer for their detailed and constructive feedback, and below outline the steps we have taken to answer their concerns.

      (1) In assessing the double KO (result #1), why are different regions of the brains shown for substance P and CGRPα (for example, midbrain for substance P and amygdala for CGRPα)? Since the authors mentioned that these peptides co-expressed in the brain (as in the introduction), shouldn't the same brain regions be shown for both IHC? It would be ideal if the authors could show both regions (midbrain and amygdala) in addition to the DRG and spinal cord for both peptides in their findings.<br /> In addition, since this is double KO, the authors should show more representative IHC-stained brain regions (spanning from the anterior to posterior).

      We could not co-stain both SP and CGRP in the same sections as the DKO mouse has endogenous GFP and RFP fluorescence, limiting us to one channel (far red). Specifically, we use a Calca KO that is a Cre:GRP knock-in/knockout (Chen et al 2018, PMID30344042) and Tac1 KO is a tagRFP knock-in/knockout (Wu et al 2018 PMID29485996). This is why we show different brain sections.

      (2) It is also unclear as to why the authors only assessed the loss of substance P signaling in the double KO mice. Shouldn't the same be done for CGRPα signaling? Either the authors assess this, or the authors have to provide clear explanations as to why only substance P signaling was assessed.

      As noted in our response to Reviewer 1, imaging of CGRP release is more challenging using the ‘sniffer’ approach because functional CGRP receptors require the expression of two genes: Calcrl (or Calcr) along with Ramp1. We have now generated this cell line and performed the experiment (see revised Figure 1 and Figure 1 Supplement).

      (3) Has these animal's naturalistic behavior been assessed after the double KO (food intake, sleep, locomotion for example)? I think this is important as changes to these naturalistic behaviors can affect pain processes or outcomes.

      We agree that assessment of naturalistic behavior including food intake, sleep and locomotion would be interesting to look at in DKO mice. However, our study is focused on acute and chronic pain behavior of these animals, and therefore a comprehensive phenotypic assessment of naturalistic home-cage behavior is outside the scope of our study.

      (4) Figure 2H: The authors acknowledge that there is a trend to decrease with capsaicin-evoked coping-like responses. However, a close look at the graph suggests that the lack of significance could be driven by 1 mouse. Have the authors run an outlier test? Alternatively, the authors should consider adding more n to these experiments to verify their conclusions.

      We were reluctant to add more animals searching for significance. Instead, we investigated the potential phenotype further by looking at cfos staining in the cord and found no differences (Figure 2, supplement 1). This result suggests loss of the two peptides does not grossly disrupt capsaicin evoked pain signal transmission between the nociceptor and post-synaptic dorsal neurons in the spinal cord.

      (5) Similarly, the values for WT in the evoked cFos activity (Figure 2- Suppl Figure 1) are pretty variable. Considering that the n number is low (n = 5), authors should consider adding more n.<br /> Also, since the n number is low in this experiment (eg. 5 vs 4), does this pass the normality test to run a parametric unpaired t-test? Either the authors increase their n numbers or run the appropriate statistical test.

      As described in the statistical tables, the Shapiro-Wilk test indicates these data do pass the normality test. Therefore, we retain the use of the unpaired t test, which demonstrates no significant difference between the groups.

      (6) In most of the results, authors ran a parametric test despite the low n number. Authors have to ensure that they are carrying out the appropriate statistical test for their dataset and n number.

      We now provide a table of the statistical results, which provides detailed information about all statistical tests performed in this study. For experiments where we make a single comparison between the two distributions (WT vs DKO), we have run a Shapiro-Wilk test. Where the data from both groups pass the normality test, we retain the use of the unpaired t test. Where the Shapiro-Wilk test indicates data from either group are unlikely to be normally distributed, we now use a Mann-Whitney U test to compare the groups, as this non-parametric test makes no assumptions about the underlying distribution.

      Many experiments involved two factors (genotype, and e.g. temperature, drug, time-point). These data were analyzed in the original submission using 2-WAY ANOVA or Repeated Measures 2-WAY ANOVA, followed by post-hoc Sidak’s tests to compute p values adjusted for multiple comparisons. Because there is no widely agreed non-parametric alternative to 2-WAY ANOVA for analyzing data with two factors and that enables us to account for multiple comparisons, we used 2-WAY ANOVA as is typically used in the field for these kinds of experiments. We reasoned sticking with the 2-WAY ANOVA was the best course of action based on information provided by the statistical software used for this study - https://www.graphpad.com/support/faq/with-two-way-anova-why-doesnt-prism-offer-a-nonparametric-alternative-test-for-normality-test-for-homogeneity-of-variances-test-for-outliers/

      We note that regardless of the test, our conclusion that there are no major changes in acute or chronic pain behaviors are clear and strongly supported.

      (7) Along the same line of comment with the previous, authors should increase the n number for DKO for staining (Figure 4) as n number is only 3 and there is variability in the cFos quantification in the ipsilateral side.

      We believe this is not necessary as the finding is clear that there is no difference.

      (8) Authors should provide references for statement made in Line 319-321 as authors mentioned that there are accumulating evidence indicating that secretion of these neuropeptides from nociceptor peripheral terminals modulates immune cells and the vasculature in diverse tissues.

      We now provide several references to primary papers and reviews supporting this statement.

      (9) Authors state that the sample size used was similar to those from previous studies, but no references were provided. Also, even though the sample sizes used were similar, I believe that the right statistic test should be used to analyze the data.

      We have now cited several classic studies phenotyping mouse KOs in pain in the methods that used similar sample sizes. As detailed above, we have taken the reviewer’s feedback on board and performed normality testing to ensure the correct statistical test is used for each experiment.

      (10) In the discussion, the authors noted that knocking out of a gene remains the strongest test of whether the molecule is essential for a biological phenomenon. At the same time, it was acknowledged that Substance P infusion into the spinal cord elicits pain, but it is analgesic in the brain. The authors might want to expand more on this discussion, including how we can selectively assess the role of these neuropeptides in areas of interest. For example, knocking out both Substance P and CGRPα in selected areas instead of the global KO since there are reported compensatory effects.

      This is highlighted in the closing paragraph: “Emerging approaches to image and manipulate these molecules (Girven et al., 2022; Kim et al., 2023), as well as advances in quantitating pain behaviors (Bohic et al., 2023; MacDonald and Chesler, 2023), may ultimately reveal the fundamental roles of neuropeptides in generating our experience of pain.” The Kim preprint (now published, and so the citation has been updated in the text) describes a method of inactivating neuropeptide transmission in select brain regions in a cell-type specific manner.

      Recommendations for the authors:

      Reviewer #2 (Recommendations For The Authors):

      I do not have any major comments. My minor comments are as follows:

      (1) What was the control group for all behavioral studies? Was it WT from an independent colony or one of the littermates was used for generating controls?

      We used C57/Bl6 mice from Jax. This is now mentioned in methods.

      (2) In Fig. 2H, it seems that the effect will become significant if several mice are added.

      We are reluctant to add mice searching for significance. Sample sizes were determined before we collected the data blind.

      (3) There is no figure 3, but two figures 4.

      Thank you. This has been corrected.

      (4) Multiple typos in the legend for figure 4 (lines 234-254). Line 242 (& n=8 (3M, 3F)), line 243 (swelling and plasma), line 252 ((n=8 for) & n=6 for DKO (4M, 4F)).

      Thank you. This has been corrected.

      (5) In Figure 4 (lines 273-285), the contralateral side is mentioned in B but no images are shown.

      Thank you. We removed the mention.

      (6) Although ligand knockouts cannot be compared directly with receptor inhibition, the readers could benefit from discussing studies of receptor ablation and/or pharmacological inhibition.

      We do discuss the classic studies of receptor KO, and the clinical data on receptor blockers here –

      “However, selective antagonists of the Substance P receptor NKR1 failed to relieve chronic pain in human clinical trials (Hill, 2000). Although CGRP monoclonal antibodies and receptor blockers have proven effective for subsets of migraine patients, their usefulness for other types of pain in humans is unclear (De Matteis et al., 2020; Jin et al., 2018). In line with this, knockout mice deficient in Substance P, CGRPα or their receptors have been reported to display some pain deficits, but the analgesic effects are neither large nor consistent between studies (Cao et al., 1998; De Felipe et al., 1998; Guo et al., 2012; Salmon et al., 2001, 1999; Zimmer et al., 1998).” 

      Reviewer #3 (Recommendations For The Authors):

      Minor comments:

      (1) Figure 1E: What does chambers mean? Additionally, are the 12 chambers equally from the male and female samples (6 from male and 6 from female)?

      We have changed this to well. Each replicate is an individual well from 8 well chamber slide. In all these experiments, the wells are approximately evenly distributed by mouse, because from each mouse we cultured around 8 wells’ worth of DRGs.

      (2) Figure 1D: What does low and high mean in the Hargreaves test?

      These refer to a low and high active intensity of the radiant heat stimulus. Number is now described in the methods. 40 and 55 in the intensity units used by the instrument.

      (3) Figure 2-Suppl Figure 1: Authors should provide a bigger image of the image so that it is clearer to the readers.

      We think the image is of a reasonable size and comparable to the images used elsewhere in the paper.

      (4) Authors should consider labeling their supplementary figures in running numbers or combining supplementary figures together to avoid confusion. For example, Figure 2-Supplementary Figure 1 and Figure 2- Supplementary Figure 2 can be combined as just Supplementary Figure 2.

      We agree with the reviewer this would be clearer, but we have followed eLife’s convention for labelling and numbering supplements.

      (5) Figure 3 is mislabeled as Figure 4.

      Thank you. We have corrected this.

      (6) Only female mice were used in the CFA experiment, which does not go in line with the rest of the results which consist of both sexes.

      We have repeated the experiment with additional male mice. To be consistent with the von frey data, these were followed for 7 days, and so the figure now shows a 7 day time course.

      (7) Typo in line 243. The word "and" is subscript.

      Thank you. We have corrected this.

      (8) There is a typo in the legend for Figure 4 where E is labeled I, G is labeled as F, and J is labeled as J.

      Thank you. We have corrected this.

      (9) Authors should specify what "several weeks" means (Line 263).

      It means three weeks. We tested to 21 days. We will replace with three.

      (10) Authors should specify what "one day" means (Line 267). For example, how many days after the intraplantar oxaliplatin treatment? Also, authors should justify why that specific time point was selected or have a reference for it.

      This means one day after - 24 hours. Please see PMID: 33693512. Two references are provided in them methods.

      (11) Figure 4 legend: authors should again be specific on what "prolonged" entails (Line 277).

      We have replaced prolonged with 30 minutes brushing. Specifically, 3 x 10 min stim period, with 1 min rest between stim. It is in the methods.

      (12) In the methods section, authors state that both male and female mice were used for all experiments. However, only female mice were used in the CFA experiment (see minor comment #6). Authors should verify and correct this.

      This is correct. We only used female mice for one of the groups. We have since repeated with males, now included in the data.

      (13) Authors should be more specific in the methods section on how long the habituation per day, how many days and what were the mice habituation to (experimenter, room, chamber, etc)?

      As noted in the methods, mice are habituated for at least an hour to the chambers, and thus implicitly to the room. We do not perform explicit habituation to the investigator such as repeated handling.

      (14) Authors need to provide more information on the semi-automated procedure they are referring to in Line 397. Also, authors should also provide the criteria for cFos quantification (eg. Intensity, etc). If this has been published before, they should provide the reference.

      We have added this. We used the ‘Find maxima’ and ‘Analyze particles’ functions in FIJI, followed by a manual curation step.

      (15) How much acetone was applied and how was it applied to the paw? (Line 495)

      We used the same applicator (1ml syringe with a well at the top) to generate a droplet of acetone that was used for all mice. This has been added to methods.

      (16) Authors should specify the amount of capsaicin injected in μl (Line 500).

      20 ul. We have added this.

      (17) Authors should explain or reference why they are analyzing the 15 min interval between 5 and 20 minutes for injection (Line507-508).

      Acetic acid behaviour lasts around 30 mins in our hands. We chose the 15 minute interval because it reduces burdensome hand scoring time by 50% versus doing the whole 30 mins. We reasoned that in the first 5 mins post injection the animal behaviour may be contaminated by stress related to handling, injection and return to chamber. Thus, 5 and 20 minutes provided a sensible time-frame for scoring the behavior when it is at its peak.

      (18) Authors have to provide more information/explanation on how they decide on the conditioned taste aversion protocol. Like why they do 30 mins exposure to a single water-containing bottle followed 90 mins exposure to both bottles. If this has been published before, they should provide the reference.

      We read dozens of different published protocols in the literature, and piloted one that was something of an amalgam of some of them with various adaptations of convenience. Because it worked on our first attempt, we stuck to it. The advantage of the CTA assay is it is incredibly robust to changes in the specificities of the paradigm, evincing the clear survival value of learning to avoid tastes that make you sick.

      (19) Authors again should provide more detail in their methods section.

      a. Specify the time frame that they are assessing here (Line 533).

      This can be seen in the Figure. 0 to 120 mins. We have added it to the methods.

      b. How long were the mice allowed to recover post-SNI before mechanical allodynia was assessed (Line 545)?

      This is apparent in the figures. 2 days to 21 days. We have added it to the methods.

      c. How much of the oxaliplatin was injected into the mice?

      40 ug / 40 ul (see PMID:33693512)

      Editors note: Reviewers agreed that addressing the concerns about power, outliers, and statistics, as well as functional validation of CGRPα would raise the strength of evidence to compelling, and inclusion of comparison to single KO would raise it to exceptional.

      Should you choose to revise your manuscript, please check to ensure full statistical reporting including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05.

    1. Author response:

      Regarding a future revised version, we plan to:

      • refer to the "MoMac-VERSE" study according to the original report.

      • modify incorrectly formatted references.

      • modify the text to acknowledge the heterogeneity and variability in the response of primary cells to the GSK3 inhibitor.

      • improve the explanation of the reanalysis of single cell RNAseq data in Figure 7 (ref. 47, GSE120833), and re-adapt the graphs of the scRNA-Seq data using different plot parameters (e.g., reduction = "umap.scvi") to provide a more friendly-user visualization including bona fide macrophage markers for each subpopulation.

      • include statistical analyses in each one of the figure legends

      • perform additional analyses (e.g., dose-response and kinetics of CHIR-99021 effects) and mechanistic studies (e.g., role of proteasome) to further dissect the re-programming ability of the GSK3/MAFB axis.

    1. Author response:

      eLife Assessment

      This study provides valuable insights into the behavioral, computational, and neural mechanisms of regime shift detection, by identifying distinct roles for the frontoparietal network and ventromedial prefrontal cortex in sensitivity to signal diagnosticity and transition probabilities, respectively. The findings are supported by solid evidence, including an innovative task design, robust behavioral modeling, and well-executed model-based fMRI analyses, though claims of neural selectivity would benefit from more rigorous statistical comparisons. Overall, this work advances our understanding of how humans adapt belief updating in dynamic environments and offers a framework for exploring biases in decision-making under uncertainty.

      Thank you for reviewing our manuscript. We appreciate the editors’ assessment and the reviewers’ constructive comments. Below we address the reviewers’ comments. In particular, we addressed Reviewer 1’s comments on (1) neural selectivity by performing statistical comparisons and (2) parameter estimation by providing more details on how the system-neglect model was parameterized. We addressed Reviewer 2’s comments on (1) our neuroimaging results regarding frontoparietal network and (2) model comparisons.  

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The study examines human biases in a regime-change task, in which participants have to report the probability of a regime change in the face of noisy data. The behavioral results indicate that humans display systematic biases, in particular, overreaction in stable but noisy environments and underreaction in volatile settings with more certain signals. fMRI results suggest that a frontoparietal brain network is selectively involved in representing subjective sensitivity to noise, while the vmPFC selectively represents sensitivity to the rate of change.

      Strengths:

      (1) The study relies on a task that measures regime-change detection primarily based on descriptive information about the noisiness and rate of change. This distinguishes the study from prior work using reversal-learning or change-point tasks in which participants are required to learn these parameters from experiences. The authors discuss these differences comprehensively.

      Thank you for recognizing our contribution to the regime-change detection literature and our effort in discussing our findings in relation to the experience-based paradigms.

      (2) The study uses a simple Bayes-optimal model combined with model fitting, which seems to describe the data well.

      Thank you for recognizing the contribution of our Bayesian framework and system-neglect model.

      (3) The authors apply model-based fMRI analyses that provide a close link to behavioral results, offering an elegant way to examine individual biases.

      Thank you for recognizing our execution of model-based fMRI analyses and effort in using those analyses to link with behavioral biases.

      Weaknesses:

      My major concern is about the correlational analysis in the section "Under- and overreactions are associated with selectivity and sensitivity of neural responses to system parameters", shown in Figures 5c and d (and similarly in Figure 6). The authors argue that a frontoparietal network selectively represents sensitivity to signal diagnosticity, while the vmPFC selectively represents transition probabilities. This claim is based on separate correlational analyses for red and blue across different brain areas. The authors interpret the finding of a significant correlation in one case (blue) and an insignificant correlation (red) as evidence of a difference in correlations (between blue and red) but don't test this directly. This has been referred to as the "interaction fallacy" (Niewenhuis et al., 2011; Makin & Orban de Xivry 2019). Not directly testing the difference in correlations (but only the differences to zero for each case) can lead to wrong conclusions. For example, in Figure 5c, the correlation for red is r = 0.32 (not significantly different from zero) and r = 0.48 (different from zero). However, the difference between the two is 0.1, and it is likely that this difference itself is not significant. From a statistical perspective, this corresponds to an interaction effect that has to be tested directly. It is my understanding that analyses in Figure 6 follow the same approach.

      Relevant literature on this point is:

      Nieuwenhuis, S, Forstmann, B & Wagenmakers, EJ (2011). Erroneous analyses of interactions in neuroscience: a problem of significance. Nat Neurosci 14, 1105-1107. https://doi.org/10.1038/nn.2886

      Makin TR, Orban de Xivry, JJ (2019). Science Forum: Ten common statistical mistakes to watch out for when writing or reviewing a manuscript. eLife 8:e48175. https://doi.org/10.7554/eLife.48175

      There is also a blog post on simulation-based comparisons, which the authors could check out: https://garstats.wordpress.com/2017/03/01/comp2dcorr/

      I recommend that the authors carefully consider what approach works best for their purposes. It is sometimes recommended to directly compare correlations based on Monte-Carlo simulations (cf Makin & Orban). It might also be appropriate to run a regression with the dependent variable brain activity (Y) and predictors brain area (X) and the model-based term of interest (Z). In this case, they could include an interaction term in the model:

      Y = \beta_0 + \beta_1 \cdot X + \beta_2 \cdot Z + \beta_3 \cdot X \cdot Z

      The interaction term reflects if the relationship between the model term Z and brain activity Y is conditional on the brain area of interest X.

      Thank you for this great suggestion. We tested the difference in correlation both parametrically and nonparametrically. Their results were identical. In our parametric test, we used the Fisher z transformation to transform the difference in correlation coefficients to the z statistic (Fisher, 1921). That is, for two correlation coefficients, r<sub>blue</sub> (the correlation between behavioral slope, and neural slope estimated at change-consistent signals; sample size n<sub>blue</sub>) and  r<sub>red</sub>, (the correlation between behavioral slope, and neural slope estimated at change-consistent signals; sample size n<sub>red</sub>), the z statistic of the difference in correlation is given by

      We found that among the five ROIs in the frontoparietal network, two of them, namely the left IFG and left IPS, the difference in correlation was significant (one-tailed z test; left IFG: z=1.8355, p=0.0332; left IPS: z=2.3782, p=0.0087). For the remaining three ROIs, the difference in correlation was not significant (dmPFC: z=0.7594, p=0.2238 ; right IFG: z=0.9068, p=0.1822; right IPS: z=1.3764, p=0.0843). We chose one-tailed test because we already know the correlation under the blue signals was significantly greater than 0. Hence the alternative hypothesis is that r<sub>blue</sub>r<sub>red</sub>>0.

      In our nonparametric test, we performed nonparametric bootstrapping to test for the difference in correlation. That is, we resampled with replacement the dataset (subject-wise) and used the resampled dataset to compute the difference in correlation. We then repeated the above for 100,000 times so as to obtain the distribution of the correlation difference. We then tested for significance and estimated p-value based on this distribution. Consistent with our parametric tests, here we also found that the difference in correlation was significant in left IFG and left IPS (left IFG: r<sub>blue</sub>r<sub>red</sub>=0.46, p=0.0496; left IPS: r<sub>blue</sub>r<sub>red</sub>=0.5306, p=0.0041), but was not significant in dmPFC, right IFG, and right IPS (dmPFC: r<sub>blue</sub>r<sub>red</sub>=0.1634, p=0.1919; right IFG: r<sub>blue</sub>r<sub>red</sub>=0.2123, p=0.1681; right IPS: r<sub>blue</sub>r<sub>red</sub>=0.3434, p=0.0631).

      We will update these results in the revised manuscript. In summary, we found that the left IFG and left IPS in the frontoparietal network differentially responded to signals consistent with change (blue signals) compared with signals inconsistent with change (red signals). First, the neural sensitivity to signal diagnosticity measured when signals consistent with change appeared (blue signals) significantly correlated with individual subjects’ behavioral sensitivity to signal diagnosticity (r<sub>blue</sub>). By contrast, neural sensitivity to signal diagnosticity measured when signals inconsistent with change appeared did not significantly correlate with behavioral sensitivity (r<sub>red</sub>). Second, the difference in correlation, r<sub>blue</sub>r<sub>red</sub>, was statistically significant between correlation obtained at signals consistent with change and correlation obtained at signals inconsistent with change.

      Another potential concern is that some important details about the parameter estimation for the system-neglect model are missing. In the respective section in the methods, the authors mention a nonlinear regression using Matlab's "fitnlm" function, but it remains unclear how the model was parameterized exactly. In particular, what are the properties of this nonlinear function, and what are the assumptions about the subject's motor noise? I could imagine that by using the inbuild function, the assumption was that residuals are Gaussian and homoscedastic, but it is possible that the assumption of homoscedasticity is violated, and residuals are systematically larger around p=0.5 compared to p=0 and p=1. Relatedly, in the parameter recovery analyses, the authors assume different levels of motor noise. Are these values representative of empirical values?

      We thank the reviewer for this excellent point. The reviewer touched on model parameterization, assumption of noise, and parameter recovery analysis, which we answered below.

      On our model was parameterized

      We parameterized the model according to the system-neglect model in Eq. (2) and estimated the alpha parameter separately for each level of transition probability and the beta parameter separately for each level of signal diagnosticity. As a result, we had a total of 6 parameters (3 alpha and 3 beta parameters) in the model. The system-neglect model is then called by fitnlm so that these parameters can be estimated. The term ‘nonlinear’ regression in fitnlm refers to the fact that you can specify any model (in our case the system-neglect model) and estimate its parameters when calling this function. In our use of fitnlm, we assume that the noise is Gaussian and homoscedastic (the default option).

      On the assumptions about subject’s motor noise

      We wish to emphasize that we did not call the noise ‘motor’ because it can be estimation noise as well. Regardless, in the context of fitnlm, we assume that the noise is Gaussian and homoscedastic.

      On the possibility that homoscedasticity is violated

      In the revision, we plan to examine this possibility (residuals larger around p=0.5 compared with p=0 and p=1).

      On whether the noise levels in parameter recovery analysis are representative of empirical values

      To address the reviewer’s question, we conducted a new analysis using maximum likelihood estimation to estimate the noise level of each individual subject. We proceeded in the following steps. First, for each subject separately, we used the parameter estimates of the system-neglect model to compute the period-wise probability estimates of regime shift. As a reminder, we referred to a ‘period’ as the time when a new signal appeared during a trial (for a given transition probability and signal diagnosticity). Each trial consisted of 10 successive periods. Second, we computed the period-wise likelihood, the probability of observing the subject’s actual probability estimate given the probability estimate predicted by the system-neglect model and the noise level. Here we define noise as the standard deviation of a Gaussian distribution centered at the model-predicted probability estimate. We then summed over all periods the negative logarithm of likelihood and used MATLAB’s minimization algorithm (the ‘fmincon’ function) to obtain the noise estimate that minimized the sum of negative log likelihood (thus the noise estimate that maximized the sum of log likelihood). Across subjects, we found that the mean noise estimate was 0.1480 and ranged from 0.0816 to 0.3239. The noise estimate of each subject can be seen in the figure below.

      Author response image 1.

      Compared with our original parameter recovery analysis where the maximum noise level was set at 0.1, our data indicated that some subjects’ noise was larger than this value. Therefore, we expanded our parameter recovery analysis to include noise levels beyond 0.1 to up to 0.3. We found good parameter recovery across different levels of noise, with the Pearson correlation coefficient between the input parameter values used to simulate data and the estimated parameter values greater 0.95 (Supplementary Fig. S3). The results will be updated in Supplementary Fig. S3.

      Author response image 2.

      Parameter recovery. We simulated probability estimates according to the system-neglect model. We used each subject’s parameter estimates as our choice of parameter values used in the simulation. Using simulated data, we estimated the parameters (𝛼 and 𝛽) in the system-neglect model. To examine parameter recovery, we plotted the parameter values we used to simulate the data against the parameter estimates we obtained based on simulated data and computed their Pearson correlation. Further, we added different levels of Gaussian white noise with standard deviation 𝜎 = 0.01, 0.05, 0.1,0.2, 0.3 to the simulated data to examine parameter recovery and show the results respectively in Fig. A, B, C, D, and E. For each noise level, we show the parameter estimates in the left two graphs. In the right two graphs, we plot the parameter estimates based on simulated data against the parameter values used to simulate the data. A. Noise 𝜎 = 0.01. B. Noise 𝜎 = 0.05. C. Noise 𝜎 = 0.1. D. Noise 𝜎 = 0.2. E. Noise 𝜎 = 0.3.

      We will update the parameter recovery section (p. 44) and Supplementary Figure S3 to incorporate these new results:

      “We implemented 5 levels of noise with σ={0.01,0.05,0.1,0.2,0.3} and examined the impact of noise on parameter recovery for each level of noise. These noise levels covered the range of empirical noise levels we estimated from the subjects. To estimate each subject’s noise level, we carried out maximum likelihood estimation in the following steps. First, for each subject separately, we used the parameter estimates of the system-neglect model to compute the period-wise probability estimates of regime shift. As a reminder, we referred to a ‘period’ as the time when a new signal appeared during a trial (for a given transition probability and signal diagnosticity). Each trial consisted of 10 successive periods. Second, we computed the period-wise likelihood, the probability of observing the subject’s actual probability estimate given the probability estimate predicted by the system-neglect model and the noise level. Here we define noise as the standard deviation of a Gaussian distribution centered at the model-predicted probability estimate. We then summed over all periods the negative natural logarithm of likelihood and used MATLAB’s minimization algorithm (the ‘fmincon’ function) to obtain the noise estimate that minimized the sum of negative log likelihood (thus the noise estimate that maximized the sum of log likelihood). Across subjects, we found that the mean noise estimate was 0.1480 and ranged from 0.0816 to 0.3239 (Supplementary Figure S3).”

      The main study is based on N=30 subjects, as are the two control studies. Since this work is about individual differences (in particular w.r.t. to neural representations of noise and transition probabilities in the frontoparietal network and the vmPFC), I'm wondering how robust the results are. Is it likely that the results would replicate with a larger number of subjects? Can the two control studies be leveraged to address this concern to some extent?

      It would be challenging to use the control studies to address the robustness concern. The control studies were designed to address the motor confounds. They were less suitable, however, for addressing the individual difference issue raised by the reviewer. We discussed why this is the case below.

      The two control studies did not allow us to examine individual differences – in particular with respect to neural selectivity of noise and transition probability – and therefore we think it is less likely to leverage the control studies. Having said that, it is possible to look at neural selectivity of noise (signal diagnosticity) in the first control experiment where subjects estimated the probability of blue regime in a task where there was no regime change (transition probability was 0). However, the fact that there were no regime shifts in the first control experiment changed the nature of the task. Instead of always starting at the Red regime in the main experiment, in the first control experiment we randomly picked the regime to draw the signals from. It also changed the meaning and the dynamics of the signals (red and blue) that would appear. In the main experiment the blue signal is a signal consistent with change, but in the control experiment this is no longer the case. In the main experiment, the frequency of blue signals is contingent upon both noise and transition probability where blue signals are less frequent than red signals because of the small transition probabilities. But in the first control experiment, the frequency of blue signals is not less frequent because the regime was blue in half of the trials. Due to these differences, we do not see how analyzing the control experiments could help in establishing robustness because we do not have a good prediction as to whether and how the neural selectivity would be impacted by these differences.

      We can address the issue of robustness through looking at the effect size. In particular, with respect to individual differences in neural sensitivity of transition probability and signal diagnosticity, since the significant correlation coefficients between neural and behavioral sensitivity were between 0.4 and 0.58 for signal diagnosticity in frontoparietal network (Fig. 5C), and -0.38 and -0.37 for transition probability in vmPFC (Fig. 5D), the effect size of these correlation coefficients was considered medium to large (Cohen, 1992). Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155-159.

      It seems that the authors have not counterbalanced the colors and that subjects always reported the probability of the blue regime. If so, I'm wondering why this was not counterbalanced.

      We are aware of the reviewer’s concern. The first reason we did not do these (color counterbalancing and report blue/red regime balancing) was to not confuse the subjects in an already complicated task. Balancing these two variables also comes at the cost of sample size, which was the second reason we did not do it. Although we can elect to do these balancing at the between-subject level to not impact the task complexity, we could have introduced another confound that is the individual differences in how people respond to these variables. This is the third reason we were hesitant to do these counterbalancing.

      Reviewer #2 (Public review):

      Summary:

      This paper focuses on understanding the behavioral and neural basis of regime shift detection, a common yet hard problem that people encounter in an uncertain world. Using a regime-shift task, the authors examined cognitive factors influencing belief updates by manipulating signal diagnosticity and environmental volatility. Behaviorally, they have found that people demonstrate both over and under-reaction to changes given different combinations of task parameters, which can be explained by a unified system-neglect account. Neurally, the authors have found that the vmPFC-striatum network represents current belief as well as belief revision unique to the regime detection task. Meanwhile, the frontoparietal network represents cognitive factors influencing regime detection i.e., the strength of the evidence in support of the regime shift and the intertemporal belief probability. The authors further link behavioral signatures of system neglect with neural signals and have found dissociable patterns, with the frontoparietal network representing sensitivity to signal diagnosticity when the observation is consistent with regime shift and vmPFC representing environmental volatility, respectively. Together, these results shed light on the neural basis of regime shift detection especially the neural correlates of bias in belief update that can be observed behaviorally.

      Strengths:

      (1) The regime-shift detection task offers a solid ground to examine regime-shift detection without the potential confounding impact of learning and reward. Relatedly, the system-neglect modeling framework provides a unified account for both over or under-reacting to environmental changes, allowing researchers to extract a single parameter reflecting people's sensitivity to changes in decision variables and making it desirable for neuroimaging analysis to locate corresponding neural signals.

      Thank you for recognizing our task design and our system-neglect computational framework in understanding change detection.

      (2) The analysis for locating brain regions related to belief revision is solid. Within the current task, the authors look for brain regions whose activation covary with both current belief and belief change. Furthermore, the authors have ruled out the possibility of representing mere current belief or motor signal by comparing the current study results with two other studies. This set of analyses is very convincing.

      Thank you for recognizing our control studies in ruling out potential motor confounds in our neural findings on belief revision.

      (3) The section on using neuroimaging findings (i.e., the frontoparietal network is sensitive to evidence that signals regime shift) to reveal nuances in behavioral data (i.e., belief revision is more sensitive to evidence consistent with change) is very intriguing. I like how the authors structure the flow of the results, offering this as an extra piece of behavioral findings instead of ad-hoc implanting that into the computational modeling.

      Thank you for appreciating how we showed that neural insights can lead to new behavioral findings.

      Weaknesses:

      (1) The authors have presented two sets of neuroimaging results, and it is unclear to me how to reason between these two sets of results, especially for the frontoparietal network. On one hand, the frontoparietal network represents belief revision but not variables influencing belief revision (i.e., signal diagnosticity and environmental volatility). On the other hand, when it comes to understanding individual differences in regime detection, the frontoparietal network is associated with sensitivity to change and consistent evidence strength. I understand that belief revision correlates with sensitivity to signals, but it can probably benefit from formally discussing and connecting these two sets of results in discussion. Relatedly, the whole section on behavioral vs. neural slope results was not sufficiently discussed and connected to the existing literature in the discussion section. For example, the authors could provide more context to reason through the finding that striatum (but not vmPFC) is not sensitive to volatility.<br />

      We thank the reviewer for the valuable suggestions.

      With regard to the first comment, we wish to clarify that we did not find frontoparietal network to represent belief revision. It was the vmPFC and ventral striatum that we found to represent belief revision ( in Fig. 3). For the frontoparietal network, we identified its involvement in our task through finding that its activity correlated with strength of change evidence (Fig. 4) and individual subjects’ sensitivity to signal diagnosticity (Fig. 5). Conceptually, these two findings reflect how individuals interpret the signals (signals consistent or inconsistent with change) in light of signal diagnosticity. This is because (1) strength of change evidence is defined as signals (+1 for signal consistent with change, and -1 for signal inconsistent with change) multiplied by signal diagnosticity and (2) sensitivity to signal diagnosticity reflects how individuals subjectively evaluate signal diagnosticity. At the theoretical level, these two findings can be interpreted through our computational framework in that both the strength of change evidence and sensitivity to signal diagnosticity contribute to estimating the likelihood of change (Eqs. 1 and 2). We added a paragraph in Discussion to talk about this.

      We will add on p. 35:

      “For the frontoparietal network, we identified its involvement in our task through finding that its activity correlated with strength of change evidence (Fig. 4) and individual subjects’ sensitivity to signal diagnosticity (Fig. 5). Conceptually, these two findings reflect how individuals interpret the signals (signals consistent or inconsistent with change) in light of signal diagnosticity. This is because (1) strength of change evidence is defined as signals (+1 for signal consistent with change, and -1 for signal inconsistent with change) multiplied by signal diagnosticity and (2) sensitivity to signal diagnosticity reflects how individuals subjectively evaluate signal diagnosticity. At the theoretical level, these two findings can be interpreted through our computational framework in that both the strength of change evidence and sensitivity to signal diagnosticity contribute to estimating the likelihood of change (Eqs. 1 and 2).”

      With regard to the second comment, we added discussion on the behavioral and neural slope comparison. We pointed out previous papers conducting similar analysis (Vilares et al., 2012; Ting et al., 2015; Yang & Wu, 2020), their findings and how they relate to our results. Vilares et al. found that sensitivity to prior information (uncertainty in prior distribution) in the orbitofrontal cortex (OFC) and putamen correlated with behavioral measure of sensitivity to prior. In the current study, transition probability acts as prior in the system-neglect framework (Eq. 2) and we found that ventromedial prefrontal cortex represents subjects’ sensitivity to transition probability. Together, these results suggest that OFC and vmPFC are involved in the subjective evaluation of prior information in both static (Vilares et al., 2012) and dynamic environments (current study). In addition, we added to the literature by showing that distinct from vmPFC in representing sensitivity to transition probability or prior, the frontoparietal network represents how sensitive individual decision makers are to the diagnosticity of signals in revealing the true state (regime) of the environment.

      We will add on p. 36:

      “In the current study, our psychometric-neurometric analysis focused on comparing behavioral sensitivity with neural sensitivity to the system parameters (transition probability and signal diagnosticity). We measured sensitivity by estimating the slope of behavioral data (behavioral slope) and neural data (neural slope) in response to the system parameters. Previous studies had adopted a similar approach (Vilares et al., 2012; Ting et al., 2015; Yang & Wu, 2020). For example, Vilares et al. (2012) found that sensitivity to prior information (uncertainty in prior distribution) in the orbitofrontal cortex (OFC) and putamen correlated with behavioral measure of sensitivity to the prior. In the current study, transition probability acts as prior in the system-neglect framework (Eq. 2) and we found that ventromedial prefrontal cortex represents subjects’ sensitivity to transition probability. Together, these results suggest that OFC and vmPFC are involved in the subjective evaluation of prior information in both static (Vilares et al., 2011) and dynamic environments (current study). In addition, we added to the literature by showing that distinct from vmPFC in representing sensitivity to transition probability or prior, the frontoparietal network represents how sensitive individual decision makers are to the diagnosticity of signals in revealing the true state (regime) of the environment.” 

      (2) More details are needed for behavioral modeling under the system-neglect framework, particularly results on model comparison. I understand that this model has been validated in previous publications, but it is unclear to me whether it provides a superior model fit in the current dataset compared to other models (e.g., a model without \alpha or \beta). Relatedly, I wonder whether the final result section can be incorporated into modeling as well - i.e., the authors could test a variant of the model with two \betas depending on whether the observation is consistent with a regime shift and conduct model comparison.

      Thank you for the great suggestion.

      To address the reviewer’s question on model comparison, we tested 4 variants of the system-neglect model and incorporated them into the final result section. The original system-neglect model and its four models are:

      – Original system-neglect model: 6 total parameters, 3 beta parameters (one for each level of signal diagnosticity) and 3 alpha parameters (one for each level of transition probability).  

      – M1: System-neglect model with signal-dependent beta parameters (alpha parameters, and beta parameters separately estimated at change-consistent and change-inconsistent signals): 9 total parameters, 3 beta parameters for change-consistent signals, 3 beta parameters for change-inconsistent signals, and 3 alpha parameters.

      – M2: System-neglect model with signal-dependent alpha parameters (alpha parameters separately estimated at change-consistent and change-inconsistent signals, and beta parameters): 9 total parameters, 3 alpha parameters for change-consistent signals, 3 alpha parameters for change-inconsistent signals, and 3 beta parameters.

      – M3: System-neglect model without alpha parameters (only the beta parameters): 3 total parameters, all are beta parameters (one for each level of signal diagnosticity).

      – M4: System-neglect model without beta parameters (only the alpha parameters): 3 total parameters, all are alpha parameters (one for each level of transition probability).

      We compared these four models with the original system-neglect model. In the figure below, we plot  where  is the Akaike Information Criterion (AIC) of one of the new models minus the AIC of the original model. ∆AIC<0  indicates that the new model is better than the original model. By contrast, ∆AIC>0 suggests that the new model is worse than the original model.

      Author response image 3.

      When we separately estimated the beta parameter (M1) for change-consistent signals and change-inconsistent signals, we found that its AIC is significantly smaller than the original model (p<0.01). The same was found for the model where we separately estimated the alpha parameters for change-consistent and change-inconsistent signals (M2). When we took out either the alpha (M3) or the beta parameters (M4), we found that these models were worse than the original model (p<0.01). In summary, we found that models where we separately estimated the alpha/beta parameters for change-consistent and change-inconsistent signals were better than the original model. This is consistent with the insight the neural data provided.

      To show these results, we will add a new figure (Figure 7) in the revised manuscript.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Insects and their relatives are commonly infected with microbes that are transmitted from mothers to their offspring. A number of these microbes have independently evolved the ability to kill the sons of infected females very early in their development; this male killing strategy has evolved because males are transmission dead-ends for the microbe. A major question in the field has been to identify the genes that cause male killing and to understand how they work. This has been especially challenging because most male-killing microbes cannot be genetically manipulated. This study focuses on a male-killing bacterium called Wolbachia. Different Wolbachia strains kill male embryos in beetles, flies, moths, and other arthropods. This is remarkable because how sex is determined differs widely in these hosts. Two Wolbachia genes have been previously implicated in male-killing by Wolbachia: oscar (in moth male-killing) and wmk (in fly male-killing). The genomes of some male-killing Wolbachia contain both of these genes, so it is a challenge to disentangle the two.

      This paper provides strong evidence that oscar is responsible for male-killing in moths. Here, the authors study a strain of Wolbachia that kills males in a pest of tea, Homona magnanima. Overexpressing oscar, but not wmk, kills male moth embryos. This is because oscar interferes with masculinizer, the master gene that controls sex determination in moths and butterflies. Interfering with the masculinizer gene in this way leads the (male) embryo down a path of female development, which causes problems in regulating the expression of genes that are found on the sex chromosomes.

      We would like to thank you for evaluating our manuscript.

      Strengths:

      The authors use a broad number of approaches to implicate oscar, and to dissect its mechanism of male lethality. These approaches include:

      (1) Overexpressing oscar (and wmk) by injecting RNA into moth eggs.

      (2) Determining the sex of embryos by staining female sex chromosomes.

      (3) Determining the consequences of oscar expression by assaying sex-specific splice variants of doublesex, a key sex determination gene, and by quantifying gene expression and dosage of sex chromosomes, using RNASeq.

      (4) Expressing oscar along with masculinizer from various moth and butterfly species, in a silkmoth cell line.

      This extends recently published studies implicating oscar in male-killing by Wolbachia in Ostrinia corn borer moths, although the Homona and Ostrinia oscar proteins are quite divergent. Combined with other studies, there is now broad support for oscar as the male-killing gene in moths and butterflies (i.e. order Lepidoptera). So an outstanding question is to understand the role of wmk. Is it the master male-killing gene in insects other than Lepidoptera and if so, how does it operate?

      Thank you for your comments. Wolbachia strains often carry wmk genes, but as observed in this study, the homologs in Homona showed no apparent MK ability. These showed strong male lethality in D. melanogaster, but it is still unclear whether the genes are the master male-killing gene in Diptera. It is also possible that the genes show toxicities in other lepidopteran insects as well as in other insect taxa. Further functional validation assays in different insects are warranted to clarify whether wmk shows toxicity in different insect taxa. We have also discussed the functions of wmk in the Discussion section (lines 301-306).

      Weaknesses:

      I found the transfection assays of oscar and masculinizer in the silkworm cell line (Figure 4) to be difficult to follow. There are also places in the text where more explanation would be helpful for non-experts (see recommendations).

      Thank you for your suggestion. We have thoroughly revised the manuscript to address all the questions, comments and suggestions you raised in “recommendations”. In particular, we have revised the section on the transfection assays of Oscar and Masc in Bm-N4 cells (result section “Hm-oscar suppresses the masculinizing functions of lepidopteran masc genes” starts on line 214 and Fig. 4; materials and methods section ”Transfection assays and quantification of BmIMP<sup>M</sup>”, starts on line 483). We have also provided more detailed explanations for non-experts in some contexts (in response to your recommendation). We believe that the resulting revisions have significantly improved the quality and comprehensiveness of our manuscript.

      Reviewer #2 (Public review):

      Summary:

      Wolbachia are maternally transmitted bacteria that can manipulate host reproduction in various ways. Some Wolbachia induce male killing (MK), where the sons of infected mothers are killed during development. Several MK-associated genes have been identified in Homona magnanima, including Hm-oscar and wmk-1-4, but the mechanistic links between these Wolbachia genes and MK in the native host are still unclear.

      In this manuscript, Arai et al. show that Hm-oscar is the gene responsible for Wolbachia-induced MK in Homona magnanima. They provide evidence that Hm-Oscar functions through interactions with the sex determination system. They also found that Hm-Oscar disrupts sex determination in male embryos by inducing female-type dsx splicing and impairing dosage compensation. Additionally, Hm-Oscar suppresses the function of Masc. The manuscript is well-written and presents intriguing findings. The results support their conclusions regarding the diversity and commonality of MK mechanisms, contributing to our understanding of the mechanisms and evolutionary aspects of Wolbachia-induced MK.

      We would like to thank you for evaluating our manuscript.

      Strengths/weaknesses:

      (1) The authors found that transient overexpression of Hm-oscar, but not wmk-1-4, in Wolbachia-free H. magnanima embryos induces female-biased sex ratios. These results are striking and mirror the phenotype of the wHm-t infected line (WT12). However, Table 1 lists the "male ratio," while the text presents the "female ratio" with standard deviation. For consistency, the calculation term should be uniform, and the "ratio" should be listed for each replicate.

      We have revised the first results section (Hm-oscar induces female-biased sex ratios, starting from line 147) accordingly to maintain the consistency in the calculation term. In the revised manuscript, the 'male ratio' is now consistently used, in alignment with Fig. 1. In addition, we have included all sex ratio information (number of males and females) in the supplementary data file for transparency and clarity.

      (2) The error bars in Figure 3 are quite large, and the figure lacks statistical significance labels. The authors should perform statistical analysis to demonstrate that Hm-oscar-overexpressed male embryos have higher levels of Z-linked gene expression.

      The large error bar on each chromosome (Fig.3a-d) likely reflect the overall variation in expression levels across different transcripts. Accordingly, we have included statistical data for Figure 3 based on the Steel-Dwass test for expression levels. However, displaying statistical significance directly on the whisker plots would make the figure too cluttered due to the numerous combinations. Instead, we have provided all the statistical data in the supplementary data file. To further support the claim that Z-linked genes are more highly expressed in wHm-t-infected/Hb-Oscar-injected embryos, we have included the expression data for a Z-linked gene tpi, along with its statistical data in the revised manuscript (Fig. 3e, lines 210-212).

      (3) The authors demonstrated that Hm-Oscar suppresses the masculinizing functions of lepidopteran Masc in BmN-4 cells derived from the female ovaries of Bombyx mori. They should clarify why this cell line was chosen and its biological relevance. Additionally, they should explain the rationale for evaluating the expression levels of the male-specific BmIMP variant and whether it is equivalent to dsx.

      Thank you for your suggestion. We selected BmN-4 cell line because previous studies have established it as a reliable model for investigating the functions of lepidopteran masc genes and the interactions between masc and Oscar genes (Katsuma et al., 2019; 2022). In addition, BmIMP<sup>M</sup> is a male-specific regulator of the male-type dsx, making it an ideal target for assessing the 'maleness' induced by transfection of the masc gene in female-derived BmN-4 cells (Suzuki et al., 2010; Katsuma et al., 2015). We have included more detailed background information in the revised manuscript and have thoroughly revised this section (Hm-oscar suppresses the masculinizing functions of lepidopteran masc genes, starting at line 214) and Figure 4 for better clarity.

      (4) Although the authors show that Hm-oscar is involved in Wolbachia-induced MK in Homona magnanima and interacts with the sex determination system in lepidopteran insects, the precise molecular mechanism of Hm-oscar-induced MK remains unclear. Further studies are needed to elucidate how Hm-oscar regulates Homona magnanima genes to induce MK, though this may be beyond the scope of the current manuscript.

      Based on our findings and previous studies in Homona, Ostrinia and Bombyx (Arai et al., 2023a; Katsuma et al., 2023; Kiuchi et al., 2014), we hypothesize that the molecular mechanisms underlying _w_Hm-induced MK are likely linked to impaired dosage compensation caused by the inhibition of Masc function by the Hm-Oscar protein. While the precise mechanisms remain unclear, unbalanced Z-linked gene expression due to the impaired dosage compensation (i.e., 2-fold higher Z-linked gene expression compared to normal males) is known to be lethal for lepidopteran males (Kiuchi et al., 2014; Fukui et al., 2015; Visser et al., 2021). We have outlined this hypothesis in the Discussion section (lines 245-254).

      Reviewer #3 (Public review):

      Summary:

      Overall, this is a clearly written manuscript with nice hypothesis testing in a non-model organism that addresses the mechanism of Wolbachia-mediated male killing. The authors aim to determine how five previously identified male-killing genes (encoded in the prophage region of the wHm Wolbachia strain) impact the native host, Homona magnanima moths. This work builds on the authors' previous studies in which:

      (1) They tested the impact of these same wHm genes via heterologous expression in Drosophila melanogaster.

      (2) They examined the activity of other male-killing genes (e.g., from the wFur Wolbachia strain in its native host: Ostrinia furnacalis moths).

      Advances here include identifying which wHm gene most strongly recapitulates the male-killing phenotype in the native host (rather than in Drosophila), and the finding that the Hm-Oscar protein has the potential for male-killing in a diverse set of lepidopterans, as inferred by the cell-culture assays.

      Strengths:

      Strengths of the manuscript include the reverse genetics approaches to dissect the impact of specific male-killing loci, and the use of a "masculinization" assay in Lepidopteran cell lines to determine the impact of interactions between specific masc and oscar homologs.

      We would like to thank you for evaluating our manuscript.

      Weaknesses:

      My major comments are related to the lack of statistics for several experiments (and the data normalization process), and opportunities to make the manuscript more broadly accessible.

      Thank you for your suggestions. We have thoroughly revised the manuscript to provide clearer explanations for non-experts. In addition, we have included more detailed statistical data for Figure 3 and Figure 4 based on the Steel-Dwass tests. For Figure 3a-d, displaying statistical significance directly on the whisker plots would make the figure too cluttered due to the numerous combinations. Therefore, we have provided all the statistical data in the supplementary data file. To further support the claim that Z-linked genes are more highly expressed in w_Hm-t-infected/Hm-Oscar-injected embryos, we have included the expression data for a Z-linked gene _tpi, along with its statistical data in the revised manuscript (Fig.3e, lines 210-212). Regarding Figure 4, we have revised the Figure based on the reviewer’s suggestions, and provided more detailed information on how the expression data were analyzed (Transfection assays and quantification of BmIMP<sup>M</sup>, lines 495-520). We have also included more detailed background information on the assay system (Hm-oscar suppresses the masculinizing functions of lepidopteran masc genes, lines 215-237). Although we did not observe statistical significance based on the Steel-Dwass test, likely due to limited replicates, the observed changes in the IMP gene expression remain clearly evident.

      The manuscript I think would be much improved by providing more details regarding some of the genes and cross-lineage comparisons. I know some of this is reported in previous publications, but some summary and/or additional analysis would make this current manuscript much more approachable for a broader audience, and help guide readers to specific important findings. For example, a graphic and/or more detail on how the wmk/oscar homologs (within and across Wolbachia strains) differ (e.g., domains, percent divergence, etc) would be helpful for contextualizing some of the results. I recognize the authors discuss this in parts (e.g., lines 223-227), but it does require some bouncing between sections to follow. Similarly, the experiments presented in Figure 4 indicate that Hm-oscar has broad spectrum activity: how similar are the masc proteins from these various lepidopterans? Are they highly conserved? Rapidly evolving? Do the patterns of masc protein evolution provide any hints at how Oscar might be interacting with masc?

      Thank you for your valuable suggestion. To address this, we have included a visualization of the structural differences between the Oscar and wmk homologs in Figure 1a of the revised manuscript. In addition, we have included more detailed information for these genes and revised the introduction (lines 110-114; 124-137) and discussion (lines 255-266) to provide a clearer and more comprehensive overview. We have also described the similarity of the Masc proteins and Oscar proteins that we used, which is now reflected in the revised Figure 4b and 4d. More detailed information on these proteins is available in the supplementary data. Notably, Masc proteins exhibit high sequence variability with conserved domains (Figure 4d). Previous study identified the N-terminal region of Masc as crucial for the Oscar function (Katsuma et al., 2022). The wide spectrum of the actions of Hm-Oscar likely stems from these conserved structures of Masc, but the effects might have undergone evolutionary tuning through interactions with the native host as discussed in lines 293-294.

      It is clear from Figure 1 that the combinations of wmk homologs do not cause male killing on their own. Did the authors test if any of the wmk homologs impact the MK phenotype of oscar? It looks like a previous study tested this in wFur (noted in lines 250-252), but given that the authors also highlight the differences between the wFur-oscar and Hm-oscar proteins, this may be worth testing in this system. Related to this, what is the explanation for why there would be 4 copies of wmk in Hm?

      Thank you for your valuable suggestion. Unfortunately, we have not yet tested the effects of co-expression of wmk and Oscar. Due to a technical issue, the mixing of multiple constructs results in a reduced amount of mRNA (i.e. mixing wmk-3 and Hm-Oscar at the same concentration results in a 2-fold lower concentration in mRNA for both genes compared to mono-injected groups). In addition, we have previously tested injecting mRNA at the twofold higher concentration (i.e. 2 ug/ul mRNA), which resulted in very low hatchability regardless of the genes. Katsuma et al (2022) tested the effect of wmk on the sex determination system, but did not test the effect of co-injection/transfection of wmk and Oscar. Considering the results of this and previous studies (Katsuma et al., 2022; Arai et al., 2023), it is likely that the targets of the wmk and oscar genes are different (as discussed in lines 267-289). Co-injection of wmk and oscar may not produce additive effects. Nevertheless, we would like to test the results in future studies using the Drosophila system as well.

      As you point out, it is an interesting point that the moth-derived MK Wolbachia w_Hm-t encodes four _wmk genes, although they have no apparent effect on host survival. The exact functional relevance of these wmk homologs remains unclear. However, they may play a role in Wolbachia biology as transcriptional regulators, given that they encode HTH domains. Wolbachia generally encode several wmk homologs in their genome, regardless of whether they induce MK. This suggests that the functions of the wmk genes may be 'suppressed' in certain Wolbachia-host systems. The wmk and Hm-oscar genes are located within a prophage region, and some wmk genes are tandemly arrayed (as described in Arai et al., 2023). These wmk homologs may have increased in number by horizontal phage transfer, and the region containing wmk and adjacent sequences may act as a genomic island for virulence. So far, the function of wmk homologs has only been tested in D. melanogaster and H. magnanima, and further studies in other Wolbachia-host systems are highly warranted to test whether wmk exerts MK effects in other insect models. These points have been briefly discussed in the revised manuscript (lines 301-306; 318-320).

      Why are some of the broods male-biased (2/3) rather than ~50:50? (Lines 170-175, Figure 2a). For example, there is a strong male bias in un-hatched oscar-injected and naturally infected embryos, whereas the control uninfected embryos have normal 50:50 sex ratios. It is difficult to interpret the rate of male-killing given that the sex ratios of different sets of zygotes are quite variable.

      The observed male-biased sex ratios in unhatched embryos are due to the occurrence of MK during embryogenesis. In the unhatched groups, the skew towards males reflects that fact that the male embryos were targeted and killed by Wolbachia/Oscar, leading to a surplus of unhatched male embryos. Conversely, hatched individuals show a higher proportion of females because many of the males were eliminated during embryogenesis. Thus, the unhatched embryos are more male-biased, while the hatched individuals are more female-biased in the Hm-oscar/_w_Hm-t treated groups. We have revised the relevant section (Males are killed mainly at the embryonic stage, lines 179-186) and provided more detailed information to clarify this explanation.

      Figure 2b - it appears there are both male and female bands in the HmOsc male lane. I think this makes sense (likely a partial phenotype due to the nature of the overexpression approach), but this is worth highlighting, especially in the context of trying to understand how much of the MK phenotype might be recapitulated through these methods. Related, there is no negative control for this PCR.

      Thank you for your suggestion. As you noted, a faint dsx-M band is visible in the Hm-oscar treated group in Figure 2b. This is consistent with previous findings by Arai et al. (2023), which reported that male embryos with low-density w_Hm-t showed double bands of _dsx-M and dsx-F, similar to what we observed in this study. This information has been included in the revised manuscript in lines 196-198, as follows:

      “Notably, male embryos expressing Hm-oscar also exhibited weak male-type dsx splicing in addition to the female-type splicing, resembling the previously observed pattern in male embryos infected with low-titer _w_Hm-t (Arai et al., 2023a).”

      Also, we appreciate your comment regarding the missing of negative control. The figure has now been revised as we realised that the negative control lane had been lost during the preparation of the figure. We also included the relevant molecular marker information in both the figure legends and Figure 2b.

      It appears the RNA-seq analysis (Figure 3) is based on a single biological replicate for each condition. And, there are no statistical comparisons that support the conclusions of a shift in dosage compensation. Finally, it is unclear what exactly is new data here: the authors note "The expression data of the wHm-t-infected and non-infected groups were also calculated based on the transcriptome data included in Arai et al. (2023a)" - So, are the data in Figure 3c and 3d a re-print of previous data? The level of dosage compensation inferred by visually comparing the control conditions in 3b and 3d does not appear consistent. With only one biological replicate library per condition, what looks like a re-print of previous data, and no statistical comparisons, this is a weakly supported conclusion.

      Thank you for your suggestion. In this study, we generated the RNA-seq data for the Hm-oscar/GFP-injected groups, but did not sequence the w_Hm-t-infected/NSR lines. Instead, the previously generated RNA-seq data of _w_Hm-t-infected/NSR (Arai et al., 2023) were re-analyzed (rather than simply reprinted) to evaluate whether the expression patterns of _Hm-oscar-injected and w_Hm-t-infected groups are similar. We have revised the Results section (_Hm-oscar impairs dosage compensation in male embryos, lines 200-212), the Materials and methods section (Quantification of Z chromosome-linked genes, lines 454-456), and the figure legends to provide more precise information about this analysis.

      Although we did not perform replicates for the RNA-seq comparisons, it is important to note that each RNA-seq sample contains 50-60 male/female individuals. We believe the results are still robust and clearly indicative of the trends we observe. This was further supported by the quantification of Hmtpi gene expression, which we have visualized in Figure 3e (and lines 210-212). As you noted, the expression patterns in Figure 3b (GFP injected) and Figure 3d (NSR) are not completely identical. This discrepancy may be due to the differences between injection treatments and natural infections. Nevertheless, both treatments are consistent in showing that gene expressions on the Z chromosome (Chr01 and Chr15) are not upregulated.

      We have also added more detailed statistical data for Figure 3 based on the Steel-Dwass tests. For Figure 3a-d, however, showing the statistical significance directly on the whisker plots would create excessive clutter due to the numerous combinations of chromosomes. Instead, we have provided the full statistical data in the supplementary data file. Furthermore, to support/strengthen our conclusion that Z-linked genes are highly expressed in w_Hm-t-infected/_Hm-Oscar-injected embryos, we have included expression data for the Z-linked gene tpi, along with statistical data, in the revised manuscript (Fig. 3e, lines 210-212).

      In Figure 4: There are no statistics to support the conclusions presented here. Additionally, the data have gone through a normalization process, but it is difficult to follow exactly how this was done. The control conditions appear to always be normalized to 100 ("The expression levels of BmImpM in the Masc and Hm-Oscar/Oscar co-transfected cells were normalized by setting each Masc-transfected cell as 100"). I see two problems with this approach:

      (1) This has eliminated all of the natural variation in BmImpM expression, which is likely not always identical across cells/replicates.

      (2) How then was the percentage of BmImpM calculated for each of the experimental conditions? Was each replicate sample arbitrarily paired with a control sample? This can lead to very different outcomes depending on which samples are paired with each other. The most appropriate way to calculate the change between experimental and control would be to take the difference between every single sample (6 total, 3 control, 3 experimental) and the mean of the control group. The mean of the control can then be set at 100 as the authors like, but this also maintains the variability in the dataset and then eliminates the issue of arbitrary pairings. This approach would also then facilitate statistical comparisons which is currently missing.

      Thank you for your suggestion. As you pointed out in (1), the previous analysis did indeed eliminate the natural variation in BmIMP-M expression. In the revised manuscript and Figure 4, we have reanalyzed the data following your suggestion and have described the variation across replicates.

      For (2), the data shown in the previous manuscript were normalized to 100 for each Masc-treated group. In doing so, each replicate sample was arbitrarily paired with a control sample from the same cell lot to account for variations that might occur due to differences in cell lots. However, following your recommendation, we have revised the figure to set the average of the Hm-masc treated group to 100, rather than using arbitrary pairings. More detailed normalization procedures have been provided in the section 'Transfection assays and quantification of BmIMP' (lines 483-520). Additionally, we have provided more detailed background information on the assay system in lines 218-223. Although we did not observe statistical significance based on the Steel-Dwass test, likely due to the limited number of replicates, the differences in IMP gene expression between the Masc-treated and Masc&Hm-oscar-treated groups remain evident.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Line 38: change to: 'Wolbachia are maternally transmitted'.

      Revised accordingly (line 38).

      Line 69: remove 'seemingly'.

      Revised accordingly (line 69).

      Paragraph starting line 123: I don't think this is so clear to a reader who is not familiar with the work and system. It would be helpful to more clearly explain that candidate male-killing genes from Wolbachia that infect Homona were inserted into Drosophila melanogaster, and that their expression was then induced, with interesting patterns (and that it can be a bit difficult to interpret the transgenic expression of genes from a moth male-killer that are inserted into a fly). Also, the sentence about the combined action of cifA and cifB in Drosophila cytoplasmic incompatibility is also confusing to a non-expert. I would suggest removing it.

      Thank you for your suggestion. We have revised the paragraph (lines 124-139) to provide clearer background information, making it easier for non-experts to follow. We have also removed the sentence regarding the combined effect of cifA and cifB to improve the flow and overall clarity.

      Line 170: what is the explanation for the male-biased sex ratio instead of 50-50?

      The male-biased sex ratio occurs because MK happens during embryogenesis. Unhatched embryos include males that were killed by Wolbachia/Oscar, resulting in a higher proportion of unhatched male embryos. Conversely, the hatched individuals display a female bias, as most of the males were eliminated during embryogenesis. Thus, the unhatched embryos are more male-biased, while the hatched individuals are more female-biased in the Hm-oscar/_w_Hm-t treated groups. We have revised the section “Males are killed mainly at the embryonic stage” (lines 170-186) to include more detailed information explaining this phenomenon.

      Line 190: please explain what are the Z chromosomes in Bombyx and Homona and Lepidoptera in general (chromosomes 1 and 15?), as this is not so clear for a non-expert.

      Thank you for your suggestion. I have revised the section (lines 200-212) to include more precise background information about the chromosome constitutions in lines 202-204 as follows:

      “Unlike other lepidopteran species, Tortricidae, including H. magnanima, generally possess a large Z chromosome that is homologous to B. mori chromosomes 1 (Z) and 15 (autosome).”

      Line 222: please explain oscar diversity and classification in more detail, as this is not so clear for a non-expert.

      Thank you for your suggestion. We have revised the sentences to provide clearer background information on the diversity of oscar genes (lines 255-264).

      Figure 4: I found this difficult to follow. Why are there 2 rows (HmOscar and Oscar)? Does oscar here refer to oscar from Ostrinia? I am also a bit confused about the baseline control of Masc in these cell lines. If I understand Lepidoptera sex determination, then these cell lines are expressing high levels of female-specific piRNAs that suppress Masc. How specific are these piRNAs (i.e. do Bombyx piRNAs suppress Mascs from other Lepidoptera)? How much extra Masc will override endogenous piRNA? Information is lost by setting Masc expression to 100% in each separate comparison.

      Yes, the Oscar indicates the w_Fur-encoded _oscar (Oscar from Ostrinia) that was tested to compare function with the Homona-derived Hm-oscar gene. In addition, following the reviewer's suggestions, we have revised the figure and included more detailed information on how we adjusted the expressions in the M&M section.

      A previous study (Shoji et al., 2017, RNA 23:86–97) demonstrated that the Fem piRNA (29 bp) in Bombyx mori requires a 17 bp complementary sequence from its 5' region for its function. However, in species other than B. mori, no significant homology (i.e., over 17 bp matches) was found between the B. mori Fem piRNA and the masc genes analyzed in this study. Therefore, it is likely that the Fem piRNA expressed in BmN-4 cells is unable to suppress the masculinizing function driven by masc genes in other lepidopteran species. In addition, we did not quantify the levels of piRNA in this system, but the expression levels of masc are probably too high to be suppressed.

      Figure 4 legend: spelling of Spodoptera.

      Revised accordingly.

      Reviewer #2 (Recommendations for the authors):

      In Figure 2, what is the dsx splicing type for the hatched male in the Hm-oscar-injected group and the wHm-t infected line? Dsx-F or dsx-M?

      Thank you for your suggestion. Unfortunately, we have not tested splicing in the hatched male neonates (1st instar larvae), partly due to difficulties in obtaining sufficient material for RNA extraction. Based on the previous publication in the Ostrinia system, where Oscar-bearing w_Sca induces MK, the hatched males (ZZ) exhibit female type _dsx as observed in the male embryos (Herran et al., 2022). The hatched Homona males may show double bands for dsx-M and dsx-F as observed in this study.

      The size of the markers (in kilobase pairs) should be indicated in Figure 2.

      We have accordingly included the marker information in the revised Figure 2b and the figure legends.

      In Figure 3, could the authors identify which genes exhibit higher expression levels in the Hm-oscar-injected group and the wHm-t infected line? Could they provide hints for the possible mechanism of male-killing?

      In the RNA-seq data shown in Figure 3a-d, we observed that both the Hm-oscar-injected and w_Hm-infected groups generally exhibited upregulated expression of Z-linked genes. Rather than the upregulation or downregulation of a specific gene, we consider that global upregulation of Z-linked genes, caused by improper dosage compensation, is lethal for males. The Z chromosome contains various genes involved in key biological processes such as endocrine function and detoxification, and disruption of these processes may contribute to male lethality. Additionally, in this revised manuscript, we have provided more detailed information on the expression level of the Z-linked gene _tpi. We have also discussed the potential mechanisms of MK in the Discussion section (lines 245-254).

      The format of the references should be consistent. Gene and species names should be italicized.

      We have accordingly formatted.

      Reviewer #3 (Recommendations for the authors):

      The authors use the term "upstream" (e.g., Oscar suppressed the function of masculinizer, the upstream male sex determinant...), which was sometimes confusing. In many cases, it reads as though the masculinizer was upstream of oscar, but what I think the authors are trying to convey is that masculinizer is a primary sex-determining factor.

      Thank you for your suggestion. We have accordingly revised the term.

      Line 101: which insect is wFur from?

      It is from Ostrinia furnacalis - line 104 has been revised.

      Figure 1: it would be helpful to indicate the statistical results on the figure.

      Accordingly, we have added statistical data (binominal test) for Figure 1. The data for the Steel-Dwass test have been included in the supplementary data.

      Figure 2b: please label the ladder on the gel.

      Thank you for your suggestion. We have accordingly labeled the DNA ladder on the gel.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Response to the Joint Public Review:

      We are indebted to eLife’s reviewing process for helping us improve our manuscript and for highlighting that our study provides new molecular insights into SFT pathogenesis.  

      Response to Reviewers:

      (1) The authors state that "NAB2-STAT6 localization is exclusively driven by EGR1 binding" yet WT1 motives are also consistently enriched. Can you please touch upon the potential involvement of WT1 (or lack thereof, and why)?

      Our data suggest that EGR1 is the primary driver of NAB2-STAT6 localization. In fact, EGR1 is the most significantly enriched motif (Fig. 4) at NAB2-STAT6 binding sites and we detect an interaction between the fusion protein and EGR1 (Fig. 5). Conversely, we did not identify an interaction between NAB2-STAT6 and WT1. However, WT1 also belongs to the C2H2 zinc finger subclass and recognizes a motif bearing striking similarities to the EGR1/2 consensus. EGR1 has been previously described to bind WT1 motifs and to function as an activator of WT1 targets (as opposed to WT1 repressive abilities). See https://www.jbc.org/article/S0021-9258(20)74720-4/fulltext and https://www.sciencedirect.com/science/article/pii/S0378111901005935.

      (2) In the description of Figure 5C the authors observe nuclear staining of both NAB2 and STAT6 following NAB2-STAT6 fusion induction. They interpret this as the fusion stimulates nuclear translocation of endogenous NAB2. This statement can only be rigorously made if the authors can unequivocally demonstrate that their antibody exclusively detects endogenous NAB2 and not the NAB2 portion of the fusion. As presented, a more likely interpretation is that the NAB2 staining detects NAB2-STAT6 fusion protein. Since there is some cytoplasmic NAB2 signal still present, the findings in Figure 5c do not support nor disprove nuclear translocation of endogenous NAB2. It may be prudent to remove this section. Figure 5B is currently the best direct evidence of nuclear translocation.

      We agree with the reviewer that Fig. 5C does not rigorously show that NAB2-STAT6 fusion proteins drag endogenous NAB2 into the nucleus. The immunostaining reveals that wt NAB2 localization is overwhelmingly cytoplasmic at steady-state conditions (and prior to expression of the fusion protein). Instead, Figure 5B shows that endogenous NAB2 translocates to the nucleus upon NAB2-STAT6 expression. Additionally, figure 5A (along with Suppl. Fig. 5 E-F) demonstrates that endogenous NAB2 co-precipitates with NAB2-STAT6 fusions in nuclear extracts of U2OS and HEK293T cells. We have rephrased the paragraph accordingly.

      (3) Figure 5D: for the interpretation of the presented data to hold up, namely, NAB1 nuclear translocation upon NAB2-STAT6 expression, it is important to demonstrate that NAB1 antibodies do not cross-react with NAB2 given the similarity between NAB1 and NAB2. Without such control, another likely interpretation of the results in Figure 5D is that NAB1 antibody detects the NAB2 portion of the overexpressed fusion protein. This needs to be acknowledged in the text.

      We had similar concerns, therefore we confirmed that the NAB1 antibody does not cross react with NAB2 by immunoblot (see figure below). We overexpressed FLAG-NAB2, HA-NAB1 and GFP constructs in HEK293T cells, we performed immunoprecipitation with either HA or FLAG from whole cell extracts followed by western blot using anti-NAB2 and anti-NAB1 polyclonal antibodies. We did not observe cross-reactivity of these antibodies. We acknowledged antibody validation in the revised text.

      Author response image 1.

      (4) Also, to support the notion that NAB2-STAT6 fusion promotes nuclear translocation of the entire complex, an imaging approach detecting EGR1 similar to Figure 5C-D would be helpful. EGR1 staining also avoids the potential pitfall of NAB1/2 antibodies detecting NAB2-STAT6 overexpressed fusion instead of endogenous proteins.

      We agree with the reviewer that this would be a helpful approach. Unfortunately, none of the commercially available EGR1 antibodies that we tested were suitable for immunocytochemistry, as they either failed to show a proper signal or were marred by high nonspecific background signal.

      (5) The authors found increased mRNA expression of certain cytokines and secreted neuropeptides in SFTs. While this may be consistent with a secretory phenotype, additional evidence such as detection of elevated levels of these proteins in tumor lysates or in culture media is necessary to formally make this claim. Please rephrase.

      We have rephrased our claims as suggested. The revised text is now as follows: “​​We also identified a distinct secretory gene signature associated with SFTs. In fact, IGF2 is the most upregulated gene, via activation of an intronic enhancer by EGR1. IGF2 was pinpointed as the cause of hypoglycemia occurring in a very small subset of SFTs (Doege–Potter syndrome)(52). Our data suggest that IGF2 (and IGF1) upregulation is a common feature of all SFTs. In addition to insulin-like growth factors, STFs may secrete a host of peptides with diverse functions in neuronal processes, chemotaxis, and growth stimulation. The previously unrecognized neuronal features and the putative secretory phenotype of STFs set them apart from mesenchymal malignancies and relate them to neuroendocrine malignancies such as pheochromocytoma, oligodendroglioma and neuroblastoma.”

      (6) GSEA with 500 randomly selected genes from target datasets needs a more detailed description to clarify the method.

      To improve clarity, we added the following description: “Gene set enrichment analysis (GSEA) was done with 500 randomly selected genes from the given set of genes across the C2 collection of the human molecular signatures database or custom signatures using the GSEA function in clusterProfiler package in R (v4.6.2).

      (7) In the IP-MS description, please double check the NaCl concentration in the second extraction step - 0.5mM seems low. Also, in the IP part, a buffer recipe appears to have been incorrectly pasted.

      We thank the reviewer for identifying this typo. Indeed, we used 0.5M NaCl instead of 0.5mM. We have corrected the co-IP buffer recipe accordingly.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1:

      The paper by Auer et. makes several contributions: (1) The study developed a novel approach to map the microstructural organization of the human amygdala by applying radiomics and dimensionality reduction techniques to high-resolution histological data from the BigBrain dataset. (2) The method identified two main axes of microstructural variation in the amygdala, which could be translated to in vivo 7 Tesla MRI data in individual subjects. (3) Functional connectivity analysis using resting-state fMRI suggests that microstructurally defined amygdala subregions had distinct patterns of functional connectivity to cortical networks, particularly the limbic, frontoparietal, and default mode networks. (4) Meta-analytic decoding was used to suggest that the superior amygdala subregion's connectivity is associated with autobiographical memory, while the inferior subregion was linked to emotional face processing. (5) Overall, the data-driven, multimodal approach provides an account of amygdala microstructure and possibly function that can be applied at the individual subject level, potentially advancing research on amygdala organization.

      We thank the Reviewer for the positive comments and insightful evaluation of the work.

      (1.1) Although these are meritorious contributions there are some concerns that I will summarize below. The paper makes little-to-no contact with the monkey literature regarding the anatomy of amygdala subregions, their functionality, and their patterns of anatomical connectivity. This is surprising because such literature on non-human primates is a very important starting point for understanding the human amygdala. I recommend taking a careful look at the work by Helen Barbas, among others. There are too many papers to cite but a notable example is: Ghashghaei, H. T., Hilgetag, C. C., & Barbas, H. (2007). Sequence of information processing for emotions based on the anatomic dialogue between prefrontal cortex and amygdala. Neuroimage, 34(3), 905-923. The work of Amaral is also highly relevant.

      As suggested, we included the important work of Amaral et al. as well as Ghashghaei et al. highlighting its contribution to mapping the intricate anatomy and function of the amygdala in non-human primates. We comment on this in the Introduction of the manuscript. Please see P.3.

      “Early research on the amygdala in non-human primates has been instrumental in understanding its intricate structure, function and patterns of anatomical connectivity (Amaral and Price 1984; Ghashghaei et al. 2007). This foundational study highlights the amygdala’s different subdivisions, most notably the basomedial nucleus (BM), basolateral nucleus (BL), and central nucleus (Ce) (Amaral et al. 1992). Furthermore, this work describes a dense network between these subdivisions and the prefrontal cortex, most strongly found in the posterior orbitofrontal and anterior cingulate areas.”

      (1.2) Furthermore, the authors subscribe to a model with LB, CM, and SF sectors. How does the SF sector relate to monkey anatomy?

      The overall organization of these subregions is largely conserved between humans and monkeys, reflecting their evolutionary relationship. While the basic subregional organization is conserved, there are still some important structural and functional differences between human and monkey amygdalae. For example, the SF subregion, often described in humans includes parts of the cortical nuclei (VCo), anterior amygdaloid area (AAA), amygdalohippocampal transition area (AHi), amygdalopiriform transition area (APir) as well as the lateral olfactory tract (LOT). This remark was added in the Discussion, on P.12:

      “However, this region has been previously described as consisting of three main subdivisions: LB, CM, and SF, each composed of smaller subnuclei with distinct connectivity patterns and functions (Amunts et al. 2005; Ball et al. 2007; Bzdok et al. 2013; de Olmos and Heimer 1999). These subregions are largely conserved between humans and monkeys, reflecting their evolutionary relationship. However, there are still some considerable differences such as in the SF subregion, where its description in monkeys additionally contains the lateral olfactory tract (LOT) (De Olmos 1990).”

      (1.3) The authors use meta-analytical decoding via NeuroSynth. If the authors like those results of course they should keep them but the quality of coordinate reporting in the literature is insufficient to conclude much in the context of amygdala subregion function in my opinion. I believe the results reported are at most "somewhat suggestive".

      We agree with the Reviewer that use of data from NeuroSynth poses unique challenges, particularly relating to investigations of a small structure such as the amygdala. However, to clarify, these analyses decode the cortex-wide functional connectivity patterns of amygdala subregions and not activations within subregions defined by our microanatomical analyses. Additionally, comments from Reviewer 2 suggested expanding the NeuroSynth decoding to the contralateral hemisphere. As such, we decided to keep this analysis in the main manuscript but rephrase the interpretation of these findings in the Discussion to emphasize their exploratory nature on P.13:

      “Functional decoding of subregional functional connectivity patterns indicated possible dissociations in cognitive (e.g., memory) and affective (e.g., emotional face processing) functions of the amygdala, echoing previous accounts of this region’s involvement in associative processing of emotional stimuli. Notably, these findings link the functional connectivity profile of a subregion partially co-localizing with LB to emotional face processing. The LB subregion has been previously linked to associative processing related to the integration of sensory information (Bzdok et al. 2013; Ghods-Sharifi, St Onge, and Floresco 2009; Pessoa 2010; Winstanley et al. 2004; Boyer 2008), which is consistent with the association with visual emotional information processing identified in the present work.”

      (1.4) Another significant concern has to do with the results in Figure 3. The red and yellow clusters identified are quite distinct but the differences in functional connectivity are very modest. Figure 3C reveals very similar functional connectivity with the networks investigated. This is very surprising, and the authors should include a careful comparison with related findings in the literature. Overall, there is limited comparison between the observed results and those obtained via other methods. On a more pessimistic note, the results of Figure 3 seem to question the validity of the general approach.

      We agree with the Reviewer that we can indeed observe considerable overlap between functional connectivity profiles of amygdala subregions. The amygdala is a relatively small structure, leading to likely interconnectivity between its subregions (Bzdok et al. 2013) in addition to considering BOLD signal autocorrelation within this region. In addition, functional signals in the amygdala are affected by relatively lower signal-to-noise ratio (SNR), a limitation extending to temporobasal and mesiotemporal regions. Despite these challenges, our technique remained sensitive to detect subtle differences in connectivity patterns even in this small group of subjects in this restricted subcortical territory.

      In the revised manuscript, we further highlight these caveats in the Discussion (P.13):

      “Although these findings are promising, we also observe considerable overlap between functional connectivity networks of both our defined subregions. Indeed, the amygdala is a relatively small structure, leading to likely interconnectivity between its subregions and locally high signal autocorrelation. Functional connectivity and microstructure in the amygdala are certainly related, however previous work suggests they do not perfectly overlap (Bzdok et al. 2013). In addition, this region is affected by relatively low signal-to-noise ratio (SNR), as is observed in broader temporobasal and mesiotemporal territories.”

      (1.5) Some statements in the Discussion feel unwarranted. For example, "significant dissociation in functional connectivity to prefrontal structures that support self-referential, reward-related, and socio-affective processes." This feels way beyond what can be stated based on the analyses performed.

      We agree that this interpretation may reach beyond the analyses performed and reported findings. We have adjusted this portion of the text accordingly in our Discussion on functional connectivity findings (P.13):

      “Qualitatively, we found that the subregion defined by the highest 25% of U1 values mainly overlapped with what is commonly defined as the superficial and centromedial subregions, whereas the lowest 25% U1 values subregion overlapped mostly with the laterobasal division. Interestingly, CM and SF characterized subregions showed significantly stronger functional connectivity to prefrontal structures. This finding aligns with previous work demonstrating unique affiliations between the CM subregion and anterior cingulate and frontal cortices (Kapp, Supple, and Whalen 1994; Barbour et al. 2010), as well as between the SF subregion and the orbitofrontal cortex (Goossens et al. 2009; Caparelli et al. 2017; Pessoa 2010; Klein-Flügge et al. 2022).”

      Additionally, we have also edited our Discussion to ensure that our interpretations are grounded in the analyses conducted, while framing the findings as potential avenues for future work. Please see P.13.

      “Functional decoding of functional connectivity results indicated possible dissociations in cognitive (e.g., memory) and affective (e.g., emotional face processing) functions of the amygdala, echoing previous accounts of this region’s functional specialization and subregional segregation of associative processing of emotional stimuli.”

      Recommendations for the authors:

      (1.6) Figure 1 has panels A-I but only A-D are discussed in the caption. The orientation of the slices is not indicated which makes it very hard to follow for most readers.

      The subpanels are now referred to in the revised Results. We also added a notation on the orientation of the slices and described them accordingly in our Figure 1 description. (P.5-6):

      “(A) The amygdala was segmented from the 100-micron resolution BigBrain dataset using an existing subcortical parcellation (Xiao et al. 2019). Slice orientation is consistent across all panels in this figure.”

      (1.7) Some figure references in the text seem to be incorrect; please check that the text refers to the correct figure number and panel.

      We thank the Reviewer for pointing this out. We thoroughly revised the correspondence between figure panel labels and their referencing in the text.

      Reviewer #2:

      This study bridges a micro- to macroscale understanding of the organization of the amygdala. First, using a data-driven approach, the authors identify structural clusters in the human amygdala from high-resolution post-mortem histological data. Next, multimodal imaging data to identify structural subunits of the amygdala and the functional networks in which they are involved. This approach is exciting because it permits the identification of both structural amygdalar subunits, and their functional implications, in individual subjects. There are, however, some differences in the macro and microscale levels of organization that should be addressed.

      Strengths:

      The use of data-driven parcellation on a structure that is important for human emotion and cognition, and the combination of this with high-resolution individual imaging-based parcellation, is a powerful and exciting approach, addressing both the need for a template-level understanding of organization as well as a parcellation that is valid for individuals. The functional decoding of rsfMRI permits valuable insight into the functional role of structural subunits. Overall, the combination of micro to macro, structure, and function, and general organization to individual relevance is an impressive holistic approach to brain mapping.

      We thank the Reviewer for their constructive and helpful feedback on our work.

      Weaknesses:

      (2.1) UMAP 1, as calculated from the histological data, appears to correlate well across individuals, and decently with the MRI data, although the medial-lateral coordinate axis is an outlier. UMAP 2, on the other hand, does not appear to correlate well with imaging data or across individuals. This does pose a problem with the claim that this paper bridges micro- and macroscale parcellations. One might certainly expect, however, that different levels of organization might parcellate differently, but the authors should address this in the discussion and offer ways forward.

      Data driven methods hold several advantages for the quantitative extraction of signal from the underlying data in an observer-independent manner. However, these techniques are also sensitive to potential idiosyncrasies in the data. In the present work, our main analyses rely on the processing of a histological dataset (BigBrain) providing a unique opportunity for high-resolution analysis of amygdala histology and in vivo translation of findings leveraging ultra-high field MRI (n=10). However, both datasets are limited by their small sample size (n=1 for BigBrain and n=10 for MICA-PNI). As a result, we speculate that signal variations captured by U2 may be sensitive to artifacts or subject-specific sources of variance. Moving forward, this hypothesis could be assessed in future work via the analysis of larger histological and neuroimaging datasets to better track recurring features picked up by U2 or the association of these unique topographies with behavioural markers.

      As suggested, we included a section in our Discussion highlighting this shortcoming and the importance for larger datasets moving forward. Please see P.11-12.

      “However, it is important to note that both datasets analyzed in this work are limited by their small sample size (n=1 for BigBrain and n=10 for MICA-PNI). We speculate that the signal variations captured by U2 may be sensitive to artifacts or subject-specific sources of variance, potentially explaining why it was not consistent between subjects and modalities. Moving forward, this hypothesis could be assessed in future work via the analysis of larger histological and neuroimaging datasets to better track recurring features picked up by U2 or the association of these unique topographies with behavioural markers.”

      (2.1) It would be interesting to see functional decoding for the right amygdala. This could be included in the supplementary material. A discussion of differences in the results in the two hemispheres could be illuminating.

      In accordance with the Reviewer’s suggestion, we added Supplementary figure S2 exploring the decoding of connectivity profiles of the right amygdala stratified by its cytoarchitectural embedding with UMAP.

      Upon analysis, dissociation in functional connectivity patterns over the right amygdala were less evident, leading to overall similar functional decoding across the two clusters. We refer to this Supplementary Figure in our Discussion on P.13.

      “For the right amygdala, dissociation in functional connectivity patterns were more subtle, leading to overall similar functional decoding across the two clusters. (Figure S2)”

      (2.3) The authors acknowledge that this mapping matches some but not all subunits that have been previously described in the amygdala. It would be helpful to neuroanatomists if the authors could discuss these differences in more detail in the discussion, to identify how this mapping differs and what the implications of this are.

      In our work, we focus on mapping the three well characterized amygdala subregions, specifically the superficial (SF), centromedial (CM) and laterobasal (LB) subdivisions. Qualitative histological accounts have indeed delineated multiple subunits within these subregions which we now describe in the revised manuscript. Due to the lower resolution of in vivo MRI data used in this work relative to post mortem histology, we focused our analyses on larger subregions that could be more reliably mapped to native quantitative T1 spaces of each participant. We now overview this issue in the Discussion. Please see P.12.

      “Although qualitative histological accounts have indeed delineated multiple subunits within these general regions, the present work focuses on three subdivisions (Amunts et al. 2005) to account for resolution disparities when translating our findings to in vivo MRI data. The LB subdivision includes the basomedial nucleus (Bm), basolateral nucleus (BL), lateral nucleus (LA) and paralaminar nucleus (PL). Moving medially, the CM subdivision includes the central (Ce) and medial nuclei (Me), while the SF subdivision includes the anterior amygdaloid area (AAA), amygdalohippocampal transition area (AHi), amygdalopiriform transition area (APir), and ventral cortical nucleus (VCo) (Heimer et al. 1999). However, disagreement on the precise attribution of nuclei to broader subdivisions motivated our investigations of probabilistic subunits of the amygdala (Kedo et al. 2018). The development of new tools to segment amygdala subnuclei in vivo offers opens opportunities for future work to further validate our framework at the precision of these nuclei within subjects (Saygin et al. 2017).”

      (2.4) The acronym UMAP is not explained. A brief explanation and description would be useful to the reader.

      We moved the expanded acronym from the Methods to the first instance of the term UMAP in our paper, found in the Introduction. As suggested, we also added a sentence describing the technique. Please see P.6.

      “We then applied Uniform Manifold Approximation and Projection (UMAP), a non-linear dimensionality reduction technique that preserves the local and global structure of high-dimensional data by projecting it into a lower-dimensional space (Becht et al. 2018), to the resulting 20-feature matrix to derive a 2-dimensional embedding of amygdala cytoarchitecture (Figure 1D).”

    1. Author response:

      Reviewer 1:

      (1) Reward Interpretation and Skin Conductance Responses (SCR):

      The reviewer raises a valid point, as the model from which we derive prediction errors describes predictive learning—specifically, the occurrence of shock—without incorporating additional reward learning effects. SCRs are used to fit the model’s hyperparameters but do not directly measure reward; rather, they serve as a marker of arousal.

      In our paradigm, SCRs are measured during CS presentation and primarily reflect predictive learning, as they are closely linked to contingency awareness. The association between estimated prediction errors during unexpected US omissions and reward remains reliant on existing literature.

      In the revised manuscript, we will further elaborate on these points to clarify the distinction between predictive learning and direct reward processing, while contextualizing our findings within the broader literature on reward signaling and fear extinction.

      (2) Reinforcement Agent and SCR Modeling:

      Notably, we do not use SCR as a personalized expectation measure due to its limited reliability at the individual level; instead, the model's hyperparameters are fitted on the entire SCR dataset, yielding per-trial prediction and prediction error estimates for each CS sequence rather than for individual participants.

      (3) Clarity and Visualization of Results:

      We recognize that the presentation of our results can be improved and will take steps to enhance figure clarity, also ensuring that trend-level results are clearly distinguished.

      (4) Theoretical Context for Paradigm Phases:

      Regarding the differences across experimental phases, we recognize the theoretical significance of these distinctions. However, our primary focus is on identifying commonalities in unexpected US omission responses across phases rather than emphasizing phase-specific differences. Nevertheless, we will provide a brief clarification on phase differences to enhance the manuscript’s interpretability.

      (5) Cerebellum-VTA Connectivity Analysis:

      Furthermore, we acknowledge that our conclusion regarding the modulation of the dopaminergic system by the cerebellum should be framed more cautiously. We will temper our claims to better reflect the bidirectional and potentially indirect nature of cerebellum-VTA interactions. Additionally, we plan to include PPI results using a cerebellar seed showing the VTA, potentially in the supplementary material.

      Reviewer 2:

      (1) Success of extinction learning based on Self-reports and SCRs?

      The reviewer points to a problem, which is inherent to extinction learning: The initial fear association is not erased, but merely inhibited, and is prone to return. Although the recall phase follows the extinction phase, we did not expect a complete inhibition of the conditioned response; instead, spontaneous recovery is expected. In fact, the spontaneous recovery observed in the recall phase provided us with an additional opportunity to investigate unexpected US omissions, which was our primary focus.

      (2) Concerns on reliability of event-based contrasts using three events:

      Regarding concerns about the reliability of analyses based on three events, we believe that the consistency of our parametric modulation analysis— which incorporates all events— combined with the three-event analysis results, provides further support for the observed patterns. We are currently discussing ways of additional analysis for further verification of the reliability of using three events.

      (3) Deviations from preregistration:

      Finally, we will carefully review all deviations from our preregistration to ensure transparency. Any methodological or analytical changes will be explicitly addressed in the revised manuscript.

    1. Author response:

      Reviewer 1:

      Summary:

      Identifying drugs that target specific disease phenotypes remains a persistent challenge. Many current methods are only applicable to well-characterized small molecules, such as those with known structures. In contrast, methods based on transcriptional responses offer broader applicability because they do not require prior information about small molecules. Additionally, they can be rapidly applied to new small molecules. One of the most promising strategies involves the use of “drug response signatures”-specific sets of genes whose differential expression can serve as markers for the response to a small molecule. By comparing drug response signatures with expression profiles characteristic of a disease, it is possible to identify drugs that modulate the disease profile, indicating a potential therapeutic connection.

      This study aims to prioritize potential drug candidates and to forecast novel drug combinations that may be effective in treating triple-negative breast cancer (TNBC). Large consortia, such as the LINCS-L1000 project, offer transcriptional signatures across various time points after exposing numerous cell lines to hundreds of compounds at different concentrations. While this data is highly valuable, its direct applicability to pathophysiological contexts is constrained by the challenges in extracting consistent drug response profiles from these extensive datasets. The authors use their method to create drug response profiles for three different TNBC cell lines from LINCS.

      To create a more precise, cancer-specific disease profile, the authors highlight the use of single-cell RNA sequencing (scRNA-seq) data. They focus on TNBC epithelial cells collected from 26 diseased individuals compared to epithelial cells collected from 10 healthy volunteers. The authors are further leveraging drug response data to develop inhibitor combinations.

      Strengths:

      The authors of this study contribute to an ongoing effort to develop automated, robust approaches that leverage gene expression similarities across various cell lines and different treatment regimens, aiming to predict drug response signatures more accurately. The authors are trying to address the gap that remains in computational methods for inferring drug responses at the cell subpopulation level.

      Weaknesses:

      One weakness is that the authors do not compare their method to previous studies. The authors develop a drug response profile by summarizing the time points, concentrations, and cell lines. The computational challenge of creating a single gene list that represents the transcriptional response to a drug across different cell lines and treatment protocols has been previously addressed. The Prototype Ranked List (PRL) procedure, developed by Iorio and co-authors (PNAS, 2010, doi:10.1073/pnas.1000138107), uses a hierarchical majority-voting scheme to rank genes. This method generates a list of genes that are consistently overexpressed or downregulated across individual conditions, which then hold top positions in the PRL. The PRL methodology was used by Aissa and co-authors (Nature Comm 2021, doi:10.1038/s41467-021-21884-z) to analyze drug effects on selective cell populations using scRNA-seq datasets. They combined PRL with Gene Set Enrichment Analysis (GSEA), a method that compares a ranked list of genes like PRL against a specific set of genes of interest. GSEA calculates a Normalized Enrichment Score (NES), which indicates how well the genes of interest are represented among the top genes in the PRL. Compared to the method described in the current manuscript, the PRL method allows for the identification of both upregulated and downregulated transcriptional signatures relevant to the drug’s effects. It also gives equal weight to each cell line’s contribution to the drug’s overall response signature.

      The authors performed experimental validation of the top two identified drugs; however, the effect was modest. In addition, the effect on TNBC cell lines was cell-line specific as the identified drugs were effective against BT20, whose transcriptional signatures from LINCS were used for drug identification, but not against the other two cell lines analyzed. An incorrect choice of genes for the signature may result in capturing similarities tied to experimental conditions (e.g., the same cell line) rather than the drug’s actual effects. This reflects the challenges faced by drug response signature methods in both selecting the appropriate subset of genes that make up the signature and managing the multiple expression profiles generated by treating different cell lines with the same drug.

      We appreciate the reviewer’s thoughtful feedback and their suggestion to refer to the Prototype Ranked List (PRL) manuscript. Unfortunately, since this methodology for the PRL isn’t implemented in an open-source package, direct comparison with our approach is challenging. Nonetheless, we investigated whether using ranks would yield similar results for the most likely active drug pairs identified by retriever. To do this, we calculated and compared the rankings of the average effect sizes provided by retriever. Although the Spearman (ρ \= 0.98) correlation coefficient was high, we observed that key genes are disadvantaged when using ranks compared to effect sizes. This difference is particularly evident in the gene set enrichment analysis, where using average ranks identified only one pathway as statistically significantly enriched. The code to replicate these analyses is available at https://github.com/dosorio/L1000-TNBC/blob/main/Code/.

      Author response image 1.

      Given the similarity in purpose between retriever and the PRL approach, we have added the following statement to the introduction: “Previously, this goal was approached using a majority-voting scheme to rank genes across various cell types, concentrations, and time points. This approach generates a prototype ranked list (PRL) that represents the consistent ranks of genes across several cell lines in response to a specific drug.”

      Regarding the experimental validation, we believe there is a misunderstanding about the evidence we provided. We would like to claridy that we used three different TNBC cell lines: CAL120, BT20, and DU4475. It’s important to note that CAL120 and DU4475 were not included in the signature generation process. Despite this, we observed effects that exceeded the additive effects expectations, particularly in the CAL120 cell line (Figure 5, Panel F).

      Reviewer 2:

      Summary:

      In their study, Osorio and colleagues present ‘retriever,’ an innovative computational tool designed to extract disease-specific transcriptional drug response profiles from the LINCS-L1000 project. This tool has been effectively applied to TNBC, leveraging single-cell RNA sequencing data to predict drug combinations that may effectively target the disease. The public review highlights the significant integration of extensive pharmacological data with high-resolution transcriptomic information, which enhances the potential for personalized therapeutic applications.

      Strengths:

      A key finding of the study is the prediction and validation of the drug combination QL-XII-47 and GSK-690693 for the treatment of TNBC. The methodology employed is robust, with a clear pathway from data analysis to experimental confirmation.

      Weaknesses:

      However, several issues need to be addressed. The predictive accuracy of ’retriever’ is contingent upon the quality and comprehensiveness of the LINCS-L1000 and single-cell datasets utilized, which is an important caveat as these datasets may not fully capture the heterogeneity of patient responses to treatment. While the in vitro validation of the drug combinations is promising, further in vivo studies and clinical trials are necessary to establish their efficacy and safety. The applicability of these findings to other cancer types also warrants additional investigation. Expanding the application of ’retriever’ to a broader range of cancer types and integrating it with clinical data will be crucial for realizing its potential in personalized medicine. Furthermore, as the study primarily focuses on kinase inhibitors, it remains to be seen how well these findings translate to other drug classes.

      We thank the reviewer for their thoughtful and constructive feedback. We appreciate your insights and agree that several important considerations need to be addressed.

      We recognize that the predictive accuracy of retriever depends on the LINCS-L1000 and single-cell datasets. These resources may not fully represent the complete range of transcriptional responses to disease and treatment across different patients. As you mentioned, this is an important limitation. However, we believe that by extrapolating the evaluation of the most likely active compound to each individual patient, we can help address this issue. This approach will provide valuable insights into which patients in the study are most likely to respond positively to treatment.

      On the in-vitro validation of drug combinations, we agree that while promising, these results are not sufficient on their own to establish clinical efficacy. Additional in-vivo studies will be essential in assessing the therapeutic potential and safety of these combinations, and clinical trials will be an important next step to validate the translational impact of our findings.

      Lastly, we appreciate the reviewer’s comment about the focus of our study on kinase inhibitors. This result was unexpected, as we tested the full set of compounds from the LINCS-L1000 project. We agree that exploring other top candidates, including different drug classes, will be important for assessing how broadly retriever approach can be applied.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      In the present work the authors explore the molecular driving events involved in the establishment of constitutive heterochromatin during embryo development. The experiments have been carried out in a very accurate manner and clearly fulfill the proposed hypotheses.

      Regarding the methodology, the use of: i) an efficient system for conversion of ESCs to 2C-like cells by Dux overexpression; ii) a global approach through IPOTD that reveals the chromatome at each stage of development and iii) the STORM technology that allows visualization of DNA decompaction at high resolution, helps to provide clear and comprehensive answers to the conclusion raised.

      The contribution of the present work to the field is very important as it provides valuable information on chromatin-bound proteins at key stages of embryonic development that may help to understand other relevant processes beyond heterochromatin maintenance.

      The study could be improved through a more mechanistic approach that focuses on how SMARCAD1 and TOPBP1 cooperate and how they functionally connect with H3K9me3, HP1b and heterochromatin regulation during embryonic development. For example, addressing why topoisomerase activity is required or whether it connects (or not) to SWI/SNF function and the latter to heterochromatin establishment, are questions that would help to understand more deeply how SMARCAD1 and TOPBP1 operate in embryonic development.

      We would like to thank the reviewer for the positive evaluation of our work and the methodology we employed. We greatly appreciated the reviewer’s recognition of our study to “provide valuable information on chromatin-bound proteins at key stages of embryonic development that may help to understand other relevant processes beyond heterochromatin maintenance”. While we acknowledge the value of including mechanistic studies, such an addition would require a substantial amount of experimental work that exceeds our current resources.

      Reviewer #1 (Recommendations For The Authors):

      In my opinion, the authors could improve the study by deciphering -to a certain extent- the possible mechanism by which SMARCAD1 and TOPBP1 are cooperating in their system to establish H3K9me3 and consequently heterochromatin; and whether it is different (or not) from that already reported in yeast (ref 27). In fact, is it only SMARCAD1 that participates in this process or the whole SWI/SNF complex? Could the lack of SMARCAD1 compromise the proper assembly of the SWI/SNF complex? In this regard, a model describing the main findings of the study and the discussion of the possible mechanisms involved -based on the current bibliography- would be appreciated. This, although speculative, would illustrate the range of possibilities that could be operating in the maintenance of heterochromatin during embryonic development. In conclusion, it would be great if the authors could link -mechanistically- the dots connecting SMARCD1, TOPBP1, H3K9me3/HP1/heterochromatin.

      As suggested by the reviewer and to enrich the discussion, we have included some additional sentences and references in the revised discussion section.

      As a minor point, In Figure 3A, left panel it appears that the protein precipitating with H3K9me3 reacts with TOPBP1 but its molecular weight does not exactly match to the TOPBP1 band found in the input. The authors should clarify this point and it is also recommended that IPs and inputs are run in the same gel. Please replace Figure 3A right panel.

      Following the reviewer’s suggestion and to improve the reading flow, we have restructured the order of the figures and removed the original Figure 3A. The revised Figure 3A-C panel illustrates the SMARCAD1 association with H3K9me3 in ESCs and 2C- cells, while capturing the reduced SMARCAD1-H3K9me3 association in 2C<sup>+</sup> cells.

      Reviewer #2 (Public Review):

      The manuscript by Sebastian-Perez describes determinants of heterochromatin domain formation (chromocenters) at the 2-cell stage of mouse embryonic development. They implement an inducible system for transition from ESC to 2C-like cells (referred to as 2C<sup>+</sup>) together with proteomic approaches to identify temporal changes in associated proteins. The conversion of ESCs to 2C<sup>+</sup> is accompanied by dissolution of chromocenter domains marked by HP1b and H3K9me3, which reform upon transition back to the 2C-like state. The innovation in this study is the incorporation of proteomic analysis to identify chromatin-associated proteins, which revealed SMARCAD1 and TOPBP1 as key regulators of chromocenter formation.

      In the model system used, doxycycline induction of DUX leads to activation of EGFP reporter regulated by the MERVL-LTR in 2C<sup>+</sup> cells that can be sorted for further analysis. A doxycycline-inducible luciferase cell line is used as a control and does not activate the MERVL-LTR GFP reporter. The authors do see groups of proteins anticipated for each developmental stage that suggest the overall strategy is effective.

      The major strengths of the paper involve the proteomic screen and initial validation. From there, however, the focus on TOPBP1 and SMARCAD1 is not well justified. In addition, how data is presented in the results section does not follow a logical flow. Overall, my suggestion is that these structural issues need to be resolved before engaging in comprehensive review of the submission. This may be best achieved by separating the proteomic/morphological analyses from the characterization of TOPBP1 and SMARCAD1.

      We appreciate the reviewer’s positive evaluation of our inducible system to trigger the transition from ESCs to 2C-like cells, and the strength of the chromatin proteomics we conducted. In response to the reviewer’s suggestion, we have reorganized the order of the figures, particularly Figure 1 and Figure 2, and revised the text to improve readability and flow.

      Reviewer #2 (Recommendations For The Authors):

      There are some very interesting components to the study but, as noted, the narrative requires changes and the rationale for focusing on TOPBP1 and SMARCAD1 is not strong at present. Specific comments are noted below

      (1) Inclusion of authentic 2C cells for comparative chromocenter analysis (or at least a more fulsome discussion of how the system has been benchmarked in previous studies).

      We have included more detail in the revised methods section, in the “Cell lines and culture conditions” paragraph. We have added: “The Dux overexpression system was benchmarked according to previously reported features. Dux overexpression resulted in the loss of DAPI-dense chromocenters and the loss of the pluripotency transcription factor OCT4 (fig. S1E) (6, 7), upregulation of specific genes of the 2-cell transcriptional program such as endogenous Dux, MERVL, and major satellites (MajSat) (fig. S1F) (6, 7, 11, 26, 58), and accumulation in the G2/M cell cycle phase (fig. S1G), with a reduced S phase consistent in several clonal lines (fig. S1H) (15).”

      (2) In Figure 1A, the text indicates a loss of chromocenters, but it may be better described as decompaction because the DAPI/H3K9me3 staining shows diffuse/expanded structures (this is in fact how it is described in relation to Figure 2).

      We have changed the text accordingly, now describing it as “decompaction”.

      (3) Table S1 has 6 separate tabs but these are not specified in the text. It would be useful to separate the 397 proteins unique to Luc and 2C- cells since they form much of the basis for the remaining analysis. This approach also assumes it is the absence of a protein in the 2C<sup>+</sup> that accounts for the lack of chromocenters (noting there are 510 proteins unique to the 2C<sup>+</sup> state that are not discussed).

      We have referenced the supplementary table as Table S1 in the text for simplicity. It includes: Table S1A - List of Protein Groups identified by mass spectrometry in -EdU, Luc, 2C- and 2C<sup>+</sup> cells; Table S1B - Input data for SAINT analysis; Table S1C - SAINT results of the comparison 2C- vs Luc and 2C<sup>+</sup> vs Luc; Table S1D - SAINT results of the comparison Luc vs 2C- and 2C<sup>+</sup> vs 2C-; Table S1E - SAINT results of the comparison Luc vs 2C<sup>+</sup> and 2C- vs 2C<sup>+</sup>; and Table S1F - Total number of PSM per protein in the different cells and conditions tested.

      (4) Since there is no change in H3K9me3 levels, loss of SUV420H2 from 2C<sup>+</sup> chromatin (figure 1G) coupled with potential changes in H4K20me3 could contribute the morphological differences. SUV420H2 is known to regulate chromocenter clustering in a way the requires H4K20me3 but this is not addressed or cited (PUBMED: 23599346).

      As suggested by the reviewer, we have added additional sentences and references in the revised manuscript.

      (5) In Figure 1C, there does appear to be overlap between the 2C<sup>+</sup> and 2C- populations (while the Luc population is distinct) even though they are morphologically distinct when imaged in Figure 2A. The 2C- cells are thought to be an intermediate, low Dux expressing population.

      Chromatome profiling through genome capture provides a snapshot of the chromatin-bound proteome in the analyzed samples (shown in revised Fig. 2B). As indicated by the reviewer and previously reported in the literature, 2C- cells are an intermediate population before reaching 2C<sup>+</sup> cells. For this study, we have focused on H3K9me3 morphological changes. Even though 2C- and 2C<sup>+</sup> cells are distinct with respect to H3K9me3 morphology (shown in revised Fig. 1B), analysis of the chromatome data from hundreds of chromatin-bound proteins revealed some overlap between these two populations. However, replicates from the same population tend to cluster together, for example, 2C<sup>+</sup> rep1 and 2C<sup>+</sup> rep3, and 2C- rep1 and 2C- rep2. Collectively, these data suggest that a defined subset of coordinated changes in the chromatome likely triggers the transition from 2C- to 2C<sup>+</sup> cells. Further experimental investigation of the chromatome dataset during the 2C-like transition would be interesting, however, we believe it is beyond the scope of this study.

      (6) Data with SUV39H1 and 2 is difficult to accommodate; what about other H3K9 methyltransferases or proteins such as TRIM28 (KAP1) and SETDB1 (this comes up in the discussion but is not assessed in the results section).

      We agree that investigating the role of TRIM28 (KAP1) and SETDB1 in this experimental setting could be of interest, however, we believe that these experiments go beyond the scope of the presented study.

      (7) Rationale for choosing TOPBP1 needs to be improved. How do TOPBP1 levels relate to TOPI/TOP2A/TOP2B levels across the 3 cell populations? By what criteria does topoisomerase inhibitor treatment increase 2C<sup>+</sup> like cells? Moreover, to what extent will inhibiting topoisomerases lead to global heterochromatin and cell cycle changes regardless of cell type.

      Following the reviewer’s suggestion, we have included some additional references throughout the text to strengthen our rationale for selecting TOPBP1, given its well-established critical role in DNA replication and repair. Additionally, we have revised the results and discussion sections to include new sentences that propose a potential mechanism by which topoisomerase inhibitors may indirectly recruit TOPBP1 to facilitate DNA repair, ultimately leading to an increase in 2C<sup>+</sup> cells.

      (8) Likewise, the decision to look at SMARCAD1 based solely on its interaction with TOPBP1 seems somewhat arbitrary and it did not seem to come up as of interest in the iPOTD analysis. Moreover, they were not able to validate the interaction with their own analyses.

      We have revised the text to clarify the connection further.

      (9) The flow of results is confusing. The first section concludes with a focus on TOPBP1 and SMARCAD1, then progresses to morphological characterization of heterochromatin regions in the next two sections before returning to TOPBP1 and SMARCAD1. It seems like it would make more sense to describe the model system and morphological characterization at the beginning of the results section and then transition to the proteomic analysis and characterization of TOPBP1 and SMARCAD1 (with the expectation that the rationale be improved).

      As suggested by the reviewer, we have reordered the figures, particularly Figure 1 and Figure 2, and rephased the text to improve the overall reading flow.

      (10) There has been considerable work done on characterizing chromatin structure, epigenetic changes, and morphology during early embryonic development. It is therefore difficult to see what validating some of these changes in the inducible model is adding much in the way of new knowledge. It may, but this is not articulated in the current text.

      As detailed before, we have rephrased the text to improve the overall reading flow, which we hope has improved the understanding of the impact of our results.

      (11) It is difficult to disentangle broader effects of both TOPBP1 and SMARCAD1 from those described here; they may induce phenotypes, but these may not be unique to this model system.

      We agree with the reviewer, but to address this point would require additional experiments which would go beyond the scope of the presented study.

      (12) One of the issues with this assay is global chromatin recovery; it is not focused on heterochromatin compartments. The statement "We identified a total of 2396 proteins, suggesting an efficient pull-down of chromatin-associated factors (fig. S2D and Table S1)" does not demonstrate efficiency. Additional functional annotation would be required to establish this claim, including what fraction are known chromatin-associated proteins (with a focus on the heterochromatin compartment).

      We have changed the text accordingly. The resulting statement reads as: “We identified a total of 2396 proteins, suggesting an effective pull-down of putative chromatin-associated factors (fig. S2D and Table S1)”.

      Reviewer #3 (Public Review):

      The manuscript entitled "SMARCAD1 and TOPBP1 contribute to heterochromatin maintenance at the transition from the 2C-like to the pluripotent state" by Sebastian-Perez et al. adopted the iPOTD method to compare the chromatin-bound proteome in ESCs and 2C-like cells generated by Dux overexpression. The authors identified 397 chromatin-bound proteins enriched only in ESC and 2C- cells, among which they further investigated TOPBP1 due to its potential role in controlling chromocenter reorganization. SMARCD1, a known interacting protein of TOPBP1, was also investigated in parallel. The authors observed increased size and decreased number of H3K9me3-heterochromatin foci in Dux-induced 2C<sup>+</sup> cells. Interestingly, depletion of TOPBP1 or SMARCD1 also led to increased size and decreased number of H3K9me3 foci. However, depletion of these proteins did not affect entry into or exit from the 2C-like state. Nevertheless, the authors showed that both TOPBP1 and SMARCD1 are required for early embryonic development.

      Although this manuscript provides new insights into the features of 2C-like cells regarding H3K9me3-heterochromatin reorganization, it remains largely descriptive at this stage. It does not provide new insights into the following important aspects: 1) how SMARCD1 associates with H3K9me3 and contributes to heterochromatin maintenance, 2) how TOPBP1 regulates the expression of SMARCD1 and facilitates its localization in heterochromatin foci, 3) whether the remodelling of chromocenter is causally related to the mutual transitions between ESCs and 2C-like cells. Furthermore, some results are over-interpreted. Additional experiments and analyses are needed to increase the strength of mechanistic insights and to support all claims in the manuscript.

      We would like to thank the reviewer for their positive and thorough evaluation of our manuscript. We have revised the text and hope that the overall flow is now clearer. Moreover, while we acknowledge the value of including mechanistic studies, such an addition would require a substantial amount of experimental work that exceeds our current resources. 

      Reviewer #3 (Recommendations For The Authors):

      Major points:

      (1) Fig.2: the DNA decompaction of the chromatin fibers shown in 2C<sup>+</sup> cells may be more related to a relaxed 3D chromatin conformation (Zhu, NAR 2021; Olbrich, Nat Commun 2021) than chromatin accessibility. The authors should discuss this point.

      As suggested by the reviewer, we have included some additional sentences and references in the revised manuscript to address this concern.

      (2) Chemical inhibition of topoisomerases resulted in an increase in the percentage of 2C<sup>+</sup> cells. Does depletion of TOPBP1 also resulted in increased percentage of 2C<sup>+</sup> cells? Please include this result in Fig. 3E. Additionally, it should be noted that DDR and p53 have been reported to activate Dux (Stashpaz, eLife 2020; Grow, Nat Genet 2021), and thus, may contribute to the increased percentage of 2C<sup>+</sup> cells observed upon topoisomerase inhibition. This point should be discussed in the manuscript.

      To address this concern, we have included some additional sentences and references in the revised manuscript.

      (3) Fig 3A: the TOPBP1 band in the IP sample is questionable, and therefore the conclusion that TOPBP1 is associated with H3K9me3 is difficult to draw from Fig 3A. Additionally, the authors mentioned that association of TOPBP1 and SMARCAD1 is undetected in ESCs, likely due to the suboptimal efficiency of available antibodies. As these are key conclusions in this study, the authors are suggested to try other commercially available TOPBP1 antibodies (e.g., Abcam #ab-105109, used by ElInati, PNAS 2017) or knock-in tags to perform the co-IP experiment.

      Following the reviewer’s suggestion and to improve the reading flow, we have restructured the order of figures and removed the original Figure 3A. The revised Figure 3A-C panel illustrates the SMARCAD1 association with H3K9me3 in ESCs and 2C- cells, while capturing the reduced SMARCAD1-H3K9me3 association in 2C<sup>+</sup> cells.

      (4) Fig. 3C-D, Fig. S3D: the authors claimed reduction of both SMARCAD1 expression and its co-localization with H3K9me3 foci in 2C<sup>+</sup> cells, but did not perform mechanistic studies. It is important to know if TOPBP1 expression also decreases in 2C<sup>+</sup> cells. Additionally, it is unclear if the reduced co-localization of SMARCAD1 with H3K9me3 foci results from its altered nuclear localization or simply from reduced expression level? In either case, please provide some mechanistic insights.

      While we acknowledge the value of including mechanistic studies, such an addition would require a substantial amount of experimental work that exceeds our current resources. 

      (5) Fig. 3K, Fig. S4D-E: does SMARCAD1 expression decrease upon TOPBP1 depletion? Statistical analysis of SMARCAD1 intensity in Fig. S4E is needed, and a Western blot analysis is strongly suggested. Additionally, it is unclear if the reduced co-localization of SMARCAD1 with H3K9me3 foci results from its altered nuclear localization or simply from reduced expression level? In Fig. 3K, TOPBP1-depleted cells appear to show decreased size and increased number of H3K9me3 foci, which is inconsistent with Fig. S4B-C. The authors should clarify this discrepancy. Furthermore, statistics should be performed to determine whether Smarcad1/Topbp1 knockdown could further increase the size and decrease the number of H3K9me3 foci in 2C<sup>+</sup> cells. This would provide additional evidence for the involvement of these proteins in heterochromatin maintenance.

      We did not observe Smarcad1 downregulation after Topbp1 knockdown (shown in fig. S4A). In Figs. S4B and S4C, we observed that the number of H3K9me3 foci decreased, and their area became larger after knocking down either Smarcad1 or Topbp1, compared to scramble controls. These results align with the reviewer’s comment. Additionally, it should be noted that these findings were derived from the quantification of tens of cells and hundreds of foci, as indicated in the figure legend. This resulted in statistical significance after applying the test indicated in the figure legend.

      (6) Fig. 3J is suggested to be moved to Fig. 4. Additionally, performing immunostaining of SMARCAD1, TOPBP1, and H3K9me3 during pre-implantation development would provide valuable information on their protein-level dynamics, interactions, and functions in early embryos. This would further strengthen the conclusions drawn in the manuscript.

      We agree that performing these additional experiments would provide additional valuable information, however this would require a substantial amount of experimental work that exceeds our current resources.

      (7) Fig. 4 and Fig. S5: the authors observed reduced H3K9me3 signal in the Smarcad1 MO embryos at the 8-cell stage, but claim that they failed to examine Topbp1 MO embryos at the 8-cell stage due to their developmental arrest at the 4-cell stage. However, based on Fig. 4A, not all Topbp1 MO embryos were arrested at the 4-cell stage, and it is still possible to examine the H3K9me3 signal in 8-cell Topbp1 MO embryos, which is critical for demonstrating its function in early embryos. Also, how to interpret the increased HP1b signal in Topbp1 MO embryos?

      For Topbp1 silencing, we observed an even more severe phenotype compared to Smarcad1 MO. All the Topbp1 MO-injected embryos (100 %) arrested at the 4-cell stage and did not develop further (shown in Fig. 4A and 4B). Therefore, the severity of the Topbp1 morpholino phenotype posed a technical challenge in evaluating the H3K9me3 signal in 8-cell Topbp1 MO embryos, as none of the injected embryos developed beyond the 4-cell stage.

      We believe the increased HP1b signal in Topbp1 MO embryos could indicate potential alterations in chromatin organization and heterochromatin stability. Specifically, we observed remodeling of heterochromatin in both 2-cell and 4-cell Topbp1 MO arrested embryos compared to controls, as evidenced by the spreading and increased HP1b signal (shown in fig. S5F-S5I). Further investigations could enhance our understanding of the underlying defects in Topbp1 knockdown embryos, extending beyond heterochromatin-related errors.

      Minor points:

      (1) Page 4, the third row from the bottom: please revise the sentence.

      We have reviewed the text and it now reads correctly in the revised manuscript.

      (2) Fig. 1C: The authors claimed "Luc replicates clustered separately from 2C<sup>+</sup> and 2C- conditions", however, Luc rep3 is apparently clustered with 2C conditions.

      (3) The GFP signal in Fig. S1E is confusing.

      (4) Please include ESC in Fig. 2D-E. Also label the colors in Fig. 2E.

      As indicated in the figure legend of the revised Fig. 1F: “Cells with a GFP intensity score > 0.2 are colored in green. Black dots indicate 2C- cells and green dots indicate 2C<sup>+</sup> cells.”

      (5) Fig. 2G: Transposition of the heatmap (show genes in rows) is suggested to improve readability.

      (6) Page 7, the third row from the bottom: incorrect citation of Fig. 1K.

      Thank you for spotting this incorrect citation. We have corrected it in the revised manuscript.

      (7) Page 8, row 15, Fig. S3D should be cited to support the decreased expression of SMARCAD1 in 2C<sup>+</sup> cells.

      We have cited the corresponding supplementary figure S3D in the mentioned sentence.

      (8) Fig. 2H: what is the difference between "2C-" and "ESC-like"?

      We named 2C- to those cells not expressing the GFP reporter in the transition from ESCs to 2C<sup>+</sup> cells. We named ESC-like cells to those cells that do not express the GFP reporter during exit, meaning from sorted and purified 2C<sup>+</sup> to a GFP negative state.

      (9) Fig. S4A-C: compared with shTopbp1#2, shTopbp1#1 appears to be slightly more effective in knockdown, but less dramatic changes in the size/number of H3K9me3 foci.

      (10) Fig. 4: please show the effectiveness of Topbp1 MO by Immunostaining of TOPBP1.

      (11) Fig. 4C: please label the developmental stage as in Fig. 4E and 4G.

      We have added a “8-cell” label in the Figure 4C, as suggested by the reviewer.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary:

      In this study, Zhao and colleagues investigate inflammasome activation by E. tarda infections. They show that E. tarda induces the activation of the NLRC4 inflammasome as well as the non-canonical pathway in human THP1 macrophages. Further dissecting NLRC4 activation, they find that T3SS translocon components eseB, eseC and eseD are necessary for NLRC4 activation and that delivery of purified eseB is sufficient to trigger NAIP-dependent NLRC4 activation. Sequence analysis reveals that eseB shares homology within the C-terminus with T3SS needle and rod proteins, leading the authors to test if this region is necessary for inflammasome activation. They show that the eseB CT is required and that it mediates interaction with NAIP. Finally, they that homologs of eseB in other bacteria also share the same sequence and that they can activate NLRC4 in a HEK293T cell overexpression system.

      Strengths:

      This is a very nice study that convincingly shows that eseB and its homologs can be recognized by the human NAIP/NLRC4 inflammasome. The experiments are well designed, controlled and described, and the papers is convincing as a whole.

      Weaknesses:

      The authors need to discuss their study in the context of previous papers that have shown an important role for E. tarda flagellin in inflammasome activation and test whether flagellin and/or E. tarda T3SSs needle or rod can activate NLRC4.

      The authors show that eseB and its homologs can activate NLRC4, but there are also other translocon proteins that are very different such as YopB or PopB. and share little homology with eseB. It would be nice to include a section comparing the different type 3 secretion systems. are there 2 different families of T3SSs, those that feature translocon components that are recognized by NAIP-NLRC4 and those that cannot be recognized?

      (1) The authors need to discuss their study in the context of previous papers that have shown an important role for E. tarda flagellin in inflammasome activation and test whether flagellin and/or E. tarda T3SSs needle or rod can activate NLRC4.

      According to the reviewer’s suggestion, we added the relevant discussion (lines 326-334) and carried out additional experiments to examine whether E. tarda flagellin, needle, and rod could activate NLRC4. The relevant results are shown in Figure S3, Figure S5, and lines 226-230 and 269-274.

      (2) The authors show that eseB and its homologs can activate NLRC4, but there are also other translocon proteins that are very different such as YopB or PopB. and share little homology with eseB. It would be nice to include a section comparing the different type 3 secretion systems. are there 2 different families of T3SSs, those that feature translocon components that are recognized by NAIP-NLRC4 and those that cannot be recognized?

      According to the reviewer’s suggestion, additional experiments were performed to examine the NLRC4-activating potentials of 14 translocator proteins that share low sequence identities with EseB. The relevant results and discussion are shown in Figure S8 and lines 289-301; 364-372, and 377-379.

      Reviewer #2 (Public Review):

      Summary:

      This work by Zhao et al. demonstrates the role of the Edwardsiella tarda type 3 secretion system translocon in activating human macrophage inflammation and pyroptosis. The authors show the requirement of both the bacterial translocon proteins and particular host inflammasome components for E. tarda-induced pyroptosis. In addition, the authors show that the C-terminal region of the translocon protein, EseB, is both necessary and sufficient to induce pyroptosis when present in the cytoplasm. The most terminal region of EseB was determined to be highly conserved among other T3SS-encoding pathogenic bacteria and a subset of these exhibited functionally similar effects on inflammasome activation. Overall, the data support the conclusions and interpretations and provide interesting insights into interactions between bacterial T3SS components and the host immune system.

      Strengths:

      The authors use established and reliable molecular biology and bacterial genetics strategies to characterize the roles of the bacterial T3SS translocon and host inflammasome pathways to E. tarda-induced pyroptosis in human macrophages. These observations are naturally expanded upon by demonstrating the specific regions of EseB that are required for inflammasome activation and the conservation of this sequence among other pathogenic bacteria.

      Weaknesses:

      The functional assessment of EseB homologues is limited to inflammasome activation at the protein level but does not include the effects on cell viability as shown for E. tarda EseB. Confirmation that EseB homologues have similar effects on cell death would strengthen this portion of the manuscript.

      According to the reviewer’s suggestion, the effects of representative EseB homologs on cell death were examined in the revised manuscripts (Figure 5D, Figure S7 and line 289).

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      I only have a few suggestions on how to improve the study:

      Activation of caspase-4 requires entry into the host cytosol. Can this be observed with E. tarda and is it T3SS dependent? The fact that deleting the translocon components abrogates all GSDMD activation (see Fig. 2D) suggests that also Casp4 activation requires an active T3SS. It would be useful for the reader to include some more information on the cellular biology of E. tarda.

      In our study, we found that E. tarda could enter THP-1 cells (Figure S1), and host cell entry was not affected by deletion of eseB-D (Δ_eseB-D_) in the T3SS system (Figure 2B, C). Additional experiments showed that Δ_eseB-D_ abolished the ability of E. tarda to activate Casp4 (Figure S2), implying that Casp4 activation required an active T3SS. Relevant changes in the revised manuscript: lines 223 and 224, 341-342.

      The data presented by the authors suggest that escB is sensed by NLRC4 when overexpressed, they do however not prove that during an infection escB is the main factor that drives NLRC4 activation, since deficiency in escB also abrogated translocation of other potential activators of NLRC4, e.g. flagellin and T3SS needle and rod subunits. I would thus find it essential to properly test if E. tarda flagellin can activate NLRC4 by comparing a WT and flagellin deficient strain, and/or by transfecting or expressing E.t. flagellin in these cells, as well as testing whether E.t. rod and needle subunits act as NLRC4 activators. This is important as previous studies suggested that flagellin is the main activator of cytotoxicity during E. tarda infection.

      Previous studies have shown that flagellin is required for E. tarda-induced macrophage death in fish [1] but not in mice [2]. In the revised manuscript, we performed additional experiments to examine whether E. tarda flagellin, needle, and rod could activate NLRC4. The relevant results are shown in Figure S3, Figure S5, and lines 226-230 and 269-274, and 326-334.

      References

      (1) Xie HX, Lu JF, Rolhion N, Holden DW, Nie P, Zhou Y, et al. Edwardsiella tarda-induced cytotoxicity depends on its type III secretion system and flagellin. Infect Immun. 2014;82(8):3436-45. doi: 10.1128/IAI.01065-13.

      (2) Chen H, Yang D, Han F, Tan J, Zhang L, Xiao J, et al. The bacterial T6SS effector EvpP prevents NLRP3 inflammasome activation by inhibiting the Ca<sup>2+</sup>-dependent MAPK-JNK pathway. Cell Host Microbe. 2017;21(1):47-58. doi: 10.1016/j.chom.2016.12.004.

      Figure 5/S4, please list the names of the eseB homologs. It is cumbersome to have to access GenBank with the accession number to be able to understand what proteins the authors define as homologs of eseB.

      The names were added to the revised Table S2, Figure 5 and Figure S6 (the original Figure S4).

      The authors mention that other translocon proteins, such as YopB/D and PopB/D, were suggested to cause inflammasome activation. How do these compare to eseB and its homologs? Do they share the CT motif?

      Additional experiments were performed to compare the inflammasome activation abilities of EseB and other translocator proteins including YopD and PopD. The relevant results and discussion are shown in Figure S8 and lines 289-301, 364-372, and 377-379.

      It would be nice to show that there are potentially two groups of translocon proteins, one group sharing homology to needle subunits within the CT region and another that is different. A quick look at the sequence of these proteins suggests that they are quite different and much larger than eseB.

      In our study, additional experiments with more translocator proteins indicated that the possession of EseB T6R-like terminal residues does not necessarily guarantee the protein to activate the NLRC4 inflammasome. Relevant results and discussion are shown in lines 289-301, 364-372, and 377-379.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      In this manuscript, Satouh et al. report giant organelle complexes in oocytes and early embryos. Although these structures have often been observed in oocytes and early embryos, their exact nature has not been characterized. The authors named these structures "endosomal-lysosomal organelles form assembly structures (ELYSAs)". ELYSAs contain organelles such as endosomes, lysosomes, and probably autophagic structures. ELYSAs are initially formed in the perinuclear region and then migrate to the periphery in an actin-dependent manner. When ELYSAs are disassembled after the 2-cell stage, the V-ATPase V1 subunit is recruited to make lysosomes more acidic and active. The ELYSAs are most likely the same as the "endolysosomal vesicular assemblies (ELVAs)", reported by Elvan Böke's group earlier this year (Zaffagnini et al. doi.org/10.1016/j.cell.2024.01.031). However, it is clear that Satouh et al. identified and characterized these structures independently. These two studies could be complementary. Although the nature of the present study is generally descriptive, this paper provides valuable information about these giant structures. The data are mostly convincing, and only some minor modifications are needed for clarification and further explanation to fully understand the results.

      Reviewer #2 (Public Review):

      Satouh et al report the presence of spherical structures composed of endosomes, lysosomes, and autophagosomes within immature mouse oocytes. These endolysosomal compartments have been named as Endosomal-LYSosomal organellar Assembly (ELYSA). ELYSAs increase in size as the oocytes undergo maturation. ELYSAs are distributed throughout the oocyte cytoplasm of GV stage immature oocytes but these structures become mostly cortical in the mature oocytes. Interestingly, they tend to avoid the region which contains metaphase II spindle and chromosomes. They show that the endolysosomal compartments in oocytes are less acidic and therefore non-degradative but their pH decreases and becomes degradative as the ELYSAs begin to disassemble in the embryos post-fertilization. This manuscript shows that lysosomal switching does not happen during oocyte development, and the formation of ELYSAs prevents lysosomes from being activated. Structures similar to these ELYSAs have been previously described in mouse oocytes (Zaffagnini et al, 2024) and these vesicular assemblies are important for sequestering protein aggregates in the oocytes but facilitate proteolysis after fertilization. The current manuscript, however, provides further details of endolysosomal disassembly post-fertilization. Specifically, the V1-subunit of V-ATPase targeting the ELYSAs increases the acidity of lysosomal compartments in the embryos. This is a well-conducted study and their model is supported by experimental evidence and data analyses.

      Reviewer #3 (Public Review):

      Fertilization converts a cell defined as an egg to a cell defined as an embryo. An essential component of this switch in cell fate is the degradation (autophagy) of cellular elements that serve a function in the development of the egg but could impede the development of the embryo. Here, the authors have focused on the behavior during the egg-to-embryo transition of endosomes and lysosomes, which are cytoplasmic structures that mediate autophagy. By carefully mapping and tracking the intracellular location of well-established marker proteins, the authors show that in oocytes endosomes and lysosomes aggregate into giant structures that they term Endosomal LYSosomal organellar Assembl[ies] (ELYSA). Both the size distribution of the ELYSAs and their position within the cell change during oocyte meiotic maturation and after fertilization. Notably, during maturation, there is a net actin-dependent movement towards the periphery of the oocyte. By the late 2-cell stage, the ELYSAs are beginning to disintegrate. At this stage, the endo-lysosomes become acidified, likely reflecting the activation of their function to degrade cellular components.

      This is a carefully performed and quantified study. The fluorescent images obtained using well-known markers, using both antibodies and tagged proteins, support the interpretations, and the quantification method is sophisticated and clearly explained. Notably, this type of quantification of confocal z-stack images is rarely performed and so represents a real strength of the study. It provides sound support for the conclusions regarding changes in the size and position of the ELYSAs. Another strength is the use of multiple markers, including those that indicate the activity state of the endo-lysosomes. Altogether, the manuscript provides convincing evidence for the existence of ELYSAs and also for regulated changes in their location and properties during oocyte maturation and the first few embryonic cell cycles following fertilization.

      At present, precisely how the changes in the location and properties of the ELYSAs affect the function of the endo-lysosomal system is not known. While the authors' proposal that they are stored in an inactive state is plausible, it remains speculative. Nonetheless, this study lays the foundation for future work to address this question.

      Minor point: l. 299. If I am not mistaken, there is a typo. It should read that the inhibitors of actin polymerization prevent redistribution from the cytoplasm to the cortex during maturation.

      Minor point: A few statements in the Introduction would benefit from clarification. These are noted in the comments to the authors.

      We sincerely appreciate the editorial board of eLife and the reviewers for their helpful and constructive comments on our manuscript. We are pleased that the reviewers acknowledged that we identified and characterized this assembly structure independently. In the revised manuscript, we have carefully considered the reviewers’ comments and conducted additional analysis to address each of them.

      Regarding the typographical errors, we revised the description to fit with our findings and the reviewers’ comments. We also found that the primer sequence was correct, and we carefully checked the accuracy of the entire manuscript.

      We hope that the revised version will now be deemed suitable for publication in eLife.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Q. 1) The authors state in the Abstract that ELYSAs contain autophagosome-like membranes in the outer layer. However, this seems to be just speculation based on the LC3 staining results and is not directly shown. Are there autophagosome-like double membrane structures in ELYSAs?

      We appreciate this comment. We also agree with this concern; however, it was difficult to assert that they are autophagosomes based on the observation of the electron micrographs. For this reason, we rephrased it to be "Most ELYSAs are also positive for an autophagy regulator, LC3.” (lines 33). In addition, we revised the notation to LC3-positive structures in the Result and Discussion section (line 165-169, 286).

      Q. 2) The data in Figure 2A, showing a decrease in the number of LAMP1 structures, seems to contradict the data in Figure 1B, showing an apparent increase in LAMP1 structures. Please explain this discrepancy. If the authors did not count structures just below the plasma membrane, please explain the rationale for this.

      We really appreciate the valuable comment. Regarding the number of LAMP1-positive structures, it is not suitable for comparison with Figure 1B, etc., as pointed out by the reviewer, since the distribution of the LAMP1 signal differs from plane to plane. To avoid any potential confusion, we added new images of the Z-projection of the immunostained images that can better reflect the number of positive structures in the whole oocyte/embryo in Figure 2.

      In addition, as the reviewer pointed out, there is a technical difficulty in measuring the LAMP1-positive signal on the plasma membrane or just below it. We explained how and why we had to delete plasma membrane signals in our response #21.

      Q. 3) The actin dependence is not observed in Figure 5C. What is the difference between Figure 5C and 5E? Please explain further.

      We apologize for the lack of clarity; Figures 5C and 5E show the average number of LAMP1-positive structures (5C) and the percentage of the sum of granule volumes in LAMP1 positive structure (5E), respectively, after classifying the LAMP1 positive granules by their diameters.

      We removed Figure 5E for the sake of conciseness since we already mentioned a similar fact in Figure 5C. To clarify the corresponding explanations, we moved figures that were not classified by diameter to Supplementary Figure 8 to improve readability. Moreover, we have rewritten the main text on lines 200–211.

      Q. 4) While the actin inhibitors reduce the number of peripheral LAMP1 structures (Figure 5F), they do not affect their number in the central region (Figure 5G). How can the authors conclude that actin inhibitors inhibit the migration of LAMP1 structures?

      We appreciate the comment. As pointed out, the number of large LAMP1-positive structures in the medial region did not change. Therefore, we have avoided the description that ELYSAs migrate from the middle region to the cell periphery and have unified the description of whether large structures in the periphery occur. Please refer to the subsection title (line 188), the following descriptions (lines 189–199), the related description in the Results (lines 200–211), and the title and the legend of Figure 5.

      Q. 5) The authors show that the V1A subunit associates with the surface of LAMP1 structures as punctate structures (Figure 6B). What are these V1A-positive structures? Is V1A recruited to some specific domains of ELYSAs, or are V1A-positive active lysosomes recruited to ELYSAs? Please provide an interpretation of these data. The phrase "The V1-subunit of V-ATPase is targeted to these structures" (line 262) is not appropriate because it is indistinguishable whether only the V1 subunits are recruited or active lysosomes containing the V1 subunit are recruited.

      Thank you for the valuable comment. Indeed, our analysis, including the analysis of Fig. 8 described on line 262, did not clarify whether free V1A-mCherry molecules accessed the ELYSA periphery or whether lysosomes with V1A-mCherry molecules newly merged into the ELYSA. Therefore, we added this interpretation to lines 232–234 of the Results and revised the Discussion as "The number of membrane structures positive for V1A-mCherry increase upon ELYSA disassembly, indicating further acidification of the endosomal/lysosomal compartment" (lines 292–294).

      Q. 6) Why did the authors use LysoSensor as a marker for ELYSA instead of LAMP1 in Figure 8 and 9? Some reasons should be given.

      There is a clear technical reason for this: when LAMP1-EGFP was expressed in a zygote, it was largely migrated to the plasma membrane before and after the 2-cell stage, making it difficult to capture the change of ELYSAs. To circumvent this difficulty, we used Lysosensor to visualize ELYSAs instead of LAMP1-EGFP. This explanation was added to lines 258–260.

      Q. 7) In Figure 9A, it is not clear whether the activity of LysoSensor-positive structures is lower at this stage compared to other stages. It may be shown in Figure S7, but the data are not clearly visible. A direct comparison would be ideal.

      A new analysis similar to that shown in Fig. 9 for early 2-cells and 4-cells was performed and added to Figure S7. To support direct comparison, the ranges of axes were set to be similar.

      As a result, the quantified MagicRed signal on the isolated LysoSensor-positive punctate structure in MII oocyte was nearly the same as that in early 2-cells and 4-cells. In early 2-cells, LysoSensor gave a signal at the cellular boundary, where MagicRed staining was not observed, confirming that MagicRed activity is higher in the interior than in the cell periphery in post-fertilization embryos. We have included an additional description in the main text (lines 280–282).

      Q. 8) In the phrase "pregnant mare serum gonadotropin or an anti-inhibin antibody" (line 382), is "or" correct?

      When inducing superovulatory stimulation, an anti-inhibin antibody (distributed as CARD HyperOva) can be used as a substitute for PMSG (after additional stimulation with hCG), which results in the production of eggs of similar quality to those of PMSG. This was used in most experiments. To amend the lack of clarity, a reference (Takeo and Nakagata Plos One, 2015) was added to the description of HyperOva (line 417).

      Q. 9) In almost all graphs, please indicate what the X-axis is indicating (not just "number") so that readers can understand what number is being represented without reading the legends.

      We revised the axis titles in all figures.

      Q. 10) Since grayscale images provide better contrast than color images, it is recommended that single-color images be shown in grayscale.

      We replaced all single-color images with grayscale images.

      Reviewer #2 (Recommendations For The Authors):

      Specific comments:

      Q. 11) Figure 1 and S1- Both Rab5 and Rab7 co-localize with LAMP1. However, there seems to be a lot of LAMP1-free Rab5 dots as compared to the Lamp1-free Rab7. As a result, LAMP1 and Rab7 are co-localized more frequently than LAMP1 and Rab5 (video1). Could it be that early endosomes (Rab5+) are yet to be incorporated into ELYSAs? If so, a brief discussion of this phenomenon would be nice.

      Thank you very much for the comment. We agree with the reviewer’s interpretation. In accordance with this suggestion, we clearly stated in the main text: “Although small punctate structures that are RAB5-positive but LAMP1-negative also spread over the cytosol, most giant structures were positive for RAB5 and LAMP1 (Video 1)” (lines 91–93). In the Discussion section, a brief statement was included: “Considering the large number of RAB5-positive and LAMP1-negative punctate structures in MII oocytes, these layers may also reflect the assembly mechanism of the ELYSA” (lines 318–320).

      Q. 12) Video 3 (and Figure 6) clearly shows the dynamics of LAMP1-labelled vesicles during maturation, which is impressive. In contrast to the live cell imaging after LAMP1 mRNA injection, Figure 1 used anti-LAMP1 Ab to detect endogenous levels of LAMP1. It appears that mRNA microinjection causes LAMP1 overexpression causing more (but smaller) vesicles to form. It should be easy to quantify and compare the vesicles in Figure 1 and 6

      We appreciate the comment. As mentioned, injections of EGFP-LAMP1 mRNA are useful for the visualization of LAMP1 dynamics during the maturation phase from GV to MII by live cell imaging, which is not feasible with immunostaining. However, the fluorescence emitted by EGFP-LAMP1 is only a few tenths of that of antibody staining, and because of the technical difficulty of microinjection into GV oocytes, the signal-to-noise ratio sufficient for imaging was merely one in ten oocytes. In addition, live cell imaging of oocytes in Figure 6 had to be carried out with very low excitation light exposure to reduce the toxicity. It was also performed with a low magnification lens and a longer step size in the z-axis. For these reasons, in examining the point raised, we performed an additional 3D object analysis, in the same way as in Figure 2, on the data of IVM oocytes injected with EGFP-LAMP1 mRNA using the same lens as in Figure 1 and with a longer exposure time than in live imaging. The results were compared with the MII data of Figures 1 and 2.

      As a result, as shown in the new Figure S8, more objects with a diameter of 0.2–0.4 µm were found than in the immunostaining data, which fits the reviewer’s point. In addition, the counts were lower for the 0.6–1.0 µm diameter, but there was no significant difference in the number of larger LAMP1 positive structures corresponding to the ELYSA size. We consider that this was appropriate for the original purpose of characterizing the ELYSA formation process. A description of these points has been added to lines 221–225.

      Q. 13) In Figure 4A and B- Seems like not all LAMP1-positive structures were LC3-positive. Is there any size or location within the oocyte that determines LC3 positivity?

      We appreciate the valuable comment. To answer this comment, we proceeded with a new 3D object-based co-localization analysis on Lamp1 and LC3, determined the number, volume, and distribution within the oocyte, and incorporated the results as Supplementary Figure 6. To examine the positivity, we further analyzed the percentage of double-positive structures of all the LAMP1-positive structures. The results showed that their average diameter significantly shifted from 2.36 µm (GV) to 3.78 µm (MII). Moreover, it was clearly indicated that LAMP1-positive structures smaller than 2 µm in diameter are rarely positive for LC3. In terms of location, measuring the distance of the double positive structures from the oocyte center (the cellular geometric center) indicated that they tend to be observed at the periphery of both stages of oocytes (more than 80% in > 30 µm in the MII oocyte). Of note, no clear tendency of double positivity was observed. A description of these points has been added to lines 174–186.

      Q. 14) In discussion, line 256- Small ELYSAs are formed in GV oocytes. Since you haven't checked the smaller-sized, growing oocytes, I suggest rephrasing this sentence as 'are present' rather than 'are formed'.

      We agree with the reviewer’s suggestion and changed it to "present" (line 287).

      Q. 15) Line 188- ELISA should instead be ELYSA

      Thank you for pointing this out. We have found a few more typographical errors, and all of them have been corrected (lines 213 and 321).

      Reviewer #3 (Recommendations For The Authors):

      Q. 16) Line 42: What do you mean by 'zygotic gene expression following the degradation of the cellular components of each maternal and paternal gamete'? ZGA requires this degradation? Please provide supporting references from the literature.

      We apologize for the confusing wording. We meant to say that both ZGA and degradation of parental components are required. To avoid misunderstanding, we have revised “zygotic gene expression as well as the degradation of the cellular components of each maternal and paternal gamete” and inserted a new reference (line 44).

      Q. 17) 50: MII means metaphase II, not meiosis II.

      We corrected the clerical mistake (line 50).

      Q. 18) 51: Define LC3.

      We added the definition of LC3 (line 51-52).

      Q. 19) 60: 'lysosomal activity in oocytes is upregulated by sperm-derived factors as the oocytes grow and mature'. As written, the sentence implies that oocytes grow and mature after fertilization. This may be true for maturation, but I would be surprised to learn that there is growth of the oocyte after fertilization.

      We appreciate this valuable comment.

      The C. elegans lives mainly as a hermaphrodite, which contains a couple of U-shaped gonad arms including the ovary, spermatheca and uterus in the body. Oocytes grow in the ovary and maturate upon receiving major sperm proteins secreted from sperms and ovulated to the spermatheca for fertilization. In 2017, Kenyon’s group reported that major sperm proteins act as sperm-secreted hormones to upregulates the lysosomal activity in oocytes during oocyte growth and maturation. We have revised our manuscript to avoid misunderstanding, to ' lysosomal activity in oocytes is upregulated by major sperm proteins secreted from sperms as the oocytes grow and mature '. (L. 61-66).

      Q. 20) 94 and Figure 1B: While it is clear that many LAMP1 foci at the late 2-cell stage do not also contain RAB5, it seems that the majority of RAB5 loci also stain for LAMP1. This may be a minor point in the context of the paper but could be clarified.

      We could not easily agree with the suggestion because of the possibility that the images might give different impressions on each plane. Therefore, as a way to verify this point, we attempted to quantify the co-localization by reconstructing the 3D puncta information based on the two types of antibody staining data. Unfortunately, as shown in Fig. 1AB, Rab5 had a high cytoplasmic background, and although we were able to extract peaks, we could not reliably recalibrate the three-dimensional punctate structure (please refer to the new Supplementary Fig. 6). Therefore, co-localization on each other's punctate structure (LAMP1/RAB5 vs. RAB5/LAMP1) could not be verified. The validation using specific planes also showed large differences between planes, with overlapping punctate structures counted separately in adjacent planes, making reliable quantification difficult. This is an issue that will be addressed in the future.

      On the other hand, the newly added Z-projection figure (Fig. 1AB) shows that RAB5-positive and LAMP1-negative punctate structures tend to accumulate along the LAMP1-positive punctate structures larger than 1 µm at the late 2-cell stage in all observed embryos; we added this statement on lines 99–101.

      Q. 21) 100-102 and Figure 2A: Does the decrease in the total number of LAMP1 foci refer just to cytoplasmic or also to membrane foci? If the former, what was the reason for not including the membrane in the analysis?

      We appreciate the critical question. The LAMP1 signal on the plasma membrane interfered with the measurement of the signals just below the plasma membrane. The biological cause of this increased signal on the plasma membrane, as shown in Fig. 2E, seemed to be caused by the migration of the LAMP1 signals post-fertilization, which was also reported in a previous paper by Zaffagnini et al. (2024), published in Cell.

      In our analysis, oocytes are giant cells, and confocal imaging has a technical limitation in obtaining the same fluorescent intensity along the z-axis. However, 3D-object analysis requires thresholding based on absolute values. As a result of this situation, the presence of the plasma membrane signal caused punctate structures located close to the membrane to be captured and recognized as a single, very large LAMP1-positive structure, resulting in the loss of the punctate structure that should be measured.

      To avoid this issue, we have used several programs to correct the fluorescence difference along the z-axis; nonetheless, these attempts were unsuccessful. Therefore, as described in the Materials and Methods section, we applied only background subtraction at each z-position and then manually removed the plasma membrane signal (which was thin and continuous at the edges). Furthermore, when the plasma membrane and punctate structure signals overlapped, we paid attention not to remove the signals but to separate them. Thus, we believe that the decrease in the number and volume of LAMP1-positive structures after fertilization is still a phenomenon associated with the shift of LAMP1 to the plasma membrane.

      Q. 22) Figure 2B, F, G: As the x-axis does not represent a continuous variable, adjacent data points should not be connected by a line. The histogram representations in A, C, and E are much easier to understand. I suggest presenting all data in this format.

      We revised the line graphs to bar graphs. Besides, to make the significance among populations clearer, the significances are now expressed using alphabetical indicators.

      Q. 23) Figure 2B, C: It seems that the values for the different stages are expressed relative to the value at MII. Why not use the GV value at the base-line? This would follow the developmental trajectory of the oocyte/embryo more directly and would not (I believe) change the conclusions.

      We appreciated the comment. We meant to express that ELYSA develops most in the MII phase and that it decreases after fertilization, so considering the reviewer’s suggestion, we expressed GV-MII changes based on GV and changes after fertilization based on the MII phase (Fig. 2C, D).

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary:

      This paper details a study of endothelial cell vessel formation during zebrafish development. The results focus on the role of aquaporins, which mediate the flow of water across the cell membrane, leading to cell movement. The authors show that actin and water flow together drive endothelial cell migration and vessel formation. If any of these two elements are perturbed, there are observed defects in vessels. Overall, the paper significantly improves our understanding of cell migration during morphogenesis in organisms.

      Strengths:

      The data are extensive and are of high quality. There is a good amount of quantification with convincing statistical significance. The overall conclusion is justified given the evidence.

      Weaknesses:

      There are two weaknesses, which if addressed, would improve the paper.

      (1) The paper focuses on aquaporins, which while mediates water flow, cannot drive directional water flow. If the osmotic engine model is correct, then ion channels such as NHE1 are the driving force for water flow. Indeed this water is shown in previous studies. Moreover, NHE1 can drive water intake because the export of H+ leads to increased HCO3 due to the reaction between CO2+H2O, which increases the cytoplasmic osmolarity (see Li, Zhou and Sun, Frontiers in Cell Dev. Bio. 2021). If NHE cannot be easily perturbed in zebrafish, it might be of interest to perturb Cl channels such as SWELL1, which was recently shown to work together with NHE (see Zhang, et al, Nat. Comm. 2022).

      (2) In some places the discussion seems a little confusing where the text goes from hydrostatic pressure to osmotic gradient. It might improve the paper if some background is given. For example, mention water flow follows osmotic gradients, which will build up hydrostatic pressure. The osmotic gradients across the membrane are generated by active ion exchangers. This point is often confused in literature and somewhere in the intro, this could be made clearer.

      Reviewer #1 (Recommendations For The Authors):

      (1) The paper focuses on aquaporins, which while mediating water flow, cannot drive directional water flow. If the osmotic engine model is correct, then ion channels such as NHE1 are the driving force for water flow. Indeed this water is shown in previous studies. Moreover, NHE1 can drive water intake because the export of H+ leads to increased HCO3 due to the reaction between CO2+H2O, which increases the cytoplasmic osmolarity (see Li, Zhou and Sun, Frontiers in Cell Dev. Bio. 2021). If NHE cannot be easily perturbed in zebrafish, it might be of interest to perturb Cl channels such as SWELL1, which was recently shown to work together with NHE (see Zhang, et al, Nat. Comm. 2022).

      We thank Reviewer #1 for this very important comment and the suggestion to examine the function of ion channels in establishing an osmotic gradient to drive directional flow. We have taken on board the reviewer’s suggestion and examined the expression of NHE1 and SWELL1 in endothelial cells using published scRNAseq of 24 hpf ECs (Gurung et al, 2022, Sci. Rep.). We found that slc9a1a, slc9a6a, slc9a7, slc9a8, lrrc8aa and lrrc8ab are expressed in different endothelial subtypes. To examine the function of NHE1 and SWELL1 in endothelial cell migration, we used the pharmacological compounds, 5-(N-ethyl-Nisopropyl)amiloride (EIPA) and DCPIB, respectively. While we were unable to observe an ISV phenotype after EIPA treatment at 5, 10 and 50µM, we were able to observe impaired ISV formation after DCPIB treatment that was very similar to that observed in Aquaporin mutants. We were very encouraged by these results and proceeded to perform more detailed experiments whose results have yielded a new figure (Figure 6) and are described and discussed in lines 266 to 289 and 396 to 407, respectively, in the revised manuscript.

      (2) In some places the discussion seems a little confusing where the text goes from hydrostatic pressure to osmotic gradient. It might improve the paper if some background is given. For example, mention water flow follows osmotic gradients, which will build up hydrostatic pressure. The osmotic gradients across the membrane are generated by active ion exchangers. This point is often confused in literature and somewhere in the intro, this could be made clearer.

      Thank you for pointing out the deficiency in explaining how osmotic gradients drive water flow to build up hydrostatic pressure. We have clarified this in lines 50, 53 - 54 and 385.

      The two recommendations listed above would improve the paper. They are however not mandatory. The paper would be acceptable with some clarifying rewrites. I am not an expert on zebrafish genetics, so it might be difficult to perturb ion channels in this model organism. Have the authors tried to perturb ion channels in these cells?

      We hope that our attempts at addressing Reviewer’s 1 comments are satisfactory and sufficient to clarify the concerns outlined.

      Reviewer #2 (Public Review):

      Summary:

      Directional migration is an integral aspect of sprouting angiogenesis and requires a cell to change its shape and sense a chemotactic or growth factor stimulus. Kondrychyn I. et al. provide data that indicate a requirement for zebrafish aquaporins 1 and 8, in cellular water inflow and sprouting angiogenesis. Zebrafish mutants lacking aqp1a.1 and aqp8a.1 have significantly lower tip cell volume and migration velocity, which delays vascular development. Inhibition of actin formation and filopodia dynamics further aggravates this phenotype. The link between water inflow, hydrostatic pressure, and actin dynamics driving endothelial cell sprouting and migration during angiogenesis is highly novel.

      Strengths:

      The zebrafish genetics, microscopy imaging, and measurements performed are of very high quality. The study data and interpretations are very well-presented in this manuscript.

      Weaknesses:

      Some of the mechanobiology findings and interpretations could be strengthened by more advanced measurements and experimental manipulations. Also, a better comparison and integration of the authors' findings, with other previously published findings in mice and zebrafish would strengthen the paper.

      We thank Reviewer #2 for the critique that the paper can be strengthened by more advanced measurements and experimental manipulations. One of the technical challenges that we face is how to visualize and measure water flow directly in the zebrafish. We have therefore taken indirect approaches to assess water abundance in endothelial cells in vivo. One approach was to measure the diffusion of GEM nanoparticles in tip cell cytoplasm in wildtype and Aquaporin mutants, but results were inconclusive. The second was to measure the volume of tip cells, which should reflect water in/outflow. As the second approach produced clear and robust differences between wildtype ECs, ECs lacking Aqp1a.1 and Aqp8a.1 and ECs overexpressing Aqp1a.1 (revised Fig. 5), we decided to present these data in this manuscript.

      We have also taken Reviewer 2 advice to better incorporate previously published data in our discussion (see below and lines 374 to 383 of the revised manuscript).

      Reviewer #2 (Recommendations For The Authors):

      I have a few comments that the authors may address to further improve their manuscript analysis, quality, and impact.

      Major comments:

      (1) Citation and discussion of published literature

      The authors have failed to cite and discuss recently published results on the role of aqp1a.1 and aqp8a.1 in ISV formation and caliber in zebrafish (Chen C et al. Cardiovascular Research 2024). That study showed a similar impairment of ISV formation when aqp1a.1 is absent but demonstrated a stronger phenotype on ISV morphology in the absence of aqp8a.1 than the current manuscript by Kondrychyn I et al. Furthermore, Chen C et al show an overall decrease in ISV diameter in single aquaporin mutants suggesting that the cell volume of all ECs in an ISV is affected equally. Given this published data, are ISV diameters affected in single and double mutants in the current study by Kondrochyn I et al? An overall effect on ISVs would suggest that aquaporin-mediated cell volume changes are not an inherent feature of endothelial tip cells. The authors need to analyse/compare and discuss all differences and similarities of their findings to what has been published recently.

      We apologise for having failed and discussed the recently published paper by Chen et al. This has been corrected and discussed in lines 374 to 383.

      In the paper by Chen et al, the authors describe a role of Aqp1a.1 and Aqp8a.1 in regulating ISV diameter (ISV diameter was analysed at 48 hpf) but they did not examine the earlier stages of sprouting angiogenesis between 20 to 30 hpf, which is the focus of our study. We therefore cannot directly compare the ISV phenotypes with theirs. Nevertheless, we recognise that there are differences in ISV phenotypes from 2 dpf. For example, they did not observe incompletely formed or missing ISVs at 2 and 3 dpf, which we clearly observe in our study. This could be explained by differences in the mutations generated. In Chen et al., the sgRNA used targeted the end of exon 2 that resulted in the generation of a 169 amino acid truncated aqp1a.1 protein. However, in our approach, our sgRNA targeted exon 1 of the gene that resulted in a truncated aqp1a.1 protein that is 76 amino acid long. As for the aqp8a.1 zebrafish mutant that we generated, our sgRNA targeted exon 1 of the gene that resulted in a truncated protein that is 73 amino acids long. In Chen et al., the authors did not generate an aqp8a.1 mutant but instead used a crispant approach, which leads to genetic mosaicism and high experimental variability.

      Following the reviewer’s suggestion, we have now measured the diameters of arterial ISVs (aISVs) and venous ISVs (vISVs) in aqp1a.1<sup>-/-</sup>, aqp8a.1<sup>-/-</sup> and aqp1a.1<sup>-/-</sup>;aqp8a.1<sup>-/-</sup> zebrafish. In our lab, we always make a distinction between aISVs and vISVs are their diameters are significantly different from each other. The results are in Fig S11A. While we corroborate a decrease in diameter in both aISVs and vISVs in single aqp1a.1<sup>-/-</sup> and double aqp1a.1<sup>-/-</sup>;aqp8a.1<sup>-/-</sup>.zebrafish, we observed a slight increase in diameter in both aISVs and vISVs in aqp8a.1<sup>-/-</sup> zebrafish at 2 dpf. We also measured the diameter of aISV and vISV in Tg(fli1ep:aqp1a.1-mEmerald) and Tg(fli1ep:aqp8a.1-mEmerald) zebrafish at 2 dpf (Fig S11B) and unlike in Chen et al., we could not detect a difference in the diameter between control and aqp1a.1- or aqp8a.1-overexpressing endothelial cells.

      We also would also like to point out that, because ISVs are incompletely formed or are missing in aqp1a.1<sup>-/-</sup>;aqp8a.1<sup>-/-</sup> zebrafish (Fig. 3G – L), blood flow is most likely altered in the zebrafish trunk of these mutants, and this can have a secondary effect on blood vessel calibre or diameter. In fact, we often observed wider ISVs adjacent to unperfused ISVs (Fig. 3J) as more blood flow enters the lumenized ISV. Therefore, to determine the cell autonomous function of Aquaporin in mediating cell volume changes in vessel diameter regulation, one would need to perform cell transplantation experiments where we would measure the volume of single aqp1a.1<sup>-/-</sup>;aqp8a.1<sup>-/-</sup> endothelial cells in wildtype embryos with normal blood flow. As this is beyond the scope of the present study, we have not done this experiment during the revision process.

      (2) Expression of aqp1a.1 and aqp8a.1

      The quantification shown in Figure 1G shows a relative abundance of expression between tip and stalk cells. However, it seems aqp8a.1 is almost never detected in most tip cells. The authors could show in addition, the % of Tip and stalk cells with detectable expression of the 2 aquaporins. It seems aqp8a1 is really weakly or not expressed in the initial stages. Ofcourse the protein may have a different dynamic from the RNA.

      We would like to clarify that aqp8a.1 mRNA is not detected in tip cells of newly formed ISVs at 20hpf. At 22 hpf, it is expressed in both tip cells (22 out of 23 tip cells analysed) and stalk cells of ISVs at 22hpf. This is clarified in lines 107 - 109. We also include below a graph showing that although aqp8a.1 mRNA is expressed in tip cells, its expression is higher in stalk cells.

      Author response image 1.

      Could the authors show endogenously expressed or tagged protein by antibody staining? The analysis of the Tg(fli1ep:aqp8a.1-mEmerald)rk31 zebrafish line is a good complement, but unfortunately, it does not reveal the localization of the endogenously expressed protein. Do the authors have any data supporting that the endogenously expressed aqp8a.1 protein is present in sprouting tip cells?

      We tested several antibodies against AQP1 (Alpha Diagnostic International, AQP11-A; ThermoFisher Scientific, MA1-20214; Alomone Labs, AQP-001) and AQP8 (Sigma Aldrich, SAB 1403559; Alpha Diagnostic International, AQP81-A; Almone Labs, AQP-008) but unfortunately none worked. As such, we do not have data demonstrating endogenous expression and localisation of Aqp1a.1 and Aqp8a.1 proteins in endothelial cells.

      Could the authors perform F0 CRISPR/Cas9 mediated knockin of a small tag (i.e. HA epitope) in zebrafish and read the endogenous protein localization with anti-HA Ab?

      CRISPR/Cas9 mediated in-frame knock-in of a tag into a genomic locus is a technical challenge that our lab has not established. We therefore cannot do this experiment within the revision period.

      Given the double mutant phenotypic data shown, is aqp8a.1 expression upregulated and perhaps more important in aqp1a.1 mutants?

      In our analysis of aqp1a.1 homozygous zebrafish, there is a slight down_regulation in _aqp8a.1 expression (Fig. S5C). Because the loss of Aqp1a.1 leads to a stronger impairment in ISV formation than the loss of Aqp8a.1 (see Fig. S6F, G, I and J), we believe that Aqp1a.1 has a stronger function than Aqp8a.1 in EC migration during sprouting angiogenesis.

      Regarding the regulation of expression by the Vegfr inhibitor Ki8751, does this inhibitor affect Vegfr/ERK signalling in zebrafish and the sprouting of ISVs significantly?

      ki8751 has been demonstrated to inhibit ERK signalling in tip cells in the zebrafish by Costa et al., 2016 in Nature Cell Biology. In our experiments, treatment with 5 µM ki8751 for 6 hours from 20 hpf also inhibited sprouting of ISVs.

      The data presented suggest that tip cells overexpressing aqp1a.1-mEmerald (Figure 2C) need more than 6 times longer to migrate the same distance as tip cells expressing aqp8a.1mEmerald (Figure 2D). How does this compare with cells expressing only Emerald? A similar time difference can be seen in Movie S1 and Movie S2. Is it just a coincidence? Could aqp8a.1, when expressed at similar levels than aqp1a, be more functional and induce faster cell migration? These experiments were interpreted only for the localization of the proteins, but not for the potential role of the overexpressed proteins on function. Chen C et al. Cardiovascular Research 2024 also has some Aqp overexpression data.

      The still images prepared for Fig. 2 C and D were selected to illustrate the localization of Aqp1a.1-mEmerald and Aqp8a.1-mEmerald at the leading edge of migrating tip cells. We did not notice that the tip cell overexpressing Aqp1a.1-mEmerald (Figure 2C) needed more than 6 times longer to migrate the same distance as the tip cell expressing aqp8a.1-mEmerald (Figure 2D), which the reviewer astutely detected. To ascertain whether there is a difference in migration speed between Aqp1a.1-mEmerald and Aqp8a.1-mEmerald overexpressing endothelial cells, we measured tip cell migration velocity of three ISVs from Tg(fli1ep:aqp1a.1-mEmerald) and Tg(fli1ep:aqp8a.1-mEmerald) zebrafish during the period of ISV formation (24 to 29 hpf) using the Manual Tracking plugin in Fiji. As shown in the graph, there is no significant difference in the migration speed of ECs overexpressing Aqp1a.1-mEmerald and Aqp8a.1-mEmerald, suggesting that Aqp8a.1-overexpressing cells migrate at a similar rate as Aqp1a.1-overexpressing cells. As we have not generated a Tg(fli1ep:mEmerald) zebrafish line, we are unable to determine whether endothelial cells migrate faster in Tg(fli1ep:aqp1a.1mEmerald) and Tg(fli1ep:aqp8a.1-mEmerald) zebrafish compared to endothelial cell expressing only mEmerald. As for the observation that tip cells overexpressing aqp1a.1mEmerald (Figure 2C) need more than 6 times longer to migrate the same distance as tip cells expressing aqp8a.1-mEmerald, we can only surmise that it is coincidental that the images selected “showed” faster migration of one ISV from Tg(fli1ep:aqp8a.1-mEmerald) zebrafish. We do not know whether the Aqp1a.1 and Aqp8a.1 are overexpressed to the same levels in Tg(fli1ep:aqp1a.1mEmerald) and Tg(fli1ep:aqp8a.1-mEmerald) zebrafish.

      We would also like to point out that when we analysed the lengths of ISVs at 28 hpf in aqp1a.1<sup>-/-</sup> and aqp8a.1<sup>-/-</sup> zebrafish, ISVs were shorter in aqp1a.1<sup>-/-</sup> zebrafish compared to aqp8a.1<sup>-/-</sup> zebrafish (Fig. S6 F to J). These results indicate that the loss of Aqp1a.1 function causes slower migration than the loss of aqp8a.1 function, and suggest that Aqp1a.1 induces faster endothelial cell migration that Aqp8a.1.

      Author response image 2.

      The data on Aqps expression after the Notch inhibitor DBZ seems unnecessary, and is at the moment not properly discussed. It is also against what is set in the field. aqp8a.1 levels seem to increase only 24h after DBZ, not at 6h, and still authors conclude that Notch activation inhibits aqp8a.1 expression (Line 138-139). In the field, Notch is considered to be more active in stalk cells, where aqp8a.1 expression seems higher (not lower). Maybe the analysis of tip vs stalk cell markers in the scRNAseq data, and their correlation with Hes1/Hey1/Hey2 and aqp1 vs aqp8 mRNA levels will be more clear than just showing qRT-PCR data after DBZ.

      As our scRNAseq data did not include ECs from earlier during development when ISVs are developing, we have analysed of scRNAseq data of 24 hpf endothelial cells published by Gurung et al, 2022 in Scientific Reports during the revision of this manuscript. However, we are unable to detect separate clusters of tip and stalk cells. As such, we are unable to correlate hes1/hey1/hey2 expression (which would be higher in stalk cells) with that of aqp1a.1/aqp8a.1. Also, we have decided to remove the DBZ-treatment results from our manuscript as we agree with the two reviewers that they are unnecessary.

      The paper would also benefit from some more analysis and interpretation of available scRNAseq data in development/injury/disease/angiogenesis models (zebrafish, mice or humans) for the aquaporin genes characterized here. To potentially raise a broader interest at the start of the paper.

      We thank the reviewer for suggesting examining aquaporin genes in other angiogenesis/disease/regeneration models to expand the scope of aquaporin function. We will do this in future studies.

      (3) Role of aqp1a.1 and aqp8a.1 on cytoplasmic volume changes and related phenotypes

      In Figure 5 the authors show that Aqp1/Aqp8 mutant endothelial tip cells have a lower cytoplasmic volume than tip cells from wildtype fish. If aquaporin-mediated water inflow occurs locally at the leading edge of endothelial tip cells (Figure 2, line 314-318), why doesn't cytoplasmic volume expand specifically only at that location (as shown in immune cells by Boer et al. 2023)? Can the observed reduction in cytoplasmic volume simply be a side-effect of impaired filopodia formation (Figure 4F-I)?

      We believe that water influx not only expands filopodia but also the leading front of tip cells (see bracket region in Fig. 4D), where Aqp1a.1-mEmerald/Aqp8a.1-mEmerald accumulate (Fig. 2), to generate an elongated protrusion and forward expansion of the tip cell. The decrease in cytoplasmic volume observed in the aqp1a.1;aqp8a.1 double mutant zebrafish is a result of decreased formation of these elongated protrusions at the leading front of migration tip cells as shown in Fig. 4E (compare to Fig. 4D), not from just a decrease in filopodia number. In fact, in the method used to quantify cell volume, mEmerald/EGFP localization is limited to the cytoplasm and does not label filopodia well (compare mEmerald/EGFP in green with membrane tagged-mCherry in Fig. 5A - C). The volume measured therefore reflects cytoplasmic volume of the tip cell, not filopodia volume.

      Do the authors have data on cytoplasmic volume changes of endothelial tip cells in latrunculin B treated fish? The images in Figures 6 A,B suggest that there is a difference in cell volume upon lat b treatment only.

      No, unfortunately we have not performed single cell labelling and measurement of tip cells in Latrunculin B-treated embryos. We can speculate that as there is a decrease in actindriven membrane protrusions in this experiment, one would also expect a decrease in cell volume as the reviewer has observed.

      (4) Combined loss of aquaporins and actin-based force generation.

      Lines 331-332 " we show that hydrostatic pressure is the driving force for EC migration in the absence of actin-based force generation"....better leave it more open and stick to the data. The authors show that aquaporin-mediated water inflow partially compensates for the loss of actin-based force generation in cell migration. Not that it is the key driving/rescuing force in the absence of actin-based force.

      We have changed it to “we show that hydrostatic pressure can generate force for EC migration in the absence of actin-based force generation” in line 348.

      (5) Aquaporins and their role in EC proliferation

      In the study by Phnk LK et al. 2013, the authors have shown that proliferation is not affected when actin polymerization or filopodia formation is inhibited. However, in the current manuscript by Kondrychyn I. et al. this has not been analysed carefully. In Movie S4 the authors indicate by arrows tip cells that fail to invade the zebrafish trunk demonstrating a severe defect of sprouting initiation in these mutants. Yet, when only looking at ISVs that reach the dorsal side in Movie S4, it appears that they are comprised of fewer EC nuclei/ISV than the ISVs in Movie S3. At the beginning of DLAV formation, most ISVs in control Movie S3 consist of 3-4 EC nuclei, while in double mutants Movie S4 it appears to be only 2-3 EC nuclei. At the end of the Movie S4, one ISV on the left side even appears to consist of only a single EC when touching the dorsal roof. The authors provide convincing data on how the absence of aquaporin channels affects sprouting initiation and migration speed, resulting in severe delay in ISV formation. However, the authors should also analyse EC proliferation, as it may also be affected in these mutants, and may also contribute to the observed phenotype. We know that effects on cell migration may indirectly change the number of cells and proliferation at the ISVs, but this has not been carefully analysed in this paper.

      We thank the reviewer for highlighting the lack of information on EC number and division in the aquaporin mutants. We have now quantified EC number in ISVs that are fully formed (i.e. connecting the DA or PCV to the DLAV) at 2 and 3 dpf and the results are displayed in Figure S10A and B. At 2 dpf, there is a slight but significant reduction in EC number in both aISVs and vISVs in aqp1a.1<sup>-/-</sup> zebrafish and an even greater reduction in the double aqp1a. aqp1a.1<sup>/-</sup>;aqp8a.1<sup>-/-</sup> zebrafish. No significant change in EC number was observed in aqp8a.1<sup>-/-</sup> zebrafish. EC number was also significantly decreased at 3 dpf for aqp1a.1<sup>-/-</sup>, aqp8a.1<sup>-/-</sup> and aqp1a.1<sup>-/-</sup>;aqp8a.1<sup>-/-</sup> zebrafish. The decreased in EC number per ISV may therefore contribute to the observed phenotype.

      We have also quantified the number of cell divisions during sprouting angiogenesis (from 21 to 30 hpf) to assess whether the lack of Aquaporin function affects EC proliferation. This analysis shows that there is no significant difference in the number of mitotic events between aqp1a.1<sup>+/-</sup>; aqp8a.1<sup>+/-</sup> and aqp1a.1<sup>-/-</sup>;aqp8a.1<sup>-/-</sup> zebrafish (Figure S10 C), suggesting that the reduction in EC number is not caused by a decrease in EC proliferation.

      These new data are reported on lines 198 to 205 of the manuscript.

      Minor comments:

      - Figure 3K data seems not to be necessary and even partially misleading after seeing Figure 3E. Fig. 3E represents the true strength of the phenotype in the different mutants.

      Figure 3K has been removed from Figure 3.

      - Typo Figure 3L (VII should be VI).

      Thank you for spotting this typo. VII has been changed to VI.

      - Line 242: The word "required" is too strong because there is vessel formation without Aqps in endothelial cells.

      This has been changed to “ …Aqp1a.1 and Aqp8a.1 regulate sprouting angiogenesis…” (lines 238 - 239).

      - From Figure S2, the doublets cluster should be removed.

      We have performed a new analysis of 24 hpf, 34hpf and 3 dpf endothelial cells scRNAseq data (the previous analysis did not consist of 24 hpf endothelial cells). The doublets cluster is not included in the UMAP analysis.

      - Better indicate the fluorescence markers/alleles/transgenes used for imaging in Figures 6A-D.

      The transgenic lines used for this experiment are now indicated in the figure (this figure is now Figure 7).

      Reviewer #3 (Public Review):

      Summary:

      Kondrychyn and colleagues describe the contribution of two Aquaporins Aqp1a.1 and Aqp8a.1 towards angiogenic sprouting in the zebrafish embryo. By whole-mount in situ hybridization, RNAscope, and scRNA-seq, they show that both genes are expressed in endothelial cells in partly overlapping spatiotemporal patterns. Pharmacological inhibition experiments indicate a requirement for VEGR2 signaling (but not Notch) in transcriptional activation.

      To assess the role of both genes during vascular development the authors generate genetic mutations. While homozygous single mutants appear normal, aqp1a.1;aqp8a.1 double mutants exhibit defects in EC sprouting and ISV formation.

      At the cellular level, the aquaporin mutants display a reduction of filopodia in number and length. Furthermore, a reduction in cell volume is observed indicating a defect in water uptake.

      The authors conclude, that polarized water uptake mediated by aquaporins is required for the initiation of endothelial sprouting and (tip) cell migration during ISV formation. They further propose that water influx increases hydrostatic pressure within the cells which may facilitate actin polymerization and formation membrane protrusions.

      Strengths:

      The authors provide a detailed analysis of Aqp1a.1 and Aqp8a.1 during blood vessel formation in vivo, using zebrafish intersomitic vessels as a model. State-of-the-art imaging demonstrates an essential role in aquaporins in different aspects of endothelial cell activation and migration during angiogenesis.

      Weaknesses:

      With respect to the connection between Aqp1/8 and actin polymerization/filopodia formation, the evidence appears preliminary and the authors' interpretation is guided by evidence from other experimental systems.

      Reviewer #3 (Recommendations For The Authors):

      Figure 1 H, J:

      The differential response of aqp1/-8 to ki8751 vs DBZ after 6h treatment is quite obvious. Why do the authors show the effect after 24h? The effect is more likely than not indirect.

      We agree with the reviewer and we have now removed 24 hour Ki8751 treatment and all DBZ treatments from Figure 1.

      Figure 2:

      According to the authors' model anterior localization of Aqp1 protein is critical. The authors perform transient injections to mosaically express Aqp fusion proteins using an endothelial (fli1) promoter. For the interpretation, it would be helpful to also show the mCherry-CAAX channel in separate panels. From the images, it is not possible to discern how many cells we are looking at. In particular the movie in panel D may show two cells at the tip of the sprout. A marker labelling cell-cell junctions would help. Furthermore, the authors are using a strong exogenous promoter, thus potentially overexpressing the fusion protein, which may lead to mislocalization. For Aqp1a.1 an antibody has been published to work in zebrafish (e.g. Kwong et al., Plos1, 2013).

      We would like to clarify that we generated transgenic lines - Tg(fli1ep:aqp1a.1-mEmerald) and Tg(fli1ep:aqp8a.1-mEmerald) - to visualize the localization of Aqp1a.1 and Aqp8a.1 in endothelial cells, and the images displayed in Fig. 2 are from the transgenic lines (not transient, mosaic expression).

      To aid visualization and interpretation, we have now added mCherry-CAAX only channel to accompany the Aqp1a.1/Aqp8a.1-mEmerald channel in Fig. 2A and B. To discern how many cells there are in the ISVs at this stage, we have crossed Tg(fli1ep:aqp1a.1-mEmerald) and Tg(fli1ep:aqp8a.1-mEmerald) zebrafish to TgKI(tjp1a-tdTomato)<sup>pd1224</sup> (Levic et al., 2021) to visualize ZO1 at cell-cell junction. However, because tjp1-tdTomato is expressed in all cell types including the skin that lies just above the ISV and the signal in ECs in ISVs is very weak at 22 to 25 hpf, it was very difficult to obtain good quality images that can properly delineate cell boundaries to determine the number of cells in the ISVs at this early stage. Instead, we have annotated endothelial cell boundaries based on more intense mCherryCAAX fluorescence at cell-cell borders, and from the mosaic expression of mCherryCAAX that is intrinsic to the  Tg(kdrl:ras-mCherry)<sup>s916</sup> zebrafish line.

      In Fig. 2D, there are two endothelial cells in the ISV during the period shown but there is only 1 cell occupying the tip cell position i.e. there is one tip cell in this ISV. Unlike the mouse retina where it has been demonstrated that two endothelial cells can occupy the tip cell position side-by-side (Pelton et al., 2014), this is usually not observed in zebrafish ISVs. This is demonstrated in Movie S3, where it is clear that one nucleus (belonging to the tip cell) occupies the tip of the growing ISV. The accumulation of intracellular membranes is often observed in tip cells that may serve as a reservoir of membranes for the generation of membrane protrusions at the leading edge of tip cells.

      We agree that by generating transgenic Tg(fli1ep:aqp1a.1-mEmerald) and Tg(fli1ep:aqp8a.1mEmerald) zebrafish, Aqp1a.1 and Aqp8a.1 are overexpressed that may affect their localization. The eel anti-Aqp1a.1 antibody used in (Kwong et la., 2013) was a gift from Dr. Gordon Cramb, Univ. of St Andrews, Scotland and it was first published in 2001. This antibody is not available commercially. Instead, we have tried to several other antibodies against AQP1 (Alpha Diagnostic International , AQP11-A; ThermoFisher Scientific, MA120214; Alomone Labs, AQP-001) and AQP8 (Sigma Aldrich, SAB 1403559; Alpha Diagnostic International, AQP81-A; Almone Labs, AQP-008) but unfortunately none worked. As such, we cannot compare localization of Aqp1a.1-mEmerald and Aqp8a.1-mEmerald with the endogenous proteins.

      Figure 3:

      E: the quantification is difficult to read. Wouldn't it be better to set the y-axis in % of the DV axis? (see also Figure S6).

      We would like to show the absolute length of the ISVs, and to illustrate that the ISV length decreases from anterior to posterior of the zebrafish trunk. We have increased the size of Fig. 3E to enable easier reading of the bars.

      K: This quantification appears arbitrary.

      We have removed this panel from Figure 3.

      G-J: The magenta channel is difficult to see. Is the lifeact-mCherry mosaic? In panel J there appears to be a nucleus between the sprout and the DLAV. It would be helpful to crop the contralateral side of the image.

      No, the Tg(fli1:Lifeact-mCherry) line is not mosaic. The “missing” vessels are not because of mosaicism in transgene but because of truncated ISVs that is a phenotype of loss Aquaporin function. We have changed the magenta channel to grey and hope that by doing so, the reviewer will be able to see the shape of the blood vessels more clearly. We would like to leave the contralateral side in the images, as it shows that the defective vessel is only on one side of body. Furthermore, when we tried to remove it (reducing the number of Z-stacks) neighbour ISV looks incomplete because the embryos were not mounted flat. To clarify what the nucleus between the sprout and the DLAV is, we have indicated that it is that of the contralateral ISV.

      L: I do not quite understand the significance of the different classes of phenotypes. Do the authors propose different morphogenetic events or contexts of how these differences come about?

      Here, we report the different types of ISV phenotypes that we observe in 3 dpf aqp1a.1<sup>-/-</sup>; aqp8a.1<sup>-/-</sup> zebrafish (Fig. 3 and Fig. S7). As demonstrated in Fig. 4, most of the phenotypes can be explained by the delayed emergence of tip cells from the dorsal aorta and slower tip cell migration. However, in some instances, we also observed retraction of tip cells (Movie S4) and failure of tip cells to emerge from the dorsal aorta or endothelial cell death (see attached figure on page 14), which can give rise to the Class II phenotype. In the dominant class I phenotype (in contrast to class II), secondary sprouting from the posterior cardinal vein is unaffected, and the secondary sprout migrates dorsally passing the level of horizontal myoseptum but cannot complete the formation of vISV (it stops beneath the spinal cord). The Class III phenotype appears to result from a failure of the secondary sprout to fuse with the regressed primary ISV. In the Class IV phenotype, the ventral EC does not maintain a connection to the dorsal aorta. We did not examine how Class III and IV phenotypes arise in detail in this current study.

      Author response image 3.

      Figure 4:

      This figure nicely demonstrates the defects in cell behavior in aqp mutants.

      In panel F it would be helpful to show the single channels as well as the merge.

      We have now added single channels for PLCd1PH and Lifeact signal in panels F and G.

      In Figure 1 the authors argue that the reduction of Aqp1/8 by VEGFR2 inhibition may account for part of that phenotype. In turn, the aqp phenotype seems to resemble incomplete VEGFR2 inhibition. The authors should check whether expression Aqp1Emerald can partially rescue ki8751 inhibition.

      To address the reviewer’s comment, we have treated Tg(fli1ep:Aqp1-Emerald) embryos with ki8751 from 20 hpf for 6 hours but we were unable to observe a rescue in sprouting. It could be because VEGFR2 inhibition also affects other downstream signalling pathways that also control cell migration as well as proliferation.

      Based on previous studies (Loitto et al.; Papadopoulus et al.) the authors propose that also in ISVs aquaporin-mediated water influx may promote actin polymerization and thereby filopodia formation. However, while the effect on filopodia number and length is well demonstrated, the underlying cause is less clear. For example, filopodia formation could be affected by reduced cell polarization. This can be tested by using a transgenic golgi marker (Kwon et al., 2016).

      We have examined tip cell polarity of wildtype, aqp1a.1<sup>-/-</sup> and  aqp8a. 1<sup>-/-</sup> embryos at 24-26 hpf by analysing Golgi position relative to the nucleus. We were unable to analyze polarity in  aqp1a.1<sup>rk28/rk28</sup>; aqp8a.1<sup>rk29/rk29</sup> embryos as they exist in an mCherry-containing transgenic zebrafish line (the Golgi marker is also tagged to mCherry). The results show that tip cell polarity is similar, if not more polarised, in aqp1a.1<sup>-/-</sup> and  aqp8a. 1<sup>-/-</sup> embryos when compared to wildtype embryos (Fig. S10D). This new data is discussed in lines 234 to 237.

      Figure 5:

      Panel D should be part of Figure 4.

      Panel 5D is now in panel J of Figure 4 and described in lines 231 and 235.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This paper is an elegant, mostly observational work, detailing observations that polysome accumulation appears to drive nucleoid splitting and segregation. Overall I think this is an insightful work with solid observations.

      Thank you for your appreciation and positive comments. In our view, an appealing aspect of this proposed biophysical mechanism for nucleoid segregation is its self-organizing nature and its ability to intrinsically couple nucleoid segregation to biomass growth, regardless of nutrient conditions.

      Strengths:

      The strengths of this paper are the careful and rigorous observational work that leads to their hypothesis. They find the accumulation of polysomes correlates with nucleoid splitting, and that the nucleoid segregation occurring right after splitting correlates with polysome segregation. These correlations are also backed up by other observations:

      (1) Faster polysome accumulation and DNA segregation at faster growth rates.

      (2) Polysome distribution negatively correlating with DNA positioning near asymmetric nucleoids.

      (3) Polysomes form in regions inaccessible to similarly sized particles.

      These above points are observational, I have no comments on these observations leading to their hypothesis.

      Thank you!

      Weaknesses:

      It is hard to state weaknesses in any of the observational findings, and furthermore, their two tests of causality, while not being completely definitive, are likely the best one could do to examine this interesting phenomenon.

      It is indeed difficult to prove causality in a definitive manner when the proposed coupling mechanism between nucleoid segregation and gene expression is self-organizing, i.e., does not involve a dedicated regulatory molecule (e.g., a protein, RNA, metabolite) that we could have depleted through genetic engineering to establish causality. We are grateful to the reviewer for recognizing that our two causality tests are the best that can be done in this context.

      Points to consider / address:

      Notably, demonstrating causality here is very difficult (given the coupling between transcription, growth, and many other processes) but an important part of the paper. They do two experiments toward demonstrating causality that help bolster - but not prove - their hypothesis. These experiments have minor caveats, my first two points.

      (1) First, "Blocking transcription (with rifampicin) should instantly reduce the rate of polysome production to zero, causing an immediate arrest of nucleoid segregation". Here they show that adding rifampicin does indeed lead to polysome loss and an immediate halting of segregation - data that does fit their model. This is not definitive proof of causation, as rifampicin also (a) stops cell growth, and (b) stops the translation of secreted proteins. Neither of these two possibilities is ruled out fully.

      That’s correct; cell growth also stops when gene expression is inhibited, which is consistent with our model in which gene expression within the nucleoid promotes nucleoid segregation and biomass growth (i.e., cell growth), inherently coupling these two processes. This said, we understand the reviewer’s point: the rifampicin experiment doesn’t exclude the possibility that protein secretion and cell growth drive nucleoid segregation. We are assuming that the reviewer is envisioning an alternative model in which sister nucleoids would move apart because they would be attached to the membrane through coupled transcription-translation-protein secretion (transertion) and the membrane would expand between the separating nucleoids, similar to the model proposed by Jacob et al in 1963 (doi:10.1101/SQB.1963.028.01.048). There are several observations arguing against this cell elongation/transertion model.

      (1) For this alternative mechanism to work, membrane growth must be localized at the middle of the splitting nucleoids (i.e., midcell position for slow growth and ¼ and ¾ cell positions for fast growth) to create a directional motion. To our knowledge, there is no evidence of such localized membrane incorporation. Furthermore, even if membrane growth was localized at the right places, the fluidity of the cytoplasmic membrane (PMID: 6996724, 20159151, 24735432, 27705775) would be problematic. To circumvent the membrane fluidity issue, one could potentially evoke an additional connection to the rigid peptidoglycan, but then again, peptidoglycan growth would have to be localized at the middle of the splitting nucleoid. However, peptidoglycan growth is dispersed early in the cell division cycle when the nucleoid splitting happens in fast growing cells and only appears to be zonal after the onset of cell constriction (PMID: 35705811, 36097171, 2656655).

      (2) Even if we ignore the aforementioned caveats, Paul Wiggins’s group ruled out the cell elongation/transertion model by showing that the rate of cell elongation is slower than the rate of chromosome segregation (PMID: 23775792). In the revised manuscript, we wil clarify this point and provide confirmatory data showing that the cell elongation rate is indeed slower than the nucleoid segregation rate, indicating that it cannot be the main driver.

      (3) Furthermore, our correlation analysis comparing the rate of nucleoid segregation to the rate of either cell elongation or polysome accumulation argues that polysome accumulation plays a larger role than cell elongation in nucleoid segregation. These data were already shown in Figure 1H and Figure 1 – figure supplement 3 of the original manuscript but were not highlighted in this context. We will revise the text to clarify this point.

      (4) The asymmetries in nucleoid compaction that we described in our paper are predicted by our model. We do not see how they could be explained by cell growth or protein secretion.

      (5) We also show that polysome accumulation at ectopic sites (outside the nucleoid) results in correlated nucleoid dynamics, consistent with our proposed mechanism. These nucleoid dynamics cannot be explained by cell growth or protein secretion (transertion).

      (1a) As rifampicin also stops all translation, it also stops translational insertion of membrane proteins, which in many old models has been put forward as a possible driver of nucleoid segregation, and perhaps independent of growth. This should at last be mentioned in the discussion, or if there are past experiments that rule this out it would be great to note them.

      It is not clear to us how the attachment of the DNA to the cytoplasmic membrane could alone create a directional force to move the sister nucleoids. We agree that old models have proposed a role for cell elongation (providing the force) and transertion (providing the membrane tether).  Please see our response above for the evidence (from the literature and our work) against it. This was mentioned in the introduction and Results section, but we agree that this was not well explained. We will add experimental data and revise the text to clarify these points.

      (1b) They address at great length in the discussion the possibility that growth may play a role in nucleoid segregation. However, this is testable - by stopping surface growth with antibiotics. Cells should still accumulate polysomes for some time, it would be easy to see if nucleoids are still segregated, and to what extent, thereby possibly decoupling growth and polysome production. If successful, this or similar experiments would further validate their model.

      We reviewed the literature and could not find a drug that stops cell growth without stopping gene expression. Any drug that affects the membrane integrity or potential stops gene expression, which requires ATP.  However, our experiment in which we drive polysome accumulation at ectopic sites decouples polysome accumulation from cell growth. In this experiment, by redirecting most of chromosome gene expression to a single plasmid-encoded gene, we reduce the rate of cell growth but still create a large accumulation of polysomes at an ectopic location. This ectopic polysome accumulation is sufficient to affect nucleoid dynamics in a correlated fashion. In the revised manuscript, we will clarify this point and add model simulations to show that our experimental observations are predicted by our model.

      (2) In the second experiment, they express excess TagBFP2 to delocalize polysomes from midcell. Here they again see the anticorrelation of the nucleoid and the polysomes, and in some cells, it appears similar to normal (polysomes separating the nucleoid) whereas in others the nucleoid has not separated. The one concern about this data - and the differences between the "separated" and "non-separated" nuclei - is that the over-expression of TagBFP2 has a huge impact on growth, which may also have an indirect effect on DNA replication and termination in some of these cells. Could the authors demonstrate these cells contain 2 fully replicated DNA molecules that are able to segregate?

      We will perform the requested experiment.

      (3) What is not clearly stated and is needed in this paper is to explain how polysomes do (or could) "exert force" in this system to segregate the nucleoid: what a "compaction force" is by definition, and what mechanisms causes this to arise (what causes the "force") as the "compaction force" arises from new polysomes being added into the gaps between them caused by thermal motions.

      They state, "polysomes exert an effective force", and they note their model requires "steric effects (repulsion) between DNA and polysomes" for the polysomes to segregate, which makes sense. But this makes it unclear to the reader what is giving the force. As written, it is unclear if (a) these repulsions alone are making the force, or (b) is it the accumulation of new polysomes in the center by adding more "repulsive" material, the force causes the nucleoids to move. If polysomes are concentrated more between nucleoids, and the polysome concentration does not increase, the DNA will not be driven apart (as in the first case) However, in the second case (which seems to be their model), the addition of new material (new polysomes) into a sterically crowded space is not exerting force - it is filling in the gaps between the molecules in that region, space that needs to arise somehow (like via Brownian motion). In other words, if the polysome region is crowded with polysomes, space must be made between these polysomes for new polysomes to be inserted, and this space must be made by thermal (or ATP-driven) fluctuations of the molecules. Thus, if polysome accumulation drives the DNA segregation, it is not "exerting force", but rather the addition of new polysomes is iteratively rectifying gaps being made by Brownian motion.

      We apologize for the understandable confusion. In our picture, the polysomes and DNA (conceptually considered as small plectonemic segments) basically behave as dissolved particles. If these particles were noninteracting, they would simply mix. However, both polysomes and DNA segments are large enough to interact sterically. So as density increases, steric avoidance implies a reduced conformational entropy and thus a higher free energy per particle. We argue (based on Miangolarra et al. PNAS 2021 PMID: 34675077 and Xiang et al. Cell 2021 PMID: 34186018) that the demixing of polysomes and DNA segments occurs because DNA segments pack better with each other than they do with polysomes. This raises the free energy cost associated with DNA-polysome interactions compared to DNA-DNA interactions.  We model this effect by introducing a term in the free energy χ_np, which refer to as a repulsion between DNA and polysomes, though as explained above it arises from entropic effects. At realistic cellular densities of DNA and polysomes this repulsive interaction is strong enough to cause the DNA and polysomes to phase separate.

      This same density-dependent free energy that causes phase separation can also give rise to forces, just in the way that a higher pressure on one side of a wall can give rise to a net force on the wall. Indeed, the “compaction force” we refer to is fundamentally an osmotic pressure difference. At some stages during nucleoid segregation, the region of the cell between nucleoids has a higher polysome concentration, and therefore a higher osmotic pressure, than the regions near the poles. This results in a net poleward force on the sister nucleoids that drives their migration toward the poles. This migration continues until the osmotic pressure equilibrates. Therefore, both phase separation (due to the steric repulsion described above) and nonequilibrium polysome production and degradation (which creates the initial accumulation of polysomes around midcell) are essential ingredients for nucleoid segregation.

      This will be clarified in the revised text, with the support of additional simulation results.

      The authors use polysome accumulation and phase separation to describe what is driving nucleoid segregation. Both terms are accurate, but it might help the less physically inclined reader to have one term, or have what each of these means explicitly defined at the start. I say this most especially in terms of "phase separation", as the currently huge momentum toward liquid-liquid interactions in biology causes the phrase "phase separation" to often evoke a number of wider (and less defined) phenomena and ideas that may not apply here. Thus, a simple clear definition at the start might help some readers.

      Phase separation means that the DNA-polysome steric repulsion is strong enough to drive their demixing, which creates a compact nucleoid. As mentioned in a previous point, this effect is captured in the free energy by the χ_np term, which is an effective repulsion between DNA and polysomes, though as explained above it arises from entropic effects.

      In the revised manuscript, we will illustrate this with our theoretical model by initializing a cell with a diffuse nucleoid and low polysome concentration. For the sake of simplicity, we assume that the cell does not elongate. We observe that the DNA-polysome steric repulsion is sufficient to compact the nucleoid and place it at mid-cell.

      (4) Line 478. "Altogether, these results support the notion that ectopic polysome accumulation drives nucleoid dynamics". Is this right? Should it not read "results support the notion that ectopic polysome accumulation inhibits/redirects nucleoid dynamics"?

      We think that this is correct; the ectopic polysome accumulation drives nucleoid dynamics. In our theoretical model, we can introduce polysome production at fixed sources to mimic the experiments where ectopic polysome production is achieved by high plasmid expression (Fig. 6). The model is able to recapitulate the two main phenotypes observed in experiments. These new simulation results will be added to the revised manuscript.

      (5) It would be helpful to clarify what happens as the RplA-GFP signal decreases at midcell in Figure 1- is the signal then increasing in the less "dense" parts of the cell? That is, (a) are the polysomes at midcell redistributing throughout the cell? (b) is the total concentration of polysomes in the entire cell increasing over time?

      It is a redistribution—the RplA-GFP signal remains constant in concentration from cell birth to division (Figure 1 – Figure Supplement 1E). This will be clarified in the revised text.

      (6) Line 154. "Cell constriction contributed to the apparent depletion of ribosomal signal from the mid-cell region at the end of the cell division cycle (Figure 1B-C and Movie S1)" - It would be helpful if when cell constriction began and ended was indicated in Figures 1B and C.

      Good idea. We will add markers to indicate the start of cell constriction. We will also indicate that cell birth and division correspond to the first and last images/timepoint in Fig. 1B and C, respectively.

      (7) In Figure 7 they demonstrate that radial confinement is needed for longitudinal nucleoid segregation. It should be noted (and cited) that past experiments of Bacillus l-forms in microfluidic channels showed a clear requirement role for rod shape (and a given width) in the positing and the spacing of the nucleoids.

      Wu et al, Nature Communications, 2020 . "Geometric principles underlying the proliferation of a model cell system" https://dx.doi.org/10.1038/s41467-020-17988-7

      Good point. We will add this reference. Thank you.

      (8) "The correlated variability in polysome and nucleoid patterning across cells suggests that the size of the polysome-depleted spaces helps determine where the chromosomal DNA is most concentrated along the cell length. This patterning is likely reinforced through the displacement of the polysomes away from the DNA dense region"

      It should be noted this likely functions not just in one direction (polysomes dictating DNA location), but also in the reverse - as the footprint of compacted DNA should also exclude (and thus affect) the location of polysomes

      We agree that the effects could go both ways at this early stage of the story. We will revise the text accordingly.  

      (9) Line 159. Rifampicin is a transcription inhibitor that causes polysome depletion over time. This indicates that all ribosomal enrichments consist of polysomes and therefore will be referred to as polysome accumulations hereafter". Here and throughout this paper they use the term polysome, but cells also have monosomes (and 2 somes, etc). Rifampicin stops the assembly of all of these, and thus the loss of localization could occur from both. Thus, is it accurate to state that all transcription events occur in polysomes? Or are they grouping all of the n-somes into one group?

      In the discussion, we noted that our term “polysomes” also includes monosomes for simplicity, but we agree that the term should have been defined much earlier. This will be done in the revised manuscript.

      Thank you for the valuable comments and suggestions!

      Reviewer #2 (Public review):

      Summary:

      The authors perform a remarkably comprehensive, rigorous, and extensive investigation into the spatiotemporal dynamics between ribosomal accumulation, nucleoid segregation, and cell division. Using detailed experimental characterization and rigorous physical models, they offer a compelling argument that nucleoid segregation rates are determined at least in part by the accumulation of ribosomes in the center of the cell, exerting a steric force to drive nucleoid segregation prior to cell division. This evolutionarily ingenious mechanism means cells can rely on ribosomal biogenesis as the sole determinant for the growth rate and cell division rate, avoiding the need for two separate 'sensors,' which would require careful coupling.

      Terrific summary! Thank you for your positive assessment.

      Strengths:

      In terms of strengths; the paper is very well written, the data are of extremely high quality, and the work is of fundamental importance to the field of cell growth and division. This is an important and innovative discovery enabled through a combination of rigorous experimental work and innovative conceptual, statistical, and physical modeling.

      Thank you!

      Weaknesses:

      In terms of weaknesses, I have three specific thoughts.

      Firstly, my biggest question (and this may or may not be a bona fide weakness) is how unambiguously the authors can be sure their ribosomal labeling is reporting on polysomes, specifically. My reading of the work is that the loss of spatial density upon rifampicin treatment is used to infer that spatial density corresponds to polysomes, yet this feels like a relatively indirect way to get at this question, given rifampicin targets RNA polymerase and not translation. It would be good if a more direct way to confirm polysome dependence were possible.

      The heterogeneity of ribosome distribution inside E. coli cells has been attributed to polysomes by many labs (PMID: 25056965, 38678067, 22624875, 31150626, 34186018, 10675340).  The attribution is also consistent with single-molecule tracking experiments showing that slow-moving ribosomes (polysomes) are excluded by the nucleoid whereas fast-diffusing ribosomes (free ribosomal subunits) are distributed throughout the cytoplasm (PMID: 25056965, 22624875).

      Furthermore, inhibition of translation initiation with kasugamycin treatment, which decreases the pool of polysomes, results in a homogenization of ribosomes and expansion of the nucleoid (see Author response image 1). This further supports the rifampicin experiments. Given that the attribution of ribosome heterogeneity to polysomes is well accepted in the field, we would prefer to not include these kasugamycin data in the revised manuscript because long-term exposure to this drug leads to nucleoid re-compaction (PMID: 25250841 and PMID: 34186018). This secondary effect may possibly be due to a dysregulated increase in synthesis of naked rRNAs (PMID: 14460744, PMID: 2114400, and PMID: 2448483) or ribosome aggregation, which we are currently investigating.

      Author response image 1.

      Effects of kasugamycin treatment on the intracellular distribution of ribosomes and nucleoids. Representative single cell (CJW7323) growing in M9gluCAAT.  Kasugamycin (3 mg/mL) was added at time = 0 min. Show is the early response (0-30 min) to the drug characterized by the homogenization of the ribosomal RplA-GFP fluorescence and the expansion of the HupA-mCherry-labeled nucleoids. For each segmented cell, the RplA-GFP and HupA-mCherry signals were normalized by the average fluorescence.

      Second, the authors invoke a phase separation model to explain the data, yet it is unclear whether there is any particular evidence supporting such a model, whether they can exclude simpler models of entanglement/local diffusion (and/or perhaps this is what is meant by phase separation?) and it's not clear if claiming phase separation offers any additional insight/predictive power/utility. I am OK with this being proposed as a hypothesis/idea/working model, and I agree the model is consistent with the data, BUT I also feel other models are consistent with the data. I also very much do not think that this specific aspect of the paper has any bearing on the paper's impact and importance.

      We appreciate the reviewer’s comment, but the output of our reaction-diffusion model is a bona fide phase separation (spinodal decomposition). So, we feel that we need to use the term when reporting the modeling results. Inside the cell, the situation is more complicated. As the reviewer points out, there likely are entanglements (not considered in our model) and other important factors (please see our discussion on the model limitations). This said, we will revise our text to clarify our terms and proposed mechanism.

      Finally, the writing and the figures are of extremely high quality, but the sheer volume of data here is potentially overwhelming. I wonder if there is any way for the authors to consider stripping down the text/figures to streamline things a bit? I also think it would be useful to include visually consistent schematics of the question/hypothesis/idea each of the figures is addressing to help keep readers on the same page as to what is going on in each figure. Again, there was no figure or section I felt was particularly unclear, but the sheer volume of text/data made reading this quite the mental endurance sport! I am completely guilty of this myself, so I don't think I have any super strong suggestions for how to fix this, but just something to consider.

      We agree that there is a lot to digest. We will add schematics and a didactic simulation. We will also try to streamline the text.

      Reviewer #3 (Public review):

      Summary:

      Papagiannakis et al. present a detailed study exploring the relationship between DNA/polysome phase separation and nucleoid segregation in Escherichia coli. Using a combination of experiments and modelling, the authors aim to link physical principles with biological processes to better understand nucleoid organisation and segregation during cell growth.

      Strengths:

      The authors have conducted a large number of experiments under different growth conditions and physiological perturbations (using antibiotics) to analyse the biophysical factors underlying the spatial organisation of nucleoids within growing E. coli cells. A simple model of ribosome-nucleoid segregation has been developed to explain the observations.

      Weaknesses:

      While the study addresses an important topic, several aspects of the modelling, assumptions, and claims warrant further consideration.

      Thank you for your feedback. Please see below for a response to each concern. 

      Major Concerns:

      Oversimplification of Modelling Assumptions:

      The model simplifies nucleoid organisation by focusing on the axial (long-axis) dimension of the cell while neglecting the radial dimension (cell width). While this approach simplifies the model, it fails to explain key experimental observations, such as:

      (1) Inconsistencies with Experimental Evidence:

      The simplified model presented in this study predicts that translation-inhibiting drugs like chloramphenicol would maintain separated nucleoids due to increased polysome fractions. However, experimental evidence shows the opposite-separated nucleoids condense into a single lobe post-treatment (Bakshi et al 2014), indicating limitations in the model's assumptions/predictions. For the nucleoids to coalesce into a single lobe, polysomes must cross the nucleoid zones via the radial shells around the nucleoid lobes.

      We do not think that the results from chloramphenicol-treated cells are inconsistent with our model. Our proposed mechanism predicts that nucleoids will condense in the presence of chloramphenicol, consistent with experiments. It also predicts that nucleoids that were still relatively close at the time of chloramphenicol treatment could fuse if they eventually touched through diffusion (thermal fluctuation) to reduce their interaction with the polysomes and minimize their conformational energy. Fusion is, however, not expected for well-separated nucleoids since their diffusion is slow in the crowded cytoplasm. This is consistent with our experimental observations: In the presence of a growth-inhibitory concentration of chloramphenicol (70 μg/mL), nucleoids in relatively close proximity can fuse, but well-separated nucleoids condense and do not fuse. Since the growth rate inhibition is not immediate upon chloramphenicol treatment, many cells with well-separated condensed nucleoids divide during the first hour. As a result, the non-fusion phenotype is more obvious in non-dividing cells, achieved by pre-treating cells with the cell division inhibitor cephalexin (50μg/mL). In these polyploid elongated cells, well-separated nucleoids condensed but did not fuse, not even after an hour in the presence of chloramphenicol (as illustrated in Author response image 2).

      In Bakshi et al, 2014, nucleoid fusion was shown for a single cell in which the sister nucleoids were relatively close to each other at the time of chloramphenicol treatment. Population statistics were provided for the relative length and width of the nucleoids, but not for the fusion events. So, it is unclear whether the illustrated fusion was universal or not. Also, we note that Bakshi et al (2014) used a chloramphenicol concentration of 300 μg/mL, which is 20-fold higher than the minimal inhibitory concentration for growth, compared to 70 μg/mL in our experiments.

      Author response image 2.

      Effects of chloramphenicol treatment on the intracellular distribution of ribosomes and nucleoids in non-dividing cells. Exponentially growing cells (M9glyCAAT at 30°C) were pre-treated with cephalexin for one hour before being spotted on an 1% agarose pad for time-lapse imaging. The agarose pad contained M9glyCAAT, cephalexin, and chloramphenicol.  (A) Phase contrast, RplA-GFP fluorescence and HupA-mCherry fluorescence images of a representative single cell. Three timepoints are shown, including the first image after spotting on the agarose pad (at 0 min), 30 minutes and one hour of chloramphenicol treatment. (B) One-dimensional profiles of the ribosomal (RplA-GFP) and nucleoid (HupA-mCherry) fluorescence from the cells shown in panel A. These intensity profiles correspond to the average fluorescence along the medial axis of the cell considering a 6-pixel region (0.4 μm) centered on the central line of the cell. The fluorescence intensity is plotted along the relative cell length, scaled from 0 to 100% between the two poles, illustrating the relative nucleoid length (L<sub>DNA</sub>/L<sub>cell</sub>) that was plotted by Bakshi et al in 2014 (PMID: 25250841).

      (2) The peripheral localisation of nucleoids observed after A22 treatment in this study and others (e.g., Japaridze et al., 2020; Wu et al., 2019), which conflicts with the model's assumptions and predictions. The assumption of radial confinement would predict nucleoids to fill up the volume or ribosomes to go near the cell wall, not the nucleoid, as seen in the data.

      The reviewer makes a good point that DNA attachment to the membrane through transertion likely contributes to the nucleoid being peripherally localized in A22 cells. We will revise the text to add this point. However, we do not think that this contradicts the proposed nucleoid segregation mechanism based on phase separation and out-of-equilibrium dynamics described in our model. On the contrary, by attaching the nucleoid to the cytoplasmic membrane along the cell width, transertion might help reduce the diffusion and thus exchange of polysomes across nucleoids. We will revise the text to discuss transertion over radial confinement.

      (3) The radial compaction of the nucleoid upon rifampicin or chloramphenicol treatment, as reported by Bakshi et al. (2014) and Spahn et al. (2023), also contradicts the model's predictions. This is not expected if the nucleoid is already radially confined.

      We originally evoked radial confinement to explain the observation that polysome accumulations do not equilibrate between DNA-free regions. We agree that transertion is an alternative explanation. Thank you for bringing it to our attention. However, please note that this does not contradict the model. In our view, it actually supports the 1D model by providing a reasonable explanation for the slow exchange of polysomes across DNA-free regions. The attachment of the nucleoid to the membrane along the cell width may act as diffusion barrier. We will revise the text and the title of the manuscript accordingly.

      (4) Radial Distribution of Nucleoid and Ribosomal Shell:

      The study does not account for well-documented features such as the membrane attachment of chromosomes and the ribosomal shell surrounding the nucleoid, observed in super-resolution studies (Bakshi et al., 2012; Sanamrad et al., 2014). These features are critical for understanding nucleoid dynamics, particularly under conditions of transcription-translation coupling or drug-induced detachment. Work by Yongren et al. (2014) has also shown that the radial organisation of the nucleoid is highly sensitive to growth and the multifork nature of DNA replication in bacteria.

      We will discuss the membrane attachment. Please see the previous response.

      The omission of organisation in the radial dimension and the entropic effects it entails, such as ribosome localisation near the membrane and nucleoid centralisation in expanded cells, undermines the model's explanatory power and predictive ability. Some observations have been previously explained by the membrane attachment of nucleoids (a hypothesis proposed by Rabinovitch et al., 2003, and supported by experiments from Bakshi et al., 2014, and recent super-resolution measurements by Spahn et al.).

      We agree—we will add a discussion about membrane attachment in the radial dimension. See previous responses.

      Ignoring the radial dimension and membrane attachment of nucleoid (which might coordinate cell growth with nucleoid expansion and segregation) presents a simplistic but potentially misleading picture of the underlying factors.

      As mentioned above, we will discuss membrane attachment in the revised manuscript.

      This reviewer suggests that the authors consider an alternative mechanism, supported by strong experimental evidence, as a potential explanation for the observed phenomena:

      Nucleoids may transiently attach to the cell membrane, possibly through transertion, allowing for coordinated increases in nucleoid volume and length alongside cell growth and DNA replication. Polysomes likely occupy cellular spaces devoid of the nucleoid, contributing to nucleoid compaction due to mutual exclusion effects. After the nucleoids separate following ter separation, axial expansion of the cell membrane could lead to their spatial separation.

      This “membrane attachment/cell elongation” model is reminiscent to the hypothesis proposed by Jacob et al in 1963 (doi:10.1101/SQB.1963.028.01.048). There are several lines of evidence arguing against it as the major driver of nucleoid segregation:

      (Below is a slightly modified version of our response to a comment from Reviewer 1—see page 3)

      (1) For this alternative model to work, axial membrane expansion (i.e., cell elongation) would have to be localized at the middle of the splitting nucleoids (i.e., midcell position for slow growth and ¼ and ¾ cell positions for fast growth) to create a directional motion. To our knowledge, there is no evidence of such localized membrane incorporation.  Furthermore, even if membrane growth was localized at the right places, the fluidity of the cytoplasmic membrane (PMID: 6996724, 20159151, 24735432, 27705775) would be problematic. To go around this fluidity issue, one could potentially evoke a potential connection to the rigid peptidoglycan, but then again, peptidoglycan growth would have to be localized at the middle of the splitting nucleoid to “push” the sister nucleoid apart from each other. However, peptidoglycan growth is dispersed prior to cell constriction (PMID: 35705811, 36097171, 2656655).

      (2) Even if we ignore the aforementioned caveats, Paul Wiggins’s group ruled out the cell elongation/transertion model by showing that the rate of cell elongation is slower than the rate of chromosome segregation (PMID: 23775792). In the revised manuscript, we will provide additional data showing that the cell elongation rate is indeed slower than the nucleoid segregation rate.

      (3) Furthermore, our correlation analysis comparing the rate of nucleoid segregation to the rate of either cell elongation or polysome accumulation argues that polysome accumulation plays a larger role than cell elongation in nucleoid segregation. These data were already shown in the original manuscript (Figure 1I and Figure 1 – figure supplement 3) but were not highlighted in this context. We will revise the text to clarify this point.

      (4) The membrane attachment/cell elongation model does not explain the nucleoid asymmetries described in our paper (Figure 3), whereas they can be recapitulated by our model.

      (5) The cell elongation/transertion model cannot predict the aberrant nucleoid dynamics observed when chromosomal expression is largely redirected to plasmid expression. In the revised manuscript, we will add simulation results showing that these nucleoid dynamics are predicted by our model.

      In line of these arguments, we do not believe that a mechanism based on membrane attachment and cell elongation is the major driver of nucleoid segregations. However, we do believe that it may play a complementary role (see “Nucleoid segregation likely involves multiple factors” in the Discussion). We will revise this section to clarify our thoughts and mention the potential role of transertion.

      Incorporating this perspective into the discussion or future iterations of the model may provide a more comprehensive framework that aligns with the experimental observations in this study and previous work.

      As noted above, we will revise the text to mention about transertion.

      Simplification of Ribosome States:

      Combining monomeric and translating ribosomes into a single 'polysome' category may overlook spatial variations in these states, particularly during ribosome accumulation at the mid-cell. Without validating uniform mRNA distribution or conducting experimental controls such as FRAP or single-molecule measurements to estimate the proportions of ribosome states based on diffusion, this assumption remains speculative.

      Indeed, for simplicity, we adopt an average description of all polysomes with an average diffusion coefficient and interaction parameters, which is sufficient for capturing the fundamental mechanism underlying nucleoid segregation. To illustrate that considering multiple polysome species does not change the physical picture, we consider an extension of our model, which contains three polysome species, each with a different diffusion coefficient (D<SUB>P</SUB> = 0.018, 0.023, or 0.028 μm<sup>2</sup>/s), reflecting that polysomes with more ribosomes will have a lower diffusion coefficient. Simulation of this model reveals that the different polysome species have essentially the same concentration distribution, suggesting that the average description in our minimal model is sufficient for our purposes. We will present these new simulation results in the revised manuscript.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The manuscript is dedicated heavily to cell type mapping and identification of sub-type markers in the human testis but does not present enough results from cross-investigation between NOA cases versus control. Their findings are mostly based on transcriptome and the authors do not make enough use of the scATAC-seq data in their analyses as they put forward in the title. Overall, the authors should do more to include the differential profile of NOA cases at the molecular level - specific gene expression, chromatin accessibility, TF binding, pathway, and signaling that are perturbed in NOA patients that may be associated with azoospermia.

      Strengths:

      (1) The establishment of single-cell data (both RNA and ATAC) from the human testicular tissues is noteworthy.

      (2) The manuscript includes extensive mapping of sub-cell populations with some claimed as novel, and reports marker gene expression.

      (3) The authors present inter-cellular cross-talks in human testicular tissues that may be important in adequate sperm cell differentiation.

      Weaknesses:

      (1) A low sample size (2 OA and 3 NOA cases). There are no control samples from healthy individuals.

      Thank you for your comments. We recognize that the small sample size in this study somewhat limits its generalizability. However, in transcriptomic research, limited sample sizes are a common issue due to the complexities involved in acquiring samples, particularly in studies about the reproductive system. Healthy testicular tissue samples are difficult to obtain, and studies (doi: 10.18632/aging.203675) have used obstructive azoospermia as a control group in which spermatogenesis and development are normal.

      (2) Their argument about interactions between germ and Sertoli cells is not based on statistical testing.

      Thank you for your comments. Due to limited funding, we have not yet fully and deeply conducted validation experiments, but we plan to carry out related experiments in the later stage. We hope that the publication of this study will help to obtain more financial support to further investigate the interactions between germ cells and Sertoli cells.

      (3) Rationale/logic of the study. This study, in its present form, seems to be more about the role of sub-Sertoli population interactions in sperm cell development and does not provide enough insights about NOA.

      Thank you for your comments. In Figure 6, we conducted an in-depth analysis and comparison of the differences between the Sertoli cell subtypes and the germ cell subtypes involved in spermatogenesis in the OA and NOA groups. The results revealed that in the NOA group, especially in the NOA3 group, which has a lower sperm count compared to NOA2 and NOA1, there is a significant loss of Sertoli cell subtypes including SC3, SC4, SC5, SC6, and SC8. The NOA1 group, with a sperm count close to that of the OA group, also had a Sertoli cell profile similar to the OA group. The NOA2 group, with a sperm count between that of NOA1 and NOA3, also exhibited an intermediate profile of Sertoli cell subtypes. Therefore, we suggest that change in Sertoli cell subtypes is a key factor affecting sperm count, rather than just the total number of Sertoli cells. We believe that through these analyses, we can provide in-depth insights into NOA, and we hope that the publication of this study will help obtain more funding support to further validate and expand on these findings.

      (4) The authors do not make full use of the scATAC-seq data.

      Thank you for your comments.We have added analysis of the scATAC-seq data and shown in the revised manuscript.

      Reviewer #2 (Public Review):

      Summary:

      Shimin Wang et al. investigated the role of Sertoli cells in mediating spermatogenesis disorders in non-obstructive azoospermia (NOA) through stage-specific communications. The authors utilized scRNA-seq and scATAC-seq to analyze the molecular and epigenetic profiles of germ cells and Sertoli cells at different stages of spermatogenesis.

      Strengths:

      By understanding the gene expression patterns and chromatin accessibility changes in Sertoli cells, the authors sought to uncover key regulatory mechanisms underlying male infertility and identify potential targets for therapeutic interventions. They emphasized that the absence of the SC3 subtype would be a major factor contributing to NOA.

      Weaknesses:

      Although the authors used cutting-edge techniques to support their arguments, it is difficult to find conceptual and scientific advances compared to Zeng S et al.'s paper (Zeng S, Chen L, Liu X, Tang H, Wu H, and Liu C (2023) Single-cell multi-omics analysis reveals dysfunctional Wnt signaling of spermatogonia in non-obstructive azoospermia. Front. Endocrinol. 14:1138386.). Overall, the authors need to improve their manuscript to demonstrate the novelty of their findings in a more logical way.

      Thank you for your detailed review of our work. We greatly appreciate your feedback and have made revisions to our manuscript accordingly.

      Regarding the novelty of our research, we believe our study offers conceptual and scientific advances in several ways:

      We have systematically revealed the stage-specific roles of Sertoli cell subtypes in different stages of spermatogenesis, particularly emphasizing the crucial role of the SC3 subtype in non-obstructive azoospermia (NOA). Additionally, we identified that other Sertoli cell subtypes (SC1, SC2, SC3...SC8, etc.) also collaborate in a stage-specific manner with different subpopulations of spermatogenic cells (SSC0, SSC1/SSC2/Diffed, Pa...SPT3). These findings provide new insights into the understanding of spermatogenesis disorders.

      Compared to the study by Zeng S et al., our research not only focuses on the functional alterations in Sertoli cells but also comprehensively analyzes the interaction patterns between Sertoli cells and spermatogenic cells using scRNA-seq and scATAC-seq technologies. We uncovered several novel regulatory networks that could serve as potential targets for the diagnosis and treatment of NOA.

      We sincerely appreciate your constructive comments and will continue to explore this area further, aiming to make a more significant contribution to the understanding of NOA mechanisms.

      Reviewer #3 (Public Review):

      Summary:

      This study profiled the single-cell transcriptome of human spermatogenesis and provided many potential molecular markers for developing testicular puncture-specific marker kits for NOA patients.

      Strengths:

      Perform single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) on testicular tissues from two OA patients and three NOA patients.

      Weaknesses:

      Most results are analytical and lack specific experiments to support these analytical results and hypotheses.

      Thank you for your thorough review of our work. We highly value your feedback and have made revisions to our manuscript accordingly. Indeed, we have conducted immunofluorescence (IF) experiments to validate the data obtained from single-cell sequencing and have expanded the sample size to enhance the reliability of our results. To better present these validation experiments, we have reorganized and renamed the sample information, making it easier for you to understand which samples were used in the specific experiments. Following the publication of this paper, we plan to secure additional funding to deepen our research, particularly in the area of experimental validation. We sincerely appreciate your support and insightful suggestions, which have greatly helped guide our future research directions.

      Reviewer #1 (Recommendations For The Authors):

      (1) The authors should include results from cross-investigation comparing NOA/OA patients versus controls.

      Thank you for your comments. In this study, OA was the control group. Healthy testicular tissue samples are difficult to obtain, and studies (doi: 10.18632/aging.203675) have used OA as a control group in which spermatogenesis and development are normal.

      (2) In Table S1, the authors should also include the metric for scATAC-seq, and do more to show the findings the authors obtained in RNA is replicated with chromatin accessibility.

      Thank you for your comments. We have added Table S2, which includes the metric for scATAC-seq.

      (3) A single sample from each OA and NOA group may not be enough to confirm colocalization. The authors should include results from all available samples and use quantitative measures.

      Thank you for your comments. I apologize that the sample size in this study was less than three and we could not conduct quantitative analysis. We will increase the sample size and conduct corresponding experiments in subsequent research.

      (4) The Methods section does not include enough description to follow how the analyses were carried out, and is missing information on some of the key procedures such as velocity and cell cycle analyses.

      Thank you for your comments. The method about velocity and cell cycle analyses was added in the revised manuscript. The description is as follows:

      “Velocity analysis

      RNA velocity analysis was conducted using scVelo's (version 0.2.1) generalized dynamical model. The spliced and unspliced mRNA was quantified by Velocity (version 0.17.17).”

      “Cell cycle analysis

      To quantify the cell cycle phases for individual cell, we employed the CellCycleScoring function from the Seurat package. This function computes cell cycle scores using established marker genes for cell cycle phases as described in a previous study by Nestorowa et al. (2016). Cells showing a strong expression of G2/M-phase or S-phase markers were designated as G2/M-phase or S-phase cells, respectively. Cells that did not exhibit significant expression of markers from either category were classified as G1-phase cells.”

      (5) For the purpose of transparency, the authors should upload codes used for analyses so that each figure can be reproduced. All raw and processed data should be made publicly available.

      Thank you for your comments. We have deposited scRNA-seq and scATAC-seq data in NCBI. ScRNA-seq data have been deposited in the NCBI Gene Expression Omnibus with the accession number GSE202647, and scATAC-seq data have been deposited in the NCBI database with the accession number PRJNA1177103.

      Reviewer #2 (Recommendations For The Authors):

      The detailed points the authors need to improve are attached below.

      The results presented in the study have several weaknesses:

      In Figure 1A, it's required to show HE staining results of all patients who underwent single-cell analysis were provided.

      Thank you very much for your valuable suggestions. In Figure 1, we present the HE staining results paired with the single-cell data, covering all patients involved in the single-cell analysis.

      - Saying "identification of novel potential molecular markers for distinct cell types" seems unsupported by the data.

      Thank you for your comments. I'm sorry for the inaccuracy of my description. We have revised this sentence. The description is as follows: These findings indicate that the scRNA-seq data from this study can serve for cellular classification.

      - The methods suggest an integrated analysis of scRNA-seq and scATAC-seq, but from the figures, it seems like separate analyses were performed. It's necessary to have data showing the integrated analysis.

      Thank you for your comments. We have added an integrated analysis of scRNA-seq and scATAC-seq. The results were shown in Figure S2.

      Figure 2 does not seem to well cover the diversity of germ cell subtypes. The main content appears to be about the differentiation process, and it seems more focused on SSCs (stem cell types), but the intended message is not clearly conveyed.

      Thank you for your comments. Figure S1 revealed the diversity of germ cell subtypes. The second part of the results described the integrated findings from Figures 2 and S1.

      - In Figure 2B, pseudotime could be shown, and I wonder if the pseudotime in this analysis shows a similar pattern as in Figure 2D.

      Thank you for your comments. Figure 2B revealed the pseudotime analysis of 12 germ cell subpopulation. Figure 2D revealed RNA velocity of 12 germ cell subpopulation. The two methods are both used for cell trajectory analysis. The pseudotime in Figure 2B showed a similar pattern as in Figure 2D.

      - While staining occurs within one tissue, saying they are co-expressed seems inaccurate as the staining locations are clearly distinct. For example, the staining patterns of A2M and DDX4 (a classical marker) are quite different, so it's hard to claim A2M as a new potential marker just because it's expressed. Also, TSSK6 was separately described as having a similar expression pattern to DDX4, but from the IF results, it doesn't seem similar.

      Thank you for your comments. We have revised the Figure.

      - It was described that A2M (expressed in SSC0-1), and ASB9 (expressed in SSC2) have open promoter sites in SSC0, SSC2, and Diffing_SPG, but it doesn't seem like they are only open in the promoters of those cell types. For example, there doesn't seem to be a peak in Diffing for either gene. The promoter region of the tracks is not very clear, so overall figure modification seems necessary.

      Thank you for your comments. We have revised the Figure.

      - The ATAC signal scale for each genomic region should be included, and clear markings for the TSS location and direction of the genes are needed.

      Thank you for your comments. We have revised the figure and shown in the revised manuscript.

      Figure 3A mostly shows the SSC2 in the G2/M phase, so it seems questionable to call SSC0/1 quiescent. Also, I wonder if the expression of EOMES and GFRA1 is well distinguished in the SSC subtypes as expected.

      Thank you for your comments. We will validate in subsequent experiments whether the expression of EOMES and GFRA1 is clearly distinguished in the SSC subtypes.

      - In Figure 3C, it would be good to have labels indicating what the x and y axes represent. The figure seems complex, and the description does not seem to fully support it.

      Thank you for your comments. We have added labels indicating what the x and y axes represent in the Figure 3C. The x and y axes represent spliced and unspliced mRNA ratios, respectively.

      - While TFs are the central focus, it's disappointing that scATAC-seq was not used.

      Thank you for your comments. TFs analysis using scATAC-seq will be carried out in the future.

      Figure 4: It would be good to have a more detailed discussion of the differences between subtypes, such as through GO analysis. The track images need modification like marking the peaks of interest and focusing more on the promoter region, similar to the previous figures.

      Thank you for your comments. GO analysis results were put in Figure S5. The description is as follows:

      As shown in Figure S5, SC1 were mainly involved in cell differentiation, cell adhesion and cell communication; SC2 were involved in cell migration, and cell adhesion; SC3 were involved in spermatogenesis, and meiotic cell cycle; SC4 were involved in meiotic cell cycle, and positive regulation of stem cell proliferation; SC5 were involved in cell cycle, and cell division; SC6 were involved in obsolete oxidation−reduction process, and glutathione derivative biosynthetic process; SC7 were involved in viral transcription and translational initiation; SC8 were involved in spermatogenesis and sperm capacitation.

      In Figure 5, it would be good to have criteria for the novel Sertoli cell subtype presented. CCDC62 is presented as a representative marker for the SC8 cluster, but from Figure 4C, it seems to be quite expressed in the SC3 cluster as well. Therefore, in Figure 5E's protein-level check, it's unclear if this truly represents a novel SC8 subtype.

      Thank you for your comments. CCDC62 expression was higher in SC8 cluster than in SC3. Since some molecular markers were not commercially available in the market, CCDC62 was selected as SC8 marker for immunofluorescence verification. Immunofluorescence results showed that CCDC62 is a novel SC8 marker.

      - It might have been more meaningful to use SOX9 as a control and show that markers in the same subtype are expressed in the same location.

      Thank you for your comments. To determine PRAP1, BST2, and CCDC62 as new markers for the SC subtype, we co-stained them with SOX9 (a well-known SC marker).

      - Figures 4 and 5 could potentially be combined into one figure.

      Thank you for your comments. Since combining Figures 4 and 5 into a single image would cause the image to be unclear, two images are used to show it.

      In Figure 6, it would be good to support the results with more NOA patient data.

      Thank you for your comments. Patient clinical and laboratory characteristics has been presented in Table 1.

      - Rather than claiming the importance of SC3 based on 3 single-cell patient data, it would be better to validate using public data with SC3 signature genes (e.g., showing the correlation between germ cell and SC3 ratios).

      Thank you for your comments. I'm sorry I didn't find public data with SC3 signature genes. In the future, we will verify the importance of SC3 through in vivo and in vitro experiments.

      - 462: It seems to be referring to Figure 6G, not 6D.

      Thank you for your comments. We have revised it. The description is as follows: As shown in Figure 6G, State 1 SC3/4/5 were tended to associated with PreLep, SSC0/1/2, and Diffing and Diffed-SPG sperm cells (R > 0.72).

      In Figure 7, the spermatogenesis process is basically well-known, so it would be better to emphasize what novel content is being conveyed here. Additionally, emphasizing the importance of SC3 in the overall process based on GO results leaves room for a better approach.

      Thank you for your valuable suggestions. Regarding Figure 7, we recognize that the spermatogenesis process is well-known, and we will focus on highlighting the novel content, particularly the role and significance of the SC3 subtype in spermatogenesis disorders. As for the importance of SC3 in the overall process based on GO results, we have validated this in Figure 8 through co-staining experiments between Sertoli cells and spermatogenic cells in OA and NOA groups. The results demonstrate a significant correlation between the number of SC3-positive cells and SPT3 spermatogenic cells, particularly in the NOA5-P8 group, where both SC3 and SPT3 cell counts are notably lower than in the NOA4-P7 group. This further supports the critical role of SC3 in the spermatogenesis process. Your suggestions have prompted us to refine our data presentation and more clearly emphasize the novel aspects of our research. We will continue to strive to ensure that every part of our research contributes meaningfully to the academic community. Thank you again for your guidance.

      In Figure 8, only the contents of the IF-stained proteins are listed, which seems slightly insufficient to constitute a subsection on its own. It might have been better to conclude by emphasizing some subtypes.

      Thank you for your comments. We have combined this part of the results with other results into one section. The description is as follows:

      “Co-localization of subpopulations of Sertoli cells and germ cells

      To determine the interaction between Sertoli cells and spermatogenesis, we applied Cell-PhoneDB to infer cellular interactions according to ligand-receptor signalling database. As shown in Figure 6G, compared with other cell types, germ cells were mainly interacted with Sertoli cells. We futher performed Spearman correlation analysis to determine the relationship between Sertoli cells and germ cells. As shown in Figure 6H, State 1 SC3/4/5 were tended to be associated with PreLep, SSC0/1/2, and Diffing and Diffed-SPG sperm cells (R > 0.72). Interestingly, SC3 was significantly positively correlated with all sperm subpopulations (R > 0.5), suggesting an important role for SC3 in spermatogenesis and that SC3 is involved in the entire process of spermatogenesis. Subsequently, to understand whether the functions of germ cells and Sertoli cells correspond to each other, GO term enrichment analysis of germ cells and sertoli cells was carried out (Figure S3, S4). We found that the functions could be divided into 8 categories, namely, material energy metabolism, cell cycle activity, the final stage of sperm cell formation, chemical reaction, signal communication, cell adhesion and migration, stem cells and sex differentiation activity, and stress reaction. These different events were labeled with different colors in order to quickly capture the important events occurring in the cells at each stage. As shown in Figure S3, we discovered that SSC0/1/2 was involved in SRP-dependent cotranslational protein targeting to membrane, and cytoplasmic translation; Diffing SPG was involved in cell division and cell cycle; Diffied SPG was involved in cell cycle and RNA splicing; Pre-Leptotene was involved in cell cycle and meiotic cell cycle; Leptotene_Zygotene was involved in cell cycle and meiotic cell cycle; Pachytene was involved in cilium assembly and spermatogenesis; Diplotene was involved in spermatogenesis and cilium assembly; SPT1 was involved in cilium assembly and flagellated sperm motility; SPT2 was involved in spermatid development and flagellated sperm motility; SPT3 was involved in spermatid development and spermatogenesis. As shown in Figure S4, SC1 were mainly involved in cell differentiation, cell adhesion and cell communication; SC2 were involved in cell migration, and cell adhesion; SC3 were involved in spermatogenesis, and meiotic cell cycle; SC4 were involved in meiotic cell cycle, and positive regulation of stem cell proliferation; SC5 were involved in cell cycle, and cell division; SC6 were involved in obsolete oxidation−reduction process, and glutathione derivative biosynthetic process; SC7 were involved in viral transcription and translational initiation; SC8 were involved in spermatogenesis and sperm capacitation. The above analysis indicated that the functions of 8 Sertoli cell subtypes and 12 germ cell subtypes were closely related.

      To further verify that Sertoli cell subtypes have "stage specificity" for each stage of sperm development, we firstly performed HE staining using testicular tissues from OA3-P6, NOA4-P7 and NOA5-P8 samples. The results showed that the OA3-P6 group showed some sperm, with reduced spermatogenesis, thickened basement membranes, and a high number of sertoli cells without spermatogenic cells. The NOA4-P7 group had no sperm initially, but a few malformed sperm were observed after sampling, leading to the removal of affected seminiferous tubules. The NOA5-P8 group showed no sperm in situ (Figure 7A). Immunofluorescence staining in Figure 7B was performed using these tissues for validation. ASB9 (SSC2) was primarily expressed in a wreath-like pattern around the basement membrane of testicular tissue, particularly in the OA group, while ASB9 was barely detectable in the NOA group. SOX2 (SC2) was scattered around SSC2 (ASB9), with nuclear staining, while TF (SC1) expression was not prominent. In NOA patients, SPATS1 (SC3) expression was significantly reduced. C9orf57 (Pa) showed nuclear expression in testicular tissues, primarily extending along the basement membrane toward the spermatogenic center, and was positioned closer to the center than DDX4, suggesting its involvement in germ cell development or differentiation. BEND4, identified as a marker fo SC5, showed a developmental trajectory from the basement membrane toward the spermatogenic center. ST3GAL4 was expressed in the nucleus, forming a circular pattern around the basement membrane, similar to A2M (SSC1), though A2M was more concentrated around the outer edge of the basement membrane, creating a more distinct wreath-like arrangement. In cases of impaired spermatogenesis, this arrangement becomes disorganized and loses its original structure. SMCP (SC6) was concentrated in the midpiece region of the bright blue sperm cell tail. In the OA group, SSC1 (A2M) was sparsely arranged in a rosette pattern around the basement membrane, but in the NOA group, it appeared more scattered. SSC2 (ASB9) expression was not prominent. BST2 (SC7) was a transmembrane protein primarily localized on the cell membrane. In the OA group, A2M (SSC1) was distinctly arranged in a wreath-like pattern around the basement membrane, with expression levels significantly higher than ASB9 (SSC2). TSSK6 (SPT3) was primarily expressed in OA3-P6, while CCDC62 (SC8) was more abundantly expressed in NOA4-P7, with ASB9 (SCC2) showing minimal expression. Taken together, germ cells of a particular stage tended to co-localize with Sertoli cells of the corresponding stages. Germ cells and sertoli cells at each differentiation stage were functionally heterogeneous and stage-specific (Figure 8). This suggests that each stage of sperm development requires the assistance of sertoli cells to complete the corresponding stage of sperm development.”

      Reviewer #3 (Recommendations For The Authors):

      The authors revealed 11 germ cell subtypes and 8 Sertoli cell subtypes through single-cell analysis of two OA patients and three NOA patients. And found that the Sertoli cell SC3 subtype (marked by SPATS1) plays an important role in spermatogenesis. It also suggests that Notch1/2/3 signaling and integrins are involved in germ cell-Stotoli cell interactions. This is an interesting and useful article that at least gives us a comprehensive understanding of human spermatogenesis. It provides a powerful tool for further research on NOA. However, there are still some issues and questions that need to be addressed.<br /> (1) How to collect testicular tissue, please explain in detail. Extract which part of testicular tissue. It's better to make a schematic diagram.

      Thank you for your comments. The process is as follows: Testicular tissues were obtained from two OA patients (OA1-P1 and OA2-P2) and three NOA patients (NOA1-P3, NOA2-P4, NOA3-P5) using micro-dissection of testicular sperm extraction separately.

      (2) Whether the tissues of these patients are extracted simultaneously or separately, separated into single cells, and stored, and then single cell analysis is performed simultaneously. Please be specific.

      Thank you for your comments. The testicular tissues of these patients were extracted separately, then separated into single cells, and single cell analysis was performed simultaneously.

      (3) When performing single-cell analysis, cells from two OA patients were analyzed individually or combined. The same problem occurred in the cells of three NOA patients.

      Thank you for your comments. Cells from two OA patients and three NOA patients were analyzed individually.

      (4) Can you specifically point out the histological differences between OA and NOA in Figure 1A? This makes it easier for readers to understand the structure change between OA and NOA. Please also label representative supporting cells.

      Thank you for your comments. We have revised the description and it was shown in the revised manuscript.

      (5) The authors demonstrate that "We speculate that this lack of differentiation may be due to the intense morphological changes occurring in the sperm cells during this period, resulting in relatively minor differences in gene expression." Please provide some verification of this hypothesis? For example, use immunofluorescence staining to observe morphological changes in sperm cells.

      Thank you for your comments. Due to limited funds, we will verified this hypothesis in future studies.

      (6) The authors demonstrate that " As shown in Figure 5E, we discovered that PRAP1, BST2, and CCDC62 were co-expressed with SOX9 in testes tissues." The staining in Figure 5D is unclear, and it is difficult to explain that SOX9 is co-expressed with PRAP1 BST2 CCDC62 based on the current staining results. The staining patterns of SOX9 (green) and SOX9 (red) are also different. (SOX9 (red) appears as dots, while the background for SOX9 (green) is too dark to tell whether its staining is also in the form of dots.) In summary, increasing the clarity of the staining makes it more convincing. Alternatively, use high magnification to display these results.

      Thank you for your comments. I have redyed and updated this part of the immunofluorescence staining results. Please refer to the files named Figure 1, Figure 2, Figure 5, and Figure 8.

      (7) In Figure 8, the author emphasized the co-localization of Sertoli cells and Germ cells at corresponding stages and did a lot of staining, but it was difficult to distinguish the specific locations of co-localization, which was similar to Figure 5E. If possible, please mark specific colocalizations with arrows or use high magnification to display these results, in order to facilitate readers to better understand.

      Thank you for your comments. We have re-stained and updated this part of the data. Please refer to the immunofluorescence staining data in the updated Figure 8.

      (8) The authors emphasize that macrophages may play an important role in spermatogenesis. Therefore, adding relevant macrophage staining to observe the differences in macrophage expression between NOA and OA should better support this idea.

      Thank you for your comments. Macrophage-related experiments will be further explored in the future.

      (9) Notch1/2/3 signaling and integrin were discovered to be involved in germ cell-Sertoli cell interaction. However there are currently no concrete experiments to support this hypothesis. At least simple verification experiments are needed.

      Thank you for your comments. Due to limited funding, studies will be carried out in the future.

      (10) Data availability statements should not be limited to the corresponding author, especially for big data analysis. This is crucial to the credibility of this data (Have the scRNA-seq and scATAC-seq in this study been deposited in GEO or other databases, and when will they be released to the public?) The data for such big data analysis needs to be saved in GEO or other databases in advance so that more research can use it.

      Thank you for your comments. We have deposited scRNA-seq and scATAC-seq data in NCBI. “ScRNA-seq data have been deposited in the NCBI Gene Expression Omnibus with the accession number GSE202647, and scATAC-seq data have been deposited in the NCBI database with the accession number PRJNA1177103.”

    1. Author response:

      The following is the authors’ response to the original reviews.

      Joint Public Review:

      Strengths:

      The paper is solidly based on the ability of the authors to master molecular simulations of highly complex systems. In my opinion, this paper shows no major weaknesses. The simulations are carried out in a technically sound way. Comparative analyses of different systems provide valuable insights, even within the well-known limitations of MD. Plus, the authors further investigate why xCas9 exhibits improved recognition of the TGG PAM sequence compared to SpCas9 via well-tempered metadynamics simulations focusing on the binding of R1335 to the G3 nucleobase and the DNA backbone in both SpCas9 and xCas9. In this context, the authors provide a free-energy profiling that helps support their final model.

      The implementation of FEP calculations to mimic directed evolution improvement of DNA binding is also interesting, original and well-conducted.

      We thank the reviewer for their positive evaluation of our computational strategy. To further substantiate our findings, we have incorporated additional molecular dynamics and Free Energy Perturbation (FEP) calculations for the system bound to GAT. These results corroborate our previous observations obtained with AAG, reinforcing our conclusions.

      Overall, my assessment of this paper is that it represents a strong manuscript, competently designed and conducted, and highly valuable from a technical point of view.

      Weaknesses:

      To make their impact even more general, the authors may consider expanding their discussion on entropic binding to other recent cases that have been presented in the literature recently (such as e.g. the identification of small molecules for Abeta peptides, or the identification of "fuzzy" mechanisms of binding to protein HMGB1). The point on flexibility helping adaptability and expansion of functional properties is important, and should probably be given more evidence and more direct links with a wider picture.

      We have expanded our discussion on the role of entropy in favoring TGG binding to xCas9. To this end, we performed entropy calculations using the Quasi-Harmonic approximation (details provided in the Materials and Methods section). This analysis reveals that R1335 in xCas9 experiences an entropy increase compared to SpCas9, enhancing its adaptability and interaction with the DNA. This analysis and its explanation are detailed on pages 8-9.

      Additionally, we have enriched the Discussion section by clarifying how DNA binding is entropically favored in xCas9, thereby facilitating the recognition of alternative PAM sequences. A refined explanation is also included in the Conclusions section, where we contextualize xCas9 within a broader evolutionary framework of protein-DNA recognition. This highlights how structural flexibility can enable sequence diversity while maintaining high specificity.

      Recommendations for the authors:

      Overall, this is a very interesting and elegant manuscript with compelling results that shed light on the atomistic determinants of genetic-editing technologies.

      Since the paper proposes new findings that may be helpful for experimentalists, it would be interesting if the authors point out (in their discussion/conclusions) specific amino acids to mutate/target for future tests by the experimental community. This should just appear as an open hypothesis/proposal for new experiments.

      In the Conclusions, we have incorporated a discussion on how modifications in the PAM-binding cleft can enhance the recognition of alternative PAM sequences. As an illustrative example, we reference the recently developed SpRY Cas9 variant, which is capable of recognizing a broader range of PAMs. This variant includes mutations within the PAM-binding cleft that likely increase the flexibility of the interacting residues, as suggested by recent cryo-EM structures (Hibshman et al. Nat. Commun. 2024). The importance of fine-tuning the flexibility of the PAM-interacting cleft for engineering strategies has also been highlighted in the abstract.

      Overall, in light of the reviewer’s comments and in consideration of our findings, we revised the manuscript title in: “Flexibility in PAM Recognition Expands DNA Targeting in xCas9.” This new title better highlights the key findings from our research and contextualizes them within the broader goal of expanding DNA targeting capabilities, a critical priority for developing enhanced CRISPR-Cas systems.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      This study by Wu et al. provides valuable computational insights into PROTAC-related protein complexes, focusing on linker roles, protein-protein interaction stability, and lysine residue accessibility. The findings are significant for PROTAC development in cancer treatment, particularly breast and prostate cancers.

      The authors' claims about the role of PROTAC linkers and protein-protein interaction stability are generally supported by their computational data. However, the conclusions regarding lysine accessibility could be strengthened with more in-depth analysis. The use of the term "protein functional dynamics" is not fully justified by the presented work, which focuses primarily on structural dynamics rather than functional aspects.

      Strengths:

      (1) Comprehensive computational analysis of PROTAC-related protein complexes.

      (2) Focus on critical aspects: linker role, protein-protein interaction stability, and lysine accessibility.

      Weaknesses:

      (1) Limited examination of lysine accessibility despite its stated importance.

      (2) Use of RMSD as the primary metric for conformational assessment, which may overlook important local structural changes.

      Reviewer #1 (Recommendations for the authors):

      (1) The authors' claims about the role of PROTAC linkers and protein-protein interaction stability are generally supported by their computational data. However, the conclusions regarding lysine accessibility could be strengthened with more in-depth analysis. Expand the analysis of lysine accessibility, potentially correlating it with other structural features such as linker length.

      We thank the reviewers for the suggestions! We performed time dependent correlation analysis to correlate the dihedral angles of the PROTACs and the Lys-Gly distance (Figures 6 and S17). We included detailed explanation on page 16:

      “To further examine the correlation between PROTAC rotation and the Lys-Gly interaction, we performed a time-dependent correlation analysis. This analysis showed that PROTAC rotation translates motion over time, leading to the Lys-Gly interaction, with a correlation peak around 60-85 ns, marking the time of the interaction (Figure 6 and Figure S17). In addition, the pseudo dihedral angles also showed a high correlation (0.85 in the case of dBET1) with Lys-Gly distance. This indicated that degradation complex undergoes structural rearrangement and drives the Lys-Gly interaction.”

      (2) The use of the term "protein functional dynamics" is not fully justified by the presented work, which focuses primarily on structural dynamics rather than functional aspects. Consider changing "protein functional dynamics" to "protein dynamics" to more accurately reflect the scope of the study.

      Thanks to the reviewer for the suggestion to use the more accurate terminology! We agreed with the reviewer that if we keep “protein functional dynamics” in the title, we should focus on how the “overall protein dynamic” links to the “function” – The function is directly related to PROTAC-induced structural dynamics which is commonly seen in “protein-structural-function” relationship, but it is not our main focus. Therefore, we changed the title to replace “functional” by “structural”.  

      (3) Incorporate more local and specific characterization methods in addition to RMSD for a more comprehensive conformational assessment.

      We thank the reviewer for the suggestion. We performed time dependent correlation analysis to understand how the rotation of PROTACs can translate to the Lys-Gly interaction. In addition, we performed dihedral entropies analysis for each dihedral angle in the linker of the PROTACs to better examine the flexibility of each PROTAC.

      We included detailed explanation at page 18: “Our dihedral entropies analysis showed that dBET57 has ~0.3 kcal/mol lower entropies than the other three linkers, suggesting dBET57 is less flexible than other PROTACs (Figure S18).”

      Reviewer #2 (Public review):

      Summary:

      The manuscript reports the computational study of the dynamics of PROTAC-induced degradation complexes. The research investigates how different linkers within PROTACs affect the formation and stability of ternary complexes between the target protein BRD4BD1 and Cereblon E3 ligase, and the degradation machinery. Using computational modeling, docking, and molecular dynamics simulations, the study demonstrates that although all PROTACs form ternary complexes, the linkers significantly influence the dynamics and efficacy of protein degradation. The findings highlight that the flexibility and positioning of Lys residues are crucial for successful ubiquitination. The results also discussed the correlated motions between the PROTAC linker and the complex.

      Strengths:

      The field of PROTAC discovery and design, characterized by its limited research, distinguishes itself from traditional binary ligand-protein interactions by forming a ternary complex involving two proteins. The current understanding of how the structure of PROTAC influences its degradation efficacy remains insufficient. This study investigated the atomic-level dynamics of the degradation complex, offering potentially valuable insights for future research into PROTAC degradability.

      Reviewer #2 (Recommendations for the authors):

      (1) Regarding the modeling of the ternary complex, the BRD4 structure (3MXF) is from humans, whereas the CRBN structure in 4CI3 is derived from Gallus gallus. Is there a specific reason for not using structures from the same species, especially considering that human CRBN structures are available in the Protein Data Bank (e.g., 8OIZ, 4TZ4)?

      We appreciate the reviewer’s insightful comment regarding the choice of crystal structures of BRD4 and CRBN structures from two species. Our initial selection of 4CI3 for CRBN structure was based on its high resolution and publication in Nature journal. Furthermore, the Gallus gallus CRBN structure shares high degree of sequence and structural similarity with Homo sapiens CRBN, especially in the ligand binding region. At the time of our study, we were aware of 4TZ4 as Homo sapiens CRBN, however, we did not use this structure since no publication or detailed experimental was associated with it. Additionally, PDB 8OIZ, was not publicly available yet for other researchers to use at the time.

      (2) Based on the crystal structure (PDB ID: 6BNB) discussed in Reference 6, the ternary complex of dBET57 exhibits a conformation distinct from other PROTACs, with CRBN adopting an "open" conformation. Using the same CRBN structure for dBET57 as for other PROTACs might result in inaccurate docking outcomes.

      Thank you for the reviewer’s comment! As noted by the authors in Reference 6, the observed open conformation of CRBN in the dBET57 ternary complex may result from the high salt crystallization conditions, which could drive structural rearrangement, and crystal contacts that may induce this conformation. The authors also mentioned that this open conformation could, in part, reflect CRBN’s intrinsic plasticity. However, they acknowledged that further studies are needed to determine whether this conformational flexibility is a characteristic feature of CRBN that enables it to accommodate a variety of substrates. Despite these observations, we believe that the compatibility of the observed BRD4<sup>BD1</sup> binding conformation with both open and closed CRBN states suggests that these conformational changes are all possible. Therefore, we believe using the same initial CRBN structure for dBET57 as for other PROTACs can still reasonably reveal the dynamic nature of the ternary complex and would not significantly affect the accuracy of our docking outcomes either.

      (3) Figure 2 displays only a single frame from the simulations, which might not provide a comprehensive representation. Could a contact frequency heatmap of PROTAC with the proteins be included to offer a more detailed view?

      We thank the reviewer for the suggestion! We performed the contact map analysis to observe the average distance between PROTACs and BRD4<sup>BD1</sup> over 400ns of MD simulation (new Figure S4 added).

      We included detailed explanation at page 8 and 9: “The residues contact map throughout the 400ns MD simulation also showed different pattern of protein-protein interactions, indicating that the linkers were able to adopt different conformations (Figure S4).”

      (4) The conclusions in Figure 3 and S11 are based on a single 400 ns trajectory. The reproducibility of these results is therefore uncertain.

      We thank the reviewer for the suggestion! We added one more random seed MD simulation for each PROTAC to ensure the reproducibility of the results. The Result is shown in Figure S21 and the details for each MD run are updated in Table 1.

      (5) Figure 4 indicates significant differences between the first and last 100 ns of the simulations. Does this suggest that the simulations have not converged? If so, how can the statistical analysis presented in this paper be considered reliable?

      We thank the reviewers for the question. The simulation was initiated with a 10-15A gap between BRD4 and Ub to monitor the movement of degradation machinery and Lys-Gly interaction. The significant changes in pseudo dihedral in Figure 4 shows that the large-scale movement of the degradation complex can initiate the Lys-Gly binding. It does not relate to unstable sampling because the system remains very stable when BRD4 comes close to Ub.

      (6) In Figure 5, the dihedral angle of dBET57_#9MD1 is marked on a peptide bond. Shouldn't this angle have a high energy barrier for rotation?

      We thank the reviewers for catching the error! Indeed, it was an error that the dihedral angles were marked on the peptide bond. We reworked the figure and double checked our dihedral correlation analysis. The updated correlate dihedral angle selection and the correlation coefficient is shown in Figure 5.

      (7) Given that crystal structures for dBET 70, 23, and 57 are available, why is there a need to model the complex using protein-protein docking?

      We thank the reviewer for the feedback. Only dBET23 has the ternary complex available in a crystal structure, which has the PROTAC and both proteins, while dBET1, dBET57 and dBET70 are not completed as ternary complexes. Although dBET70 has a crystal structure, its PROTAC’s conformation is not resolved, and thus we decided to still perform protein-protein docking with dBET70. 

      We includeed the explanation at page 8: “Only dBET23 crystal structure is available with the PROTAC and both proteins, while the experimentally determined ternary complexes of dBET1, dBET57 and dBET70 are not available. “

      (8) On page 9, it is mentioned that "only one of the 12 PDB files had CRBN bound to DDB1 (PDB ID 4TZ4)." However, there are numerous structures of the DDB1-CRBN complex available, including those used for docking like 4CI3, as well as 4CI1, 4CI2, 8OIZ, etc.

      We thank the reviewers for the comment! We acknowledged the existence of several DDB1-CRBN complex crystal structures, such as PDB IDs 4CI1, 4CI2, 4CI3, and the more recent 8OIZ. For our study, we chose to use 4TZ4 to maintain consistency in complex construction and to align with the methodology established in a previously published JBC paper (https://doi.org/10.1016/j.jbc.2022.101653), which successfully utilized the same structure for a similar construct. At the time our study was conducted, the 8OIZ structure had not yet been released. We appreciate your suggestion and will consider incorporating alternative structures in future studies to further investigate our findings.

      (9) Table 2 is first referenced on page 8, while Table 1 is mentioned first on page 10. The numbering of these tables should be reversed to reflect their order of appearance in the text.

      We thank the reviewer for catching the error! We switched the order of Table 1 and Table 2.

      Reviewer #3 (Public review):

      The authors offer an interesting computational study on the dynamics of PROTAC-driven protein degradation. They employed a combination of protein-protein docking, structural alignment, atomistic MD simulations, and post-analysis to model a series of CRBN-dBET-BRD4 ternary complexes, as well as the entire degradation machinery complex. These degraders, with different linker properties, were all capable of forming stable ternary complexes but had been shown experimentally to exhibit different degradation capabilities. While in the initial models of the degradation machinery complex, no surface Lys residue(s) of BRD4 were exposed sufficiently for the crucial ubiquitination step, MD simulations illustrated protein functional dynamics of the entire complex and local side-chain arrangements to bring Lys residue(s) to the catalytic pocket of E2/Ub for reactions. Using these simulations, the authors were able to present a hypothesis as to how linker property affects degradation potency. They were able to roughly correlate the distance of Lys residues to the catalytic pocket of E2/Ub with observed DC50/5h values. This is an interesting and timely study that presents interesting tools that could be used to guide future PROTAC design or optimization.

      Reviewer #3 (Recommendations for the authors):

      (1) My most important comment refers to the MM/PBSA analysis, the results of which are shown in Figure S9: binding affinities of -40 to -50 kcal/mol are unrealistic. This would correspond to a dissociation constant of 10^-37 M. This analysis needs to be removed or corrected.

      We thank the reviewer for the comment! MM/PBSA analysis indeed cannot give realistic binding free energy. It does not include the configurational entropy loss which should be a large positive value. In addition, while the implicit PBSA solvent model computes solvation free energy, the absolute values may not be very accurate. However, because this is a commonly used energy calculation, and some readers may like to see quantitative values to ensure that the systems have stable intermolecular attractions, we kept the analysis in SI. We edited the figure legend, moved the Figure S10 in SI page 19, and added sentences to clearly state that the calculations did not include configuration entropy loss “Note that the energy calculations focus on non-bonded intermolecular interactions and solvation free energy calculations using MM/PBSA, where the configuration entropy loss during protein binding was not explicitly included. “.

      (2) I think that the analysis of what in the different dBETx makes them cause different degradation potency is underdeveloped. The dihedral angle analysis (Figure 4B) did not explain the observed behavior in my opinion. Please add additional, clearer analysis as to what structural differences in the dBETx make them sample very different conformations.

      We thank the reviewer for the suggestions! Based on the suggestion, we further performed dihedral entropy analysis for each dihedral angle in the linker part of the PROTAC to examine the flexibility of each PROTAC. Because each PROTAC has a different linker, we now clearly label them in a new Figure S18 in SI page 27. Low dihedral entropies indicate a more rigid structure and thus less flexibility to make a PROTAC more difficult to rearrange and facilitate the protein structural dynamic necessary for ubiquitination.

      We added detailed explanation on page 18: “Our dihedral entropy analysis showed that dBET57 has ~0.3 kcal/mol lower configuration entropies than the other dBETs with three different linkers, suggesting that dBET57 is less flexible than the other PROTACs (Figure S18).”

      (3) "The movement of the degradation machinery correlated with rotations of specific dihedrals of the linker region in dBETs (Figure 5).": this is not sufficiently clear from the figure. Definitely not in a quantitative way.

      We thank the reviewers for the suggestions! To further understand the correlation between PROTACs dihedral angles and the movement of degradation machinery, we performed time dependent correlation analysis to correlate the dihedral angles of the PROTACs and the Lys-Gly distance (Figures 6 and S17).

      We included detailed explanation on page 16:

      “To further examine the correlation between PROTAC rotation and the Lys-Gly interaction, we performed a time-dependent correlation analysis. This analysis showed that PROTAC rotation translates motion over time, leading to the Lys-Gly interaction, with a correlation peak around 60-85 ns, marking the time of the interaction (Figure 6 and Figure S17). In addition, the pseudo dihedral angles also showed a high correlation (0.85 in the case of dBET1) with Lys-Gly distance. This indicated that degradation complex undergoes structural rearrangement and drives the Lys-Gly interaction.

      (4) Cartoons are needed at multiple stages throughout the paper to enhance the clarity of what the modeled complexes looked like (e.g. which subunits they contained).

      We thank the reviewers for the suggestions. We added and remade several Figures with cartoons to better represent the stages. We also used higher resolution and included clearer labels for each protein system.

      (5) The difference between CRL4A E3 ligase and CRBN E3 ligase is not clear to the non-expert reader.

      Thanks for the reviewer’s comment! To clarify the terms "CRL4A E3 ligase" and "CRBN E3 ligase", which refer to different levels of description for the protein complexes, we added a couple of sentences in the Figure 1 legend. As a result, the non-expert readers can clearly know the differences.

      As illustrated in Figure 1,

      • CRL4A E3 ligase refers to the full E3 ligase complex, which includes all protein components such as CRBN, DDB1, CUL4A, and RBX1.

      • CRBN E3 ligase, on the other hand, is a more colloquial term typically used to describe just the CRBN protein, often in isolation from the full CRL4A complex.

      (6) Figure 1, legend: unclear why it's E3 in A and E2 in B.

      We thank the reviewer for the question! E3 ligase in Figure 1A refers to CRBN E3 ligase, where researchers also simply term it CRBN. We have added a sentence to specify that CRBN E3 ligase is also termed CRBN for simplicity. In Figure 1B, E2 was unclear in the sentences. The full name of E2 should be E2 ubiquitin-conjugating enzyme. Because the name is a bit long, researchers also call it E2 enzyme. We have corrected it and used E2 enzyme to make it clearer. 

      (7) "Although the protein-protein binding affinities were similar, other degraders such as dBET1 and dBET57 had a DC50/5h of about 500 nM". It's unclear what experimental data supports the assertion that the protein-protein binding affinities are similar.

      We thank reviewer for the question. Indeed, the statement is unclear.

      We corrected the sentence in page 6: “Although utilizing the exact same warheads, other degraders such as dBET1 and dBET57 had a DC<sub>50/5h</sub> of about 500 nM.”

      (8) Was the construction of the degradation machinery complex guided by experimental data (maybe cryo-EM or tomography)? If not, what is the accuracy of the starting complex for MD? This may impact the reliability of the obtained results.

      Thank you for your insightful comments! Yes, the construction of the degradation machinery complex was guided by available high-resolution crystal structures, which was selected to maintain consistency and align with the methodology established in a previously published JBC paper (https://doi.org/10.1016/j.jbc.2022.101653).

      We acknowledged that static crystal structures represent only a single snapshot of the system and may not capture the full conformational flexibility of the complex. To address this limitation, we performed MD simulations using multiple starting structures. This approach allowed us to explore a broader conformational landscape and reduced the dependence on any single starting configuration, thereby enhancing the reliability of the results.

      We hope this clarifies the robustness of our methodology and the steps taken to ensure accuracy in our simulations.

      (9) "With quantitative data, we revealed the mechanism underlying dBETx-induced degradation machinery": I think this may be too strong of an assertion. The authors may have developed a mechanistic hypothesis that can be tested experimentally in the future.

      We thank the reviewer for the suggestion. This is indeed a strong assertion and needs to be modified. We edited the sentence in page 7: “With quantitative data, we revealed the importance of the structural dynamics of dBETx-induced motions, which arrange positions of surface lysine residues of BRD4<sup>BD1</sup> and the entire degradation machinery.”

      (10) Figure S2: are the RMSDs calculated over all residues? Or just the BRD4 residues? Given that the structures are aligned with respect to CRBN, the reported RMSD numbers might be artificially low since there are many more CRBN residues than there are BRD4 residues. Also, why weren't the crystal structures used for dBET 23 and 70 for the modeling? Wouldn't you want to use the most accurate possible structures? Simulations were run for 23. Why not for 70?

      We thank the reviewer for the suggestion. We added a sentence to more clearly explain the RMSD calculations in Figure S2: “The structural superposition is performed based on the backbone of CRBN and RMSD calculation is conducted based on the backbone of BRD4<sup>BD1</sup>.”

      Although dBET70 has crystal structure, its PROTAC structure is not resolved, and thus we decided to still perform protein-protein docking with dBET70.  dBET1 and dBET57 do not have a crystal structure for the ternary complexes.

      We included the explanation at page 8: “Only dBET23 crystal structure is available with the PROTACs and both proteins, while the experimentally determined ternary complexes of dBET1, PROTACs of dBET57 and dBET70 are not available. “

      a. And there are no crystal structures available for 1 and 57? If so, please clearly say that. Otherwise please report the RMSD.

      We thank the reviewer for the suggestion. We included the explanation at page 8: “Only dBET23 crystal structure is available with the PROTACs and both proteins, while the experimentally determined ternary complexes of dBET1, PROTACs of dBET57 and dBET70 are not available.”

      (11) Table 2 is referenced before Table 1.

      We thank the reviewer for catching the error! We switched the order for Table 1 and Table 2.

      (12) Figure S3 is not referenced in the main paper.

      We thank the reviewer for catching the error! We now referred Figure S3 on page7.

      (13) Minor comments on grammar and sentence structure:

      a. It should be "binding of a ternary complex"

      b. "Our shows the importance": word missing.

      c. "...providing insights into potential orientations for ubiquitination. observe whether the preferred conformations are pre-organized for ubiquitination." Word or words missing.

      We thank reviewer for catching the errors! We corrected grammatical errors and unclear sentences throughout the entire paper and revised the sentences to make them easily understandable for non-expert readers.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife assessment

      This work describes a convincingly validated non-invasive tool for in vivo metabolic phenotyping of aggressive brain tumors in mice brains. The analysis provides a valuable technique that tackles the unmet need for patient stratification and hence for early assessment of therapeutic efficacy. However, wider clinical applicability of the findings can be attained by expanding the work to include more diverse tumor models.

      We thank the Editors for their comments. This concern was also raised by Reviewer 1 in the Public Review, where we address in more detail – please refer to comment PR-R1.C1. In brief, we agree that a more clinically relevant model should provide more translatable results to patients, and acknowledge this better in the revised manuscript: page 18 (lines 14-17), “While patient-derived xenografts and de novo models would be more suited to recapitulate human GBM heterogeneity and infiltration features, and genetic manipulation of glycolysis and mitochondrial oxidation pathways potentially relevant to ascertain DGE-DMI sensitivity for their quantification, (…)”. However, we also believe that the potential of DGE-DMI for application to different glioblastoma models or patients is demonstrated clearly enough with the two immunocompetent models we chose, extensively reported in the literature as reliable models of glioblastoma.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      This work introduces a new imaging tool for profiling tumor microenvironments through glucose conversion kinetics. Using GL261 and CT2A intracranial mouse models, the authors demonstrated that tumor lactate turnover mimicked the glioblastoma phenotype, and differences in peritumoral glutamate-glutamine recycling correlated with tumor invasion capacity, aligning with histopathological characterization. This paper presents a novel method to image and quantify glucose metabolites, reducing background noise and improving the predictability of multiple tumor features. It is, therefore, a valuable tool for studying glioblastoma in mouse models and enhances the understanding of the metabolic heterogeneity of glioblastoma.

      Strengths:

      By combining novel spectroscopic imaging modalities and recent advances in noise attenuation, Simões et al. improve upon their previously published Dynamic Glucose-Enhanced deuterium metabolic imaging (DGE-DMI) method to resolve spatiotemporal glucose flux rates in two commonly used syngeneic GBM mouse models, CT2A and GL261. This method can be standardized and further enhanced by using tensor PCA for spectral denoising, which improves kinetic modeling performance. It enables the glioblastoma mouse model to be assessed and quantified with higher accuracy using imaging methods.

      The study also demonstrated the potential of DGE-DMI by providing spectroscopic imaging of glucose metabolic fluxes in both the tumor and tumor border regions. By comparing these results with histopathological characterization, the authors showed that DGE-DMI could be a powerful tool for analyzing multiple aspects of mouse glioblastoma, such as cell density and proliferation, peritumoral infiltration, and distant migration.

      Weaknesses:

      (1) Although the paper provides clear evidence that DGE-DMI is a potentially powerful tool for the mouse glioblastoma model, it fails to use this new method to discover novel features of tumors. The data presented mainly confirm tumor features that have been previously reported. While this demonstrates that DGE-DMI is a reliable imaging tool in such circumstances, it also diminishes the novelty of the study.

      PR-R1.C1 – We thank the Reviewer for the detailed analysis and reply below to each point. PR-R1.C1.1 - novelty: We thank the Reviewer for the comments and understand their perspective. While we acknowledge that our paper is more methodologically oriented, we also believe that significant methodological advances are critical for new discoveries. This was our main motivation and is demonstrated in the present work, showing the ability to map in vivo metabolic fluxes in mouse glioma, a “hot topic” and very desirable in the cancer field. 

      PR-R1.C1.2 – additional tumor features: To strengthen the biological relevance of this methodologic novelty, we have now included immune cell infiltration among the tumor features assessed, besides perfusion, histopathology, cellularity and cell proliferation. For this, we performed iba-1 immunostaining for microglia/ macrophages, now included in Fig. 2-B. These new results demonstrate significantly higher microglia/macrophage infiltration in CT2A tumors compared to GL261, particularly at the tumor border. This is very consistent with the respective tumor phenotypes, namely differences in cell density and cellularity between the 2 cohorts and across pooled cohorts, as we now report: page 9 (lines 10-18), “Such phenotype differences were reflected in the regional infiltration of microglia/macrophages: significantly higher at the CT2A peritumoral rim (PT-Rim) compared to GL261, and slightly higher in the tumor region as well (Fig 2B). Further quantitative regional analysis of Tumor-to-PT-Rim ROI ratios revealed: (i) 47% lower cell density (p=0.004) and 32% higher cell proliferation (p=0.026) in GL261 compared to CT2A (Fig 2C, Table S3); and (ii) strong negative correlations in pooled cohorts between microglia/macrophage infiltration and cellularity (R=-0.91, p=<0.001) or cell density (R=-0.77, p=0.016), suggesting more circumscribed tumor growth with higher peripheral/peritumoral infiltration of immune cells.”; and page 16 (lines 13-19), “GL261 tumors were examined earlier after induction than CT2A (17±0 vs. 30±5 days, p = 0.032), displaying similar volumes (57±6 vs. 60±14, p = 0.813) but increased vascular permeability (8.5±1.1 vs 4.3±0.5 10<sup>3</sup>/min: +98%, p=0.001),  more disrupted stromal-vascular phenotypes and infiltrative growth (5/5 vs 0/5), consistent with significantly lower tumor cell density (4.9±0.2 vs. 8.2±0.3 10<sup>-3</sup> cells/µm<sup>2</sup>: -40%, p<0.001) and lower peritumoral rim infiltration of microglia/macrophages (2.1±0.7 vs. 10.0±2.3 %: -77%, p=0.008)”.

      PR-R1.C1.3 – new tumor features and DGE-DMI: Importantly, such regional differences in cellularity/cell density and immune cell infiltration between the two cohorts were remarkably mirrored by the lactate turnover maps (Fig 3-C), as we now report in the manuscript: page 12 (lines 6-15), “GL261 tumors accumulated significantly less lactate in the core (1.60±0.25 vs 2.91±0.33 mM: -45%, p=0.013) and peritumor margin regions (0.94±0.09 vs 1.46±0.17 mM: 36%, p=0.025) than CT2A – Fig 3 A-B, Table S1. Consistently, tumor lactate accumulation correlated with tumor cellularity in pooled cohorts (R=0.74, p=0.014). Then, lower tumor lactate levels were associated with higher lactate elimination rate, k<sub>lac</sub> (0.11±0.1 vs 0.06±0.01 mM/min: +94%, p=0.006) – Fig 3B – which in turn correlated inversely with peritumoral rim infiltration of microglia/macrophages in pooled cohorts (R=-0.73, p=0.027) – Fig 3-C. Further analysis of Tumor/P-Margin metabolic ratios (Table S3) revealed: (i) +38% glucose (p=0.002) and -17% lactate (p=0.038) concentrations, and +55% higher lactate consumption rate (p=0.040) in the GL261 cohort; and (ii) lactate ratios across those regions reflected the respective cell density ratios in pooled cohorts (R=0.77, p=0.010) – Fig 3-C”. This is a novel, relevant feature compared to our previous work, as highlighted in our discussion: page 17 (lines 1-8), “Tumor vs peritumor border analyses further suggest that lactate metabolism reflects regional histologic differences:

      lactate accumulation mirrors cell density gradients between and across the two cohorts; whereas lactate consumption/elimination rate coarsely reflects cohort differences in cell proliferation, and inversely correlates with peritumoral infiltration by microglia/macrophages across both cohorts. This is consistent with GL261’s lower cell density and cohesiveness, more disrupted stromal-vascular phenotypes, and infiltrative growth pattern at the peritumor margin area, where less immune cell infiltration is detected and relatively lower cell division is expected [43]”.

      We trust that these new features recovered from DGE-DMI (Fig 2-B and Fig 3-C) show its potential for new discoveries in glioblastoma.

      (2) When using DGE-DMI to quantitatively map glycolysis and mitochondrial oxidation fluxes, there is no comparison with other methods to directly identify the changes. This makes it difficult to assess how sensitive DGE-DMI is in detecting differences in glycolysis and mitochondrial oxidation fluxes, which undermines the claim of its potential for in vivo GBM phenotyping.

      PR-R1.C2: We thank the reviewer for raising this important point. The validity of the method for mapping specific metabolic kinetics in mouse glioma was reported in our previous work, using the same animal models, as specified in the introduction (page 4, lines 10-13): “we recently (…) propose[d] Dynamic Glucose-Enhanced (DGE) 2H-MRS [31], demonstrating its ability to quantify glucose fluxes through glycolysis and mitochondrial oxidation pathways in vivo in mouse GBM (…)”. Therefore, this was not reproduced in the present work. 

      In brief, our DGE-DMI results are very consistent with our previous study, where DGE single voxel deuterium spectroscopy was performed in the same tumor models with higher temporal resolution and SNR (as state on page 16, lines 9-10: glycolytic lactate synthesis rate, 0.59±0.04 vs. 0.55±0.07 mM/min; glucose-derived glutamate-glutamine synthesis rate, 0.28±0.06 vs. 0.40±0.08 mM/min), which in turn matched well the values reported by others for glucose consumption rate through: 

      (i) glycolysis, in different tumor models including mouse lymphoma in vivo (0.99 mM/min, by DGE-DMI (Kreis et al. 2020), rat breast carcinoma in situ (1.43 mM/min, using a biochemical assay (Kallinowski et al. 1988), and even perfused GBM cells (1.35 fmol min<sup>−1</sup> cell<sup>−1</sup>, according to Hyperpolarized 13C-MRS (Jeong et al. 2017), very similar to our previous in vivo measurements in GL261 tumors: 0.50 ± 0.07 mM min<sup>−1</sup> = 1.25 ± 0.16 fmol min<sup>−1</sup> cell<sup>−1</sup> (Simoes et al. 2022)); 

      (ii) mitochondrial oxidation, very similar to previous in vivo measurements in mouse GBM xenografts (0.33 mM min<sup>−1</sup>, using 13C spectroscopy (Lai et al. 2018)), and particularly to our in situ measurements in cell culture for (GL261, 0.69 ± 0.09 fmol min<sup>−1</sup> cell<sup>−1</sup>; and CT2A 0.44 ± 0.08 fmol min<sup>−1</sup> cell<sup>−1</sup>), remarkably similar to the in vivo measurements in the respective tumors in vivo (Gl261, 0.32 ± 0.10 mM min<sup>−1</sup> = 0.77 ± 0.23 fmol min<sup>−1</sup> cell<sup>−1</sup>; and CT2A, 0.51 ± 0.11 mM min<sup>−1</sup> = 0.60 ± 0.12 fmol min<sup>−1</sup> cell<sup>−1</sup>) (Simoes et al. 2022)). 

      (3) The study only used intracranial injections of two mouse glioblastoma cell lines, which limits the application of DGE-DMI in detecting and characterizing de novo glioblastomas. A de novo mouse model can show tumor growth progression and is more heterogeneous than a cell line injection model. Demonstrating that DGE-DMI performs well in a more clinically relevant model would better support its claimed potential usage in patients.

      PR-R1.C3: We agree that a more clinically relevant model, such as the one suggested by the Reviewer, would in principle be better suited to provide more translatable results to patients. We however believe that the potential of DGE-DMI for application to different glioblastoma models or patients, with GBM or any other types of brain tumors for that matter, is demonstrated clearly enough with the two syngeneic models we chose, given their robustness and general acceptance in the literature as reliable immunocompetent models of GBM, and for their different histologic and metabolic properties. This way we could fully focus on the novel metabolic imaging method, as compared to our previous single-voxel approach. While both tumor cohorts (GL261 and CT2A) were studied at more advanced stages of tumor progression, the metabolic differences depicted are consistent with the histopathologic features reported, as discussed in the manuscript; namely, the lower glucose oxidation rates. We have now modified the manuscript to highlight this point: page 18 (lines 12-14), “While patient-derived xenografts and de novo models would be more suited to recapitulate human GBM heterogeneity and infiltration features, and genetic manipulation of glycolysis and mitochondrial oxidation pathways could be relevant to ascertain DGE-DMI sensitivity for their quantification, (…)”.

      Reviewer #2 (Public Review):

      Summary:

      In this work, the authors attempt to noninvasively image metabolic aspects of the tumor microenvironment in vivo, in 2 mouse models of glioblastoma. The tumor lesion and its surrounding appearance are extensively characterized using histology to validate/support any observations made with the metabolic imaging approach. The metabolic imaging method builds on a previously used approach by the authors and others to measure the kinetics of deuterated glucose metabolism using dynamic 2H magnetic resonance spectroscopic imaging (MRSI), supported by de-noising methods.

      Strengths:

      Extensive histological evaluation and characterization.

      Measurement of the time course of isotope labeling to estimate absolute flux rates of glucose metabolism.

      Weaknesses:

      (1) The de-noising method appears essential to achieve the high spatial resolution of the in vivo imaging to be compatible with the dimensions of the tumor microenvironment, here defined as the immediately adjacent rim of the mouse brain tumors. There are a few challenges with this approach. Often denoising methods applied to MR spectroscopy data have merely a cosmetic effect but the actual quantification of the peaks in the spectra is not more accurate than when applied directly to original non-denoised data. It is not clear if this concern is applicable to the denoising technique applied here. However, even if this is not an issue, no denoising method can truly increase the original spatial resolution at which data were acquired. A quick calculation estimates that the spatial resolution of the 2H MRSI used here is 30-40 times too low to capture the much smaller tumor rim volume, and therefore there is concern that normal brain tissue and tumor tissue will be the dominant metabolic signal in so-called tumor rim voxels. This means that the conclusions on metabolic features of the (much larger) tumor are much more robust than the observations attributed to the (much smaller) tumor microenvironment/tumor rim.

      PR-R2.C1: We thank the Reviewer for the constructive comments regarding resolution and tumor rim, and denoising. These issues were raised more extensively in the section Recommendations For The Authors, where they are addressed in detailed (RA-R2.C2). In summary, we agree with the Reviewer that no denoising method can increase the nominal resolution; not was that our purpose. Thus, we clarify the relevance of spectral matrix interpolation in MRSI, and how our display resolution should in principle provide a better approximation to the ground truth than the nominal resolution, relevant for ROI analysis in the tumor margin. While we further show relevant correlations between metabolic maps and histologic features in tumor core and margin, we agree with the reviewer that our observations in the tumor core are more robust than those in the margin, and acknowledge this in the Discussion: page 19, lines 6-10: “Therefore, further DGE-DMI preclinical studies aimed at detecting and quantifying relatively weak signals, such as tumor glutamate-glutamine, and/or increase the nominal spatial resolution to better correlate those metabolic results with histology findings (e.g. in the tumor margin), should improve basal SNR with higher magnetic field strengths, more sensitive RF coils, and advanced DMI pulse sequences [55]).”

      (2) To achieve their goal of high-level metabolic characterization the authors set out to measure the deuterium labeling kinetics following an intravenous bolus of deuterated glucose, instead of the easier measurement of steady-state after the labeling has leveled off. These dynamic data are then used as input for a mathematical model of glucose metabolism to derive fluxes in absolute units. While this is conceptually a well-accepted approach there are concerns about the validity of the included assumptions in the metabolic model, and some of the model's equations and/or defining of fluxes, that seem different than those used by others.

      PR-R2.C2: These concerns about the metabolic model, were also raised in more detail in the section Recommendations For The Authors, where they are addressed more extensively – please refer to RA-R2.C3 (glucose infusion protocol) and RA-R2.C4 (equations). In brief, we explain that the total volume injected (100uL/25g animal) is standard for i.v. administration in mice, and clarify this better in the manuscript (page 24, line 23); as well as the differences between our kinetic model and the original one reported by Kreis et al. (Radiology 2020), who quantified glycolysis kinetics on a subcutaneous mouse model of lymphoma, exclusively glycolytic and thus estimating the maximum glucose flux rate was from the lactate synthesis rate (Vmax = Vlac). Instead, we extended this model to account for glucose flux rates for lactate synthesis (Vlac) and also for glutamate-glutamine synthesis (Vglx) in mouse glioblastoma, where Vmax = Vlac + Vglx, also acknowledging its simplistic approach in the Discussion (page 20, lines 22-24: “(…) metabolic fluxes [estimations] through glycolysis and mitochondrial oxidation (…) could potentially benefit from an improved kinetic model simultaneously assessing cerebral glucose and oxygen metabolism, as recently demonstrated in the rat brain with a combination of 2H and 17O MR spectroscopy [62] (…)”).

      Reviewer #3 (Public Review):

      Summary:

      Simoes et al enhanced dynamic glucose-enhanced (DGE) deuterium spectroscopy with Deuterium Metabolic Imaging (DMI) to characterize the kinetics of glucose conversion in two murine models of glioblastoma (GBM). The authors combined spectroscopic imaging and noise attenuation with histological analysis and showcased the efficacy of metabolic markers determined from DGE DMI to correlate with histological features of the tumors. This approach is also potent to differentiate the two models from GL261 and CT2A.

      Strengths:

      The primary strength of this study is to highlight the significance of DGE DMI in interrogating the metabolic flux from glucose. The authors focused on glutamine/glutamate and lactate. They attempted to correlate the imaging findings with in-depth histological analysis to depict the link between metabolic features and pathological characteristics such as cell density, infiltration, and distant migration.

      Weaknesses:

      (1) A lack of genetic interrogation is a major weakness of this study. It was unclear what underlying genetic/epigenetic aberrations in GL261 and CT2A account for the metabolic difference observed with DGE DMI. A correlative metabolic confirmation using mass spectrometry of the two tumor specimens would give insight into the observed imaging findings.

      PR-R3.C1: We thank the Reviewer for the helpful comments, which we break down below.

      PR-R3.C1.1 - genetic interrogation/manipulation: While we did not have access to conditional models for key enzymes of each metabolic pathway, for their genetic manipulation, we did however assess the mitochondrial function in each cell line, showing a significantly higher respiration buffer capacity and more efficient metabolic plasticity between glycolysis and mitochondrial oxidation in GL261 cells compared to CT2A (Simoes et al. NIMG:Clin 2022). This could drive e.g. more active recycling of lactate through mitochondrial metabolism in GL261 cells, aligned with our observations of increased glucose-derived lactate consumption rate in those tumors compared to CT2A. We have now included this in the discussion (page 17, lines 812): “our results suggest increased lactate consumption rate (active recycling) in GL261 tumors with higher vascular permeability, e.g. as a metabolic substrate for oxidative metabolism [44] promoting GBM cell survival and invasion [45], aligned with the higher respiration buffer capacity and more efficient metabolic plasticity of GL261 cells than CT2A [31].”

      PR-R3.C1.2 - correlation with post-mortem metabolic assessment: implementing this validation step would require an additional equipment, also not accessible to us: focalized irradiator, to instantly halt all metabolic reactions during animal sacrifice. We do believe that DGE-DMI could guide further studies of such nature, aimed at validating the spatio-temporal dynamics of regional metabolite concentrations in mouse brain tumors. Thus, the importance of end-point validation is now stressed more clearly in the manuscript (page 20, lines 13-16): “(…) mapping pathway fluxes alongside de novo concentrations (…) may be determinant for the longitudinal assessment of GBM progression, with end-point validation (…)”.

      These concerns and recommendations were also raised by the Reviewer in the Recommendations to Authors section, where we address them more extensively – please see RA-R1.C3 and RA-R1.C2, respectively.

      (2) A better depiction of the imaging features and tumor heterogeneity would support the authors' multimodal attempt.

      PR-R3.C2: We agree with the Reviewer that including more imaging features would improve the non-invasive characterization of each tumor. Due to the RF coil design and time constraints, we did not acquire additional data, such as diffusion MRI to assess tissue microstructure. Instead, our multi-modal protocol included two dynamic MRI studies on each animal, for multiparametric assessment of tumor volume, metabolism and vascular permeability, using 1H-MRI, 2H-spectroscopy during 2H-labelled glucose injection, and 1H-imaging during Gd-DOTA injection, respectively. Rather than aiming at tumor radiomics, we focused on the dynamic assessment of tumor metabolic turnover with heteronuclear spectroscopy, which is challenging per se and particularly in mouse brain tumors, given their very small size. For such multi-modal studies we used a previously developed dual tuned RF coil: the deuterium coil (2H) positioned in the mouse head, for optimal SNR; whereas the proton coil (1H) had suboptimal performance compared a conventional single tuned coil, and was used only for basic localization and adjustments, reference imaging and tumor volumetry (T2-weighted), and DCE-T1 MRI (T1weighted). The latter was analyzed pixel-wise to assess spatial correlations between tumor permeability and metabolic metrics, as shown in Fig S3. Whereas the limited T2w MRI data collected was only analyzed for tumor volume assessment; no additional imaging features were extracted (e.g. kurtosis/skewness), since such assessment did not shown any differences between the two tumor cohorts in our previous study (Simoes et al NIMG:Clin 2022).

      (3) Integration of the various cell types in the tumor microenvironment, as allowed with the resolution of DGE DMI, will explain the observed difference between GL261 and CT2A. Is there a higher percentage of infiltrative "other cells" observed in GL261 tumor?

      PR-R3.C3: While DGE-DMI resolution is far larger than brain and brain tumor cell sizes, we now performed additional analysis to assess the percentage of microglia/macrophages in both cohorts. The results are now included in the manuscript, namely Fig. 2B, as previously explained in PR-R1.1. Interestingly though, we observed a lower percentage of infiltrative "other cells" in GL261 tumors compared to CT2A, which we discuss in the manuscript: pages 19-20 (lines 20-24 and 1-4), “Finally, our results are indicative of higher microglia/macrophage infiltration in CT2A than GL261 tumors, which is inconsistent with another study reporting higher immunogenicity of GL261 tumors than CT2A for microglia and macrophage populations [56]. Such discrepancy could be related to methodologic differences between the two studies, namely the endpointguided assessment of tumor growth (bioluminescence vs MRI, more precise volumetric estimations) and the stage when tumors were studied (GL261 at 23-28 vs 16-18 days postinjection, i.e. less time for immune cell to infiltration in our case), presence/absence of a cell transformation step (GFP-Fluc engineered vs we used original cell lines), or perhaps media conditioning effects during cell culture due to the different formulations used (DMEM vs RPMI).”

      (4) This underlying technology with DGE DMI is capable of identifying more heterogeneous GBM tumors. A validation cohort of additional in vivo models will offer additional support to the potential clinical impact of this study.

      PR-R3.C4: We agree with the Reviewer that applying DGE-DMI to more clinically-relevant models of human brain tumors will enhance its translational impact to patients, as also suggested by Reviewer 1 and addressed in PR-R1.C3. We also believe that the feasibility and potential of DGE-DMI for application to different glioblastoma models or patients, with GBM or any other primary or secondary brain tumors, is clearly demonstrated in our work, using two reliable and well-described immunocompetent models of GBM. In any case, we have now modified the manuscript to better acknowledge this point: page 18 (lines 14-16), “(…) patient-derived xenografts and de novo models would be more suited to recapitulate human GBM heterogeneity and infiltration features (…)”.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) The authors utilize longitudinal MRI to track tumor volumes but perform DMI at endpoint with late-stage tumors. Their previous publication applied metabolic imaging in tumors before the presence of necrosis. It would be valuable to perform longitudinal DMI to examine the evolution of glucose flux metabolic profile over time in the same tumor.

      RA-R1.C1: We thank the Reviewer for the very useful comments to our manuscript. We agree – in this work, we aimed at “extending” our previous DGE-2H single-voxel methodology to multivoxel (DMI), thoroughly demonstrating (1) its in vivo application to the same immunocompetent models of glioblastoma and (2) the ability to depict their phenotypic differences, and therefore (3) the potential for the metabolic characterization of more advanced models of GBM and/or their progression stages. We believe these objectives were achieved. Our results indeed open several possibilities, from longitudinal assessment of the spatio-temporal metabolic changes during GBM progression (and treatment-response) to its application to other models recapitulating more closely the human disease. Now that we have comprehensively demonstrated a protocol for DGE-DMI acquisition, processing and analysis in mouse GBM (a very challenging methodology), and demonstrate it in different mouse GBM cell lines, new studies can be designed to tackle more specific questions, like the one suggested here by the Reviewer. We have modified the manuscript to make this point clearer: page 20 (lines 15-17), “This may be determinant for the longitudinal assessment of GBM progression, with end-point validation; and/or treatment-response, to help selecting among new therapeutic modalities targeting GBM metabolism (…)”; page 21 (lines 5-8), “(…) we report a DGE-DMI method for quantitative mapping of glycolysis and mitochondrial oxidation fluxes in mouse GBM, highlighting its importance for metabolic characterization and potential for in vivo GBM phenotyping in different models and progression stages.”.

      (2) The authors demonstrate a promising correlation between metabolic phenotypes in vivo and key histopathological features of GBM at the endpoint. Directly assessing metabolites involved in glucose fluxes on endpoint tumor samples would strengthen this correlation.

      RA-R1.C2: While we acknowledge the Reviewer’s point, there were two main limitations to implementing such validation step in our protocol: 

      (1) Since we performed dynamic experiments, at the end of each study most 2H-glucose-derived metabolites were already below their maximum concentration (or barely detectable in some cases), as depicted by the respective kinetic curves (Fig 1-D and Fig S7), and thus no longer detectable in the tissues. Importantly, DGE-DMI could guide further studies towards selecting the ideally time-point for validating different metabolite concentrations in specific brain regions.

      (2) Such validation would require sacrificing the animals with a focalized irradiator (which we did not have), to instantly halt all metabolic reactions. Only then we could collect and analyze the metabolic profile of specific brain regions, either by in vitro MS or high-resolution NMR following extraction, or by ex vivo HRMAS analysis of the intact tissue, as reported previously by some of the authors for validation of glucose accumulation in different regions of mouse GL261 tumors (Simões et al. NMRB 2010: https://doi.org/10.1002/nbm.1421). Importantly, even if we did have access to a focalized irradiator, such protocols for metabolic characterization would compromise tissue integrity and thus the histopathologic analysis performed in this study. 

      We do agree with the importance of end-point validation and therefore stress it more clearly in the revised manuscript (page 20, lines 14-16): “(…) mapping pathway fluxes alongside de novo concentrations (…) may be determinant for the longitudinal assessment of GBM progression, with end-point validation (…)”.

      (3) Genetic manipulation of key players in the metabolic pathways studied in this paper (glycolysis and mitochondrial oxidation) would offer a strong validation for the sensitivity of DGE-DMI in accurately distinguishing metabolites (lactate, glutamate-glutamine) and their dynamics.

      RA-R1.C3: Thank you for this comment, we agree. This would be particularly relevant in the context of treatment-response monitoring. While such models were not available to us (conditional spatio-temporal manipulation of metabolic pathway fluxes), we believe our results can still demonstrate this point: We previously used in vivo DGE 2H-MRS to show evidence of decreased glucose oxidation fraction (Vglx/Vlac) in GL261 tumors under acute hypoxia (FiO2=12 %) compared to regular anesthesia conditions (FiO2=31 %), consistent with the inhibition of OXPHOS due to lower oxygens tensions (Simoes et al. NIMG:Clin 2022). In the present work, enhanced glycolysis in tumors vs peritumoral brain regions was clearly observed in all the animals studied, from both cohorts, as shown in Fig 1-B and Fig S4. Moreover, the spectral background (before glucose injection) is limited to a single peak in all the voxels: basal DHO, used as internal reference for spatio-temporal quantification of glucose, glutamine-glutamate, and lactate, all de novo and extensively characterized in healthy and glioma-bearing rodent brain (Lu et al. JCBFM 2018; Zhang et al. NMR Biomed 2024, de Feyter et al. SciAdv 2018; Batsios et al ClinCancerRes 2022;  Simoes et al. NIMG:Clin 2022) and other rodent tumors (Kreis et al. Radiology 2020, Montrazi et al. SciRep 2023). We have modified the manuscript to clarify this point (page 18, lines 14-17) “(…) patient-derived xenografts and de novo models would be more suited to recapitulate human GBM heterogeneity and infiltration features, and genetic manipulation of glycolysis and mitochondrial oxidation pathways could be relevant to ascertain DGE-DMI sensitivity for their quantification (…)”.

      (4) Please explain more why DEG-DMI can distinguish different glucose metabolites and how accurate it is.

      RA-R1.C4: DGE-DMI is the imaging extension of our previous work based on single-voxel deuterium spectroscopy, therefore relying on the same fundamental technique and analysis pipeline but moving from a temporal analysis to a spatio-temporal analysis for each metabolite, and thus dealing with more data. Unlike conventional proton spectroscopy (1H), only metabolites carrying the deuterium label (2H) will be detected in this case, including the natural abundance DHO (~0.03%), the deuterated glucose injected and its metabolic derivatives, namely deuterated lactate and deuterated glutamate-glutamine. Due to their different molecular structures, the deuterium atoms will resonate at specific frequencies (chemical shifts, ppm) during a 2H magnetic resonance spectroscopy experiment, as illustrated in Fig 1-A. The method is fully reproducible and accurate, and has been extensively reported in the literature from high-resolution NMR spectroscopy to in vivo spectroscopic imaging of different nuclei, such as proton (1H), deuterium (2H), carbon (13C), phosphorous (31P), and fluorine (19F). Since the fundamental principles of DMI and its application to brain tumors have been very well described in the flagship article by de Feyter et al., we have now highlighted this in the manuscript: page 4 (lines 4-7), “Deuterium metabolic imaging (DMI) has been (…) demonstrated in GBM patients, with an extensive rationale of the technique and its clinical translation [18], and more recently in mouse models of patient-derived GBM subtypes (…)”.

      (5) When mapping glycolysis and mitochondrial oxidation fluxes, add a control method to compare the reliability of DEG-DMI.

      RA-R1.C5: This concern (“lack of a control method”) was also raised by the Reviewer in the section Public Reviews section, where we already address it (PR-R1.2).

      (6) If using peritumoral glutamate-glutamine recycling as a marker of invasion capacity, what would be the correct rate of the presence of secondary brain lesions?

      RA-R1.C6: While our results suggest the potential of peritumoral glutamate-glutamine recycling as a marker for the presence of secondary brain lesions, this remains to be ascertained with higher sensitivity for glutamate-glutamine detection. Therefore, we cannot make further conclusions in this regard.  

      To make this point clear, we state in different sections of the discussion: page 19 (lines 1-2), “(…) recycling of the glutamate-glutamine pool may reflect a phenotype associated with secondary brain lesions.”; and page 19 (lines 6-10), “Therefore, further DGE-DMI preclinical studies aimed at detecting and quantifying relatively weak signals, such as tumor glutamateglutamine, and/or increase spatial resolution to correlate those metabolic results with histology findings (e.g in the tumor margin), should improve basal SNR with higher magnetic field strengths, more sensitive RF coils, and advanced DMI pulse sequences [55]).”).  

      (7) There are duplicated Vlac in Figure S3 B.

      RA-R1.C7: This was a typo that has now been corrected. Thank you.

      (8) Figure 4, it would be better to add a metabolic map of a tumor without secondary brain lesions to compare.

      RA-R1.C8: We fully agree and have modified Fig 4 accordingly, together with its legend.

      Particularly, we have included tumors C4 (without secondary lesions) vs G4 (with) for this “comparison”, since details of their histology, including the secondary lesions, are provided in Fig 2.

      (9) Full name of SNR and FID should be listed when first mentioned.

      RA-R1.C9: Agreed and modified accordingly, on pages 6-7 (lines 22-1), ”signal-to-noise-ratio (SNR)”, and page 19 (lines 5-6), “free induction decay (FID)”.

      (10) Page 2, Line 14: (59{plus minus}7 mm3) is not needed in the abstract.

      RA-R1.C10: As requested we have removed this specification from the Abstract.

      (11) Page 4, Line 22: Closing out the Introduction section with a statement on broader implications of the present work would enhance the effectiveness of the section.

      RA-R1.C11: We have added an additional sentence in this regard – pages 4-5 (lines 24-2): “Since DMI is already performed in humans, including glioblastoma patients [18], DGE-DMI could be relevant to improve the metabolic mapping of the disease.”

      (12) Define all acronyms to facilitate comprehension. For example, principal component analysis (PCR) and signal-to-noise ratio (SNR).

      R1.C12: Thank you for the comment. We have now defined all the acronyms when first used, including PCA (page 4 (line 11), “Marcheku-Pastur Principal Component Analysis (MP-PCA)”) and SNR (pages 6-7 (lines 22-1), as indicated above in comment R1.9).

      (13) Some elements within the figures have lower resolution, specifically bar graphs.

      RA-R1.C13: We apologize for this oversight. All the Figures have been revised accordingly, to correct this problem. Thank you.

      (14) Page 13, Line 8: "underly" should be spelled "underlie."

      RA-R1.C14: The typo has been corrected on page 15 (line 8), thank you.

      (15) Page 14, Line 13: "better vascular permeability" would be more effectively phrased as "increased vascular permeability."

      RA-R1.C15: This has also been corrected on page 16 (line 14), thank you.

      Reviewer #2 (Recommendations For The Authors):

      (1) I strongly suggest adding a scale bar in the histology figures.

      RA-R2.C1: Thank you for spotting our oversight! This has now been added as requested to Fig 2.

      (2) The 2H MRSI data were acquired at a nominal resolution of 2.25 x 2.27 x 2.25 mm^3, resulting in a nominal voxel volume of 11.5 uL. (In reality, this is larger due to the point spread function leading to signal bleeding from adjacent voxels.) If we estimate the volume of the tumor rim, as indicated by the histology slides, as (generously) ~ 50 um in width, 3.2 mm long (the diagonal of a 2.25 x 2.25 mm^2 square, and 2.27 mm high, we get a volume of 0.36 uL. Therefore the native spatial resolution of the 2H MRSI is at least 30 times larger than the volume occupied by the tumor rim/microenvironment. Normal tissue and tumor tissue will contribute the majority of the metabolic signal of that voxel. I feel an opposite approach could have been pursued: find out the spatial resolution needed to characterize the tumor rim based on the histology, then use a de-noising method to bring the SNR of those data to be acceptable. (this is just a thought experiment that assumes de-noising actually works to improve quantification for MRS data instead of merely cosmetically improve the data, so far the jury is still out on that, in my view).

      RA-R2.C2 – We thank the Reviewer for the detailed analysis and reply below to each point.

      RA-R2.C2.1 – spatial resolution and tumor rim: Our nominal voxel volume was indeed 11.5 uL, defined in-plane by the PSF which explains signal bleeding effects, as in any other imaging modality. The DMI raw data were Fourier interpolated before reconstruction, rendering a final in-plane resolution of 0.56 mm (0.72 uL voxel volume). The tumor rim (margin) analyzed was roughly 0.1 mm width (please note, not 0.05 mm), as explained in the methods section (page 28, line 16) and now more clearly defined with the scale bars in Fig 2. According to the Reviewer’s analysis, this would correspond to 0.1*3.2*2.27 = 0.73 uL, which we approximated with 1 voxel (0.72 uL), as displayed in Fig 3-A. Importantly, it has long been demonstrated that Fourier interpolation provides a better approximation to the ground truth compared to the nominal resolution, and even to more standard image interpolation performed after FT - see for instance Vikhoff-Baaz B et al. (MRI 2001. 19: 1227-1234), now citied in the Methods section: page 24, line 24 ([69]). While we do agree that both normal brain and tumor should contribute significantly to the metabolic signal in this relatively small region, we rely on extensive literature to maintain that despite its smoothing effect, the display resolution provides a better approximation to the ground truth and is therefore more suited than the nominal resolution for ROI analysis in this region. Still, we acknowledge this potential limitation in the Discussion: page 19, lines 6-10: “Therefore, further DGE-DMI preclinical studies aimed at detecting and quantifying relatively weak signals, such as tumor glutamate-glutamine, and/or increase the nominal spatial resolution to better correlate those metabolic results with histology findings (e.g. in the tumor margin), should improve basal SNR with higher magnetic field strengths, more sensitive RF coils, and advanced DMI pulse sequences [55]).”

      RA-R2.C2.2 – metabolic and histologic features at the tumor rim: Furthermore, we also performed ROI analysis of lactate metabolic maps in tumor and peritumoral rim areas closely reflected regional differences in cellularity and cell density, and immune cell infiltration between the 2 tumor cohorts and across pooled cohorts, as explained in the Public Review section - PR-R1.1 – and now report in the manuscript: page 12 (lines 6-16), “GL261 tumors accumulated significantly less lactate in the core (1.60±0.25 vs 2.91±0.33 mM: -45%, p=0.013) and peritumor margin regions (0.94±0.09 vs 1.46±0.17 mM: -36%, p=0.025) than CT2A – Fig 3 A-B, Table S1. Consistently, tumor lactate accumulation correlated with tumor cellularity in pooled cohorts (R=0.74, p=0.014). Then, lower tumor lactate levels were associated with higher lactate elimination rate, k<sub>lac</sub> (0.11±0.1 vs 0.06±0.01 mM/min: +94%, p=0.006) – Fig 3B – which in turn correlated inversely with peritumoral margin infiltration of microglia/macrophages in pooled cohorts (R=-0.73, p=0.027) - Fig 3-C. Further analysis of Tumor/P-Margin metabolic ratios (Table S3) revealed: (i) +38% glucose (p=0.002) and -17% lactate (p=0.038) concentrations, and +55% higher lactate consumption rate (p=0.040) in the GL261 cohort; and (ii) lactate ratios across those regions reflected the respective cell density ratios in pooled cohorts (R=0.77, p=0.010) – Fig 3-C”; page 17 (lines 1-8), “Tumor vs peritumor border analyses further suggest that lactate metabolism reflects regional histologic differences: lactate accumulation mirrors cell density gradients between and across the two cohorts; whereas lactate consumption/elimination rate coarsely reflects cohort differences in cell proliferation, and inversely correlates with peritumoral infiltration by microglia/macrophages across both cohorts. This is consistent with GL261’s lower cell density and cohesiveness, more disrupted stromal-vascular phenotypes, and infiltrative growth pattern at the peritumor margin area, where less immune cell infiltration is detected and relatively lower cell division is expected [43]”.

      RA-R2.C2.3 – alternative method: Regarding the alternative method suggested by the Reviewer, we have tested a similar approach in another region (tumor) and it did not work, as explained the Discussion section (page 19, lines 5-6) and Fig S11. Essentially, Tensor PCA performance improves with the number of voxels and therefore limiting it to a subregion hinders the results. In any case, if we understand correctly, the Reviewer suggests a method to further interpolate our data in the spatial dimension, which would deviate even more from the original nominal resolution and thus sounds counter-intuitive based on the Reviewer’s initial comment about the latter. More importantly, we would like to remark the importance of spectral denoising in this work, questioned by the Reviewer. There are several methods reported in the literature, most of them demonstrated only for MRI. We previously demonstrated how MPPCA denoising objectively improved the quantification of DCE-2H MRS in mouse glioma by significantly reducing the CRLBs: 19% improved fitting precision. In the present study, Tensor PCA denoising was applied to DGE-DMI, which led to an objective 63% increase in pixel detection based on the quality criteria defined, unambiguously reflecting the improved quantification performance due to higher spectral quality. 

      (3) Concerns re. the metabolic model: 2g/kg of glucose infused over 120 minutes already leads to hyperglycemia in plasma. Here this same amount is infused over 30 seconds... such a supraphysiological dose could lead to changes in metabolite pool sizes -which are assumed to not change since they are not measured, and also fractional enrichment which is not measured at all. Such assumptions seem incompatible with the used infusion protocol.

      RA-R2.C3:  We understand the concern. However, the protocol was reproduced exactly as originally reported by Kreis et al (Radiology 2020) that performed the measurements in mice and measured the fraction of deuterium enrichment (f=0.6). Since we also worked with mice, we adopted the same value for our model. The total volume injected was 100uL/25g animal, and adjusted for animal weight (96uL/24g average – Table S1), as we reported before (Simões et al. NIMG:Clin 2022), which is standard for i.v. bolus administration in mice as it corresponds to ~10% of the total blood volume. This volume is therefore easily diluted and not expected to introduce significant changes in the metabolic pool sizes. Continuous infusion protocols on the other hand will administer higher volumes, easily approaching the mL range when performed over periods as large as 120 min. This would indeed be incompatible with our bolus infusion protocol. We have now clarified this in the manuscript – page 24 (line 23): “i.v. bolus of 6,6<sup>′2</sup>H<sub>2</sub>-glucose (2 mg/g, 4 µL/g injected over 30 s (…)”.

      (4) Vmax = Vlac + Vglx. This is incorrect: Vmax = Vlac.

      RA-R2.C4: Thank you for raising this concern. As indicated in RA-R2.C3, our model (Simões et al. NIMG:Clin 2022) was adapted from the original model proposed by Kreis et al. (Radiology 2020), where the authors quantified glycolysis kinetics on a subcutaneous mouse model of lymphoma, exclusively glycolytic and thus estimating the maximum glucose flux rate was from the lactate synthesis rate (Vmax = Vlac). However, we extended this model to account for glucose flux rates for lactate synthesis (Vlac) and also for glutamate-glutamine synthesis (Vglx), where Vmax = Vlac + Vglx, as explained in our 2022 paper. While we acknowledge the rather simplistic approach of our kinetic model compared to others - reported by 13C-MRS under continuous glucose infusion in healthy mouse brain (Lai et al. JCBFM 2018) and mouse glioma (Lai et al. IJC 2018) – and acknowledge this in the Discussion (page 20, lines 22-24: “(…) metabolic fluxes [estimations] through glycolysis and mitochondrial oxidation (…) could potentially benefit from an improved kinetic model simultaneously assessing cerebral glucose and oxygen metabolism, as recently demonstrated in the rat brain with a combination of 2H and 17O MR spectroscopy [62] (…)”), our Vlac and Vglx results are consistent with our previous DGE 2H-MRS findings in the same glioma models, and very aligned with the literature, as discussed in PR-R1.C2.1.

      (5) Some other items that need attention: 0.03 % is used as the value for the natural abundance of DHO. The natural abundance of 2H in water can vary somewhat regionally, but I have never seen this value reported. The highest seen is 0.015%.

      RA-R2.C5: The Reviewers is referring to the natural abundance of deuterium in hydrogen: 1 in ~6400 is D, i.e. 0.015 %. The 2 hydrogen atoms in a water molecule makes ~3200 DHO, i.e. 0.03%. Indeed the latter can have slight variations depending on the geographical region, as nicely reported by Ge et al (Front Oncol 2022), who showed a 16.35 mM natural-abundance of DHO in the local tap water of St Luis MO, USA (55500/16.35 = 1/3364 = 0.034%).

      (6) Based on the color scale bar in Figure 1, the HDO concentration appears to go as high as 30 mM. Even if this number is off because of the previous concern (HDO), it appears to be a doubling of the HDO concentration. Is this real? What would be the origin of that? No study using [6,6'-2H2]-glucose that I'm aware of has reported such an increase in HDO.

      RA-R2.C6: As explained before (RA-R2.C3 and RA-R2.C4), we based our protocol and model on Kreis et al (Radiology 2020), who reported ~10 mM basal DHO levels raising up to ~27 mM after 90min, which are well within the ~30 mM ranges we report over a longer period (132 min).

      Similar DHO levels were mapped with DGE-DMI in mouse pancreatic tumors (Montrazi et al. SciRep 2023).

      (7) "...the central spectral matrix region selected (to discard noise regions outside the brain, as well as the olfactory bulb and cerebellum)". This reads as if k-space points correspond one-toone with imaging pixels, which is not the case.

      RA-R2.C7: We rephrased the sentence to avoid such potential misinterpretation, specifically: page 25 (lines 19-21): “Each dataset was averaged to 12 min temporal resolution and the noise regions outside the brain, as well as the olfactory bulb and cerebellum, were discarded (…)”.

      (8) The use of the term "glutamate-glutamine recycling" is not really appropriate since these metabolites are not individually detected with 2H MRS, which is a requirement to measure this neurotransmitter cycling.

      RA-R2.C8: Thank you for this comment. To avoid this misinterpretation, we have now rephrased "glutamate-glutamine recycling" to “recycling of the glutamate-glutamine pool” in all the sentences, namely: page 2 (lines 14-15); page 15 (line 8); page 15 (line 8); page 19 (line 1); page 21 (line 10).

      Reviewer #3 (Recommendations For The Authors):

      (1) One major issue is the lack of underlying genetics, and therefore it is hard for readers to put the observed difference between GL261 and CT2A into context. The authors might consider perturbing the genetic and regulatory pathways on glycolysis and glutamine metabolism, repeating DGE DMI measure, in order to enhance the robustness of their findings.

      RA-R3.C1: We thank the reviewer for the helpful revision and comments. The point made here is aligned with Reviewer 1’s, addressed in RA-R1.C3; and also with our previous reply to the Reviewer, PR-R3.C1. Thus, we agree that conditional spatio-temporal manipulation of metabolic pathway fluxes would be relevant to further demonstrate the robustness of DGEDMI, particularly for treatment-response monitoring. While such models were not available to us, our previous findings seem compelling enough to demonstrate this point. Thus, we previously showed a significantly higher respiration buffer capacity and more efficient metabolic plasticity between glycolysis and mitochondrial oxidation in GL261 cells compared to CT2A (Simoes et al. NIMG:Clin 2022), which could enhance lactate recycling through mitochondrial metabolism in GL261 cells and thus explain our observations of increased glucose-derived lactate consumption rate in those tumors compared to CT2A. We have now included this in the discussion (page 17, lines 8-12): “our results suggest increased lactate consumption rate (active recycling) in GL261 tumors with higher vascular permeability, e.g. as a metabolic substrate for oxidative metabolism [44] promoting GBM cell survival and invasion [45], aligned with the higher respiration buffer capacity and more efficient metabolic plasticity of GL261 cells than CT2A [31].” Moreover, we previously showed evidence of DGE-2H MRS’ ability to detect decreased glucose oxidation fraction (Vglx/Vlac) in GL261 tumors under acute hypoxia (FiO2=12 %) compared to regular anesthesia conditions (FiO2=31 %), consistent with the inhibition of OXPHOS due to lower oxygens tensions (Simoes et al. NIMG:Clin 2022).

      (2) Is increased resolution possible for DGE DMI to correlate with histological findings?

      RA-R3.C2: The resolution achieved with DGE DMI, or any other MRI method, is limited by the signal-to-noise ratio (SNR), which in turn depends on the equipment (magnetic field strength and radiofrequency coil), the pulse sequence used, and post-processing steps such as noiseremoval. Thus, increased resolution could be achieved with higher magnetic field strengths, more sensitive RF coils, more advanced DMI pulse sequences, and improved methods for spectral denoising if available. We have used the best configuration available to us and discussed such limitations in the manuscript, including now a few modifications to address the Reviewer’s point more clearly – page 19 (lines 6-10): “Therefore, further DGE-DMI preclinical studies aimed at detecting and quantifying relatively weak signals, such as tumor glutamateglutamine, and/or increase the nominal spatial resolution to better correlate those metabolic results with histology findings (e.g in the tumor margin), should improve basal SNR with higher magnetic field strengths, more sensitive RF coils, and advanced DMI pulse sequences [55])”.

      (3) The authors might consider measuring the contribution of stromal cells and infiltrative immune cells in the analysis of DGE DMI data, to construct a more comprehensive picture of the microenvironment.

      RA-R3.C3: Thank you for this important point. We now added additional Iba-1 stainings of infiltrating microglia/macrophages, for each tumor, as suggested by the Reviewer; stromal cells would be more difficult to detect and we did not have access to a validated staining method for doing so. Our new data and results - now included in Fig 2B – indicate significantly higher levels of Iba-1 positive cells in CT2A tumors compared to GL261, which are particularly noticeable in the periphery of CT2A tumors and consistent with their better-defined margins and lower infiltration in the brain parenchyma. This has been explained more extensively in PRR1.1.

      (4) Additional GBM models with improved understanding of the genetic markers would serve as an optimal validation cohort to support the potential clinical translation.

      RA-R3.C4: We agree with the Reviewer and direct again to RA-R1.3, where we already addressed this suggestion in detail and introduced modifications to the manuscript accordingly.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this report, the authors investigated the effects of reproductive secretions on sperm function in mice. The authors attempt to weave together an interesting mechanism whereby a testosterone-dependent shift in metabolic flux patterns in the seminal vesicle epithelium supports fatty acid synthesis, which they suggest is an essential component of seminal plasma that modulates sperm function by supporting linear motility patterns.

      Strengths:

      The topic is interesting and of general interest to the field. The study employs an impressive array of approaches to explore the relationship between mouse endocrine physiology and sperm function mediated by seminal components from various glandular secretions of the male reproductive tract.

      Thank you for your positive evaluation of our study's topic and approach. We are pleased that you found our investigation into the effects of reproductive secretions on sperm function to be of general interest to the field. We appreciate your positive feedback on the diverse methods we employed to explore this complex relationship.

      Weaknesses:

      Unfortunately, support for the proposed mechanism is not convincingly supported by the data, and the experimental design and methodology need more rigor and details, and the presence of numerous (uncontrolled) confounding variables in almost every experimental group significantly reduce confidence in the overall conclusions of the study.

      The methodological detail as described is insufficient to support replication of the work. Many of the statistical analyses are not appropriate for the apparent designs (e.g. t-tests without corrections for multiple comparisons). This is important because the notion that different seminal secretions will affect sperm function would likely have a different conclusion if the correct controls were selected for post hoc comparison. In addition, the HTF condition was not adjusted to match the protein concentrations of the secretion-containing media, likely resulting in viscosity differences as a major confounding factor on sperm motility patterns.

      We appreciate you highlighting concerns regarding our weak points and apologize for our unclear description. We revised the manuscript to be as rigorous and detailed as possible. In addition, some experimental designs were changed to simpler direct comparisons, and additional experiments were conducted (New Figure 1A-F, lines 103-113). We have made our explanations more consistent with the provided data, which includes further experimentation with additional controls and larger sample sizes to increase the reliability of the findings.

      To address the multiple testing problem, a multiple testing correction was made by making the statistical tests more stringent (Please see Statistical analysis in the Methods section and the Figure legends). Based on different statistical methods, the analysis results did not require significant revisions of the previous conclusions.

      Because the experiments on mixing extracts from the seminal vesicles were exploratory, we planned to avoid correcting for multiple comparisons. Repeating the t-test could lead to a Type I error in some results, so we apologize for not interpreting and annotating them. In the revised version, we removed the dataset for experiments on mixing extracts from the seminal vesicles and prostate, and we changed the description to refer to the clearer dataset mentioned above.

      The viscosity of the secretion-containing medium was measured with a viscometer, confirming that secretions did not significantly affect the viscosity of the solution. In addition, as the reviewer pointed out, we addressed the issue that the HTF condition could not be used as a control because of the heterogeneity in protein concentration (New Fig.1G, lines 110-111).

      Overall, we concluded that seminal vesicle secretion improves the linear motility of sperm more than prostate secretion.

      There is ambiguity in many of the measurements due to the lack of normalization (e.g. all Seahorse Analyzer measurements are unnormalized, making cell mass and uniformity a major confounder in these measurements). This would be less of a concern if basal respiration rates were consistently similar across conditions and there were sufficient independent samples, but this was not the case in most of the experiments.

      We apologize for the many ambiguities in the first manuscript. Cell culture experiments in the paper, including the flux analysis, were performed under conditions normalized or fixed by the number of viable cells. The description has also been revised to emphasize that the measurement values are standardized by cell count (lines 183-185, 189-190, 194-197). We emphasize that testosterone affects metabolism under the same number of viable cells (New Fig.4). This change in basal respiration is thought to be due to the shift in the metabolic pathway of seminal vesicle epithelial cells to a “non-normal TCA cycle” in which testosterone suppresses mitochondrial oxygen consumption, even under aerobic conditions (New Figs.3, 4, 5).

      The observation that oleic acid is physiologically relevant to sperm function is not strongly supported. The cellular uptake of 10-100uM labeled oleic acid is presumably due to the detergent effects of the oleic acid, and the authors only show functional data for nM concentrations of exogenous oleic acid. In addition, the effect sizes in the supporting data were not large enough to provide a high degree of confidence given the small sample sizes and ambiguity of the design regarding the number of biological and technical replicates in the extracellular flux analysis experiments.

      Thank you for your important critique. As you noted, the too-high oleic acid concentration did not reflect physiological conditions. Therefore, we changed the experimental design of an oleic acid uptake study and started again. We added an in vitro fertilization experiment corresponding to the functional data of exogenous oleic acid at nM concentrations (New Fig.7J,K, Lines 274-282).

      For the flux data to determine the effect of oleic acid on sperm metabolism, we have indicated in the text that the data were obtained based on eight male mice and two technical replicates. Pooled sperm isolated and cultured from multiple mice were placed in one well. The measurements were taken in three different wells, and each experiment was repeated four times. We did not use the extracellular flux analyzers XFe24 or XFe96. The measurements were also repeated because the XF HS Mini was used in an 8-well plate (only a maximum of 6 samples at a run since 2 wells were used for calibration).

      Overall, the most confident conclusion of the study was that testosterone affects the distribution of metabolic fluxes in a cultured human seminal vesicle epithelial cell line, although the physiological relevance of this observation is not clear.

      We thank the comments that this finding is one of the more robust conclusions of our study. Below we have written our thoughts on the physiological relevance of the observation results and our proposed revisions. In the mouse experiments, when the action of androgens was inhibited by flutamide, oleic acid was no longer synthesized in the seminal vesicles. The results of the experiments using cultured seminal vesicle epithelial cells showed that oleic acid was not being synthesized because of a change in metabolism dependent on testosterone. We have also added IVF data on the effects of oleic acid on sperm function (New Fig.7 and Supplementary Fig. 5, lines 274-282).<br /> As you can see, we have obtained consistent data in vitro and in vivo in mice. Our data also showed that the effects of testosterone on metabolic fluxes in vitro are similar in mouse and human seminal vesicle epithelial cells (New Fig.9). Therefore, it can be assumed that a decrease in testosterone levels causes abnormalities in the components of human semen. However, the conclusion was overestimated in the original manuscript, so we changed the wording as follows: It could be assumed that a decrease in testosterone levels causes abnormalities in the components of human semen. (lines 422-423)

      In the introduction, the authors suggest that their analyses "reveal the pathways by which seminal vesicles synthesize seminal plasma, ensure sperm fertility, and provide new therapeutic and preventive strategies for male infertility." These conclusions need stronger or more complete data to support them.

      We appreciate your comments about the suggestion presented in the introduction.

      We also removed our conclusions regarding treatment and prevention strategies for male infertility (lines 96-98). We wanted to discuss our findings not conclusively but as future applications that could result from further research based on our initial findings.

      The last sentence of the introduction has been revised to tone down these assertions as follows: These analyses revealed that testosterone promotes the synthesis of oleic acid in seminal vesicle epithelial cells and its secretion into seminal plasma, and the oleic acid ensures the linear motility and fertilization ability of sperm.

      We are grateful for your suggestions, which have prompted us to refine our manuscript.

      Reviewer #2 (Public Review):

      Summary:

      Using a combination of in vivo studies with testosterone-inhibited and aged mice with lower testosterone levels, as well as isolated mouse and human seminal vesicle epithelial cells, the authors show that testosterone induces an increase in glucose uptake. They find that testosterone induces differential gene expression with a focus on metabolic enzymes. Specifically, they identify increased expression of enzymes that regulate cholesterol and fatty acid synthesis, leading to increased production of 18:1 oleic acid.

      Strength:

      Oleic acid is secreted by seminal vesicle epithelial cells and taken up by sperm, inducing an increase in mitochondrial respiration. The difference in sperm motility and in vivo fertilization in the presence of 18:1 oleic acid and the absence of testosterone is small but significant, suggesting that the authors have identified one of the fertilization-supporting factors in seminal plasma.

      Thank you for your positive comments regarding our work on the role of testosterone in regulating metabolic enzymes and the subsequent production of 18:1 oleic acid in seminal vesicle epithelial cells. We are pleased that the strength of our findings, particularly identifying oleic acid as a factor influencing sperm motility and mitochondrial respiration, has been recognized.

      Weaknesses:

      Further studies are required to investigate the effect of other seminal vesicle components on sperm capacitation to support the author's conclusions. The author's experiments focused on potential testosterone-induced changes in the rate of seminal vesicle epithelial cell glycolysis and oxphos, however, provide conflicting results and a potential correlation with seminal vesicle epithelial cell proliferation should be confirmed by additional experiments.

      Thank you very much for your valuable criticism. Although we fully agree with your comment, conducting experiments to investigate the effects of other seminal vesicle components on the fertilization potential of sperm would be a great challenge for us. This is because it has taken us the last three years to identify oleic acid as a key factor in seminal plasma. We are considering a follow-up study to explore the effect of other seminal vesicle components on sperm capacitation. Therefore, we have revised the Introduction and conclusions to tone down our assertions .

      The revised manuscript also includes additional data showing a correlation between changes in metabolic flux and the proliferation of seminal vesicle epithelial cells using shRNA. As a result, it was shown that cell proliferation is promoted when mitochondrial oxidative phosphorylation is promoted by ACLY knockdown (New Fig.8D, lines 303-305). This shows a close relationship between the metabolic shift in seminal vesicle epithelial cells and cell proliferation. The revised manuscript includes an interpretation and discussion of these results (lines 369-379).

      We are grateful for your suggestions, which have prompted us to refine our manuscript.

      Reviewer #3 (Public Review):

      Summary:

      Male fertility depends on both sperm and seminal plasma, but the functional effect of seminal plasma on sperm has been relatively understudied. The authors investigate the testosterone-dependent synthesis of seminal plasma and identify oleic acid as a key factor in enhancing sperm fertility.

      Strengths:

      The evidence for changes in cell proliferation and metabolism of seminal vesicle epithelial cells and the identification of oleic acid as a key factor in seminal plasma is solid.

      Weaknesses:

      The evidence that oleic acids enhance sperm fertility in vivo needs more experimental support, as the main phenotypic effect in vitro provided by the authors remains simply as an increase in the linearity of sperm motility, which does not necessarily correlate with enhanced sperm fertility.

      We appreciate the positive feedback on the solid evidence of cell proliferation and metabolic changes in seminal vesicle epithelial cells and the identification of oleic acid as an important factor in seminal plasma. We fully agree with the assessment that the evidence linking oleic acid and increased sperm fertility in vivo needs further experimental support. To address this concern, we changed the experimental design of an oleic acid study and started again to be more physiological regarding the effect of oleic acid on fertility outcomes, increased the replicates of artificial insemination, and added in vitro fertilization assessments (New Fig.7 and supplementary Fig.5, lines 274-282). The revised manuscript describes these experiments and discusses the association between oleic acid and fertility.

      We are grateful for your suggestions, which have prompted us to refine our manuscript.

      Recommendations for the authors:

      Reviewing Editor's note:

      As you can see from the three reviewers' comments, the reviewers agree that this study can be potentially important if major concerns are adequately addressed. The major concern common to all the reviewers is the incomplete mechanistic link between the physiological androgen effect on the production of oleic acid and its effect on sperm function. Statistical analyses need more rigor and consideration of other important capacitation parameters are needed to address these concerns and to improve the manuscript to support the current conclusions.

      Thank you for summarizing the reviewers' feedback and for your insights regarding the major concerns raised. We appreciate the reviewers' understanding of the potential importance of our work and have addressed the issues highlighted to strengthen the manuscript. We believe these changes will improve the quality of the manuscript and provide a clearer and more complete understanding of the role of androgens and oleic acid in sperm function.

      Reviewer #1 (Recommendations For The Authors):

      The following comments are provided with the hope of aiding the authors in improving the alignment between the data and their interpretations.

      Thank you for allowing us to strengthen our manuscript with your valuable comments and queries. We have made our best efforts to reflect your feedback.

      Major Comments:

      (1) The methodological detail is not sufficient to reproduce the work. For example:<br /> a. Manufacturer protocols are referred to extensively. These protocols are neither curated nor version-controlled. Please consider describing the underlying components of the assays. If information is not available, please consider providing catalog numbers and lot numbers in the methods (if appropriate for journal style requirements).

      We appreciate this suggestion, which we believe is important to ensure reproducibility. We described the catalog number in our Methodology and included as much information as possible.

      b. Please consider describing the analyses in full, with consideration given to whether blinding was part of the design. For example- line 492: "apoptotic cells were quantified using ImageJ". How was this quantified? How were images pre-processed? Etc.

      Although blinding was not performed, experiments and analyses based on Fisher's three principles were conducted to eliminate bias (lines 549-552). In order to avoid false-positive or false-negative results, it is clearly stated that tissue sections treated with DNAse were used as positive controls, and tissue sections without TdT were used as negative controls for apoptosis. We have added detailed quantification methods (lines 544-546).

      c. Please consider providing versions of all acquisition and analysis software used.

      We have added software version information in Materials and Methods.

      (2) Please consider revisiting the statistical analyses. Many of the analyses don't seem appropriate for the design. For example, the use of a t-test with multiple comparisons for repeated measures design in Figure 2 and the use of t-test for two-factor design in Figure 8. etc.

      To address the multiple testing issues, the statistical methodology was changed to a more rigorous one. Details are given in the Statistical analysis in the Methods section and the Figure legends.

      (3) The increase in % LIN in Figure 1 may be confounded by differences in viscosity between HTF and the fluid secretion mixtures. For this reason, HTF may not be an appropriate control for the ANOVA post hoc analysis. HTF protein was not adjusted to the same concentration as the secretion mixtures, correct? Ultimately, it does not appear that there would be a significant statistical effect of the different fluid mixtures if appropriate statistical comparisons were made. This detracts from the notion that the secretions impact sperm function.

      (4) Figure 1, the statistical analysis in the legend suggests that the experiments were analyzed with a t-test. Were corrections made for multiple comparisons in B-D? An ANOVA would probably be more appropriate.

      We used a viscometer to measure the viscosity of a solution of prostate and seminal vesicle secretions adjusted to a protein concentration of 10 mg/mL. The results showed that the secretions did not cause any significant viscosity changes (New Fig.1G, Lines 110-111).

      As you pointed out, the protein levels in the HTF medium and the secretion mixture are not adjusted to the same concentration. In addition, the original manuscript was not a controlled experiment because the two factors, seminal vesicle and prostate extracts, were modified. Therefore, to investigate the effect of prostate and seminal vesicle secretions on sperm motility, we modified the experimental design to directly compare the effects of the two groups: seminal vesicle and prostate extracts (New Fig.1A-G, lines 101-113). To show the sperm quality used in this study, motility data from sperm cultured in the HTF medium are presented independently in New Supplemental Fig.1A.

      (5) Additionally in Figure 1, there is no baseline quality control data to show that there are no intrinsic differences between sperm sampled from the two treatment groups. So baseline differences in sperm quality/viability remain a potential confounder.

      We thank you for this important point. Epididymal sperm were collected from healthy mice. We recovered only the seminal vesicle secretions from the flutamide-treated mice to pursue its role in the accessory reproductive glands, since testosterone targets the testes and accessory reproductive organs. So, there was no qualitative difference between the epididymal sperm before treatment. Nevertheless, incubation with seminal vesicle secretion for one hour altered the sperm motility pattern and in vivo fertilization results. Sperm function was altered by seminal vesicle secretion in a short period of culture time. We apologize for the confusion, and we have revised the text and figure to carry a clearer message (lines 128-132).

      (6) Figure 1E, did the authors confirm that flutamide-treated mice had decreased serum androgens? How often were mice treated with flutamide? This is important because flutamide has a relatively short half-life and is rapidly metabolized to inert hydroxyflutamide.

      Serum testosterone levels were unchanged. Flutamide was administered every 24 hours for 7 consecutive days. Although there was no change in blood testosterone levels (New Supplemental Fig.1B), a decrease in the weight of the seminal vesicles, prostate, and epididymis was confirmed. This is thought to be due to the pharmacological activity of flutamide.

      (7) Figure 1H, the meaning of 'relative activity of mitochondria' isn't clear. JC-1 does not measure 'activity'. A decreased average voltage potential across the inner mitochondrial membrane may indicate that more of the sperm from the flutamide group were dead. Additionally, J-aggregates are slow to form, generally requiring long incubation periods of at least 90 minutes or more. Additional positive and negative controls for predictable mitochondrial transmembrane voltage potential polarization states would have improved the quality of this experiment.

      Thank you for pointing this out. We have replaced the relative activity of mitochondria with high mitochondrial membrane potential (New Fig.1M, lines 125-128). Actually, it is thought that the sperm cultured in seminal vesicle secretions from mice that had been administered flutamide died because the motility of the sperm was also significantly reduced. Since antimycin reduces mitochondrial membrane potential, we have added an experiment in which 10 µM antimycin-treated sperm were used as a control to confirm that the JC-1 reaction is sensitive to changes in membrane potential.

      (8) Figure 4, the extracellular flux data appear to be unnormalized. The Seahorse instruments are extremely sensitive to the mass and uniformity of the cells at the bottom of the well. This may be a significant confounder in these results. For example, all of the observed differences between groups could simply be a product of differential cell mass, which is in line with the reduced growth potential of testosterone-treated cells indicated by the authors in the results section.

      We thank you for this important point. After correcting for cell viability, we seeded the same number of viable cultured cells into wells between experimental groups before measuring them in the flux analyzer. There were no significant differences in survival rates in all experiments. As a result, an increase in glucose-induced ECAR and a suppression of mitochondrial respiration were observed. We would like to emphasize that this difference based on metabolic data does not imply a reduction in the growth potential of the cells due to testosterone treatment.

      We described that these measurements are normalized based on cell count and viability (lines 184, 190, 195).

      (9) How did the authors know that the isolated mouse primary cells were epithelial cells? Was this confirmed? What was the relative sample purity?

      The cells were labeled with multiple epithelial cell markers (cytokeratin) and confirmed using immunostaining and flow cytometry. The percentage of cells positive for epithelial cell markers was approximately 80%. A stromal cell marker (vimentin) was also used to confirm purity, but only a few percent of cells were positive. The contaminating cell type was considered to be mainly muscle cells because the gene expression levels of muscle cell markers verified by RNA-seq were relatively high.

      (10) It is misleading to include the lactate/pyruvate media measurements in the middle of the figure in Figure 4 D and E because it seems at first glance like these measurements were made in the seahorse media but they are completely unrelated. Additionally, these measures are not normalized and are sensitive to confounding differences in cell viability, seeding density, mass, etc.

      Thank you for pointing this out. We have placed the lactate and pyruvate measurement graphs after the flux data of ECAR. We noted that these measurements are normalized based on cell count and viability (lines 189-190). The doubling time of seminal vesicle epithelial cells was approximately 3 days, and testosterone inhibited cell proliferation. Therefore, the seeding concentration of cells was increased 4-fold in the testosterone-treated group compared to the control, and experiments were conducted to ensure that the confluency at the time of measurement after 7 days of culture was comparable between groups.

      (11) The flux analyzer assays sold by Agilent have many ambiguities and problems of interpretation. Unfortunately, Agilent's interest in marketing/sales has outpaced their interest in scientific rigor. Please consider revising some of the language regarding the measurements. For example, 'ATP production rate' is not directly measured. Rather, oligomycin-sensitive respiration rate is measured. The conversion of OCR to ATP production rate is an estimation that depends on complex assumptions often requiring additional testing and validation. The same is true for other ambiguous terms such as 'maximal respiration' referring to FCCP uncoupled respiration, and glycolytic rate- which is also not measured directly. If the authors are interested in a more detailed description of the problems with Agilent's interpretation of these assays please see the following reference (PMID: 34461088).

      Thank you for your critical criticism and thoughtful advice, as well as for sharing the excellent reference. We agree with you on the flux analyzer ambiguities and data interpretation problems. The description of the measured values has been revised as follows.

      We have replaced the “ATP production rate” with the “oligomycin-sensitive respiratory rate.” Similarly, we have replaced “maximal respiration” with “FCCP-induced unbound respiration.” (lines 197-202) We chose not to deal with the conversion of OCR to ATP production rate because it is outside the scope of interest in our study.

      Avoid using the term "glycolytic capacity". We use “Oligomycin-sensitive ECAR.” (line 186) We recognize that the ECARs measured in this study reflect experimental conditions and may not fully represent physiological glycolytic flux in vivo. So, the main section includes a data set of glucose uptake studies to emphasize the significance of the changes obtained with the flux analyzer assay. (New Fig.6, lines 230-254)

      Figure 6, it's not surprising to see the accumulation of labeled oleic acid in the cells, however, this does not mean that oleic acid is participating in normal metabolic processes. Oleic acid will have detergent effects at high (uM) concentrations. The observation that sperm 'take up' OA at 10-100 uM concentrations should also be validated against sperm function the health of the cells is very likely to be negatively impacted. Additionally, no apparent accumulation is noted in the fluorescence imaging at 1uM, but the authors insinuate that uptake occurs at low nM concentrations. The effects in Figure 6D-F are nominal at best and are likely a result of the small sample sizes.

      Thank you for your good suggestion. We agree with the reviewer that high concentrations of oleic acid had a detergent effect. To improve the consistency of functional data and observations, oleic acid uptake tests were performed under the same concentration range as the sperm motility tests (New Fig.7A-C). The oleic acid concentration at this time was calculated regarding the oleic acid concentration in seminal fluid recovered from mice as detected by GCMS to reflect in vivo conditions.

      Epididymal sperm were incubated with fluorescently labeled oleic acid and observed after quenching of extracellular fluorescence. Fluorescent signals were detected selectively in the midpiece of the sperm. The fluorescence intensity of sperm quantified by flow cytometry increased significantly in a dose-dependent manner (New Fig.7A-C, lines 261-264).

      Furthermore, increasing the sample size did not change the trend of the sperm motility data. Although the effect size of oleic acid on sperm motility was small (New Fig.7D-G, lines 265-268), an improvement in fertilization ability was observed both in vitro (IVF) and in vivo (AI) (New Fig.7J-L, lines 274-282, 286-291). We conclude that the effect of oleic acid on sperm is of substantial significance. These data and interpretations have been revised in the text in the Results section.

      (12) Figure 6H, I applaud the authors for attempting intrauterine insemination experiments to test their previous findings. That said, there is no supporting data included to show that the sperm from the treatment groups had comparable starting viability/quality. Additionally, it is difficult to tell if the results are due to the small sample sizes and particularly the apparent outlier in the flutamide-only group.

      Thanks for the praise and comments for improvement. As we answered in your comment #5 above, the epididymal sperm was collected from healthy mice. Therefore, there is no qualitative difference in the epididymal sperm before treatment. This is described in the figure legend (lines 1130-1131). We apologize again for this complication. We also more than doubled the number of replications of the experiment. The impact of the outlier would have been minimal.

      (13) One final question related to Figure 6H: how did the authors know they were retrieving all of the possible 2-cell embryos from the uterus? Perhaps the authors could provide the raw counts of unfertilized eggs and 2-cell embryos so we can see if there were differences between the mice.

      We retrieved the pronuclear stage embryos from the fallopian tubes. It is not certain whether all embryos were recovered. Therefore, we added the number of embryos in the graph and in the supplementary data.

      (14) Figure 7 has the same seahorse assay normalization problem as mentioned earlier. Without normalization, it is difficult to tell if the effects are simply due to differences in cell mass. Were the replicates indicated in the graphs run on the same plate? If so, it would be much more convincing to see a nested design, with technical replicates within plates, and additional replicates run on separate plates.

      As we answered in your comment #8 above, these measurements were normalized based on sperm count. This has been corrected to be noted in the text and the figure legend (lines 1123-1124).

      Pooled sperm isolated and cultured from multiple mice were placed in one well. The measurements were taken in three different wells, and each experiment was repeated four times. We did not use the extracellular flux analyzers XFe24 or XFe96. The measurements were also repeated because the XF HS Mini was used in an 8-well plate (only a maximum of 6 samples at a run since 2 wells were used for calibration).

      (15) The statistical test in Figures 8E and F described in the legend is inappropriate (t-test), this appears to be a two-factor design.

      Thank you for pointing this out. Differences between groups were assessed using a two-way analysis of variance (ANOVA). When the two-way ANOVA was significant, differences among values were analyzed using Tukey's honest significant difference test for multiple comparisons.

      (16) The data in Figure 8 are interesting, and the effects appear to be a little more consistent compared with the mouse primary cells, potentially due to cell uniformity. However, the data are unnormalized, causing significant ambiguity, and there are no measures of cell viability to determine if the effects are due to cell death (or at least relative cell mass).

      As we answered in your comments #8 and #14 above, these measurements were normalized based on cell count and viability. This has been corrected to be noted in the figure legend (lines 1185-1186).

      Minor Comments:

      (1) The section title indicating the beginning of the results section is missing.

      A section title has been added to indicate the beginning of the results section.

      (2) There were several typos and confusingly worded statements throughout. Please consider additional editing.

      We used a proofreading service and corrected as much as possible.

      (3) In the introduction, a brief description of seminal fluid physiology is provided, but the reference is directed toward human physiology. Given that the research is performed solely in the mouse, a brief comparative description of mouse physiology would be helpful. For example, what is the role of mouse seminal fluid in the formation of the mating plug? What are the implications of the relative size disparity in seminal vesicles in mice versus humans? Etc.

      The third paragraph of the introduction has been revised (lines 57-60).

      Reviewer #2 (Recommendations For The Authors):

      Thank you for allowing us to strengthen our manuscript with your valuable comments and queries. We have made our best efforts to reflect your feedback.

      (1) The abstract is confusing and partly misleading and should be revised to more clearly and accurately summarize the study.

      The abstract was revised to be clearer and more accurate (lines 20-34).

      (2) The introduction should be revised to more accurately describe the sperm life cycle. Spermatogenesis, per definition, for example, exclusively takes place in the testis, sperm do not gain fertilization competence in the epididymis, sperm isolated from the epididymis cannot fertilize an oocyte unless in vitro capacitated, etc. In the last paragraph the connection between changes in fructose and citrate concentration, sperm metabolism and testicular-derived testosterone and AR remain unclear.

      The introduction was revised to be clearer and more accurate (lines 44-45).

      Citric acid and fructose are chemical components that are the subject of biochemical testing and are commonly used as semen testing items for humans and livestock. This is because the secretory function of the prostate and seminal vesicles is dependent on androgens. The measurement of citric acid and fructose concentrations in semen is routinely used to indicate testicular androgen production function (ISBN: 978-1-4471-1300-3, 978 92 4 0030787).

      (3) Throughout the manuscript the concept of (in vitro) capacitation is missing. Mixing sperm with seminal plasma is not the only way to achieve sperm that can fertilize the oocyte. Since media containing bicarbonate and albumin is the standard procedure in the field to capacitate epididymal mouse sperm rein vitro, the manuscript would gain value from a comparison between the effect of seminal plasma and in vitro capacitating media. Interesting readouts in addition to motility would i.e. be sAC activation, PKA-substrate phosphorylation, and acrosomal exocytosis.

      Thank you for pointing out this important point. As the reviewer points out, fertilization can be achieved in artificial insemination and in vitro fertilization using epididymal sperm which have not been exposed to seminal plasma. This has historically led to an underestimation of the role of accessory reproductive glands, such as the prostate and seminal vesicles. However, it has been reported that the removal of seminal vesicles in rodents decreases the fertilization rate after natural mating. This has been shown to be due to multiple factors affecting sperm motility rather than factors involved in plug formation (PMID: 3397934), but details of these factors and the whole picture of the role of the accessory glands were not known. This led us to become interested in the effects of sperm plasma on sperm other than fertilization and led us to begin research on the role of the accessory glands that synthesize sperm plasma.

      Early in our study, we found that simply exposing sperm to seminal vesicle extracts for 1 hour before IVF dramatically reduced fertilization rates, even in HTF medium containing bicarbonate and albumin. The experiment was designed on the assumption that seminal plasma contains factors that inhibit sperm from acquiring fertilizing ability. Therefore, we conducted experiments using modified HTF without albumin to avoid unintended motility patterns.

      However, we also respect the reviewer's opinion, and we have added our preliminary data related to IVF (New supplementary Fig.5).

      (4) In the introduction and throughout the manuscript it is unclear what the authors mean by "linear motility". An increase in VSL doesn't mean that the sperm swim in a more linear or straight way, or even that the sperm are 'straightened', it means that they swim faster from point A to point B. Do the authors mean progressive or hyperactivated motility? Please clarify.

      For all conditions tested the authors should follow the standard in the field and include the % of motile, progressively motile, and hyperactivated sperm.

      Thank you for pointing this out. We appreciate your feedback regarding the terminology. In our manuscript, "linear motility" refers to the degree to which sperm move in a straight line. We have clarified this by explaining that VSL (Straight-Line Velocity) and LIN (Linearity) are used to quantify and describe linear motility in sperm analysis: Higher VSL values indicate more direct, linear movement. A higher LIN value indicates a straighter path, thus representing greater linear motility. These terms have been standardized, and explanations have been added to the main text (lines 111-113).

      In response to your suggestion, we have included the percentage of motility and progressive motility for all conditions tested. However, since the experiment was performed using modified HTF without albumin, we have decided not to report the percentage of hyperactivation to avoid confusion.

      (5) Did the authors confirm that the injection of flutamide decreases androgen levels? That control needs to be included in the experiment to validate the conclusion.

      Injection of flutamide did not reduce androgen levels (see reviewer #1, comment 6). This is because flutamide's mechanism of action is based on antagonizing androgen and inhibiting its binding to the androgen receptor (New Fig.2A).

      (6) The role of mitochondrial activity in sperm progressive motility is still under investigation. PMID: 37440924 i.e. showed that inhibition of the ETC does not affect progressive but hyperactivated motility. The authors should either include additional experiments to confirm the correlation between mitochondrial activity and sperm progressive motility or tone down that conclusion.

      We have previously shown that treatment with D-chloramphenicol, an inhibitor of mitochondrial translation, significantly reduced sperm mitochondrial membrane potential, ATP levels, and linear motility (PMID: 31212063). Also, in the previous manuscript, we did not address progressive motility or hyperactivated motility in our analysis. We have chosen to discuss the effect of mitochondrial activity on linear motility rather than on progressive motility and hyperactivation of sperm.

      Was mitochondrial activity also altered in epididymal sperm incubated with and without seminal plasma or in aged mice?

      The mitochondrial membrane potential of epididymal sperm cultured with seminal vesicle extract (SV) was higher than that of epididymal sperm cultured without seminal vesicle extract (without SV: 67.3 ± 0.8%, with SV: 83.4 ± 1.8%). On the other hand, the mitochondrial membrane potential of epididymal sperm cultured with seminal vesicle extract recovered from aged mice was decreased (SV from aged: 60.3 ± 2.7%). It should be noted that the epididymal spermatozoa used in these experiments were healthy individuals, different from those from which seminal vesicle extracts were collected. (See also the response to reviewer 1's comment #5.)

      (7) The quality of the provided images showing AR, Ki67, and TUNEL staining should be improved or additional images should be included. Especially the AR staining is hard to detect in the provided images. The authors should also include a co-staining between AR and vesicle epithelial cells. That epithelial cells are multilayered does not come across in the pictures provided.

      We apologize for any inconvenience caused. The image has been replaced with one of higher resolution. The multilayered structure of the epithelial cells will also be seen.

      For the 12-month-old mice, an age-matched control should be included to support the authors' conclusion.

      To clarify the seminal vesicle changes associated with aging, we included images of 3-month-old mice as controls (New Supplementary Fig.2D).

      Overall, the rationale for the experiment does not become clear. How are the amount of seminal vesicle epithelial cells, testosterone, and AR expression connected to seminal plasma secretions? Why is it a disadvantage to have proliferating seminal vesicle epithelial cells? How is proliferation connected to the proposed switch in metabolic pathway activity?

      We have added some explanations and supporting data to the manuscript (New Fig.8D, lines 303-305, 315-319, 369-379). Cell proliferation stopped when the metabolic shift occurred, redirecting glucose toward fatty acid synthesis. Fatty acid synthesis is an important function of the seminal vesicle, and in the presence of testosterone, fatty acid synthesis enhancement and arrest of proliferation occur simultaneously. The connection between metabolism and cell proliferation was further demonstrated when ACLY was knocked down by shRNA, which stopped fatty acid synthesis and released the proliferative arrest induced by testosterone, allowing the cells to proliferate again. However, we do not know what effects occur when cell proliferation is stopped.

      (8) The experiments provided for glycolysis and oxphos are inconsistent and insufficient to support the authors' conclusion that testosterone shifts glycolytic and oxphos activity of seminal vesicle epithelial cells. Multiple groups (PMID 37440924, 37655160, 32823893) have shown that the increased flux through central carbon metabolism during capacitation is accompanied by an accumulation of intracellular lactate and increased secretion of lactate into the surrounding media. How do the authors explain that they see an increase in glucose uptake and ECAR but not in lactate and a decrease in pyruvate? Did the authors additionally quantify intracellular pyruvate and lactate? Since pyruvate and lactate are in constant equilibrium, it is odd that one metabolite is changing and the other one is not.

      Thank you for pointing this out. Since ECAR is often used as an alternative to lactate production but does not directly measure lactate levels, we measured changes in lactate and pyruvate concentrations in the culture medium. Under our experimental conditions, glucose appeared to be directed primarily towards anabolic processes, such as fatty acid synthesis, rather than the OXPHOS pathway, which may explain the lack of lactate production. The observed decrease in pyruvate might indicate its conversion to acetyl-CoA in the mitochondria, supporting both fatty acid synthesis and the TCA cycle. This shift would be consistent with the metabolic reprogramming toward anabolic activity.

      What do the authors mean by "the glycolytic pathway was not enhanced despite the activation of glycolysis" Seahorse, especially using a series of pathway inhibitors, only provides an indirect measurement of glycolysis and oxphos since the instrument does not provide a distinction from which pathways the detected protons are originating. The authors should consider a more optimized experimental design, i.e. the authors could monitor ECAR and OCR in the presence of glucose over time with and without the addition of testosterone. That would be less invasive since the sperm are not starved at the beginning of the experiment and would provide a more direct read-out. Did the authors normalize cell numbers in their experiment? Alternatively, the authors could consider performing metabolomics experiments.

      I agree with the reviewer. Buzzwords such as “glycolytic capacity” simply do not make sense, so we have removed them from the phrases noted by the reviewer. Please refer to the response to some of reviewer 1's points regarding the ambiguity of the data measured by the flux analyzer. Nevertheless, the assay design of the flux analysis could be used as a good “starting point” and provide information on the glycolytic system and respiratory control. Therefore, the interpretation of the flux analysis is supported by subsequent data sets.

      (9) The authors would strengthen their results by confirming their gene expression data by quantifying the expression of the respective proteins.

      Does testosterone treatment increase GLUT4 protein levels in isolated seminal vesicle epithelial cells? Or does it change the localization of the transporter? Are GLUT4 gene and protein levels altered in flutamide-treated cells? How do the authors explain that testosterone increases glucose uptake without changing Glut gene expression?

      We performed Western blot analysis to measure GLUT4 protein levels in seminal vesicle epithelial cells after testosterone treatment. The results showed that testosterone does not alter the expression of GLUT4 protein but simply changes its subcellular localization (New Fig.6C,D, lines 238-244).

      The discussion includes the interpretation of the observation that testosterone increases glucose uptake by altering localization without altering GLUT4 gene expression, a phenomenon commonly seen in other cells, such as cardiomyocytes (lines 362-364). The revised main figure also includes a data set of changes in GLUT4 localization, including flutamide-treated data. See also Reviewer 3's main comment #1.

      (10) Considering that the authors claim that SV secretions are crucial for sperm fertilization capacity, how do they explain that fertilization rates are still at 40 % when sperm are treated with flutamide?

      It is actually about 50% fertilized with HTF because it is fertilized without SV. Considering this baseline, we found that seminal vesicle secretions positively affect sperm in vivo fertilization. On the other hand, seminal plasma from flutamide-treated mice reduced the fertilization ability of healthy sperm. These are described in the text (lines 283-294).

      (11) It would be beneficial for the reader to include a schematic summarizing the results.

      Thank you for your advice from the reader's point of view. We have visualized the summaries of this study and added them to the manuscript (New Fig.10).

      Minor comments:

      Line 38: Male fertility, no article, please revise.

      I have changed “The male fertility” to “Male fertility” and added some references (lines 42-43).

      Line 55: Seminal plasma or TGFb? Please clarify.

      Corrected as follows. “TGFβ, a component of seminal plasma, increases antigen-specific Treg cells in the uterus of mice and humans, which induces immune tolerance, resulting in pregnancy.” (lines 60-62)

      Line 63: Why do the authors find it surprising that blood and seminal plasma have different compositions?

      This is because seminal plasma contains unique biochemical components that are not normally found in blood or only in small quantities. The intention was to emphasize the unique function of seminal plasma in supporting the physiological functions of sperm and to highlight its complex role by comparing it to blood. We clarified these intentions and reflected them in the revised text (lines 62-67).

      Line 94: The headline causes confusion. Seminal plasma does not induce sperm motility, it increases progressive sperm motility.

      Corrected as follows. “The effect of androgen-dependent changes in mouse seminal vesicle secretions on the linear motility of sperm” (lines 101-102)

      Reviewer #3 (Recommendations For The Authors):

      Thank you for allowing us to strengthen our manuscript with your valuable comments and queries. We have made our best efforts to reflect your feedback.

      Major:

      Figure 4 and Figure 5: The trend shows that GLUT3 is up-regulated and GLUT4 is downregulated although both of them are not statistically significant. However, GLUT4 is picked for all the following experiments based on protein localization. Providing other evidence/discussion why not to further consider other GLUTs will help to justify. Also, this reviewer suggests including GLUT4 localization data in the main figure as it is important data for the logical flow to link the following figures.

      We focused on GLUT4 because it was known that testosterone increases glucose uptake by changing the localization of GLUT4 without changing its expression (lines 230-231). In the revised manuscript, the increasing trend in Glut3 gene expression was also mentioned in the discussion, in addition to GLUT4 (lines 360-362). In any case, the results showed that testosterone increased glucose uptake by regulating the function of glucose transporters.

      Immunostaining of GLUT1~4 was performed to compare seminal vesicles from flutamide-treated mice with controls, and localization changes were observed only in GLUT4. Therefore, we hypothesized that GLUT4 is regulated by testosterone and performed the experiment. Fortunately, we were able to obtain a GLUT4-specific inhibitor, which dramatically inhibited the testosterone-dependent glucose uptake and subsequent lipid synthesis in seminal epithelial cells, leading us to believe that GLUT4 is a major glucose transporter.

      Increasing sperm linearity by oleic acid is observed and interpreted as enhanced sperm fertilizing potential. It is not clear why and how sperm linearity can be a determinant factor for enhancing sperm fertility in vivo. Providing an explanation of the effect of oleic acid on another key motility parameter more proven to be directly correlated with fertility (i.e., hyperactivation), and more direct evidence of oleic acid on enhancing sperm linearity indeed increasing sperm fertilization using IVF, is strongly recommended to support the author's main conclusion.

      Thank you for pointing this out. It is known that proteins derived from the seminal vesicles inhibit the hyperactivation of sperm and the acrosome reaction. Therefore, we conducted an experiment to add oleic acid, focusing on fatty acid synthesis caused by the metabolic shift of the seminal vesicles, which had not been known until now.

      Sperm were pretreated with an oleic acid-containing medium before IVF and oleic acid enhanced sperm linearity. When the sperm number was sufficient, there was no change in the cleavage rate after in vitro fertilization, but when the sperm count was reduced to one-tenth of the normal, the cleavage rate increased compared to the control (lines 274-282). In other words, the physiological role of oleic acid is to increase the probability of fertilization by keeping the sperm motility pattern linear or progressive. This increases the likelihood of the sperm passing through the female reproductive tract and environments that are unfavorable to sperm survival. Our research has uncovered significant insights into the role of seminal vesicle fluid and oleic acid in sperm fertilization. Due to the strong effect of the decapacitation factor, we found that seminal vesicle fluid reduces the fertilization rate in IVF. However, it does not interfere with the fertilization rate in in vivo during artificial insemination. This emphasizes the importance of oleic acid, along with other protein components of seminal plasma, in ensuring the in vivo fertilization ability of sperm.

      Minor:

      Please correct a typo in Line 173: sifts to shifts

      All typographical errors have been corrected.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Cystinosis is a rare hereditary disease caused by biallelic loss of the CTNS gene, encoding two cystinosin protein isoforms; the main isoform is expressed in lysosomal membranes where it mediates cystine efflux whereas the minor isoform is expressed at the plasma membrane and in other subcellular organelles. Sur et al proceed from the assumption that the pathways driving the cystinosis phenotype in the kidney might be identified by comparing the transcriptome profiles of normal vs CTNS-mutant proximal tubular cell lines. They argue that key transcriptional disturbances in mutant kidney cells might not be present in non-renal cells such as CTNS-mutant fibroblasts.

      Using cluster analysis of the transcriptomes, the authors selected a single vacuolar H+ATPase (ATP6VOA1) for further study, asserting that it was the "most significantly downregulated" vacuolar H+ATPase (about 58% of control) among a group of similarly downregulated H+ATPases. They then showed that exogenous ATP6VOA1 improved CTNS(-/-) RPTEC mitochondrial respiratory chain function and decreased autophagosome LC3-II accumulation, characteristic of cystinosis. The authors then treated mutant RPTECs with 3 "antioxidant" drugs, cysteamine, vitamin E, and astaxanthin (ATX). ATX (but not the other two antioxidant drugs) appeared to improve ATP6VOA1 expression, LC3-II accumulation, and mitochondrial membrane potential. Respiratory chain function was not studied. RTPC cystine accumulation was not studied.

      In this manuscript, as an initial step, we have studied the first step in respiratory chain function by performing the Seahorse Mito Stress Test to demonstrate that the genetic manipulation (knocking out the CTNS gene and plasmid-mediated expression correction of ATP6V0A1) impacts mitochondrial energetics. We did not investigate the respirometry-based assays that can identify locations of electron transport deficiency, which we plan to address in a follow-up paper.

      We would like to draw attention to Figure 3D, where cystine accumulation has been studied. This figure demonstrates an increased intracellular accumulation of cystine.

      The major strengths of this manuscript reside in its two primary findings.

      (1) Plasmid expression of exogenous ATP6VOA1 improves mitochondrial integrity and reduces aberrant autophagosome accumulation.

      (2) Astaxanthin partially restores suboptimal endogenous ATP6VOA1 expression.

      Taken together, these observations suggest that astaxanthin might constitute a novel therapeutic strategy to ameliorate defective mitochondrial function and lysosomal clearance of autophagosomes in the cystinotic kidney. This might act synergistically with the current therapy (oral cysteamine) which facilitates defective cystine efflux from the lysosome.

      There are, however, several weaknesses in the manuscript.

      (1) The reductive approach that led from transcriptional profiling to focus on ATP6VOA1 is not transparent and weakens the argument that potential therapies should focus on correction of this one molecule vs the other H+ ATPase transcripts that were equally reduced - or transcripts among the 1925 belonging to at least 11 pathways disturbed in mutant RPTECs.

      The transcriptional profiling studies on ATP6V0A1 have been fully discussed and publicly shared. Table 2 lists the v-ATPase transcripts that are significantly downregulated in cystinosis RPTECs. We have also clarified and justified the choice of further studies on ATP6V0A1, where we state the following: "The most significantly perturbed member of the V-ATPase gene family found to be downregulated in cystinosis RPTECs is ATP6V0A1 (Table 2). Therefore, further attention was focused on characterizing the role of this particular gene in a human in vitro model of cystinosis."

      (2) A precise description of primary results is missing -- the Results section is preceded by or mixed with extensive speculation. This makes it difficult to dissect valid conclusions from those derived from less informative experiments (eg data on CDME loading, data on whole-cell pH instead of lysosomal pH, etc).

      We appreciate the reviewer highlighting areas for further improving the manuscript's readership. In our resubmission, we have revised the results section to provide a more precise description of the primary findings and restrict the inferences to the discussion section only.

      (3) Data on experimental approaches that turned out to be uninformative (eg CDME loading, or data on whole=cell pH assessment with BCECF).

      We have provided data whether it was informative or uninformative. Though lysosome-specific pH measurement would be important to measure, it was not possible to do it in our cells as they were very sick and the assay did not work. Hence we provide data on pH assessment with BCECF, which measures overall cytoplasmic and organelle pH, which is also informative for whole cell pH that is an overall pH of organelle pH and cytoplasmic pH.

      (4) The rationale for the study of ATX is unclear and the mechanism by which it improves mitochondrial integrity and autophagosome accumulation is not explored (but does not appear to depend on its anti-oxidant properties).

      We have provided rationale for the study of ATX; provided in the introduction and result section, where we mentioned the following: “correction of ATP6V0A1 in CTNS-/- RPTECs and treatment with antioxidants specifically, astaxanthin (ATX) increased the production of cellular ATP6V0A1, identified from a custom FDA-drug database generated by our group, partially rescued the nephropathic RPTEC phenotype. ATX is a xanthophyll carotenoid occurring in a wide variety of organisms. ATX is reported to have the highest known antioxidant activity and has proven to have various anti-inflammatory, anti-tumoral, immunomodulatory, anti-cancer, and cytoprotective activities both in vivo and in vitro_”._

      We are still investigating the mechanism by which ATX improves mitochondrial integrity, and this will be the focus of a follow-on manuscript.

      (5) Thoughtful discussion on the lack of effect of ATP6VOA1 correction on cystine efflux from the lysosome is warranted, since this is presumably sensitive to intralysosomal pH.

      In the revised manuscript, we have included a detailed discussion on the plausible reasons why ATP6V0A1 correction has no effect on cysteine efflux from the lysosome. We have now added to the Discussion – “However, correcting ATP6V0A1 had no effect on cellular cystine levels, likely because cystinosin is known to have multiple roles beyond cystine transport Cystinosin is demonstrated to be crucial for activating mTORC1 signaling by directly interacting with v-ATPases and other mTORC1 activators. Cystine depletion using cysteamine does not affect mTORC1 signaling. Our data, along with these observations, further supports that cystinosin has multiple functions and that its cystine transport activity is not mediated by ATP6V0A1.”

      (6) Comparisons between RPTECs and fibroblasts cannot take into account the effects of immortalization on cell phenotype (not performed in fibroblasts).

      The purpose of examining different tissue sources of primary cells in nephropathic cystinosis was to assess if any of the changes in these cells were tissue source specific. We used primary cells isolated from patients with nephropathic cystinosis—RPTECs from patients' urine and fibroblasts from patients' skin—these cells are not immortalized and can therefore be compared. This is noted in the results section - “Specific transcriptional signatures are observed in cystinotic skin-fibroblasts and RPTECs obtained from the same individual with cystinosis versus their healthy counterparts”.

      We next utilized the immortalized RPTEC cell line to create CRISPR-mediated CTNS knockout RPTECs as a resource for studying the pathophysiology of cystinosis. These cells were not compared to the primary fibroblasts.

      (7) This work will be of interest to the research community but is self-described as a pilot study. It remains to be clarified whether transient transfection of RPTECs with other H+ATPases could achieve results comparable to ATP6VOA1. Some insight into the mechanism by which ATX exerts its effects on RPTECs is needed to understand its potential for the treatment of cystinosis.

      In future studies we will further investigate the effect of ATX on RPTECs for treatment of cystinosis- this will require the conduct of Phase 1 and Phase 2 clinical studies which are beyond the scope of this current manuscript.

      Reviewer #2 (Public Review):

      Sur and colleagues investigate the role of ATP6V0A1 in mitochondrial function in cystinotic proximal tubule cells. They propose that loss of cystinosin downregulates ATP6V0A1 resulting in acidic lysosomal pH loss, and adversely modulates mitochondrial function and lifespan in cystinotic RPTECs. They further investigate the use of a novel therapeutic Astaxanthin (ATX) to upregulate ATP6V0A1 that may improve mitochondrial function in cystinotic proximal tubules.

      The new information regarding the specific proximal tubular injuries in cystinosis identifies potential molecular targets for treatment. As such, the authors are advancing the field in an experimental model for potential translational application to humans.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) There is a lack of care with precise wording and punctuation, which negatively affects the text. Importantly, the manuscript lacks a clear description of experimental Results. This section begins with speculation, then wanders through experimentation that didn't work (could be deleted). Figure 1A and lines 94-102 could be deleted. Data from CDME loading was found to be a "poor surrogate" for cystinosis and could be deleted from the manuscript or mentioned as a minor point in the discussion. The number of individual patient cell lines used for experimentation is unclear - 8 patients are mentioned on line 109, Figure 2B shows 6 normal fibroblasts, 3 CDME-loaded fibroblasts, and an indeterminate number of normal vs CDME-loaded cells (both colored red). Cluster analysis refers to two large gene clusters - data supporting this key conclusion is not shown. It is unclear why ATP6VOA1 was selected as the most significantly reduced H+ATPase from Table II. Thus, the focus on this particular gene appears to be largely "a hunch".

      In this study, we aim to establish a new concept by using multiple cell types and various assays tailored to each affected organelle, which might be confusing. Therefore, we believe Figure 1a provides a roadmap and helps clarify what to expect from this paper.

      This study was started a decade back, when CDME-mediated lysosomal loading was regularly used as a surrogate in vitro model to study cystinosis tissue injury. That was the reason to include CDME in the study design. Since we already had the CDME-treated data and in this article we are talking about another superior in vitro cystinosis model, we would like to include it.

      In the Result and Methods section, we mentioned “8 patients” with nephropathic cystinosis from whom we collected the RPTECs and Fibroblasts. These cystinotic cells are shown in blue and purple dots, respectively in figure 2B. Normal RPTEC and fibroblast cells were purchased from company and these cells were then treated with CDME to artificially load lysosomes with cystine. Details on the cell types and its procurement can be found in the Methods section under “Study design and Samples”. Normal and CDME-loaded RPTECs are shown in red and orange dots, whereas normal and CDME-loaded fibroblasts are shown in green and yellow dots, respectively in figure 2B.

      We removed this figure from the manuscript because the data is already detailed in Tables 1 and 2. As a sub-figure, the string pathway analysis output was illegible and did not add any new information. However, for your reference, we have now provided this data below.

      Author response image 1.

      STRIG pathway analysis using the microarray transcriptomic data from normal vs.cystinotic RPTECs. Ysing K-mean clustering on the genes in these significantly enriched pathways, we identified 2 distinct clusters, red and green nodes. Red nodes are enriched in nucleus-encoded mitochondrial genes and v-ATPases family, which are crucial for lysosomes and kidney tubular acid secretion. ATP6VOA1, the topmost v-ATPase in our cystinotic transcriptome dataset is highlighted in cyan. Green nodes are enriched in genes needed for DNA replication.

      (2) It was decided to use transcriptional profiling of CTNS mutant vs wildtype renal proximal tubular cells (RPTECs) as a way to uncover defective secondary molecular pathways that might be upstream drivers of the cystinosis phenotype. Since the kidneys are the first organs to deteriorate in cystinosis, it is postulated that transcriptome differences might be more obvious in kidney cells than in non-renal tissues, such as fibroblasts. A potential pitfall is that the RPTECs were transformed cell lines whereas fibroblasts were not.

      Transcriptional profiling was done on primary cells isolated from patients with nephropathic cystinosis—RPTECs from patients' urine and fibroblasts from patients' skin—these cells are not immortalized and can therefore be compared. This is noted in the results section - “Specific transcriptional signatures are observed in cystinotic skin-fibroblasts and RPTECs obtained from the same individual with cystinosis versus their healthy counterparts”.

      We utilized the immortalized RPTEC cell line to create CRISPR-mediated CTNS knockout RPTECs as a resource for studying the pathophysiology of cystinosis. These cells were not compared to the primary fibroblasts.

      (3) The authors wanted to study intralysosomal pH but could not, so used a pH-sensitive dye that reflects whole cell pH. It would be incorrect to take this measurement as support for their hypothesis that intralysosomal pH is increased. Since these experiments cannot be interpreted, they should be deleted from the manuscript.

      We have now corrected the term to "intracellular pH." Although measuring lysosome-specific pH would be important, it was not feasible in our cells as knocking out cystinosin gene made them fragile, making the assay ineffective. Therefore, we provide data on pH assessment using BCECF, which measures the overall pH of the cytoplasm and organelles. This information is still valuable for understanding the whole cell pH, encompassing both organelle and cytoplasmic pH. We have mentioned this as one of our limitations in the Discussion section.

      (4) The choice of ATX as a potential therapy is puzzling. Its antioxidant properties seem to be irrelevant since two other antioxidants had no effect. The mechanism by which it appears to correct some aspects of the cystinosis phenotype remains unknown and this should be pointed out. A key experiment to assess whether ATX reduces lysosomal cystine accumulation is missing. While the impact of ATX on cystinosis is interesting, the mechanism is unexplored.

      A detailed study on the mechanism by which ATX corrects certain aspects of the cystinosis phenotype is currently underway and will be presented in a follow-up paper. We have measured the effect of ATX and cysteamine, both individually and combined, on cystine accumulation using HPLC, as shown in the figure below. Our results indicate a significant increase in cystine levels with ATX treatment alone, while the combined ATX and cysteamine treatment significantly reduced cystine accumulation to the normal level. This suggests that ATX addresses specific aspects of the cystinosis phenotype through a different mechanism, not by reducing the accumulated cystine levels. When co-administered with cysteamine, they have the potential to complement each other's shortcomings. We believe that the increase in cystine with ATX alone may be due to interactions between ATX's ketone or hydroxyl groups and cystine's amine or carboxylic groups. Further research on this interaction is ongoing.

      We have now added to the Discussion – “We noticed a significant increase in cystine levels with ATX treatment alone (data not shown in the manuscript), while the combined ATX and cysteamine treatment significantly reduced cystine accumulation to the normal level. This may suggest that when co-administered with cysteamine, they have the potential to complement each other's shortcomings. We believe that the increase in cystine with ATX alone could be due to interactions between ATX's ketone or hydroxyl groups and cystine's amine or carboxylic groups. Further research on this interaction is ongoing.”

      Author response image 2.

      (5) The effects of exogenous ATP6VOA1 are interesting but had no effect on lysosomal cystine efflux, a hallmark of the cystinosis cellular phenotype. A discussion of this issue would be important.

      In the revised manuscript, we have included a detailed discussion on the plausible reasons why ATP6V0A1 correction has no effect on cysteine efflux from the lysosome. We have added to the Discussion – “However, correcting ATP6V0A1 had no effect on cellular cystine levels (Figure 7C), likely because cystinosin is known to have multiple roles beyond cystine transport. Cystinosin is demonstrated to be crucial for activating mTORC1 signaling by directly interacting with v-ATPases and other mTORC1 activators. Cystine depletion using cysteamine does not affect mTORC1 signaling (47). Our data, along with these observations, further supports that cystinosin has multiple functions and that its cystine transport activity is not mediated by ATP6V0A1.”

      (6) The arguments on lines 260-273 are not comprehensible. The authors confirm that RPTC LC3-II levels are increased, a marker of active processing of autophagosome cargo, prior to delivery to lysosomes. Discussion of balfilomycin (not used), mTORC activity, and endocytosis are not directly relevant and wander from interpretation of the LC3-II observation. One possibility is that the 50% decrease in ATP6VOA1 transcript is sufficient to slow the transfer of LC3-II-tagged cargo from autophagosome to lysosome - however, it would be important to offer a plausible explanation for why decreased ATP6VOA1 expression alone does not appear to be the key limitation on lysosomal cystine efflux.

      We have now rephrased our explanation in the Discussion section – “Cystinotic cells are known to have an increased autophagy or reduced autophagosome turnover rate. Autophagic flux in a cell is typically assessed by examining the accumulation of the autophagosome or autophagy-lysosome marker LC3B-II. This accumulation can be artificially induced using bafilomycin, which targets the V-ATPase, thereby inhibiting lysosomal acidification and degradation of its contents. Taken together, the observed innate increase in LC3B-II in cystinotic RPTECs (Figure 5A) without bafilomycin treatment suggests dysfunctional lysosomal acidification and thus could be linked to inhibited v-ATPase activity”.

    1. Author response:

      We plan to submit a revised version of our manuscript eLife-RP-RA-2024-105013, in which we address all comments raised by the two expert reviewers.

      Below we describe what we like to address in this revision. We understand that the provisional response is not meant to be a point-by-point reply. Therefore, our revision plan more generally summarizes the comments of the reviewers and how we plan to address them.

      Reviewer #1:

      This reviewer is overall very positive and states that our ‘work is likely to become the go-to resource for quantification in this field’. This reviewer raises few weaknesses of the manuscript that are explicitly described as minor.

      Microscopic resolution sufficient to support quantitative spine assessments?

      In the detailed revision, we will provide quantification of microscopic resolution and will relate this to the spine comparisons offered. Where needed, we will add caveats discussing measurement limits.

      Age of the human tissue.

      Most analysis is based on the study of three brains from elderly individuals. For the analysis of dendritic spines, we added measures from a younger brain (37 years-old). We will make it more clear, which datasets contained these measures and what the results of our comparative analysis have been.

      Genetic diversity contributing to species differences?

      We provide an updated discussion on this interesting topic.

      Reviewer #2:

      This reviewer also expresses a largely positive view of the manuscript, noting that ‘..the data will be of widespread interest to the cerebellar field…’. 

      Microscopic resolution:

      see above.

      Figure panels / Fig. 3:

      We will make sure that the figures are readable and will provide a clarification of gray scales used in Fig. 3.

      Vertical vs horizontal dendrite orientation:

      This is a point that requires clarification. Per our definition, all dendrites fall either into the vertical or horizontal category. We will make sure that this is defined sufficiently well.

    1. Author response:

      Response to Referee 1

      We agree that convex walls increase the time that consortia remain trapped in pores at high magnetic fields. Since the non-monotonic behavior of the drift velocity with the Scattering number arises largely due to these long trapping times, we agree that experiments using concave pores are likely to show a peak drift velocity that is diminished or erased.

      However, we disagree that a random packing of spheres or similar particles provides an appropriate model for natural sediment, which is not composed exclusively of hard particles in a pure fluid. Pore geometry is also influenced by clogging. Biofilms growing within a network of convex pillars in two-dimensional microfluidic devices have been observed to connect neighboring pillars, thereby forming convex pores. Similar pore structures appear in simulations of biofilm growth between spherical particles in three dimensions. Moreover, the salt marsh sediment in which MMB live is more complex than simple sand grains, as cohesive organic particles are abundant. Experiments in microfluidic channels show that cohesive particles clog narrow passageways and form pores similar to those analyzed here. Thus, we expect convex pores to be present and even common in natural sediment where clogging plays a role.

      The concentration of convex pores in the experiments presented here is almost certainly much higher than in nature. Nonetheless, since magnetotactic bacteria continuously swim through the pore space, they are likely to regularly encounter such convexities. Efficient navigation of the pore space thus requires that magnetotactic bacteria be able to escape these traps. In the original version of this manuscript, this reasoning was reduced to only one or two sentences. That was a mistake, and we thank the reviewer for prompting us to expand on this point. As the reviewer notes, this reasoning is central to the analysis and should have been featured more prominently. In the final version, we will devote considerable space to this hypothesis and provide references to support the claims made above.

      The reviewer suggests that the generality of this work depends on our finding a "positive correlation between the swimming speed and alignment [rate] based on parameters derived from literature." We wish to emphasize that, in addition to predicting this correlation, our theory also predicts the function that describes it. The black line in Figure 3 is not fitted to the parameters found in the literature review; it is a pure prediction.

      Response to Referee 2

      In the "Recommendations for the Authors," this reviewer drew our attention to a manuscript that absolutely should have been prominently cited. As the reviewer notes, our manuscript meaningfully expands upon this work. We are pleased to learn that the phenomena discussed here are more general than we initially understood. It was an oversight not to have found this paper earlier. The final version will better contextualize our work and give due credit to the authors. We sincerely appreciate the reviewer for bringing this work to our attention.

      We disagree that the use of non-culturable organisms and our unrealistic array should be considered serious weaknesses. While any methodological choice comes with trade-offs, we believe these choices best advance our aims. First, the goal of our research, both within and beyond this manuscript, is to understand the phenotypes of magnetotactic bacteria in nature. While using pure cultures enables many useful techniques, phenotypic traits may drift as strains undergo domestication. We therefore prioritize studying environmental enrichments.

      Clearly, an array of obstacles does not fully represent natural heterogeneity. However, using regular pore shapes allows us to average over enough consortium-wall collisions to enable a parameter-free comparison between theory and experiment. Conducting an analysis like this with randomly arranged obstacles would require averaging over an ensemble of random environments, which is practically challenging given the experimental constraints. Since we find good agreement between theory and experiment in simple geometries, we are now in a position to justify extending our theory to more realistic geometries. Additionally, we note that a microfluidic device composed of a random arrangement of obstacles would also be a poor representation of environmental heterogeneity, as pore shape and network topology differ between two and three dimensions.